https://orcid.org/0009-0000-8111-4197
Figures
Abstract
One of the critical challenges in managing colorectal cancer (CRC) is the development of oxaliplatin (OXP) resistance. Long non-coding RNAs (lncRNAs) have a crucial role in CRC progression and chemotherapy resistance, with exosomal lncRNAs emerging as potential biomarkers. This study aimed to predict key lncRNAs involved in OXP-resistance using in-silico methods and validate them using RT-qPCR methods in CRC cells and their isolated exosomes. Two public datasets, GSE42387 and GSE119481, were downloaded from the GEO database to identify differentially expressed genes (DEGs) and miRNAs (DEmiRNAs) associated with OXP-resistance in the HCT116 cell line. The analysis of GSE42387 revealed 210 DEGs, and GSE119481 identified 73 DEmiRNAs. A protein-protein interaction (PPI) network analysis of the DEGs identified 133 interconnected genes, from which the top ten genes with the highest degree scores were selected. By intersecting predicted miRNAs targeting these genes with the DEmiRNAs, 38 common miRNAs were found. Subsequently, 224 lncRNAs targeting these common miRNAs were predicted. LncRNA-miRNA-mRNA network were constructed and the top five lncRNAs with the highest degree scores were identified. Analysis using the Kaplan-Meier plotter database revealed that the key lncRNAs NEAT1, OIP5-AS1, and MALAT1 are significantly associated with the overall survival of CRC patients. To validate these lncRNAs, OXP-resistant HCT116 sub-cell line (HCT116/OXR) was developed by exposing parental HCT116 cells to gradually increasing concentrations of OXP. Exosomes derived from both HCT116 and HCT116/OXR cells were isolated and characterized utilizing dynamic light scattering (DLS), transmission electron microscopy (TEM), and Western blotting. RT-qPCR confirmed elevated levels of NEAT1, OIP5-AS1, and MALAT1 in HCT116/OXR cells and their exosomes compared to parental HCT116 cells and their exosomes. This study concludes that NEAT1, OIP5-AS1, and MALAT1 are associated with the OXP-resistance in CRC. The high levels of these lncRNAs in exosomes of resistant cells suggest their involvement in intercellular communication and resistance propagation. This positioning makes them promising biomarkers for OXP-resistance in CRC.
Citation: Sahebnasagh R, Deli H, Shadboorestan A, Vakili-Ghartavol Z, Salehi N, Komeili-Movahhed T, et al. (2024) Identification of key lncRNAs associated with oxaliplatin resistance in colorectal cancer cells and isolated exosomes: From In-Silico prediction to In-Vitro validation. PLoS ONE 19(10): e0311680. https://doi.org/10.1371/journal.pone.0311680
Editor: Lu Zhang, Fudan University, CHINA
Received: June 20, 2024; Accepted: September 23, 2024; Published: October 14, 2024
Copyright: © 2024 Sahebnasagh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: This study was part of a PhD thesis funded and supported by Tehran University of Medical Sciences Grant #42069. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Colorectal cancer (CRC) is the third most common diagnosed malignany and the second most cause of cancer-related death worldwide [1]. In 2020, approximately 1.9 million new cases of CRC were diagnosed and 935,000 deaths were occurred globally [2]. The incidence rate of CRC is increasing and is predicted to reach over 2.2 million new cases and 1.1 million deaths annually by 2030 [3]. The available treatment options for CRC include surgical intervention, radiotherapy, chemotherapy, immunotherapy, and targeted therapy [4, 5]. Cytotoxic chemotherapy is considered the cornerstone of CRC treatment [6].
Oxaliplatin (OXP), a third-generation platinum compound, is one of the main component in cytotoxic chemotherapy which commonly used in combination with other cytotoxic drugs as first-line therapy for CRC patients. It exhibits extensive anti-tumor activity in metastatic cancer and various cell lines through the formation of covalent platinum-DNA adducts, which inhibit DNA replication and cell growth, ultimately leading to cell death [5, 7, 8]. The DNA damage induced by OXP results in transient S phase delay and G2/M arrest in cell cycle progression and enhances cell apoptosis processes [9–11]. Despite its effectiveness, most cases of advanced CRC suffer from the development of OXP-resistance, which often results in treatment failure and poor overall survival (OS) [11, 12]. OXP-resistance occur through various mechanisms, including the regulation of cell death, detoxification, cellular influx/efflux, alterations in DNA adduct repair, and epigenetic modifications [13]. Discovering the molecular mechanisms of OXP-resistance and associated biomarkers will help develop new treatments designed to overcome such resistance and aid in identifying patients who are unlikely to benefit from treatment with OXP [11].
Recent studies have demonstrated that long non-coding RNAs (lncRNAs), which exceed 200 nucleotides in length, play crucial roles in regulating various cellular processes. LncRNAs can interact with RNA, DNA, and proteins to form complexes that modulate transcription, RNA stability, translation, and alternative splicing [14, 15]. It has been shown that the intricate interaction between lncRNAs and microRNAs (miRNAs) shows their significance in RNA regulation processes. Some lncRNAs act as competitive endogenous RNAs (ceRNAs), functioning as sponges for miRNAs within the tumor regulation network to regulate gene expression [16]. LncRNAs can function as oncogenes or tumor suppressors by modulating crucial signaling pathways through interactions with regulatory molecules [17–19]. These lncRNAs significantly contribute to the development of malignant behaviors in various cancers by regulating critical cellular processes such as cell proliferation, apoptosis, cell cycle, autophagy, metastasis, epithelial-mesenchymal transition (EMT), invasion, and chemotherapy resistance [20]. Recent studies have identified several lncRNAs that are notably contribute to CRC. For instance, MALAT1, NEAT1, HOTAIR, Linc00152, CRNDE, GAS5, KCNQ1OT1, TUG1, MEG3, PVT1, MEIS1, and H19 have been shown to influence cancer initiation, progression, and resistance to drugs such as OXP and 5-fluorouracil (5-FU) [21–32]. LncRNAs also transcend cellular boundaries through encapsulation in extracellular vesicles, where they influence the functions of recipient cells. This encapsulation protects lncRNAs from degradation, thereby preserving their functional integrity during cell-to-cell transfer [33].
Exosomes, a type of extracellular vesicle typically ranging from 30 to 150 nm in diameter, originate from the endocytic pathway and are secreted into the extracellular environment by various cell types [34]. They selectively package biomolecular cargo and deliver it to adjacent or distant cells, thereby mediating cellular communication, modulating gene expression, and altering the phenotypes and biology of recipient cells [35, 36]. Interestingly, recent research indicates that exosomes secreted by drug-resistant cancer cells play a critical role in spreading resistance traits through their paracrine effects, enabling sensitive cancer cells to acquire drug resistance. Exosomes contribute to this mechanism by transferring various drug-resistance-associated materials, including proteins (such as P-glycoprotein, ABC transporters, and multidrug resistance-associated proteins), metabolites (such as TGF-β and prostaglandin E2), and nucleic acids (such as DNA, miRNAs, and lncRNAs) to drug-sensitive recipient cells [37, 38]. Through these transfers, exosomes protect cancer cells from the cytotoxic effects of chemotherapeutic agents by mediating alterations in signal transduction, inhibiting apoptosis, enhancing drug tolerance, forming tumor niches, regulating drug efflux pumps, promoting immune escape, enhancing DNA repair, and altering the tumor microenvironment (such as hypoxia, EMT, and angiogenesis) [37, 39, 40]. Recent studies have demonstrated that exosomes carrying specific lncRNAs can influence CRC progression and OXP resistance. For instance, Ren et al. reported that exosomal lncRNA H19 contributes to OXP resistance in CRC by acting as a sponge for miR-141 and activating the β-catenin pathway [41]. Similarly, Deng et al. demonstrated that exosomes secreted by cancer-associated fibroblasts (CAFs), enriched with the lncRNA CCAL, promote OXP resistance in CRC cells by upregulating β-catenin and inhibiting apoptosis [42]. Additionally, CAF-derived exosomal FOSL1 has been shown to enhance cell proliferation, stemness, and OXP resistance in CRC cells by activating ITGB4 [43].
Given the roles of lncRNAs and exosomes in cancer drug resistance, they hold promise as biomarkers for predicting CRC progression and responses to chemotherapy [41, 44–46]. This study hypothesizes that specific lncRNAs within exosomes contribute to OXP-resistance in CRC, aiming to identify and validate these lncRNAs through a combined in-silico and in-vitro approach. To achieve this, differentially expressed genes (DEGs) and miRNAs (DEmiRNAs) between parental and OXP-resistant CRC cells were identified from two datasets. Using in-silico methods, mRNA-miRNA-lncRNA networks were constructed and key lncRNAs based on specific criteria were selected. To validate these, an OXP-resistant CRC cell line was established and characterized and exosomes from the parental and resistant cells were isolated. The expression levels of the identified key lncRNAs were then evaluated using qRT-PCR in both the cells and their derived exosomes.
Materials and methods
Data acquisition
In this study, an initial search was performed in the Gene Expression Omnibus (GEO) database to obtain microarray and RNA-seq data related to differentially expressed genes (DEGs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) associated with acquired OXP-resistance in CRC. Ultimately, two publicly available datasets, GSE42387 and GSE119481, were selected based on their comprehensive data for parental (HCT116) cells and OXP-resistant HCT116 (HCT116/OXR) sub-cell. Although other datasets, such as GSE119603, also explore the gene expression profiles of OXP-resistant in HCT116 cells, we chose to focus on the GSE42387 and GSE119481 datasets. The reason for this selection is that these two datasets demonstrate a higher level of resistance in the HCT116/OXR cells to OXP, making them more suitable for our research objectives. The selection of these datasets was also based on several key criteria. Firstly, these datasets focus on the HCT116 cell line, a well-established model for studying drug resistance in CRC, allowing for direct comparison of results and enhancing the relevance of the findings to the current study. Secondly, the methods used to establish the HCT116/OXR sub-cell lines in these studies are consistent with those employed in the current study, involving a stepwise increase in OXP concentration. This consistency ensures that the resistance mechanisms observed are comparable and directly applicable. Additionally, the integration of data from microarray and RNA-seq technologies provides a comprehensive view by leveraging the strengths of each method while compensating for their respective limitations. This combined approach offers more robust and detailed results, which are valuable for both bioinformatics analyses and experimental design.
Bibliography of data sources.
The GEO dataset GSE42387, generated by Jensen et al., contains mRNA expression profiles of HCT116 and HCT116/OXR sub-cell lines. The OXP-resistant cells were established by exposing the parental HCT116 cell line to stepwise increasing OXP concentrations over 241 days. To confirm drug resistance, an MTT assay was performed. The HCT116/OXR sub-cell lines were then recovered in a drug-free culture medium for two weeks. Subsequently, total RNA was extracted from both HCT116 and HCT116/OXR cells. Each set of samples was obtained in triplicate: control parental HCT116 cell lines (GSM1038651, GSM1038652, and GSM1038653) and HCT116/OXR sub-cell lines (GSM1038654, GSM1038655, and GSM1038656). This triplicate sampling ensured the robustness and reliability of the findings. Labeled cRNAs were then hybridized to the Agilent Whole Human Genome Microarray 4x44K G4112F, based on the GPL16297 platform, providing a comprehensive gene expression profile with 32,750 probes [47].
The GEO dataset GSE119481, generated by Gasiule et al., contains the miRNA expression profile of parental HCT116 cells and the HCT116/OXR sub-cell line. The OXP-resistant sub-cell line were generated by exposing the parental HCT116 cells to stepwise increasing OXP-concentrations over nine months until stable resistance cells were achieved. MTT assays confirmed the resistance of HCT116 to OXP, showing a 27.7-fold increase in HCT116/OXR sub-cell compared to the parental HCT116. Total RNA was extracted from both the HCT116 and HCT116/OXR cells. Each set of samples was obtained in triplicate: control parental HCT116 cell lines (GSM3375480, GSM3375481, and GSM3375482) and HCT116/OXR sub-cell lines (GSM3375477, GSM3375478, and GSM3375479). cDNA libraries were then prepared, and miRNAs were examined using high-throughput sequencing on an Illumina MiSeq instrument (platform GPL15520) [48].
Data preprocessing for the identification of DEGs and DElncRNAs from the GSE42387 dataset.
The pre-processed and normalized gene expression matrix sourced from the microarray dataset GSE42387 was acquired. To analyze gene expression data and perform a pairwise comparison of DEGs and DElncRNAs between the parental HCT116 cells and HCT116/OXR sub-cell line, the R packages ‘limma’ version 3.52.2 within the R software version 4.2.1 were utilized. The criteria set to determine DEGs were │log2 fold change (FC) │> 1 and a p-value < 0.05. Visualizations were conducted using the ggplot2 version 3.3.6 and pheatmap version 1.0.12 R package. Principal component analysis (PCA) was performed using the Base R prcomp function, and gene annotations were conducted using data.table version1.14.4. Gene annotation and functional analysis were conducted using the Ensembl (https://asia.ensembl.org/index.html), GeneCard (https://www.genecards.org/) and NCBI (https://www.ncbi.nlm.nih.gov/) databases.
Data preprocessing for the identification of DEmiRNAs from the GSE119481 dataset.
To obtain DEmiRNAs, the miRNA expression profiles from GSE119481 dataset were downloaded for subsequent analysis. Data analysis and visualization were conducted using R software v4.2.1 and multiple R packages, including DESeq2 (version 1.42.0), RColorBrewer (version 1.1.3), gplots (version 3.1.3), ggplot2 (version 3.4.0), and org.Hs.eg.db (version 3.18.0), to obtain a comprehensive list of DEmiRNAs. DESeq2, a user-friendly RNA-Seq data analysis package for handling high-dimensional count data, provides functionalities such as normalization, visualization and differential analysis [49]. The org.Hs.eg.db package was utilized for annotation, while visualizations were generated using the ggplot2, gplots, and RColorBrewer R package. The criteria set to determine DEmiRNAs.were │log2 fold change (FC)│> 0.6 and a p-value < 0.05.
Construction of Protein-Protein Interaction (PPI) network
To explore the connected genes, a PPI network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) online databe version 12 (https://string-db.org) with a confidence score > 0.4 [50] and visualized using Cytoscape software (version 3.10.0) (https://cytoscape.org/) [51].
Gene Ontology (GO) and pathway enrichment analysis
To explore the functions and pathways of the identified connected genes in the PPI network, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/home.jsp) was used to perform Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis [52, 53]. A cutoff criterion of an EASE score < 0.05 was set, indicating significant enrichment.
Screening central genes in PPI network
To identify the central nodes (genes) in the PPI network, the CytoNCA plug-in (version 2.1.6) in Cytoscape was used to calculate the degree scores of the gene nodes, representing the number of edges connecting the genes [54]. The top 10 genes with the highest degree scores were selected as the central genes for further analysis.
Prediction of miRNAs targeting central genes
DIANA-TarBase version 9 (https://dianalab.e-ce.uth.gr/tarbasev9) and miRTarBase version 9 (https://mirtarbase.cuhk.edu.cn/) were used as data sources to predict upstream miRNAs targeting top10 genes based on experimentally validated miRNA-target interactions. These databases provide valuable insights into miRNA-mRNA interactions [55, 56]. Subsequently, an intersection was performed between these predicted miRNAs and the DEmiRNAs obtained from the GSE119481 dataset analysis. This process resulted in a list of overlapped miRNAs, from which miRNA-mRNA pairs were constructed.
Prediction and enrichment analysis of lncRNAs
To identify lncRNAs targeting miRNAs, the starBase version 2. (https://rnasysu.com/encori/) and DIANA-lncBase version 3. (https://diana.e-ce.uth.gr/lncbasev3) online database were used to construct lncRNA-miRNA pairs. These databases provide a user-friendly interface and comprehensive resource for experimentally validated lncRNA-miRNA interaction networks [57–59]. Additionally, DIANA-lncBase version 3. was employed to predict miRNA targets of DElncRNA, obtained from the GSE42387 dataset, resulting in the construction of DElncRNA-miRNA pairs.
Enrichment analysis of predicted lncRNAs was performed using the LncSEA version 1.0 platform (https://bio.liclab.net/LncSEAv1/index.php). LncSEA provides a comprehensive and freely accessible platform for conducting enrichment analysis of lncRNAs [60]. The analysis encompassed disease, subcellular localization, exosome and cancer hallmark categories, aiming to reveal the biological function of lncRNAs. A p-value < 0.05 was considered statistically significant.
LncRNA-miRNA-mRNA networks construction and analysis
The lncRNA-miRNA-mRNA network was constructed by assembling the miRNA-mRNA, lncRNA-miRNA, and DElncRNA-miRNA pairs. This integrated network was then visualized using Cytoscape software. To identify the key lncRNAs associated with OXP-resistance in CRC, two criteria were employed. First, the degree score of each lncRNA node in the lncRNA-miRNA-mRNA network was calculated using the CytoNCA plug-in in Cytoscape. The top five lncRNAs with the highest scores were then selected.
Second, the association between expression of these top five lncRNAs and the OS rate of CRC patients were assessed using the Kaplan-Meier plotter (KM plotter) online database (https://kmplot.com/analysis/), with log-rank p-value < 0.05 considered statistically significant. The KM plotter database provides both data sources and OS information sourced from the Cancer Genome Atlas (TCGA) database [61, 62].
To explore the potential pathways involving each identified key lncRNA, DEGs associated with candidate lncRNAs were enriched using the clusterProfiler package, with a focus on KEGG pathways provided by the SRplot bioinformatics tool (http://bioinformatics.com.cn/). SRplot is an online tool integrating clusterProfiler in R packages, facilitating data analysis and visualization. Enrichment results with a p-value < 0.05 were considered statistically significant.
Cell culture
The HCT116 human colorectal carcinoma cell line was purchased from the Pasteur Institute of Iran, Tehran. Cells were cultured in DMEM/F12 medium (Biowest, France), containing 10% FBS (Biowest, France) and 1% penicillin/streptomycin (Biowest, France). They were maintained under controlled conditions at 37°C with 5% CO2.
Establishment of OXP-resistance HCT116 sub-cell line
To establish the HCT116/OXR sub-cell line, parental HCT116 cells were exposed to gradually increasing concentrations of OXP (Sigma, USA), as described previously [8, 63]. The culture medium used to generate the OXP-resistant sub-cell line remained consistent with that of the parental HCT116 cells, comprising DMEM/F12, to prevent potential cellular shock during resistance development. In this procedure, the parental HCT116 cells were initially cultured in a drug-free medium for 24 h. Subsequently, during the logarithmic phase of cell growth, the medium was replaced with a culture medium containing an initial concentration of 1 μM OXP, which is lower than the 72 h IC50. After incubation for 72 h, the medium was removed, and the cells were maintained in drug-free medium for at least one week to allow for recovery. The rescued cells underwent passaging and were then re-exposed to the same concentration of the drug. This process was repeated several times until the cells exhibited consistent proliferation in the drug-containing medium. Once stable, the cells were treated with increasing concentrations of the drug (2.5, 5, 10, 20 and finally 30 μM) over a period of 10 months. This procedure was repeated for each concentration until consistent proliferation in the drug-containing medium was observed. OXP-resistance in cells was confirmed through cell viability assays, resistance index measurements, cell proliferation assay, cell apoptosis assays and cell cycle progression analysis. The HCT116/OXR sub-cell line was maintained in a medium containing 30 μM OXP to sustain resistance. Before further experiments, the cells were incubated in a drug-free medium for at least two weeks to allow for recovery.
Cell viability assay.
HCT116 and HCT116/OXR cells were seeded in 96-well culture plates (8×103 cells/well) and incubated overnight at 37°C. The culture medium was then replaced with fresh medium containing different concentrations of OXP (0.1–1000 μM). After incubation for 72 h, the medium was removed and 20 μl of MTT solution (5 mg/ml in PBS; Thiazolyl blue tetrazolium bromide, Life Biolab, Germany) was added to each well. The cells were incubated for an additional 3 hours at 37°C to allow MTT formazan crystal formation. The crystals were solubilized with 100 μl of DMSO, and the optical density (OD) was measured at 570 nm using an ELISA Plate Reader (Anthos, UK). Cell viability was calculated as a percentage relative to the untreated control cells. The resistance index was determined as the ratio between the IC50 values of HCT116/OXR cells and parental HCT116 cells [11]. This experiment was replicated three times to ensure consistency.
Cell proliferation assay.
To assess the proliferative activity of the HCT116 and HCT116/OXR cells, 5×10⁴ cells/well were seeded in 24-well culture plates without any drugs. The cells were counted at 24 h intervals over a continuous period. The doubling time was calculated using the formula: Doubling Time = Duration (hours) × LN (2) / LN (final concentration / initial concentration). This process was replicated three times to ensure experimental consistency.
Cell apoptosis assay.
Flow cytometry was conducted to analyze Annexin V-FITC/propidium iodide (PI) staining for assessing cell apoptosis. HCT116 and HCT116/OXR cells were seeded in 12-well culture plates at a density of 5×10⁴ cells/well and incubated overnight at 37°C. After 24 h, the culture medium was replaced with fresh medium containing 75 μM of OXP, and the cells were incubated for an additional 48 hours. The concentration of 75 μM was selected based on the IC50 value of the OXP-resistant sub-cell line, as it was required to elicit a strong apoptotic response within the 48hour timeframe. This higher concentration ensured a measurable apoptotic response while minimizing non-specific cytotoxic effects. Then, the cells were washed with PBS and trypsinized using trypsin-EDTA (Biowest, France), followed by neutralization with a complete medium. The cells were centrifuged at 1500 rpm for 5 minutes, and the cell pellets were resuspended in PBS. 100μl of Annexin V-Binding Buffer (BioLegend, UK), 5μl Annexin V-FITC (BioLegend, UK) and 10 μl PI were added to the pellet of cells. The mixture was incubated for 15 minutes at room temperature in the dark. After incubation, the labeled cells were diluted with 400 μl binding buffer and analyzed using BD FACS Calibur Flow Cytometer (BD Biosciences, San Jose, CA, USA). The data were analyzed using FlowJo software (version 7.6.1). The percentage of early and late apoptotic cells was determined to assess the apoptosis rate.
Cell cycle assay.
Flow cytometry was conducted to assess the cell cycle profile. HCT116 and HCT116/OXR cells were seeded in 12-well culture plates (5×104 cells/well) and incubated overnight at 37°C. After 24 h, the culture medium was replaced with a fresh medium containing 30 μM of OXP, and the cells were incubated for an additional 48 h. The concentration of 30 μM was chosen based on the maximum level at which the resistant cells could still grow. This concentration was adequate for observing subtle alterations in cell cycle distribution without causing excessive cell death, which could interfere with accurate measurements. Then, the cells were washed with PBS and trypsinized, followed by neutralization with a complete medium. The cells were collected and stained with 40 μl of PI, 10 μl of RNase (DNase-free) and 950 of μl PBS, and incubated for 30 minutes in the dark at room temperature. The cell cycle phase distribution was then analyzed using BD FACS Calibur Flow Cytometer (BD Biosciences, San Jose, CA, USA). The data were analyzed using FlowJo software (version 7.6.1).
Exosomes isolation and characterization
To isolate the exosomes, HCT116 and HCT116/OXR cells were cultured in an FBS-free medium for 48 h. The conditioned medium supernatants were collected and subjected to stepwise centrifugation at 4°C to remove cells, dead cells and cell debris. First, the supernatants were centrifuged at 500× g for 20 minutes. Then, the remaining supernatants were centrifuged at 2500 × g for 20 minutes. Finally, the remaining supernatants were centrifuged at 14000 × g for 45 minutes. The resulting supernatants were concentrated using an ultrafiltration Amicon Ultra-15 100 kD device (Merck Millipore, Germany) at 4000 × g. Exosomes were isolated from the concentrated supernatant using an isolation kit (Cibzist, Iran) following the manufacturer’s protocol. The isolated exosomes were characterized based on size distribution, morphology, and the presence of exosomal markers to confirm successful isolation.
Dynamic light scattering (DLS).
The size distribution of exosomes was measured using the DLS method with a HORIBA Scientific Nanoparticle Analyzer (SZ-100), following the manufacturer’s guidelines.
Transmission electron microscopy (TEM).
The morphology of exosomes was evaluated using TEM. Briefly, a drop (20 μL) of isolated exosomes was dripped onto a 300-mesh carbon-coated TEM grid (EMS, USA) for 2 min. The excess liquid was then removed using filter paper. Negative staining was performed by applying a drop (20 μL) of 2% uranyl acetate for 1 minute. The excess liquid was again removed with filter paper, and the grid was allowed to air dry. The prepared grids were examined using a TEM (Zeiss, EFM10C) operating at an accelerating voltage of 100kV for image acquisition and analysis.
Western blot analysis.
The presence of the exosomal markers CD9 and CD63 was determined by Western blot analysis. First, exosomes were lysed using RIPA lysis buffer. The concentration of proteins was assessed using the BCA Protein Quantification Kit (DNAbiotech, Iran). After denaturation at 95°C for 10 minutes, the proteins were separated by electrophoresis on a 10% SDS-polyacrylamide gel and transferred onto a polyvinylidene difluoride (PVDF) membrane (Roche, Mannheim Germany). The membrane was blocked with 2% skimmed milk (Sigma, Germany) in 1X Tris-buffered saline with 0.1% Tween-20 (TBST) for a one hour at room temperature. The blot was then incubated with specified primary antibodies against CD63 (#sc-5275, Santa Cruz Biotechnology, USA) and CD9 (#sc-13118, Santa Cruz Biotechnology, USA) overnight at 4°C. Subsequently, the membrane was washed with TBST buffer and incubated with m-IgGκBP-HRP secondary antibody (#sc-516102, Santa Cruz Biotechnology, USA) for one hour at room temperature. After another wash with TBST buffer, the signal was detected using chemiluminescence assay (ECL advanced reagents kit, Amersham, USA).
Validation of predicted key lncRNAs
Cellular and exosomal RNAs were extracted from the HCT116 cell line and HCT116/OXR sub-cell line using One Step-RNA Reagent (Bio Basic, Germany) according to the manufacturer’s protocol. RNA concentration and quantity were measured using a spectrophotometer (Biotek, USA), with A260/280 ratios between 1.8 and 2.0. Subsequently, 1 μg of extracted RNA was reverse-transcribed into cDNA using the AddScript cDNA Synthesis Kit (Addbio, Korea), following the manufacturer’s instructions. Real-Time qRT-PCR was performed in triplicate on the StepOnePlus Real-Time PCR System (Thermo Fisher Scientific, Germany) using 1 μl of cDNA as a template and RealQ Plus 2x Master Mix Green High ROX™ (Ampliqon, Denmark). The thermal cycling protocol included an initial denaturation at 95°C for 15 minutes, followed by 40 cycles of denaturation at 95°C for 20 seconds and annealing/extension at 72°C for 30 seconds. Relative gene expression levels were normalized to GAPDH and calculated using the 2-ΔΔCt method. The primers used for qRT-PCR are listed in Table 1.
Statistical analysis
The mean ± standard deviation (SD) of results from three independent experiments was reported. Statistical analysis was conducted using GraphPad Prism 8 (GraphPad Software, LLC). A Student’s t-test was employed to compare differences between the two groups. A p-value < 0.05 was considered as statistically significant.
Results
Identification of DEGs and DElncRNAs
The bioinformatics analysis flowchart conducted in the current study is shown in Fig 1.
This flowchart illustrates the stepwise bioinformatics procedure utilized for identifying key lncRNAs associated with OXP-resistance in CRC. OXP, Oxaliplatin; CRC, Colorectal Cancer.
Analysis of the GSE42387 dataset identified a total of 210 DEGs, including 128 up- and 82 down-regulated genes, as well as 9 DElncRNAs, comprising 5 up- and 4 down-regulated lncRNAs, associated with OXP-resistance in CRC, following the specified cutoff criteria. A heatmap and a volcano plot were generated to visualize the expression patterns of these genes (Fig 2 and S1 Table).
(A) Heatmap displaying gene expression patterns, with sample names on the horizontal axis and fold-change on the vertical axis. Up-regulated genes are indicated in green, while down-regulated genes are shown in red. (B) Volcano Plot demonstrating the association between alterations in gene expression fold change and their statistical significance. Green dots indicate up-regulated genes, while red dots represent down-regulated genes. Among the genes, HCLS1, EHF, KIRREL2, FGF9, ATG4A, CALB2, COL13A1, PMEPA1, BST2, and AKR1C3 were identified as the top 10 up-regulated genes, whereas IAH1, SUSD2, S100A4, FMR1, KRT23, HDGF, ZNF266, STXBP6, CYB5B, and GNE were the top 10 down-regulated genes. Additionally, the up-regulated lncRNAs LINP1, LINC00326, LINC00707, LINC00992, and CCDC144NL-AS1 were identified, while TMEM132D-AS1, SFTA1P, RARA-AS1, and LINC01405 were found to be down regulated. OXP, Oxaliplatin.
Identification of OXP-resistance -associated central genes in the PPI network and functional enrichment analysis of DEGs
The PPI network for OXP-resistance -associated DEGs were constructed. In total, 133 genes, including 87 up- and 46 down regulated, were involved in the network (Fig 3).
The PPI network consists of 133 nodes (genes) and 297 edges (interactions). Orange rectangles display up-regulated genes, while green rectangles display down-regulated genes. DEG, Differentially expressed gene; PPI, Protein-protein interaction; OXP, Oxaliplatin.
Enrichment analysis was conducted to investigate the functions and pathways of these genes. Fig 4 displays GO terms, including biological processes (BP), cellular components (CC), and molecular functions (MF), along with the KEGG pathways, ranked by their p-values (from low to high). Detailed results are presented in S2 Table. The visualizations were generated using the freely accessible online SRplot bioinformatics web server. The CC term indicates that most of the genes were components of the “extracellular exosome”, “extracellular region”, “extracellular space”, “cytosol” and “cytoplasm” (Fig 4A). From the viewpoint of BP, the majority of the genes were involved in “cell adhesion”, “positive regulation of cell proliferation”, “collagen fibril organization”, “positive regulation of peptidyl-tyrosine phosphorylation”, “nitric oxide transport”, “signal transduction” and “angiogenesis” (Fig 4B). The MF terms indicate that most of the genes were involved in “integrin binding”, “extracellular matrix structural constituent”, “growth factor activity”, “protein binding” and “signaling adaptor activity” (Fig 4C). The KEGG pathway analysis showed significant enrichment of these genes across 22 signaling pathways. The top-ranked pathways include “Focal adhesion”, “ECM-receptor interaction”, “Pathways in cancer” and “PI3K/Akt signaling pathway” and “MAPK signaling pathway” (Fig 4D). These pathways are known to regulate cellular processes such as growth, proliferation, survival, apoptosis, migration, and differentiation. Their significant enrichment underscores their pivotal role in modulating cellular processes relevant to CRC progression and chemotherapy resistance [43, 64–69]. In summary, the enrichment results suggest that these genes play significant roles in cancer development.
(A–C) Top 20 enriched GO Terms for CC, BP, and MF, respectively (D) Top 20 Significant KEGG pathway terms (p-value < 0.05). GO, Gene Ontology; CC, Cellular Component; BP, Biological Process; MF, Molecular Function; KEGG, Kyoto Encyclopedia of Genes and Genomes.
In the following analysis, the top 10 ranked genes in the network were identified using degree centrality. These genes, which exhibited the highest node degrees, include PXDN, SPP1, FGF9, NES, JUN, BDNF, ITGB4, PRKACB, IL-18, and CD274 that considered as central genes and used for future analysis (Table 2).
Predicted miRNAs
The upstream miRNAs targeting the top 10 genes were predicted by combining data from TarBase and miRTarBase, resulting in the identification of 520 experimentally validated miRNAs (S3 Table).
Additionally, analysis of the GSE119481 dataset identified 73 DEmiRNAs, comprising 28 upregulated and 45 downregulated miRNAs associated with OXP-resistance in CRC, following the specified cutoff criteria. Subsequently, a heatmap and a volcano plot were generated to visualize the expression patterns of these DEmiRNAs (Fig 5 and S4 Table).
(A) Heatmap displaying miRNA expression patterns, with sample names on the horizontal axis and fold change on the vertical axis. Up-regulated miRNA are presented in green, while down-regulated miRNA are in red. (B) Volcano Plot demonstrating the association between alterations in expression fold change and their statistical significance. Green dots indicate up-regulated miRNA, while red dots represent down-regulated miRNA. OXP, Oxaliplatin.
After performing an intersection analysis between the predicted miRNAs and the DEmiRNAs using a Venn diagram generated by the SRplot bioinformatics web server, 38 common miRNAs were identified. Subsequently, miRNA-mRNA pairs were constructed (Fig 6A and S5 Table).
(A) The intersection between the predicted miRNAs and the DEmiRNAs to identify common miRNAs. (B) The intersection between the predicted lncRNAs from the StarBase and DIANA-lncBase databases to identify common lncRNAs. DEmiRNA, Differentially Expressed miRNAs.
Predicted lncRNAs and enrichment analysis
Using StarBase and DIANA-lncBase, 224 lncRNAs targeting 36 out of 38 common miRNAs were predicted, and lncRNA-miRNA pairs were constructed (Fig 6B and S6 Table). Additionally, 10 miRNAs were predicted for DElncRNAs which obtained from the GSE42387 dataset (S6 Table), followed by the construction of DElncRNA-miRNA pairs.
The four relevant categories—disease, subcellular location, exosome, and cancer hallmark of the lncSEA platform were explored for the predicted lncRNAs. Detailed results are provided in S7 Table. Overall, in the disease category, most of the predicted lncRNAs were primarily enriched in CRC. The cytoplasm, nucleus, and exosome were the main locations for these lncRNAs in the subcellular locations category. Previous studies have supported the cooperative relationships between lncRNAs and hallmark of cancer [70, 71]. In the cancer hallmark category, the predicted lncRNAs were significantly enriched in processes such as proliferation, prognosis, invasion, apoptosis, migration, metastasis, and epithelial-mesenchymal transition (EMT).
Identification of OXP-resistance -associated lncRNAs in constructed lncRNA-miRNA-mRNA networks
To screen for the key lncRNAs that regulate OXP-resistance -associated genes in CRC, the lncRNA-miRNA-mRNA network was constructed by assembling predicted miRNA-mRNA, lncRNA-miRNA, and DElncRNA-miRNA pairs (Fig 7). The degree centrality analysis of each lncRNA node highlighted that the top five lncRNAs—NEAT1, MALAT1, XIST, OIP5-AS1, and SNHG14—exhibited the highest node scores in the network. These lncRNAs were considered involved in OXP-resistance and were selected for further analysis.
This network contains 278 nodes and 2012 edges. Orange and green rectangles display up- and down-regulated genes, respectively; yellow ellipses represent lncRNAs, and blue hexagons represent miRNAs.
The prognostic impact of selected lncRNA expression on overall survival in CRC patients: Insights from the KM plotter platform
The potential association between the expression levels of each selected lncRNA and the OS of CRC patients was assessed using an available online database. The results indicated that high expression levels of lncRNAs NEAT1, MALAT1, and OIP5-AS1 were significantly associated with reduced OS in CRC patients. However, XIST was not found to be associated with OS in CRC. Additionally, no data was available for SNHG14 (Fig 8).
The horizontal axis of the graph shows the overall survival time in years, while the vertical axis indicates the corresponding survival probability. (A) NEAT1, (B) MALAT1, (C) XIST and (D) OIP5-AS1.
Finally, the lncRNAs NEAT1, MALAT1, and OIP5-AS1, which had high degree scores in the lncRNA-miRNA-mRNA network and significant associations with the overall survival (OS) of CRC patients, were identified as key lncRNAs potentially associated with OXP-resistance in CRC. To elucidate the biological functions of the three lncRNAs, the genes associated with these lncRNAs were enriched using ClusterProfiler. The results revealed that genes connected to the lncRNAs were involved in pathways such as the MAPK signaling pathway, PI3K/Akt signaling pathway, focal adhesion, chemical carcinogenesis, cAMP signaling pathway, and Ras signaling pathway (Fig 9 and S8 Table).
This pathway performed using ClusterProfiler.
Comprehensive characterization of HCT116/OXR sub-cell line
HCT116/OXR cells were generated by exposing parental HCT116 cells to gradually increasing concentrations of OXP over a period of 10 months (Fig 10A). To ensure the establishment of acquired OXP-resistant cells, characterization assessments were performed. The cell viability assay showed that the IC50 value for parental HCT116 cells was 8.95 μM, whereas the HCT116/OXR sub-cell line exhibited an IC50 value of 71.48 μM (Fig 10B). The OXP-resistance index for HCT116/OXR was determined to be eight times higher than that of parental cells. Following a drug-free culture of HCT116/OXR cells for three weeks and an extended period, the drug-resistant cells consistently demonstrated stable IC50 and relative resistance values. The growth curve analysis showed a slower growth rate of HCT116/OXR cells compared to HCT116 cells (Fig 10C). The doubling time in the HCT116/OXR sub-cell line was notably extended, averaging approximately 47 h. This represents an increase of about 1.8-fold compared to the parental cells, which had a doubling time of 27 h. The microscopic observation of cells using inverted microscopy indicated morphological alterations in HCT116/OXR compared to parental cells. The morphology of HCT116/OXR cells exhibited a pebble-like epithelial phenotype, with cells appearing smaller and more rounded in shape (Fig 10D).
(A) The process of establishing OXP-resistant cells involved gradually exposing parental HCT116 cells to increasing concentrations of OXP over a period. (B) Cell viability dose-response curves for parental HCT116 and HCT116/OXR cells exposed to different concentrations of OXP (0.1–1000 μM). (C) Growth curve of parental HCT116 and HCT116/OXR cells. (D) Morphology of parental HCT116 and HCT116/OXR cells. OXP, Oxaliplatin; HCT116/OXR, Oxaliplatin-resistance HCT116.
The cell apoptosis assay demonstrated that OXP significantly induced apoptosis in parental HCT116 cells. However, under the same conditions, the OXP-resistant cells showed persistence despite OXP-induced apoptosis, with no significant alterations observed (Fig 11A). Analysis of cell cycle phase distribution showed that exposure to OXP led to significant S and G2/M phase arrest in parental HCT116 cells, whereas HCT116/OXR cells exhibited a delay in the G1 phase (Fig 11B). These results suggest that OXP-resistant cells overcome the cytotoxic effects of OXP by experiencing a prolonged G1 phase and reduced cell growth rate, which provides sufficient time for DNA damage repair. Moreover, by slowing down the cell cycle, they prevent the incorporation of drug metabolites into DNA. This strongly indicates the emergence of successful OXP-resistant sub-cell line s. In contrast, parental cells exhibit a reduced G1 phase and enter the S phase earlier. However, due to their inability to synthesize DNA and the presence of DNA damage, parental cells accumulate in the G2/M phase. Ultimately, because of the inability to repair DNA, apoptosis is initiated in parental cells.
(A) Left: Flow cytometric histograms of apoptosis induced by OXP in cells. Apoptosis was analyzed using Annexin V/PI staining. The diagram represents live cells (lower left), early apoptotic cells (lower right), late apoptotic cells (upper right), and necrotic cells (upper left). Right: Bar diagrams, representing the average counts of apoptotic cells obtained from at least two independent experiments. The percentage of early and late apoptotic cells determined the apoptosis rate. The data are presented as means ± SD (*p < 0.05). (B) Left: Flow cytometric histograms of cell cycle phase distribution under treatment with OXP in parental HCT116 and HCT116/OXR cells. Right: Stacked bar graphs showing the average counts of cells at cell cycle phase obtained from at least two independent experiments. OXP, Oxaliplatin; HCT116/OXR, Oxaliplatin-resistance HCT116.
Comprehensive characterization of isolated exosomes
The exosomes were isolated from the parental HCT116 and HCT116/OXR cells according to the sequential steps indicated in Fig 12A and then characterized. TEM analysis showed the presence of exosomes with typical round shapes and bilayer membranes (Fig 12B). DLS revealed the expected sizes of exosomes, indicating that the average size was approximately 93.8 nm for HCT116 and 70.8 nm for HCT116/OXR (Fig 12C). Western blot analysis confirmed the enrichment of well-known exosome markers, CD9 and CD63, in the isolated exosomes (Fig 12D). These results demonstrate the successful isolation of exosomes.
(A) The process of exosome isolation, including stepwise centrifugation and the use of an isolation kit. (B) Representative TEM images showing the presence and morphology of exosomes. (C) Size distribution graphs of exosomes detected by DLS analysis. (D) Western blot analysis demonstrating the detection of exosomal markers CD9 and CD63 in isolated exosomes.
Association of cellular and exosomal NEAT1, MALAT1, and OIP5-AS1 OXP-resistant in CRC
The experimental validation of the expression levels of the predicted key lncRNAs—NEAT1, MALAT1, and OIP5-AS1—in both HCT116/OXR and parental HCT116 cells was performed through qRT-PCR. The results revealed a significantly elevated expression level of all three lncRNAs in HCT116/OXR cells compared to parental cells (Fig 13A). This significant up-regulation shows a potential association between these lncRNAs and the observed phenotypic differences in the OXP-resistant cell line. As exosomes are well-recognized as critical mediators of intercellular communication, with their contents contributing to chemotherapy resistance [72], the exosomal form of these key lncRNAs in both parental and resistance-derived exosomes were also investigated. The results indicated that NEAT1, OIP5-AS1, and MALAT1 were detectable in these isolated exosomes, with their expression levels being higher in the OXP-resistant group compared to the parental group (Fig 13B). Our data suggest that exosomal NEAT1, OIP5-AS1, and MALAT1 could serve as promising diagnostic biomarkers for OXP-resistance in CRC.
(A) Bar diagrams present the expression levels of key lncRNAs in parental and OXP-resistance cells (B) Bar diagrams present the expression levels of key lncRNAs in isolated exosomes from parental and OXP-resistance cells. The data are presented as means ± SD. (* p < 0.05, **p < 0.01, and ***p < 0.001). OXP, Oxaliplatin; HCT116/OXR, Oxaliplatin-resistance HCT116; Exo, Exosome.
Discussion
Resistance to OXP is a cause of treatment failure and reduced survival rates in CRC patients, which remains a significant challenge in treatment [12]. Studies have identified several lncRNAs involved in OXP-resistance in CRC [14]. Considering the pivotal role of lncRNAs in the induction of chemotherapy resistance, investigating additional lncRNAs is crucial to enhancing treatment efficacy and overall patient survival. Furthermore, investigating the presence of lncRNAs in exosomes derived from OXP-resistant cells could reveal novel biomarkers for diagnosing resistance. Therefore, In the present study, we employed an in-silico approach to predict lncRNAs associated with OXP-resistance and then validated them using qRT-PCR in CRC cells and their isolated exosome.
Initially OXP-resistance associated DEGs from GSE42387 were obtained and a PPI network was constructed to identify the interconnected DEGs. Subsequently, PXDN, SPP1, FGF9, NES, JUN, BDNF, ITGB4, PRKACB, IL-18, and CD274 were selected as central genes exhibiting the highest degree in the PPI network. Previous studies have reported that these genes play critical roles in cancer progression and chemotherapy resistance by regulating signaling pathways such as PI3K/Akt and Wnt/β-catenin, which are known to influence essential cellular processes including cell proliferation, survival and migration. For example, SPP1, encoding osteopontin, plays a crucial role in cell-matrix interactions and promotes tumor progression across various cancers, including CRC, prostate cancer, ovarian cancer, cervical cancer, and head and neck squamous cell carcinoma (HNSCC) [73, 74]. Elevated SPP1 expression activates the integrin β1/FAK/Akt pathway, promoting cell proliferation, migration, and invasion [75]. Moreover, SPP1 upregulation correlates with resistance to platinum-based drug and poorer survival rates in cancer patients [76–79]. Conversely, downregulation of SPP1 expression promotes sensitivity to cisplatin by inhibiting the PI3K/Akt pathway in cervical cancer cells [80]. JUN, a component of the AP-1 (Activator Protein-1) transcription factor, promotes invasion and metastasis in various cancers when overexpressed. Zhang et al,. observed a significant correlation between SPP1 and JUN expression and reduced survival rates in oral cancer patients [81]. Additionally, Liang et al., demonstrated that the upregulation of LINC00174 induced by c-JUN contributes to proliferation and invasion in CRC [82]. FGF9, identified as a proto-oncogene, contributes to CRC progression and confers resistance to cisplatin by modulating the Wnt/β-catenin signaling pathway [83, 84]. Makondi et al. highlighted a positive correlation between FGF9 and PRKACB expression in irinotecan-resistant CRC [85]. NES, frequently overexpressed in cancer cells, correlates with poor prognosis and cisplatin resistance, indicating its role in acquired drug resistance [86–88]. Li et al. reported significantly elevated NES expression in CRC tissues compared to normal tissues, showing that NES knockdown inhibits proliferation and migration of CRC cells. Their findings suggest that NES plays a critical role in promoting CRC growth and metastasis [89]. BDNF activates tropomyosin-related receptor kinase B (TrkB), initiating critical signaling pathways such as RAS/MAPK, PI3K/Akt, and JAK2/STAT3, which influence cancer progression and affect cellular processes including proliferation and migration [90, 91]. PXDN dysregulation is observed in various cancers, influencing the tumor microenvironment and immune cell infiltration. Overexpression of PXDN correlates with increased proliferation, EMT, migration, invasion, and poor clinical outcomes [92, 93]. In pancreatic cancer, high PXDN expression is associated with higher IC50 levels for drugs, indicating resistance [94]. ITGB4, highly expressed in CRC tissues, correlates with decreased overall survival [95]. As an integrin molecule and cell-surface receptor, ITGB4 is responsible for extracellular matrix interactions and regulates various cell signaling pathways, including proliferation, differentiation, migration, and invasion. High ITGB4 expression is a prognostic factor in CRC and is associated with drug resistance mechanisms in breast cancer, such as resistance to tamoxifen-induced apoptosis via the PI3K/Akt pathway and anoikis resistance through RAC1 signaling [95, 96]. CD274, encoding PD-L1, plays a critical role in cancer by suppressing immune responses through interaction with PD-1 on T cells [97, 98]. PD-L1 influences various cancer processes such as cell growth, metastasis, and chemotherapy resistance [99]. CD274 expression on tumor cells, contributes to immune evasion and chemotherapy resistance, including OXP-resistance in CRC, as demonstrated by Yu et al. [99]. Exploring the role of the top ten central genes in cancer progression and drug resistance not only identifies them as key candidates for predicting cancer- and resistance-related lncRNAs but also deepens our comprehension of CRC biology, providing potential targets for therapeutic intervention.
The potential upstream regulatory miRNAs targeting the top ten genes were predicted and intersected with miRNAs obtained from RNA-seq data to identify common miRNAs and construct miRNA-mRNA pairs. Several studies have demonstrated the involvement of some of these miRNAs in chemotherapy resistance across various cancers by regulating gene expression involved in key cellular processes. For example, miR-195 exhibits dual roles as an oncogene and tumor suppressor, influencing cell proliferation, apoptosis, metastasis, invasion, and chemosensitivity by targeting specific genes in various cancers [100]. In hepatocellular carcinoma, miR-195-3p contributes to OXP-resistance through the TINCR/miR-195-3p/ST6GAL1/NF-κB signaling axis [101]. Similarly, in gastric cancer, it contributes to OXP-resistance via the HOTAIR/miR-195-5p/ABCG2 axis, where the lncRNA HOTAIR acts as a ceRNA to sponge miR-195-5p, thereby upregulating ABCG2 expression and promoting OXP-resistance in cancer cells [102]. The miR-27b-3p and miR-15a-5p are implicated in OXP-resistance in CRC through distinct regulatory mechanisms. MiR-27b-3p enhances the sensitivity of CRC cells to OXP by suppressing autophagy via inhibition of ATG10. The regulatory axis involving c-Myc/miR-27b-3p/ATG10 plays a pivotal role in modulating OXP-resistance [103]. Conversely, inhibition of miR-27b-3p by c-Myc promotes OXP-resistance, underscoring the critical role of miR-27b-3p in this context. On the other hand, miR-15a-5p contributes to OXP-resistance in CRC cells through the SIRT4 axis. This axis affects multiple signaling pathways including STAT3/TWIST1 and PETN/Akt, which are known to influence cellular responses to chemotherapy [104]. The miR-181a-5p/miR-382-5p/CELF1 axis is pivotal in regulating cisplatin resistance in lung squamous cell carcinoma. The lncRNA DLX6-AS1 negatively correlates with miR-181a-5p and miR-382-5p expression, thereby modulating CELF1 expression and contributing to drug resistance [105]. Additionally, lncRNAs CRNDE, ANRIL, and CASC15 interact with miR-181a-5p to promote OXP-resistance in CRC through ATP-binding protein activation [106]. MiR-181a-5p indirectly binds to glucose-related protein (GRP78), promoting tumor progression and OXP-resistance. Low miR-181a expression is significantly associated with cervical cancer growth and OXP-resistance [107]. MiR-205 exhibits a dual role as either an oncogene or a tumor suppressor depending on the cancer type and specific targets [108]. It influences proliferation, tumor progression, and invasion. In some contexts, miR-205 is targeted by lncRNA NEAT1, affecting the miR-205-5p/VEGFA axis [109], and by lncRNA ZEB1-AS1, influencing the miR-205/YAP1 axis [110]. Other miRNAs such as miR-30a [111], miR-23a [112] and miR-23b [113], miR-329 [114], miR-130 [115], miR-192 [116] and miR-190 [117] also impact various cellular processes by targeting specific genes, contributing to CRC progression and drug resistance.
The miRNAs target mRNAs to modulate their expression, whereas lncRNAs target miRNAs, thereby regulating both miRNAs and their downstream target genes [118]. lncRNAs directly or indirectly modulate expression of genes [3]. Mounting evidence suggests that analyzing the intricate lncRNA-miRNA-mRNA regulatory network provides insights into the molecular mechanisms underlying chemotherapy resistance, introducing lncRNAs as novel biomarkers for diagnosis and potential targets for drug-resistant cancers [119, 120]. In the present study, lncRNAs were predicted based on their interactions with miRNAs, and subsequently, an lncRNA-miRNA-mRNA network was constructed. Following this, the lncRNAs NEAT1, MALAT1, and OIP5-AS1 were identified as key candidates potentially associated with OXP-resistance in CRC. Examining the expression patterns of the predicted lncRNAs revealed statistically significant elevations in the OXP-resistant cells compared to the parental cells. Considering that exosomes are crucial mediators in cell communication and transfer drug resistance features from resistance cells to sensitive recipient cells by carrying cargo such as lncRNAs [33, 72], we explored the expression patterns of the predicted lncRNAs in exosomes isolated from OXP-resistant cells compared to parental cells.
NEAT1, an oncogenic lncRNA, induces cell proliferation, metastasis, invasion, and therapy resistance, while suppresses apoptosis in cancer cells [121]. Studies have shown that a high level of NEAT1 expression is associated with a poor OS and a poor prognosis in CRC patients [122]. NEAT1 was reported to activate the Wnt/β-catenin signaling pathway significantly, thereby contributing to the progression of CRC [123]. This lncRNA can act as a ceRNA by sponging miRNAs, thereby targeting them to regulate mRNA and influence signaling pathways [124]. Numerous studies have reported that NEAT1 expression is associated with chemotherapy resistance in various cancer cells and isolated exosomes. NEAT1 has been implicated in platinum resistance in various cancers. Li et al., reported that upregulated NEAT1 regulates OXP-resistance in gastric cancer [125]. Furthermore, Liu et al., demonstrated that NEAT1 contributes to cisplatin resistance by modulating Rsf-1 expression and the Ras-MAPK pathway in nasopharyngeal carcinoma [126]. Zhu et al. reported that the NEAT1/miR-770-5p/PARP1 axis mediates cisplatin resistance in ovarian cancer, with NEAT1 regulating PARP1 [127]. NEAT1 also has the potential to contribute to resistance against other chemotherapeutic agents. For instance, a study demonstrated that NEAT1 promotes EMT and sorafenib resistance in renal cell carcinoma through the miR-34a/c-Met axis [128]. NEAT1 was also found to promote 5-FU resistance and modulate autophagy in CRC by targeting miR-34a [23]. Its expression was also shown to be elevated in paclitaxel-resistant breast cancer cell lines and their isolated exosomes. The study reported that NEAT1, through targeting miR-133b, modulates CXCL12 and promotes migration, and induces resistant to paclitaxel cells [129]. Our study demonstrated significantly elevated levels of NEAT1 in OXP-resistant cells compared to parental cells, consistent with previous findings in other types of cancer This study may be the first to demonstrate the association of NEAT1 with OXP-resistance in CRC cells and to identify its exosomal form in isolated exosomes, which could potentially serve as a biomarker. However, further investigation is necessary to fully comprehend the mechanism by which NEAT1, including its exosomal form, contributes to OXP-resistance.
MALAT1, known for its oncogenic properties, has emerged as a critical player in several cancers, including CRC, where it enhances cell proliferation, angiogenesis, migration, and invasion while suppressing apoptosis [130]. This lncRNA contributes to CRC tumorigenesis and progression by modulating critical signaling pathways, such as Wnt/β-catenin and PI3K/Akt, and by targeting miRNAs that impact essential cellular processes [131, 132]. For example, a study has shown that MALAT1 contributes to the promotion of proliferation and invasion in CRC via regulating the miR-508-5p/RAB14 axis, which in RAB14, a member of the RAS oncogene family [133]. Similar to NEAT1, evidence has shown that MALAT1 is frequently overexpressed in CRC tissues and implicated in the formation of nuclear speckle bodies, correlating with poor disease prognosis [134]. Notably, its upregulation is implicated in conferring resistance to platinum-based chemotherapies like OXP and cisplatin. For example, MALAT1 promotes resistance to cisplatin in cervical cancer by inhibiting apoptosis through activation of the PI3K/Akt pathway [135]. Furthermore, it can enhance the development of cisplatin resistance in lung cancer, ultimately resulting in a significantly poor prognosis [136]. Li et al. reported that increased MALAT1 expression correlated with decreased survival rates and poorer response to oxaliplatin-based chemotherapy in advanced CRC patients [137]. Wei et al. demonstrated that MALAT1 promotes OXP-resistance in HCT116 cell by influencing the JNK pathway cells [138]. Previous studies have demonstrated that the intercellular transfer of MALAT1 through exosomes enhances proliferation, invasion, and metastasis in various cancers. For instance, in a study, it was revealed that exosomal MALAT1 promotes invasion and metastasis in CRC cells by regulating FUT4 and activating the PI3K/Akt/mTOR pathway [132]. In breast cancer tissues, elevated MALAT1 levels are associated with disease progression, and exosomal MALAT1 induces cell proliferation [139]. Studies have also reported that exosomal MALAT1 contributes to chemotherapy resistance in cancer. Hu et al., showed that exosomal MALAT1 regulates the miR-370-3p/STAT3 axis to promote cisplatin resistance in cervical cancer through the activation of the PI3K/Akt pathway [72]. However, the involvement of exosomal MALAT1 in OXP-resistance in CRC has not yet been studied. In the present study, we observed elevated MALAT1 expression in OXP-resistant CRC cells and their isolated exosomes. This finding, along with previous studies, underscores MALAT1 as a potential biomarker for predicting treatment response and targeted therapeutic strategies to overcome OXP-resistance in CRC. Furthermore, our study reports, for the first time, the involvement of exosomal MALAT1 in OXP-resistance in CRC.
Similarly, emerging evidence has demonstrated that dysregulation of OIP5-AS1 contributes to cancer progression and drug resistance across various cancers [140–142]. In gastric cancer, OIP5-AS1 modulates the miR-367-3p/HMGA2 axis to regulate the Wnt/β-catenin and PI3K/Akt pathways, promoting cancer progression [143]. Studies have shown that high expression level of this lncRNA correlate with poor overall survival, which indicates a poor prognosis [142]. OIP5-AS1 regulates cell proliferation, cell cycle, colony formation, apoptosis, migration, invasion, and resistance to chemotherapy and radiotherapy by targeting miRNAs and modulating signaling pathways [144]. Liang et al. reported high expression of OIP5-AS1 in cancer tissues and OXP-resistant cells compared to parental cells, consistent with our findings in the HCT116/OXR sub-cell line [145]. In osteosarcoma, OIP5-AS1 contributes to cisplatin resistance by inducing the LPAATβ/PI3K/Akt/mTOR pathway [146]. Exosomal OIP5-AS1 has been demonstrated to promote resistance to trastuzumab in breast cancer through the miR-381-3p/HMGB3 axis [147]. Our study also demonstrated high levels of this lncRNA in exosomes derived from OXP-resistant CRC cells. This study is the first to establish a link between exosomal OIP5-AS1 and resistance to OXP, suggesting its potential as a biomarker.
Overall, our study has provided new insights into predicting key lncRNAs and has introduced NEAT1, MALAT1, and OIP5-AS1 associated with OXP-resistance in CRC. The limitations of our study include the need to involve diverse CRC cell lines with different levels of OXP-resistance, as well as in-vivo experiment. Further experiments should explore the mechanisms by which these lncRNAs target miRNAs and regulate gene expression within our constructed ceRNA regulatory network. Additionally, it is essential to investigate their involvement in the signaling pathways predicted in this study. Our study showed that these lncRNAs are present at high levels in exosomes derived from OXP-resistant CRC cells, which highlights the need for future studies to understand the mechanisms of their packaging into exosomes. By exploring the roles of exosomal NEAT1, MALAT1, and OIP5-AS1, we could develop personalized treatment strategies.
Conclusion
In-silico approaches applied to the analysis of omics data enable the study of regulatory networks and the identification of predictive biomarkers for cancer diagnosis, progression, treatment, and prevention [148, 149]. Collectively, NEAT1, MALAT1, and OIP5-AS1 lncRNAs are crucial contributors to the development of CRC and hold promise as biomarkers for predicting OXP-resistance. In this study, for the first time, we revealed the expression patterns of NEAT1 in both cells and exosomes, as well as MALAT1 and OIP5-AS1 in the exosomes of OXP-resistant HCT116 cells. To uncover the intricate molecular mechanisms of these lncRNAs, additional in-vitro and in-vivo experiments are essential.
Supporting information
S1 Table. List of DEGs and DElncRNAs identified from GSE42387.
https://doi.org/10.1371/journal.pone.0311680.s001
(XLSX)
S2 Table. Functional enrichment analysis of oxaliplatin resistance-associated DEGs.
https://doi.org/10.1371/journal.pone.0311680.s002
(XLSX)
S3 Table. List of predicted miRNAs from TarBase and miRTarBase databases.
https://doi.org/10.1371/journal.pone.0311680.s003
(XLSX)
S4 Table. List of DEmiRNAs identified from GSE119481.
https://doi.org/10.1371/journal.pone.0311680.s004
(XLSX)
S5 Table. List of miRNAs overlapped between the predicted miRNAs and the DEmiRNAs from GSE119481.
https://doi.org/10.1371/journal.pone.0311680.s005
(XLSX)
S7 Table. Enrichment analysis of predicted lncRNAs through LncSEA.
https://doi.org/10.1371/journal.pone.0311680.s007
(XLSX)
S8 Table. Pathway analysis of genes associated with key lncRNAs.
https://doi.org/10.1371/journal.pone.0311680.s008
(XLSX)
Acknowledgments
The authors would like to thank Ms. Sepideh Karoobi and Ms. Shohreh Tavajohi for their technical assistance.
References
- 1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians. 2021;71(3):209–49. pmid:33538338
- 2. Dutta A, Pratiti R, Kalantary A, Aboulian A, Shekherdimian S. Colorectal Cancer: A Systematic Review of the Current Situation and Screening in North and Central Asian Countries. Cureus. 2023;15(1):e33424. pmid:36751203
- 3. Ahmad R, Singh JK, Wunnava A, Al-Obeed O, Abdulla M, Srivastava SK. Emerging trends in colorectal cancer: Dysregulated signaling pathways (Review). International journal of molecular medicine. 2021;47(3). pmid:33655327
- 4. Dekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB. Colorectal cancer. Lancet (London, England). 2019;394(10207):1467–80. pmid:31631858
- 5. Wang D, Lippard SJ. Cellular processing of platinum anticancer drugs. Nature reviews Drug discovery. 2005;4(4):307–20. pmid:15789122
- 6. Hammond WA, Swaika A, Mody K. Pharmacologic resistance in colorectal cancer: a review. Therapeutic advances in medical oncology. 2016;8(1):57–84. pmid:26753006
- 7. Mauri G, Gori V, Bonazzina E, Amatu A, Tosi F, Bencardino K, et al. Oxaliplatin retreatment in metastatic colorectal cancer: Systematic review and future research opportunities. Cancer Treatment Reviews. 2020;91:102112. pmid:33091698
- 8. Huang WS, Hsieh MC, Huang CY, Kuo YH, Tung SY, Shen CH, et al. The Association of CXC Receptor 4 Mediated Signaling Pathway with Oxaliplatin-Resistant Human Colorectal Cancer Cells. PloS one. 2016;11(9):e0159927. pmid:27668882
- 9. William-Faltaos S, Rouillard D, Lechat P, Bastian G. Cell cycle arrest by oxaliplatin on cancer cells. Fundamental & clinical pharmacology. 2007;21(2):165–72. pmid:17391289
- 10. Noordhuis P, Laan AC, van de Born K, Losekoot N, Kathmann I, Peters GJ. Oxaliplatin activity in selected and unselected human ovarian and colorectal cancer cell lines. Biochemical pharmacology. 2008;76(1):53–61. pmid:18508032
- 11. Cheraghi-Shavi T, Jalal R, Minuchehr Z. TGM2, HMGA2, FXYD3, and LGALS4 genes as biomarkers in acquired oxaliplatin resistance of human colorectal cancer: A systems biology approach. PloS one. 2023;18(8):e0289535. pmid:37535601
- 12. Lin Q, Luo L, Wang H. A New Oxaliplatin Resistance-Related Gene Signature With Strong Predicting Ability in Colon Cancer Identified by Comprehensive Profiling. Frontiers in oncology. 2021;11:644956. pmid:34026619
- 13. O’Dowd PD, Sutcliffe DF, Griffith DM. Oxaliplatin and its derivatives–An overview. Coordination Chemistry Reviews. 2023;497:215439.
- 14. Luo ZD, Wang YF, Zhao YX, Yu LC, Li T, Fan YJ, et al. Emerging roles of non-coding RNAs in colorectal cancer oxaliplatin resistance and liquid biopsy potential. World J Gastroenterol. 2023;29(1):1–18. pmid:36683709
- 15. Statello L, Guo C-J, Chen L-L, Huarte M. Gene regulation by long non-coding RNAs and its biological functions. Nature Reviews Molecular Cell Biology. 2021;22(2):96–118. pmid:33353982
- 16. Sun B, Liu C, Li H, Zhang L, Luo G, Liang S, et al. Research progress on the interactions between long non-coding RNAs and microRNAs in human cancer. Oncology letters. 2020;19(1):595–605. pmid:31897175
- 17. Siddiqui H, Al-Ghafari A, Choudhry H, Al Doghaither H. Roles of long non-coding RNAs in colorectal cancer tumorigenesis: A Review. Molecular and clinical oncology. 2019;11(2):167–72. pmid:31281651
- 18. Yang Y, Junjie P, Sanjun C, Ma Y. Long non-coding RNAs in colorectal cancer: progression and future directions. Journal of Cancer. 2017;8(16):3212. pmid:29158793
- 19. He Q, Long J, Yin Y, Li Y, Lei X, Li Z, et al. Emerging roles of lncRNAs in the formation and progression of colorectal cancer. Frontiers in oncology. 2020;9:1542. pmid:32010629
- 20. Zhang W, Guan X, Tang J. The long non-coding RNA landscape in triple-negative breast cancer. Cell proliferation. 2021;54(2):e12966. pmid:33314471
- 21. Fan C, Yuan Q, Liu G, Zhang Y, Yan M, Sun Q, et al. Long non-coding RNA MALAT1 regulates oxaliplatin-resistance via miR-324-3p/ADAM17 axis in colorectal cancer cells. Cancer cell international. 2020;20:473. pmid:33005106
- 22. Azizidoost S, Ghaedrahmati F, Anbiyaee O, Ahmad Ali R, Cheraghzadeh M, Farzaneh M. Emerging roles for lncRNA-NEAT1 in colorectal cancer. Cancer cell international. 2022;22(1):209. pmid:35676702
- 23. Liu F, Ai FY, Zhang DC, Tian L, Yang ZY, Liu SJ. LncRNA NEAT1 knockdown attenuates autophagy to elevate 5-FU sensitivity in colorectal cancer via targeting miR-34a. Cancer medicine. 2020;9(3):1079–91. pmid:31802650
- 24. Weng X, Liu H, Ruan J, Du M, Wang L, Mao J, et al. HOTAIR/miR-1277-5p/ZEB1 axis mediates hypoxia-induced oxaliplatin resistance via regulating epithelial-mesenchymal transition in colorectal cancer. Cell Death Discovery. 2022;8(1):310. pmid:35798695
- 25. Yue B, Cai D, Liu C, Fang C, Yan D. Linc00152 functions as a competing endogenous RNA to confer oxaliplatin resistance and holds prognostic values in colon cancer. Molecular therapy. 2016;24(12):2064–77. pmid:27633443
- 26. Gao H, Song X, Kang T, Yan B, Feng L, Gao L, et al. Long noncoding RNA CRNDE functions as a competing endogenous RNA to promote metastasis and oxaliplatin resistance by sponging miR-136 in colorectal cancer. OncoTargets and therapy. 2017;10:205–16. pmid:28115855
- 27. Meng X, Sun W, Yu J, Zhou Y, Gu Y, Han J, et al. LINC00460-miR-149-5p/miR-150-5p-Mutant p53 Feedback Loop Promotes Oxaliplatin Resistance in Colorectal Cancer. Molecular therapy Nucleic acids. 2020;22:1004–15. pmid:33251049
- 28. Li Y, Li C, Li D, Yang L, Jin J, Zhang B. lncRNA KCNQ1OT1 enhances the chemoresistance of oxaliplatin in colon cancer by targeting the miR-34a/ATG4B pathway. OncoTargets and therapy. 2019;12:2649–60. pmid:31040703
- 29. Sun J, Zhou H, Bao X, Wu Y, Jia H, Zhao H, et al. lncRNA TUG1 Facilitates Colorectal Cancer Stem Cell Characteristics and Chemoresistance by Enhancing GATA6 Protein Stability. Stem cells international. 2021;2021:1075481. pmid:34858502
- 30. Li L, Shang J, Zhang Y, Liu S, Peng Y, Zhou Z, et al. MEG3 is a prognostic factor for CRC and promotes chemosensitivity by enhancing oxaliplatin-induced cell apoptosis. Oncology reports. 2017;38(3):1383–92. pmid:28731151
- 31. Wu S, Yang X, Tang W, Familiari G, Relucenti M, Aschner M, et al. Chemotherapeutic Risk lncRNA-PVT1 SNP Sensitizes Metastatic Colorectal Cancer to FOLFOX Regimen. Frontiers in oncology. 2022;12:808889. pmid:35433465
- 32. Li Y, Gan Y, Liu J, Li J, Zhou Z, Tian R, et al. Downregulation of MEIS1 mediated by ELFN1-AS1/EZH2/DNMT3a axis promotes tumorigenesis and oxaliplatin resistance in colorectal cancer. Signal transduction and targeted therapy. 2022;7(1):87. pmid:35351858
- 33. Li S, Yi M, Dong B, Jiao Y, Luo S, Wu K. The roles of exosomes in cancer drug resistance and its therapeutic application. Clinical and translational medicine. 2020;10(8):e257. pmid:33377643
- 34. Mahmoudi-Aznaveh A, Tavoosidana G, Najmabadi H, Azizi Z, Ardestani A. The liver-derived exosomes stimulate insulin gene expression in pancreatic beta cells under condition of insulin resistance. Frontiers in Endocrinology. 2023;14. pmid:38027137
- 35. Wei H, Chen Q, Lin L, Sha C, Li T, Liu Y, et al. Regulation of exosome production and cargo sorting. International journal of biological sciences. 2021;17(1):163–77. pmid:33390841
- 36. Krylova SV, Feng D. The machinery of exosomes: biogenesis, release, and uptake. International journal of molecular sciences. 2023;24(2):1337. pmid:36674857
- 37. Ren B, Li X, Zhang Z, Tai S, Yu S. Exosomes: a significant medium for regulating drug resistance through cargo delivery. Frontiers in Molecular Biosciences. 2024;11. pmid:39135913
- 38. Li J, Gao N, Gao Z, Liu W, Pang B, Dong X, et al. The Emerging Role of Exosomes in Cancer Chemoresistance. Frontiers in Cell and Developmental Biology. 2021;9. pmid:34778252
- 39. Ye Y, Li Y, Wu C, Shan Y, Li J, Jiang D, et al. Exosomes Mediate the Production of Oxaliplatin Resistance and Affect Biological Behaviors of Colon Cancer Cell Lines. Current cancer drug targets. 2024. pmid:38956907
- 40. Stefańska K, Józkowiak M, Angelova Volponi A, Shibli JA, Golkar-Narenji A, Antosik P, et al. The Role of Exosomes in Human Carcinogenesis and Cancer Therapy-Recent Findings from Molecular and Clinical Research. Cells. 2023;12(3). pmid:36766698
- 41. Ren J, Ding L, Zhang D, Shi G, Xu Q, Shen S, et al. Carcinoma-associated fibroblasts promote the stemness and chemoresistance of colorectal cancer by transferring exosomal lncRNA H19. Theranostics. 2018;8(14):3932–48. pmid:30083271
- 42. Deng X, Ruan H, Zhang X, Xu X, Zhu Y, Peng H, et al. Long noncoding RNA CCAL transferred from fibroblasts by exosomes promotes chemoresistance of colorectal cancer cells. International journal of cancer. 2020;146(6):1700–16. pmid:31381140
- 43. Lin S, Zhu B. Exosome-transmitted FOSL1 from cancer-associated fibroblasts drives colorectal cancer stemness and chemo-resistance through transcriptionally activating ITGB4. Molecular and Cellular Biochemistry. 2024;479(3):665–77. pmid:37160555
- 44. Sun R, He XY, Mei C, Ou CL. Role of exosomal long non-coding RNAs in colorectal cancer. World journal of gastrointestinal oncology. 2021;13(8):867–78. pmid:34457192
- 45. Zhu S, Mao J, Zhang X, Wang P, Zhou Y, Tong J, et al. CAF-derived exosomal lncRNA FAL1 promotes chemoresistance to oxaliplatin by regulating autophagy in colorectal cancer. Digestive and liver disease: official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver. 2024;56(2):330–42. pmid:37400281
- 46. Chen X, Liu Y, Zhang Q, Liu B, Cheng Y, Zhang Y, et al. Exosomal Long Non-coding RNA HOTTIP Increases Resistance of Colorectal Cancer Cells to Mitomycin via Impairing MiR-214-Mediated Degradation of KPNA3. Frontiers in cell and developmental biology. 2020;8:582723. pmid:33585440
- 47. Jensen NF, Stenvang J, Beck MK, Hanáková B, Belling KC, Do KN, et al. Establishment and characterization of models of chemotherapy resistance in colorectal cancer: Towards a predictive signature of chemoresistance. Molecular oncology. 2015;9(6):1169–85. pmid:25759163
- 48. Gasiulė S, Dreize N, Kaupinis A, Ražanskas R, Čiupas L, Stankevičius V, et al. Molecular Insights into miRNA-Driven Resistance to 5-Fluorouracil and Oxaliplatin Chemotherapy: miR-23b Modulates the Epithelial–Mesenchymal Transition of Colorectal Cancer Cells. Journal of clinical medicine. 2019;8(12):2115.
- 49. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15(12):550. pmid:25516281
- 50. Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1):D638–d46. pmid:36370105
- 51. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. pmid:14597658
- 52. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216–w21.
- 53. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols. 2009;4(1):44–57. pmid:19131956
- 54. Tang Y, Li M, Wang J, Pan Y, Wu FX. CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems. 2015;127:67–72. pmid:25451770
- 55. Huang HY, Lin YC, Cui S, Huang Y, Tang Y, Xu J, et al. miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 2022;50(D1):D222–d30. pmid:34850920
- 56. Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, et al. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res. 2018;46(D1):D239–d45. pmid:29156006
- 57. Paraskevopoulou MD, Karagkouni D, Vlachos IS, Tastsoglou S, Hatzigeorgiou AG. microCLIP super learning framework uncovers functional transcriptome-wide miRNA interactions. Nature Communications. 2018;9(1):3601. pmid:30190538
- 58. Karagkouni D, Paraskevopoulou MD, Tastsoglou S, Skoufos G, Karavangeli A, Pierros V, et al. DIANA-LncBase v3: indexing experimentally supported miRNA targets on non-coding transcripts. Nucleic Acids Res. 2020;48(D1):D101–d10. pmid:31732741
- 59. Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic acids research. 2014;42(Database issue):D92–7. pmid:24297251
- 60. Chen J, Zhang J, Gao Y, Li Y, Feng C, Song C, et al. LncSEA: a platform for long non-coding RNA related sets and enrichment analysis. Nucleic Acids Res. 2021;49(D1):D969–d80. pmid:33045741
- 61. Győrffy B. Discovery and ranking of the most robust prognostic biomarkers in serous ovarian cancer. Geroscience. 2023;45(3):1889–98. pmid:36856946
- 62. Kovács SA, Fekete JT, Győrffy B. Predictive biomarkers of immunotherapy response with pharmacological applications in solid tumors. Acta Pharmacol Sin. 2023;44(9):1879–89. pmid:37055532
- 63. McDermott M, Eustace A, Busschots S, Breen L, Clynes M, O’Donovan N, et al. In vitro Development of Chemotherapy and Targeted Therapy Drug-Resistant Cancer Cell Lines: A Practical Guide with Case Studies. Frontiers in oncology. 2014;4.
- 64. Eke I, Cordes N. Focal adhesion signaling and therapy resistance in cancer. Seminars in cancer biology. 2015;31:65–75. pmid:25117005
- 65. Zhang Y, Liu X, Zhang J, Xu Y, Shao J, Hu Y, et al. Inhibition of miR-19a partially reversed the resistance of colorectal cancer to oxaliplatin via PTEN/PI3K/AKT pathway. Aging. 2020;12(7):5640–50. pmid:32209726
- 66. Chen Y, Deng G, Fu Y, Han Y, Guo C, Yin L, et al. FOXC2 Promotes Oxaliplatin Resistance by Inducing Epithelial-Mesenchymal Transition via MAPK/ERK Signaling in Colorectal Cancer. OncoTargets and therapy. 2020;13:1625–35. pmid:32110058
- 67. Nersisyan S, Novosad V, Engibaryan N, Ushkaryov Y, Nikulin S, Tonevitsky A. ECM-Receptor Regulatory Network and Its Prognostic Role in Colorectal Cancer. Front Genet. 2021;12:782699. pmid:34938324
- 68. Janiszewska M, Primi MC, Izard T. Cell adhesion in cancer: Beyond the migration of single cells. Journal of Biological Chemistry. 2020;295(8):2495–505. pmid:31937589
- 69. Fu D, Hu Z, Xu X, Dai X, Liu Z. Key signal transduction pathways and crosstalk in cancer: Biological and therapeutic opportunities. Translational oncology. 2022;26:101510. pmid:36122506
- 70. Gutschner T, Diederichs S. The hallmarks of cancer: a long non-coding RNA point of view. RNA Biol. 2012;9(6):703–19. pmid:22664915
- 71.
Leandro G, Erika Z, Carolina M, Jéssica B, Daniela G, Jaqueline O. lncRNAs in Hallmarks of Cancer and Clinical Applications. In: Lütfi T, Sümer A, Esen T, editors. Non-Coding RNAs. Rijeka: IntechOpen; 2019. p. Ch. 8.
- 72. Hu Y, Li G, Ma Y, Luo G, Wang Q, Zhang S. Effect of Exosomal lncRNA MALAT1/miR-370-3p/STAT3 Positive Feedback Loop on PI3K/Akt Pathway Mediating Cisplatin Resistance in Cervical Cancer Cells. Journal of oncology. 2023;2023:6341011. pmid:36793374
- 73. Pang X, Xie R, Zhang Z, Liu Q, Wu S, Cui Y. Identification of SPP1 as an Extracellular Matrix Signature for Metastatic Castration-Resistant Prostate Cancer. Frontiers in oncology. 2019;9:924. pmid:31620371
- 74. Gao W, Liu D, Sun H, Shao Z, Shi P, Li T, et al. SPP1 is a prognostic related biomarker and correlated with tumor-infiltrating immune cells in ovarian cancer. BMC cancer. 2022;22(1):1367.
- 75. Asare-Werehene M, Nakka K, Reunov A, Chiu C-T, Lee W-T, Abedini MR, et al. The exosome-mediated autocrine and paracrine actions of plasma gelsolin in ovarian cancer chemoresistance. Oncogene. 2020;39(7):1600–16. pmid:31700155
- 76. Wei T, Bi G, Bian Y, Ruan S, Yuan G, Xie H, et al. The Significance of Secreted Phosphoprotein 1 in Multiple Human Cancers. Frontiers in molecular biosciences. 2020;7:565383. pmid:33324676
- 77. Yi J, Liu Y, Zhang L, Fang C. Secreted phosphoprotein-1 accelerates the progression of human colorectal cancer through activating β-catenin signaling. Oncology letters. 2021;21(5):372.
- 78. Zhao K, Ma Z, Zhang W. Comprehensive Analysis to Identify SPP1 as a Prognostic Biomarker in Cervical Cancer. Front Genet. 2021;12:732822. pmid:35058964
- 79. Tang H, Chen J, Han X, Feng Y, Wang F. Upregulation of SPP1 Is a Marker for Poor Lung Cancer Prognosis and Contributes to Cancer Progression and Cisplatin Resistance. Frontiers in cell and developmental biology. 2021;9:646390. pmid:33996808
- 80. Chen X, Xiong D, Ye L, Yang H, Mei S, Wu J, et al. SPP1 inhibition improves the cisplatin chemo-sensitivity of cervical cancer cell lines. Cancer chemotherapy and pharmacology. 2019;83(4):603–13. pmid:30627777
- 81. Zhang X, Zhang L, Tan X, Lin Y, Han X, Wang H, et al. Systematic analysis of genes involved in oral cancer metastasis to lymph nodes. Cellular & molecular biology letters. 2018;23:53. pmid:30459815
- 82. Liang W, Liu D, Wu J. c-JUN-induced upregulation of LINC00174 contributes to colorectal cancer proliferation and invasion through accelerating USP21 expression. Cell biology international. 2023;47(11):1782–98. pmid:37434557
- 83. Zhang Z, Zhang Y, Qin X, Wang Y, Fu J. FGF9 promotes cisplatin resistance in colorectal cancer via regulation of Wnt/β-catenin signaling pathway. Experimental and therapeutic medicine. 2020;19(3):1711–8.
- 84. Chen T-M, Shih Y-H, Tseng JT, Lai M-C, Wu C-H, Li Y-H, et al. Overexpression of FGF9 in colon cancer cells is mediated by hypoxia-induced translational activation. Nucleic Acids Research. 2013;42(5):2932–44. pmid:24334956
- 85. Makondi PT, Chu C-M, Wei P-L, Chang Y-J. Prediction of novel target genes and pathways involved in irinotecan-resistant colorectal cancer. PloS one. 2017;12(7):e0180616. pmid:28749961
- 86. Szymańska-Chabowska A, Świątkowski F, Jankowska-Polańska B, Mazur G, Chabowski M. Nestin Expression as a Diagnostic and Prognostic Marker in Colorectal Cancer and Other Tumors. Clinical Medicine Insights Oncology. 2021;15:11795549211038256. pmid:34421318
- 87. Jia R, Song L, Fei Z, Qin C, Zhao Q. Long noncoding RNA Ftx regulates the protein expression profile in HCT116 human colon cancer cells. Proteome science. 2022;20(1):7. pmid:35490216
- 88. Sone K, Maeno K, Masaki A, Kunii E, Takakuwa O, Kagawa Y, et al. Nestin Expression Affects Resistance to Chemotherapy and Clinical Outcome in Small Cell Lung Cancer. Frontiers in oncology. 2020;10:1367. pmid:32903755
- 89. Li J, Wang R, Yang L, Wu Q, Wang Q, Nie Z, et al. Knockdown of Nestin inhibits proliferation and migration of colorectal cancer cells. International journal of clinical and experimental pathology. 2015;8(6):6377. pmid:26261513
- 90. Tanaka K, Okugawa Y, Toiyama Y, Inoue Y, Saigusa S, Kawamura M, et al. Brain-derived neurotrophic factor (BDNF)-induced tropomyosin-related kinase B (Trk B) signaling is a potential therapeutic target for peritoneal carcinomatosis arising from colorectal cancer. PloS one. 2014;9(5):e96410. pmid:24801982
- 91. Serafim V Junior, Fernandes GMdM, Oliveira-Cucolo JGd, Pavarino EC, Goloni-Bertollo EM. Role of Tropomyosin-related kinase B receptor and brain-derived neurotrophic factor in cancer. Cytokine. 2020;136:155270. pmid:32911446
- 92. Wyllie K, Panagopoulos V, Cox TR. The role of peroxidasin in solid cancer progression. Biochemical Society transactions. 2023;51(5):1881–95. pmid:37801286
- 93. Zheng YZ, Liang L. High expression of PXDN is associated with poor prognosis and promotes proliferation, invasion as well as migration in ovarian cancer. Ann Diagn Pathol. 2018;34:161–5. pmid:29661721
- 94. Zhou X, Sun Q, Xu C, Zhou Z, Chen X, Zhu X, et al. A systematic pan-cancer analysis of PXDN as a potential target for clinical diagnosis and treatment. Frontiers in oncology. 2022;12. pmid:35982948
- 95. Jiang X, Wang J, Wang M, Xuan M, Han S, Li C, et al. ITGB4 as a novel serum diagnosis biomarker and potential therapeutic target for colorectal cancer. Cancer medicine. 2021;10(19):6823–34. pmid:34414684
- 96. Fang H, Ren W, Cui Q, Liang H, Yang C, Liu W, et al. Integrin β4 promotes DNA damage-related drug resistance in triple-negative breast cancer via TNFAIP2/IQGAP1/RAC1. eLife. 2023;12.
- 97. Masugi Y, Nishihara R, Yang J, Mima K, da Silva A, Shi Y, et al. Tumour CD274 (PD-L1) expression and T cells in colorectal cancer. Gut. 2017;66(8):1463–73. pmid:27196573
- 98. Sun C, Mezzadra R, Schumacher TN. Regulation and Function of the PD-L1 Checkpoint. Immunity. 2018;48(3):434–52. pmid:29562194
- 99. Yu M, Wang H, Zhao W, Ge X, Huang W, Lin F, et al. Targeting type Iγ phosphatidylinositol phosphate kinase overcomes oxaliplatin resistance in colorectal cancer. Theranostics. 2022;12(9):4386–98. pmid:35673560
- 100. Yu W, Liang X, Li X, Zhang Y, Sun Z, Liu Y, et al. MicroRNA-195: a review of its role in cancers. OncoTargets and therapy. 2018;11:7109–23. pmid:30410367
- 101. Mei J, Lin W, Li S, Tang Y, Ye Z, Lu L, et al. Long noncoding RNA TINCR facilitates hepatocellular carcinoma progression and dampens chemosensitivity to oxaliplatin by regulating the miR-195-3p/ST6GAL1/NF-κB pathway. Journal of experimental & clinical cancer research: CR. 2022;41(1):5.
- 102. Luo Y, Lu X, Ma W, Xiao Y, Wei C, Yuan X, et al. Dampening HOTAIR sensitizes the gastric cancer cells to oxaliplatin through miR-195-5p and ABCG2 pathway. Journal of cellular and molecular medicine. 2023;27(22):3591–600. pmid:37621132
- 103. Sun W, Li J, Zhou L, Han J, Liu R, Zhang H, et al. The c-Myc/miR-27b-3p/ATG10 regulatory axis regulates chemoresistance in colorectal cancer. Theranostics. 2020;10(5):1981–96. pmid:32104496
- 104. Deng J, Wang H, Liang Y, Zhao L, Li Y, Yan Y, et al. miR-15a-5p enhances the malignant phenotypes of colorectal cancer cells through the STAT3/TWIST1 and PTEN/AKT signaling pathways by targeting SIRT4. Cellular signalling. 2023;101:110517. pmid:36332797
- 105. Zhao X, Wang J, Zhu R, Zhang J, Zhang Y. DLX6-AS1 activated by H3K4me1 enhanced secondary cisplatin resistance of lung squamous cell carcinoma through modulating miR-181a-5p/miR-382-5p/CELF1 axis. Scientific Reports. 2021;11(1):21014. pmid:34697393
- 106. Qi F-f, Yang Y, Zhang H, Chen H. Long non-coding RNAs: Key regulators in oxaliplatin resistance of colorectal cancer. Biomedicine & Pharmacotherapy. 2020;128:110329. pmid:32502843
- 107. Luo C, Qiu J. miR-181a Inhibits Cervical Cancer Development via Downregulating GRP78. Oncology research. 2017;25(8):1341–8. pmid:28245171
- 108. Qin AY, Zhang XW, Liu L, Yu JP, Li H, Wang SZ, et al. MiR-205 in cancer: an angel or a devil? European journal of cell biology. 2013;92(2):54–60. pmid:23279926
- 109. Liu H, Li A, Sun Z, Zhang J, Xu H. Long non-coding RNA NEAT1 promotes colorectal cancer progression by regulating miR-205-5p/VEGFA axis. Human cell. 2020;33(2):386–96. pmid:32065361
- 110. Jin Z, Chen B. LncRNA ZEB1-AS1 Regulates Colorectal Cancer Cells by MiR-205/YAP1 Axis. Open medicine (Warsaw, Poland). 2020;15:175–84. pmid:32190742
- 111. Wang R, Wang J, Chen Y, Chen Y, Xi Q, Sun L, et al. Circular RNA circLDLR facilitates cancer progression by altering the miR-30a-3p/SOAT1 axis in colorectal cancer. Cell death discovery. 2022;8(1):314. pmid:35821230
- 112. Deng YH, Deng ZH, Hao H, Wu XL, Gao H, Tang SH, et al. MicroRNA-23a promotes colorectal cancer cell survival by targeting PDK4. Experimental cell research. 2018;373(1):171–9. pmid:30342991
- 113. Kou CH, Zhou T, Han XL, Zhuang HJ, Qian HX. Downregulation of mir-23b in plasma is associated with poor prognosis in patients with colorectal cancer. Oncology letters. 2016;12(6):4838–44. pmid:28101227
- 114. Ye J, Lei J, Fang Q, Shen Y, Xia W, Hu X, et al. miR-4666-3p and miR-329 synergistically suppress the stemness of colorectal cancer cells via targeting TGF-β/Smad pathway. Frontiers in oncology. 2019;9:1251.
- 115. Shi L, Zhao Y, Liu X, Qian J, Yang X, Li W. Circular RNA circWHSC1 facilitates colorectal cancer cell proliferation by targeting miR-130a-5p/zeb1 signaling in vitro and in vivo. Heliyon. 2023;9(10):e20176. pmid:37810854
- 116. Chen X, Su X, Lin M, Fu B, Zhou C, Ling C, et al. Expression of miR-192-5p in colon cancer serum and its relationship with clinicopathologic features. American journal of translational research. 2021;13(8):9371–6. pmid:34540055
- 117. Hu X, Wang Y, Liang H, Fan Q, Zhu R, Cui J, et al. miR-23a/b promote tumor growth and suppress apoptosis by targeting PDCD4 in gastric cancer. Cell Death Dis. 2017;8(10):e3059. pmid:28981115
- 118. Lampropoulou DI, Pliakou E, Aravantinos G, Filippou D, Gazouli M. The Role of Exosomal Non-Coding RNAs in Colorectal Cancer Drug Resistance. International journal of molecular sciences. 2022;23(3). pmid:35163397
- 119. Kong X, Hu S, Yuan Y, Du Y, Zhu Z, Song Z, et al. Analysis of lncRNA, miRNA and mRNA-associated ceRNA networks and identification of potential drug targets for drug-resistant non-small cell lung cancer. Journal of Cancer. 2020;11(11):3357–68. pmid:32231742
- 120. Raziq K, Cai M, Dong K, Wang P, Afrifa J, Fu S. Competitive endogenous network of lncRNA, miRNA, and mRNA in the chemoresistance of gastrointestinal tract adenocarcinomas. Biomedicine & Pharmacotherapy. 2020;130:110570. pmid:32763816
- 121. Li K, Yao T, Zhang Y, Li W, Wang Z. NEAT1 as a competing endogenous RNA in tumorigenesis of various cancers: Role, mechanism and therapeutic potential. International journal of biological sciences. 2021;17(13):3428–40. pmid:34512157
- 122. Yang C, Li Z, Li Y, Xu R, Wang Y, Tian Y, et al. Long non-coding RNA NEAT1 overexpression is associated with poor prognosis in cancer patients: a systematic review and meta-analysis. Oncotarget. 2017;8(2):2672–80. pmid:27926523
- 123. Zhang M, Weng W, Zhang Q, Wu Y, Ni S, Tan C, et al. The lncRNA NEAT1 activates Wnt/β-catenin signaling and promotes colorectal cancer progression via interacting with DDX5. J Hematol Oncol. 2018;11(1):113.
- 124. Zhu Y, Wang X, Zheng L, Li D, Liu Z, Teng L. The lncRNA NEAT1 Inhibits miRNA-216b and Promotes Colorectal Cancer Progression by Indirectly Activating YY1. Journal of oncology. 2022;2022:8130132. pmid:36262350
- 125. Li Y, Peng C, Fang C, Huang K. Upregulation of nuclear-enriched abundant transcript 1 confers oxaliplatin resistance to gastric cancer. Cell biology international. 2020;44(2):446–55. pmid:31617275
- 126. Liu F, Tai Y, Ma J. LncRNA NEAT1/let-7a-5p axis regulates the cisplatin resistance in nasopharyngeal carcinoma by targeting Rsf-1 and modulating the Ras-MAPK pathway. Cancer biology & therapy. 2018;19(6):534–42. pmid:29565706
- 127. Zhu M, Yang L, Wang X. NEAT1 Knockdown Suppresses the Cisplatin Resistance in Ovarian Cancer by Regulating miR-770-5p/PARP1 Axis. Cancer management and research. 2020;12:7277–89. pmid:32884343
- 128. Liu F, Chen N, Gong Y, Xiao R, Wang W, Pan Z. The long non-coding RNA NEAT1 enhances epithelial-to-mesenchymal transition and chemoresistance via the miR-34a/c-Met axis in renal cell carcinoma. Oncotarget. 2017;8(38):62927–38. pmid:28968960
- 129. Wei X, Tao S, Mao H, Zhu H, Mao L, Pei W, et al. Exosomal lncRNA NEAT1 induces paclitaxel resistance in breast cancer cells and promotes cell migration by targeting miR-133b. Gene. 2023;860:147230. pmid:36717039
- 130. Xu W-W, Jin J, Wu X-y, Ren Q-L, Farzaneh M. MALAT1-related signaling pathways in colorectal cancer. Cancer cell international. 2022;22(1):126. pmid:35305641
- 131. Zhang J, Li Q, Xue B, He R. MALAT1 inhibits the Wnt/β-catenin signaling pathway in colon cancer cells and affects cell proliferation and apoptosis. Bosnian journal of basic medical sciences. 2020;20(3):357–64.
- 132. Xu J, Xiao Y, Liu B, Pan S, Liu Q, Shan Y, et al. Exosomal MALAT1 sponges miR-26a/26b to promote the invasion and metastasis of colorectal cancer via FUT4 enhanced fucosylation and PI3K/Akt pathway. Journal of experimental & clinical cancer research: CR. 2020;39(1):54. pmid:32209115
- 133. Zhang C, Yao K, Zhang J, Wang C, Wang C, Qin C. Long Noncoding RNA MALAT1 Promotes Colorectal Cancer Progression by Acting as a ceRNA of miR-508-5p to Regulate RAB14 Expression. BioMed research international. 2020;2020:4157606. pmid:33344634
- 134. Wang H, Huang C, Yao X. The functions of long non-coding RNAs in colorectal cancer. Translational cancer research. 2019;8(5):2192–204. pmid:35116969
- 135. Wang N, Hou MS, Zhan Y, Shen XB, Xue HY. MALAT1 promotes cisplatin resistance in cervical cancer by activating the PI3K/AKT pathway. European review for medical and pharmacological sciences. 2018;22(22):7653–9. pmid:30536307
- 136. Fang Z, Chen W, Yuan Z, Liu X, Jiang H. LncRNA-MALAT1 contributes to the cisplatin-resistance of lung cancer by upregulating MRP1 and MDR1 via STAT3 activation. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie. 2018;101:536–42.
- 137. Li P, Zhang X, Wang H, Wang L, Liu T, Du L, et al. MALAT1 Is Associated with Poor Response to Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients and Promotes Chemoresistance through EZH2. Molecular cancer therapeutics. 2017;16(4):739–51. pmid:28069878
- 138. Wei Z, Zhou J, Yu H, Pu Y, Cheng Y, Zhang Y, et al. Zuo Jin Wan Reverses the Resistance of Colorectal Cancer to Oxaliplatin by Regulating the MALAT1/miR-200s/JNK Signaling Pathway. Evidence-based complementary and alternative medicine: eCAM. 2022;2022:3032407. pmid:36248422
- 139. Zhang P, Zhou H, Lu K, Lu Y, Wang Y, Feng T. Exosome-mediated delivery of MALAT1 induces cell proliferation in breast cancer. OncoTargets and therapy. 2018;11:291–9. pmid:29386907
- 140. Wang M, Sun X, Yang Y, Jiao W. Long non‐coding RNA OIP5‐AS1 promotes proliferation of lung cancer cells and leads to poor prognosis by targeting miR‐378a‐3p. Thoracic cancer. 2018;9(8):939–49. pmid:29897167
- 141. Liu Y, Fu X, Wang X, Liu Y, Song X. Long non‑coding RNA OIP5‑AS1 facilitates the progression of ovarian cancer via the miR‑128‑3p/CCNG1 axis. Mol Med Rep. 2021;23(5):388. pmid:33760168
- 142. Ren X, He J, Qi L, Li S, Zhang C, Duan Z, et al. Prognostic and clinicopathologic significance of long non-coding RNA opa-interacting protein 5-antisense RNA 1 in multiple human cancers. Artificial cells, nanomedicine, and biotechnology. 2020;48(1):353–61. pmid:31899963
- 143. Tao Y, Wan X, Fan Q, Wang Y, Sun H, Ma L, et al. Long non-coding RNA OIP5-AS1 promotes the growth of gastric cancer through the miR-367-3p/HMGA2 axis. Digestive and liver disease: official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver. 2020;52(7):773–9. pmid:31959478
- 144. Ghafouri-Fard S, Dashti S, Farsi M, Hussen BM, Taheri M. A review on the role of oncogenic lncRNA OIP5-AS1 in human malignancies. Biomedicine & Pharmacotherapy. 2021;137:111366. pmid:33601149
- 145. Liang J, Tian XF, Yang W. Effects of long non-coding RNA Opa-interacting protein 5 antisense RNA 1 on colon cancer cell resistance to oxaliplatin and its regulation of microRNA-137. World journal of gastroenterology. 2020;26(13):1474–89. pmid:32308348
- 146. Song L, Zhou Z, Gan Y, Li P, Xu Y, Zhang Z, et al. Long noncoding RNA OIP5-AS1 causes cisplatin resistance in osteosarcoma through inducing the LPAATβ/PI3K/AKT/mTOR signaling pathway by sponging the miR-340-5p. Journal of cellular biochemistry. 2019;120(6):9656–66.
- 147. Yu Q, Li Y, Peng S, Li J, Qin X. Exosomal-mediated transfer of OIP5-AS1 enhanced cell chemoresistance to trastuzumab in breast cancer via up-regulating HMGB3 by sponging miR-381-3p. Open medicine (Warsaw, Poland). 2021;16(1):512–25. pmid:33821219
- 148. Xie L, Wang L, Zhu W, Zhao J, Guo X. Editorial: Bioinformatics tools (and web server) for cancer biomarker development, volume II. Frontiers in Genetics. 2022;13. pmid:36299589
- 149. Liu X-Y, Mei X-Y. Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches. Frontiers in Bioengineering and Biotechnology. 2023;11. pmid:37139048