https://orcid.org/0000-0002-3883-3396
Figures
Abstract
Introduction
Type 2 diabetes (T2D) is considered as a risk factor for kidney cancer (KC). However, so far, there is no study in the literature that has explored genetic factors through which T2D drive the development and progression of KC. Therefore, this study attempted to explore T2D- and KC-causing shared key genes (sKGs) for revealing shared pathogenesis and therapeutic drugs as their common treatments.
Methods
The integrated bioinformatics and system biology approaches were utilized in this study. The statistical LIMMA approach was used based web-tool GEO2R to detect differentially expressed genes (DEGs) through transcriptomics analysis. Then upregulated and downregulated DEGs for T2D and KC were combined to obtained shared DEGs (sDEGs) between T2D and KC. The STRING database was used to construct the protein-protein interaction (PPI) network of sDEGs. Then Cytohubba plugin-in Cytoscape were used in the PPI network to disclose the sKGs based on different topological measures. The RegNetwork database was used in NetworkAnalyst to analyze co-regulatory networks of sKGs with transcription factors (TFs) and micro-RNAs to identify key TFs and miRNAs as the transcriptional and post-transcriptional regulators of sKGs, respectively. AutoDock Vina is a tool used for molecular docking. ADME/T properties were 24 assessed using pkCSM and SwissADME.
Results
At first, 74 shared DEGs (sDEGs) were identified that can distinguish both KC and T2D patients from control samples. Through protein-protein interaction (PPI) network analysis, top-ranked 6 sDEGs (CD74, TFRC, CREB1, MCL1, SCARB1 and JUN) were detected as the sKGs that drive both KC and T2D development and progression. The most common sKG ‘CD74’ is associated with key pathways, such as NF-κB signaling transduction, apoptotic processes, B cell proliferation. Differential expression patterns of sKGs validated by independent datasets of NCBI database for T2D and TCGA and GTEx databases for KC. Furthermore, sKGs were found to be significant at several CpG sites in DNA methylation studies. Regulatory network analysis identified three TFs proteins (SMAD5, ATF1 and NR2F1) and two miRNAs (hsa-mir-1-3p and hsa-mir-34a-5p) as the regulators of sKGs. The enrichment analysis of sKGs with KEGG-pathways and Gene Ontology (GO) terms revealed some crucial shared pathogenetic mechanisms (sPM) between two diseases. Finally, sKGs-guided four potential therapeutic drug molecules (Imatinib, Pazopanib hydrochloride, Sorafenib and Glibenclamide) were recommended as the common therapies for KC with T2D.
Citation: Ahmmed R, Islam MA, Hasan MT, Sarker A, Ali MA, Islam MS, et al. (2025) Common molecular links and therapeutic insights between type 2 diabetes and kidney cancer. PLoS One 20(8): e0330619. https://doi.org/10.1371/journal.pone.0330619
Editor: Yanggang Hong, Wenzhou Medical University, CHINA
Received: January 26, 2025; Accepted: August 4, 2025; Published: August 20, 2025
Copyright: © 2025 Ahmmed 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: The datasets analyzed in this study were downloaded from the NCBI-GEO database with the following link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15641https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38424https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29226https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25724.
Funding: This work was supported by the Bangladesh University Grant Commission (UGC) research project (ID: Biological Science 1, 2022-2023)
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
Kidney cancer (KC) ranks among the most prevalent urological cancers [1]. It is a disease caused by uncontrolled proliferation of cells and spread throughout the body [2]. The renal parenchyma is the primary site where KC arises [3]. Among all cancers, KC ranks 14th in the world and It is the 7th most frequently occurring cancer in men and the 9th in women [4–6]. In adults, renal cell carcinoma (RCC) is the 2nd prevalent form of KC [7]. According to histopathology, the most prevalent subtypes of renal cell carcinoma are chromophobe (5%–10%), papillary (10%–15%), and clear cell (75%–85%) [4]. Despite significant diagnostic and treatment advancements, the 5-year survival rate for KC has only reached 10% [8]. Early-stage KC is treatable, but advanced or metastatic KC typically results in death [9]. Approximately 42% of KC patients died in 2020 [10]. Due to non-specific symptoms and absence of screening guidelines, most KC patients are diagnosed at an advanced-stage, prohibiting the possibility of surgical intervention [11]. One of the main causes of the rise in cancer-related mortality is older people [12,13]. In adults, 2–3% of malignant disorders are kidneys tumors [14]. Studies on KC have shown that it is generally a metabolic disease [5]. Conversely, T2D is a metabolic disorder as well, marked by dysregulation of genes, glucose, and lipid metabolism [15]. T2D is characterized by higher blood-glucose levels caused due to either insufficient insulin production by the pancreas or insulin resistance in the body [16,17]. The reasons underlying the elevated cancer risk in diabetic people may include increased oxidative stress, proinflammatory state, insulin resistance, and hyperinsulinemia [18]. The two most well-established risk factors for renal cell carcinoma are obesity and hypertension, and both are associated with type 2 diabetes through metabolic syndrome and insulin resistance [19]. According to some studies, T2D have an increased risk of developing KC [16,18–22]. More than 90% of all instances of diabetes are T2D, which is more prevalent in older people and worsens with age [23]. It has been estimated that up to 40% of people with T2D also have KC [24,25]. The above data indicates that T2D increases the risk of developing KC to the patients. The relationship between T2D and KC is shown schematically in Fig 1.
It highlights how hyperglycemia and IR in T2D contribute to oxidative stress, inflammation, and altered immune responses, which may promote KC development.
Multiple studies have been conducted to investigate the genetic factors contributing to T2D and KC separately. It is crucial to identify shared genes/proteins has been associated with T2D and KC in order to develop appropriate treatment plans for patients dealing with both of these complex conditions simultaneously. Doctors typically prescribe drugs that are specific to the patient’s disease [26]. As a result, they have to prescribe multiple drugs to the patients who are suffering from multiple diseases. However, polypharmacy can lead to drug-drug interactions (DDIs), which may cause adverse effects or toxicity, potentially worsening the patient’s health [27–30]. In such situations, doctors should aim to prescribe a smaller number of common drugs that effectively represent the specific medications of those diseases, thereby reducing toxicity. However, no research has yet to propose a common medication for the treatment of KC patients who are also suffering from T2D. Therefore, specific objectives of this bioinformatics-based study are to explore (i) T2D and KC causing shared key-genes (sKGs) highlighting their common pathogenetic mechanisms, and (ii) sKGs-guided repurposable common drug molecules for the treatment of KC with T2D.
2. Materials and methods
2.1. Sources of data and their descriptions
As shown below, we employed both transcriptomic data and meta-data related to T2D and KC in this study:
2.1.1. Collection of transcriptomic datasets to discover potential sKGs.
To determine which T2D stimulates KC, we take two types of datasets (control vs. KC and control vs. T2D). We took two KC datasets (GSE15641 [31], GSE38424 [32]) and two T2D datasets (GSE29226 [33], GSE25724 [34]) from Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI). The KC dataset with accession ID GSE38424 consisted of 4 renal-cell carcinoma (RCC) and 4 control. The KC dataset with accession ID GSE15641 consisted of 32 clear-cell RCC (cRCC), 6 chromophobe RCC (chrRCC), 11 papillary RCC (pRCC), 12 Oncocytoma (OC), 8 transitional cell carcinoma (TCC) and 20 control, where ccRCC, pRCC, chrRCC, OC, and TCC were considered as case group.
Table 1 presents comprehensive descriptions of the transcriptomics datasets utilized in the study.
2.1.2. Collection of drug molecules.
In order to identify possible medication for the treatment of patients with T2D and KC, we collected 156 drug molecules reported to be associated with both T2D and KC linked meta-drug molecules from the published articles and online database (Table S1 in S1 File).
2.2. Identification of Differentially Expressed Genes (DEGs) between case and control groups
We used the GEO2R online tool to find differential expression genes (DEGs) between cancer samples and normal samples using the linear models for microarray (LIMMA) [35] approach for each of four datasets. Each dataset was normalized using RMA software. We selected common upregulated DEGs between T2D and KC as well as common downregulated DEGs using the threshold at log2FC. > 1 and log2FC < −1, respectively. Then we combined both upregulated and downregulated DEGs to get final DEGs-set. We consider common sDEGs and then identified common key gene among T2D and KC. The average of log2 fold-change (aLog2FC) values and the adjusted P.value of the kth differentially expressed gene (DEGk) are combined to define DEGk as follows:
where aLog2FCk [12,36] is computed as
Here and
represent the expression values of kth gene for the ith case and jth control samples, respectively. Thus, we computed common upregulated and downregulated DEGs for both T2D and KC.
2.3. Identification of T2D and KC causing shared key genes (sKGs)
Generally, a protein cannot work alone. In order to perform a function of a protein, it interacts with other one or more proteins within the cells. The protein-protein interaction (PPI) network is widely used in identifying diseases related key-genes (KGs) from a set of differentially expressed genes (DEGs) [37–40]. The PPI network is created by measuring the distance “D” between the proteins (m, n) as follows:
where Nm and Nn are the corresponding nearby sets of m and n. The distance metric “D” represents the degree of functional or topological separation between two proteins within the interaction network. A lower “D” value indicates a closer functional relationship or stronger connectivity, suggesting that the proteins may share common biological pathways or regulatory mechanisms. Conversely, a higher “D” value implies weaker connectivity or distinct functional roles, potentially indicating disparate biological functions or minimal direct interaction [41]. In order to construct PPI network for DEGs, STRING [42] is popular database [43–46]. In this study, a high-confidence interaction score cutoff of 0.700 was applied to construct PPI network. The node of a PPI network shows a protein, and the edge shows how proteins interact with one another. In order to select KGs from the PPI network by prioritizing different topological measures, the CytoHubba [47] plugin in Cytoscape [48] is a popular web-tool [13,14]. This study also considered this web-tool and database to select the shared KGs (sKGs) from sDEGs by prioritizing different topological measures with threshold of Betweenness = 318.665, BottleNeck = 6, Closeness = 38.56, Radiality = 4.21, MNC = 18, EPC = 32.413, and Stress = 530.
2.4. Verification on the association of sKGs with T2D and KC by using independent datasets and databases
Using the independent datasets from the NCBI, GTEx and TCGA databases, box plot analysis was used to validate the differential expression patterns of sKGs in both disease (KC and T2D). We verified the difference sKG expression patterns between KC and control samples using the GTEx and TCGA databases in the UALCAN web-tool [49]. We used two separate datasets with accession IDs GSE16449 [50] and GSE20966 [51] from the NCBI GEO database to verify the differing sKG expression patterns between T2D and control samples.
2.5. Regulatory analysis of sKGs
We used NetworkAnalyst [52,53] platform-based RegNetwork [54]databases and performed the study of the co-regulatory network between sKGs, TFs, and miRNAs in order to identify key micro-RNAs (miRNAs) and transcription factors (TFs) of sKGs. It was determined that the Cytoscape software [55] represented the networks more accurately. Seven independent topological analyses (Betweenness [56], BottleNeck [57], Closeness [58] MNC [59], Radiality [60], EPC [61] and Stress [62]) were used to choose the core regulators.
2.6. Revealing common pathogenetic processes of T2D and KC with sDEGs
To investigate biological processes (BP), cellular components (CC), molecular functions (MF), and pathways of sKGs, sKGs-set enrichment studies were conducted using gene ontology (GO) keywords and Kyoto encyclopedia of genes and genomes (KEGG) pathways [63–66]. To find KEGG-pathways or highly enriched GO-functions (BPs, CCs, MFs,) by the sKGs-set, a 2x2 contingency table was created in Table 2.
where Nj: total number of annotated genes in jth group (j = 1, 2,…,r); Q: total number of annotated genes in all groups
such that
, since some genes are annotated in different groups. Here q: total number of sKGs, mj: number of sKGs belonging to Aj. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) [67] was used to calculation of the p-value using the Fisher exact test based on hypergeometric distribution [68], in order to identify the highly enriched GO terms or KEGG pathways associated with the sKGs.
2.7. Methylation analysis of sKGs in KC
MethSurv [69] and UALCAN [70] was employed to investigate the methylation status of the sKGs in KC. β-values, which varied from 0 to 1, were used to express the degree of DNA methylation. The values are found using the formula N/ (N + O + 100). Here, N and O represent completely methylated and completely unmethylated intensities, respectively. In our analysis, we utilized normalized β-values obtained from both MethSurv and UALCAN databases.
2.8. Homology modeling
It was used to predict the three-dimensional (3D) structure of some receptor proteins that are unavailable in Protein Data Bank (PDB) [71]. A protein sequence in FASTA format or its reference number from UniProt database [72] is submitted to SWISS-MODEL for predicting the 3D structure of that protein. Qualitative Model Energy Analysis (QMEAN) [73] and QMEANDisco analysis [74] were used to assess the quality of the protein structure. The Ramachandran plot of the receptor protein was created using ProCheck [75] to confirm the expected 3D structures for molecular docking analysis.
2.9. Molecular docking
We looked at molecular docking studies of target receptors and meta-drug-agents to find FDA-approved medications that could be used to treat KC and T2D. As possible drug target receptors, the sKGs-mediated proteins and their regulatory TFs proteins were chosen in this example. Meta-drug agents were extracted from the published literature. The three-dimensional structures of every receptor protein were extracted using the Protein Data Bank (PDB) [76] and SWISS-MODEL [77] databases. From the PubChem database, the 3D structures of all 156 meta-drug compounds were selected [78]. Drug energy minimization is performed by Avogadro software (https://avogadro.cc/). This step helps predict the preferred structure of a molecule and its interactions with target sites [79]. MMFF94 is used as the force field. MMFF94 helps generate different conformations of a molecule by rotating around bonds and adjusting bond angles and lengths [80]. Steepest descent algorithm used in energy minimization. Using Swiss PDB view [81] and AutoDock Vina [82,83], protein receptors were created by including charge added and reducing energy, respectively. After selecting the appropriate chemicals to interact with the target proteins, molecular visualization is essential in model analysis. BIOVIA Discovery studio 2021 was used to view and analyze the docking outputs [84]. Let Buv represent the binding affinity between the vth drug agent (v = 1, 2,..., s) and the uth receptor protein (u = 1, 2, …, r). To choose the top some drug molecules as possible candidate-drugs, the receptor proteins were sorted by decreasing-order of row average j = 1,2,…,r, and drug molecules were arranged by decreasing order of column average
v = 1,2,…,s.
2.10. ADME/T analysis
We used absorption, distribution, metabolism, excretion, and toxicity (ADME/T) study to look into the pharmacokinetics characteristics of the chosen drug compounds. The Lipinski rules fulfillment of drug likeness properties (such as: number of hydrogen bonds donor and acceptor, rotatable bond, molecular weight, LogP value, etc.) is assessed using the SCFBio web tool [85]. The ADMET properties were calculated using the pkCSM [86] and SwissADME [87] online databases. The optimum structures of medicinal substances were used in SMILES formats for the ADME/T computations.
2.11. The workflow from transcriptomics analysis to drug discovery
Unraveling Shared Molecular Mechanism between T2D and KC, and Therapeutic Indication: Insights from Bioinformatics and System Biology Analysis workflow is displayed in Supplementary Figure S1 in S1 File.
3. Results
3.1. DEGs identification
In each gene-expression dataset, we identified DEGs between case (T2D and KC) and control (normal) samples using the LIMMA with an r-package with an adjusted p value <0.05 and |aLog2(FC)| > 1 as described in section 2.2. In the instance of the KC dataset, we identified 1421 and 3589 downregulated DEGs, and 4475 and 2983 upregulated DEGs, respectively, from the GEO datasets with the IDs GSE15641, and GSE38424. Common DEGs (cDEGs) of KC were found to be 559 upregulated and 130 downregulated (Table S2 in S1 File). Using the GEO datasets GSE25724 and GSE29226, we identified 2,651 and 424 upregulated DEGs, respectively, along with 3,032 and 1,763 downregulated genes in the T2D datasets. We identified 225 downregulated and 33 upregulated cDEGs associated with T2D (Table S3 in S1 File).
3.2. Shared DEGs (sDEGs) identification
Based on two transcriptomics datasets, we discovered 559 upregulated and 130 downregulated DEGs for KC in the previous section. Similarly, using two transcriptomics datasets, 33 upregulated and 225 downregulated DEGs were found for T2D. As a result, we take consideration of 74 sDEGs overall for T2D and KC (Table S4 in S1 File).
3.3. sKGs identification from sDEGs
The PPI network, comprising sDEGs with 74 nodes and 295 edges (Fig 2). Utilizing 7 topological measures (BottleNeck, Closeness, Betweenness, Radiality, MNC, EPC, and Stress) within the PPI networking, we identified the top six sKGs: JUN, CD74, TFRC, CREB1, MCL1, and SCARB1 (Table S5 in S1 File).
Orange colour nodes indicate the sKGs. This network highlights the complex interactions among sDEGs and identifies sKGs. Orange colour nodes indicate the sKGs. These sKGs may play critical roles in the molecular crosstalk linking T2D and KC pathogenesis.
3.4. Verification of sKGs with T2D and KC by using independent datasets and databases
Based on the expression patterns in the GTEx and TCGA databases, we analysed the expression pattern using a box plot analysis that indicate five sKGs (TFRC, MCL1, SCARB1, CD74 and JUN) are upregulated and sKGs (CREB1) are downregulated (Supplementary Figure S2A in S1 File) in KC. We discovered with independent dataset Supplementary Figures S2B in S1 File shows that five sKGs (TFRC, MCL1, SCARB1, CD74 and JUN) are upregulated in T2D, while the other sKGs (CREB1) are downregulated, supporting our results.
3.5. Analysis of the regulatory network associated with sKGs
We analyzed the networks of transcription factors (TFs) and miRNAs with sKGs to identify regulatory factors at both transcriptional and post-transcriptional levels. Initially, we selected the top-ranked three transcription factors (SMAD5, ATF1, NR2F1) as regulators of sKGs based on two topological measures — degree (≥2) and betweenness (≥55.8), respectively (Fig 3A). Next, we selected the top two miRNAs (hsa-mir-34a-5p and hsa-mir-1-3p) as post-transcriptional regulators of sKGs based on degree (≥5) and betweenness (≥348.53) as topological criteria (Fig 3B).
(B) The TarBase database-based on miRNA-sKGs interaction network. sKGs are shown as green color octagons in both A and B, while TFs and miRNAs are displayed as pink color hexagons in A and B, respectively.
3.6. Revealing common pathogenetic processes of T2D and KC with sDEGs
We examined common functional terms and pathways of both T2D and KC by the functional enrichment analyses for 6 sKGs (Table 3). In the significantly enriched GO terms (BPs, MFs, CCs) and KEGG pathways are involving sDEGs and sKGs, which are associated the pathogenic processes of T2D in KC.
3.7. Methylation analysis of sKGs in KC
DNA methylation is an epigenetic mechanism that regulates gene expression [88]. It enables researchers to select potential biomarkers for early diagnosis, prognosis, and therapies [89]. DNA methylation of key genes helps researchers learn about how key cellular processes are regulated and how they can disruspt the expression of sKG, making it an important part of genomic research [90]. We used methsurv web-tool to check the DNA methylation status at CpG sites for all sKGs (JUN, CD74, TFRC, CREB1, MCL1, SCARB1). All of the sKGs had multiple significant CpG sites, as we observed (p-value of ≤0.01) (Table S6 in S1 File). We observed that two CpG sites (cg00543485 and cg24946133) in JUN are biologically relevant to transcriptional regulation, since in these sites JUN is hyper methylated and survival probability curves are significantly separated based on its low and high expressions. Similarly, a CpG site of CD74, SCARB1 and TFRC are biologically relevant to transcriptional regulation. The other two key-genes (MCL1 and CREB1) are significantly methylated but the status of biological relevance to transcriptional regulation are not available in the methsurv-database (Table S6 in S1 File, Figure S3 in S1 File). Upregulated genes are often associated with promoter hypomethylation, particularly in cancer. Hypomethylation in promoter regions can lead to increased gene expression, contributing to tumor development and progression [91]. On the other hand, downregulated genes are often associated with promoter hypermethylation, a common epigenetic mechanism that silences gene expression, particularly in cancer [92].
3.8. Homology modeling
SWISS-MODEL was used to generate the 3D structure of SCARB1, and the global model quality estimation (GMQE) score was 0.84. In general, a GMQE score of 0.70 or higher is considered a reliable predictor [93]. Furthermore, it was demonstrated that the SCARB1 crystal structure and the template protein exhibited 100% identity. The QMEANDisCo score for the SCARB1, calculated using SWISS-MODEL, was 0.60 ± 0.05. ProCheck performed a Ramachandran plot analysis to evaluate the stereochemical quality of the protein structure by assessing phi and psi torsion angles. In Fig 4 showed that all residues fell the preferred region (92.36%) while none fell within the inappropriate region. As a result, the model has been validated as high quality and used for further ligand–receptor interaction studies.
The red areas show the most favorable phi-psi angle combinations. The white area shows an unfavorable phi-psi combination.
3.9. Molecular docking
For exploring the candidate drugs for T2D and KC we considered 6 sKGs (CD74, TFRC, CREB1, MCL1, SCARB1 and JUN) and 3 regulatory TFs proteins (SMAD5, ATF1, NR2F1) and 156 drug receptors which has collected from Database and published article (Table S1 in S1 File). The 3D structures of CD74, TFRC, CREB1, MCL1, SCARB1, JUN, SMAD5, ATF1, and NR2F1 were downloaded from PDB database and SWISS-MODEL with codes E7EQJ3, 60KD, 5ZK1, 3WIX, Q8WTV0, 1JUN, 6FZS, P18846 and F1DAL7. The PubChem database used for downloaded the 3D structures of drug receptors. Then we applied molecular docking simulation between our 9 proposed protein and 156 drug agents. The binding affinity score matrix between the top ordered proposed protein receptors with top ordered 30 drug-ligands were presented in Fig 5 and Table S7 in S1 File. To look at the effectiveness of the top ranked five drugs in terms of resistance against the most up-to-date alternative receptors for T2D and KC. The binding affinity scores of top five drugs (Digoxin, Imatinib, Pazopanib hydrochloride and Sorafenib and Glibenclamide) average were −9.2, −8.1, −8.6, −8.9 and −9.1 (kcal/mol) respectively. The top ordered five drugs for proposed protein with 3D view, protein and ligand interaction and amino acid residues are shown in (Table S8 in S1 File). These drugs may be promising candidates for treating T2D and KC than the currently available drugs.
The X-axis represents the top 30 ranked drug agents (selected from 156), while the Y-axis shows the proposed receptors in order.
3.10. Re-docking of co-crystalized ligand with RMSD
To validate the docking study, the co-crystal ligand in the binding site of the PDB file is typically re-docked, and the RMSD of the docking poses is calculated relative to the co-crystal pose. This comparison assesses the alignment between the experimental ligand position and the docked poses. The co-crystal ligand must be re-docked at the binding site, followed by calculating the RMSD to compare it with the original co-crystal pose. In this study, the RMSD of the co-crystal ligand was calculated (Fig 6) to compare two different poses, revealing that none of the molecules had an RMSD exceeding the acceptable range, with all values remaining under 2.0 Å. The RMSD values obtained by re-docking the co-crystal ligands were 1.398 Å for Imatinib, and 0.539 Å for Glibenclamide.
As demonstrated, the docked ligand (red) closely resembles the crystallized ligand (purple) (RMSD = 1.398 Å, and 0.539 respectively).
3.11. Drug likeness and ADME/T analysis
Table 4 shows that our recommended top four drug compounds (Glibenclamide, Imatinib, Pazopanib hydrochloride, and Sorafenib) satisfy nearly all of the drug-likeness criterion. To assess the suggested drugs’ efficacy and indemnity, different parameters can be used to analyze their toxicity and ADME analyses. The compounds are anticipated to have adequate absorption in the gastrointestinal tract, suggesting they could be promising candidates for oral drugs. A substance is regarded as highly absorbed in the human gut if its Human Intestinal Absorption (HIA) score is high that is HIA ≥ 30% [94,95]. Our suggested drug molecules all had strong HIA scores of at least ≥ 65%, indicating that the body could effectively absorb them.
A compound’s blood-brain barrier (BBB) permeability index indicates its capacity to traverse the BBB. Compounds with a LogBB value of less than <−1 are thought to be poorly disseminated through to the BB barrier, whilst compounds with a value of more than 0.3 may be able to cross the BBB with easy. Following analysis, it was determined that each drug compound had a low BBB passage ability (Table 5). The toxicity of drug molecules decreases with increasing LC50 value; conversely, decreasing LC50 value denotes increasing toxicity. Our proposed compounds’ toxicity assessments (AMES, lethal dosage LD50, and Minnow toxicity (LC50)) showed that they were inactive for all of the toxicity prediction criteria used, and as a result, they were exhibiting low toxicity based on in silico AMES and LD50 analyses, these predictions require further validation through in vitro and in vivo assays. Since digoxin does not follow the Lipinski rules, we have excluded digoxin from the suggested drug list. Thus, we observed that top-ranked four drug molecules (Glibenclamide, Imatinib, Pazopanib hydrochloride, and Sorafenib) comply most of the pharmacokinetic properties and Lipinski rules. Therefore, we considered these four drug molecules for further investigation. The 3D View of four drug molecules and their complex are display in Fig 7 and Table S8 in in S1 File. Consequently, the three drugs that have been suggested may be effective inhibitors of the genes that cause T2D and KC.
Top-ranked four drug-target complexes highlighting their 3-dimension (3D) view and interacting residues. Complexes: (a) indicated MCL1-Imatinib, (b) NR2F1- Pazopanib hydrochloride, (c) SCRB1-Sorafenib, and (d) TFRC- Glibenclamide.
4. Discussion
Type 2 diabetes (T2D) is a long-term metabolic disorder marked by a gradual deterioration in β-cell function over time [96]. It is considered as one of the most type of risk factor for kidney cancer (KC) [19,97,98]. Therefore, it is crucial to identify the shared key genes (sKGs) that cause both diseases in order to study their shared pathogenetic mechanisms (sPM) and potential medications for improved diagnosis and treatment when they are co-occurrence. Nonetheless, no studies have explored the pathogenic mechanisms of sKGs or potential drug candidates as a unified treatment approach for T2D and KC. Although every disease is unique, multiple drugs may also create toxicity or unfavorable side effects for patients due to drug-drug interactions [27–30]. This study investigates the genetic relationship between T2D and KC by identifying shared differentially expressed genes (sDEGs) that distinguish these conditions from control samples, with the goal of exploring common drugs as potential treatments for both T2D and KC. We investigated into 74 shared DEGs (sDEGs) in order to find common candidate genes for T2D and KC utilizing an integrated bioinformatics-based approach. The link between sKGs, T2D, and KC is further supported by several individual studies, including TFRC [99–102], MCL1 [103–105], SCARB1 [106–109] JUN [110,111] CD74 [112–114],CREB1 [115–118], as shown in Fig 8A.
(A) Verification of the suggested T2D and KC-causing sKGs (B) Verification of the suggested drug-agents.
The transferrin receptor (TFRC) gene encodes a transmembrane glycoprotein that functions as a cell surface receptor essential for cellular iron uptake. Through a process called receptor-mediated endocytosis, the receptor moves iron from the outside to the interior of the cell, which is crucial for cell growth. In the human renal mesangial cells (HRMCs), TFRC was overexpressed. TFRC facilitates the endocytosis of iron ions from ferritin in the blood, improving absorption [99,100]. Conversely, Patients with T2D have a considerably have greater expressed TFRC gene [101,102]. TFRC gene is regulated by three transcriptional regulators (ATF1, NR2F1 and SMAD5) and two post-transcriptional regulators (has-mir-34a-5p and hsa-mir-1-3p) of sKGs. ATF1 is a transcription factor that is a member of the ATF/CREB family. It binds to the “TGACGTCA” consensus ATF/CRE site. ATF1 might be a novel therapeutic target for the treatment of KC [119]. NR2F1’s role in controlling the expression of genes involved in cell cycle regulation, metastasis, and angiogenesis, making it an important target for understanding RCC’s molecular mechanisms and potential therapeutic interventions [120]. NR2F2, play significant roles in metabolic processes and cellular functions related to diabetes [121]. In KC, the dysregulation of miR-34a-5p has been linked to its ability to modulate oncogenes and tumor suppressor genes. It can inhibit cancer progression by targeting key pathways involved in cell survival and proliferation [122]. A study demonstrated that downregulation of miR-34a-5p improved lipid metabolism and reduced liver fat accumulation in a mouse model of T2D [123]. The gene, myeloid cell leukemia-I (MCL-1) gene also significant role in primary stages of cancer. The Bcl-2 family contains the anti-apoptotic protein Mcl-1, which is highly expressed in numerous human cancer cell lines and up-regulated in a range of human cancer, including KC [103,104]. The degradation of Mcl-1 may indeed be a decisive step in the process leading to β-cell apoptosis in both Type 1 Diabetes (T1D) and T2D [105]. Genes related to cholesterol metabolism, such as SCARB1, are significant role in the accurate diagnosis and treatment of KC [106,107]. There are many functions for SCARB1, especially in T2D [108,109]. The sKGs, CD74 expression in clear cell renal cell carcinoma (ccRCC) cells has shown to be significantly associated with high infiltration of CD4 + T cells, which may have important implications for the tumor microenvironment and immune response [112,113]. CD74 expression is upregulated in the kidneys of individuals with T2D, especially in podocytes and tubular epithelial cells. High glucose levels and pro-inflammatory cytokines like TNF-α enhance CD74 expression in these cells. When activated by macrophage migration inhibitory factor (MIF), CD74 initiates signaling pathways involving ERK1/2 and p38 MAPK, leading to increased production of inflammatory mediators such as MCP-1 and TRAIL. This inflammatory cascade contributes to the development and progression of diabetic nephropathy, a common complication of T2D [113,124]. The oncogene JUN (part of the AP-1 transcription factor) links to T2D by promoting inflammation and cellular stress pathways that impair insulin signaling and damage pancreatic beta cells. This contributes to insulin resistance and reduced insulin production, which are key in T2D development [125,126]. JUN as a key regulatory hub in RCC using a network pharmacology approach. The study suggests that gut microbial metabolites target JUN via the IL-17 signaling pathway, influencing RCC progression [127]. CREB1 promotes carcinogenesis through a major impact on tumor cell growth, proliferation, survival, metastasis, and invasion. The RCC tissues and cell lines exhibited overexpression of CREB1 [115,116]. The cAMP activating transcription factor family’s cyclic adenosine monophosphate (cAMP) response element binding protein 1 (CREB1) is essential to the regulation of gluconeogenic processes, lipid metabolism, and insulin signaling pathways. For the pathophysiology and progression of T2D, CREB1 may be a promising causal gene [117,118].
We identified the top 5 GO-terms (each of BPs, CCs and MFs) and top 5 KEGG-pathways for investigation of sKGs pathogenesis process. The top BPs such as, positive regulation of canonical NF- κB signal transduction, the negative regulation of apoptotic process, the positive regulation of B cell proliferation etc. of T2D and KC was backed up by some number of additional investigations. Among the BPs, Targeting the NF-κB pathway has become an area of interest for potential therapeutic approaches in RCC, with the hope that inhibiting this pathway could reduce tumor progression and improve patient outcomes [128]. On the other hand, NF-κB plays a central role in the pathogenesis of diabetes and its associated complications, particularly through its involvement in chronic inflammation, which is a key feature of both T1D and T2D [129]. The top CCs such as, extracellular exosome, Endocytic vesicle membrane, cytosol etc. was supported by others investigation. Among the CCs, Exosomes, small extracellular vesicles secreted by various cell types, including cancer cells, have emerged as important players in the diagnosis and treatment of RCC [130]. Exosomes derived from individuals with T2D play a significant role in mediating detrimental effects, particularly by promoting inflammation, insulin resistance, and contributing to diabetic complications [131]. NOD-like receptors (NLRs) signaling pathway, Osteoclast differentiation, Kaposi sarcoma-associated herpesvirus infection, TNF signaling pathway, Relaxin signaling pathway were top 5 KEGG pathways was also supported by others articles. Among the KEGG pathways, NLRs are a class of intracellular sensors that detect metabolic stress, pathogens, and other danger signals. They play an significant role in the body’s immune response, especially in metabolic diseases such as obesity, insulin resistance, and T2D [132]. NLRs are found in various kidney tissues during pathological conditions, indicating their important role in a range of kidney diseases, including acute kidney injury (AKI), diabetic nephropathy (DN), obstructive nephropathy (ON), lupus nephritis (LN), IgA nephropathy, uric acid nephropathy, crystal nephropathy and RCC [133]. The NLRs pathway and NF-κB signaling are central to inflammation and are implicated in both metabolic diseases and cancer. Chronic activation of NF-κB contributes to insulin resistance in T2D and promotes tumorigenesis by enhancing cell survival and proliferation. Similarly, aberrant NLRs signaling can drive inflammation that supports cancer development [134,135]. The methylation of DNA is an epigenetic process where the methyl group is added to cytosine bases, particularly at the CpG sites. DNA methylation analysis revealed that most of the sKGs were hypomethylated, leading to their increased activity as oncogenes [136]. In our investigation, we found that upregulated (CD74, TFRC, CREB1, MCL1, SCARB1 and JUN) were particularly (p-value of <0.01) hypomethylated in different CpG locations (Table S6 in S1 File).
Molecular docking study was utilized to investigate sKGs-guided common therapeutic compounds for both T2D and KC. The top four drug molecules (Imatinib [12,137,138], Pazopanib hydrochloride [139], Sorafenib [140,141] and Glibenclamide [142,143]) demonstrated strong binding affinities with the sKGs-mediated target proteins. These molecules were then verified for both T2D and KC through a literature review, as shown in Fig 8B. We performed ADME/T analysis and assessed drug-likeness to validate the drug-molecules computationally. Most of the identified drug molecules satisfied Lipinski’s rule of five, demonstrating their drug-like properties (Table 4). The four drug molecules that were chosen for analysis showed favorable ADME/T profiles (Table 5), high HIA percentages ranging from 68.58% to 93.85%, sufficient water solubility, and no carcinogenic characteristics. The literature review provided further support for the potential effectiveness of our suggested drugs as treatments for T2D, and KC individually. All our proposed drugs were supported as common candidate molecules for T2D and KC by individual studies on these conditions. According to the research article, imatinib successfully lowers blood sugar levels and treats T2D in the animal model [137]. Imatinib has been shown in another experimental investigation to be a highly effective inhibitor of advancer renal carcinoma [138]. Glibenclamide is a second-generation sulfonylurea that lowers blood sugar by stimulating insulin release from pancreatic beta cells. It is a well-established drug used in the treatment of T2D [142]. In a rat model of chronic kidney disease (CKD) induced by an adenine-rich diet, glibenclamide administration improved kidney structure and function. The study observed reductions in blood urea nitrogen (BUN), plasma creatinine, and proteinuria levels. Additionally, glibenclamide decreased markers of inflammation and oxidative stress in kidney tissues [143]. Pazopanib hydrochloride is a multitargeted tyrosine kinase inhibitor (TKI) that was approved by the U.S. Food and Drug Administration (FDA) for the therapy or treatment of patients with advanced RCC [139]. Sorafenib was indeed the first oral antiangiogenic multikinase inhibitor approved for the treatment of advanced RCC [140]. Sorafenib targets pathways involved in tumor growth and angiogenesis, but its interaction with metabolic conditions like T2D may alter its efficacy and toxicity [141]. Therefore, our suggested four candidate’s drugs Imatinib, Pazopanib hydrochloride, Sorafenib and Glibenclamide may be helpful for T2D and KC treatment.
5. Conclusion
This research combinedly investigated four transcriptomics datasets both KC (GSE15641, GSE38424) and T2D (GSE29226, GSE25724) and suggested six shared significant genomic biomarkers (JUN, CD74, TFRC, CREB1, MCL1, SCARB1) by Limma approach, pathways, as well as regulators for prognosis, diagnosis and therapies of KC patients with T2D. The shared key gene (sKGs) set enrichment analysis revealed that sKGs are highly related to some KEGG pathways (e.g., NOD-like receptor signaling pathway), as well as GO-terms BPs (e.g., Positive regulation of canonical NF-κB signal transduction) CCs (Extracellular exosome) and MFs (MHC class II protein complex binding) that are connected with T2D and KC. sKGs vs microRNAs co-regulatory vs transcription factor (TFs) investigation discovered three transcriptional regulator (SMAD5, ATF1, NR2F1) and two post transcriptional regulators(hsa-mir-34a-5p and hsa-mir-1-3p) of sKGs. DNA methylation analysis indicated that, sKGs were found to be significant at various CpG sites. Four top-ranked drug molecules (Pazopanib hydrochloride, Imatinib, Sorafenib and Glibenclamide) were suggested through molecular docking analysis with sKGs. ADMET analysis results satisfied the pharmacokinetics properties of the proposed drug-molecules. Thus, the results of this study could be significant in developing an effective common treatment strategy for both KC and T2D.
6. Limitation of the study
Independent transcriptomics datasets on KC and T2D were analyzed in this study to identify both diseases causing sKGs through which T2D stimulate KC, since necessary datasets were not available in the online databases that needs to be generated from the patients during the co-occurrence/existence of both diseases. However, these in-silico results might be further Improved through single cell RNA-Seq (scRNA-Seq) profile and multi-OMICS data analysis. A key limitation in this study is that the proposed sKGs between T2D and KC as well as the proposed therapeutic agents as the common treatment for both diseases, were not yet validated experimentally. Therefore, further experimental validation is necessary to confirm the results of this study. However, the results of this study might be useful input for wet-lab researchers to find effective diagnostic and therapeutic biomarkers by reducing time and cost, to adopt an appropriate treatment strategy for KC patients with T2D as comorbidity.
References
- 1. Park J, Shin DW, Han K, Kim D, Chun S, Jang HR. Associations Between Kidney Function, Proteinuria, and the Risk of Kidney Cancer: A Nationwide Cohort Study Involving 10 Million Participants. Am J Epidemiol. 2021;190(10):2042–52. pmid:33984862
- 2. Brown JS, Amend SR, Austin RH, Gatenby RA, Hammarlund EU, Pienta KJ. Updating the Definition of Cancer. Mol Cancer Res. 2023;21(11):1142–7. pmid:37409952
- 3. Chow W-H, Dong LM, Devesa SS. Epidemiology and risk factors for kidney cancer. Nat Rev Urol. 2010;7(5):245–57. pmid:20448658
- 4. Le LN-H, Munir J, Kim E-B, Ryu S. Kidney Cancer and Potential Use of Urinary Extracellular Vesicles. Oncol Rev. 2024;18:1410450. pmid:38846051
- 5. Linehan WM, Bratslavsky G, Pinto PA, Schmidt LS, Neckers L, Bottaro DP, et al. Molecular diagnosis and therapy of kidney cancer. Annu Rev Med. 2010;61:329–43. pmid:20059341
- 6. Nepple KG, Yang L, Grubb RL 3rd, Strope SA. Population based analysis of the increasing incidence of kidney cancer in the United States: evaluation of age specific trends from 1975 to 2006. J Urol. 2012;187(1):32–8. pmid:22088338
- 7. Zhang Y, Cao H, Yang F, Wang X, Xu Z, Cheng M, et al. Comprehensive analyses of nuclear mitochondria‐related genes in the molecular features, immune infiltration, and drug sensitivity of clear cell renal cell carcinoma. Medicine Advances. 2024;2(3):238–53.
- 8. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33. pmid:33433946
- 9. Wallen EM, Pruthi RS, Joyce GF, Wise M, Urologic Diseases in America Project. Kidney cancer. J Urol. 2007;177(6):2006–18; discussion 2018-9. pmid:17509280
- 10. Bahadoram S, Davoodi M, Hassanzadeh S, Bahadoram M, Barahman M, Mafakher L. Renal cell carcinoma: an overview of the epidemiology, diagnosis, and treatment. G Ital Nefrol. 2022;39(3):2022-vol3. pmid:35819037
- 11. Klein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol. 2021;18(7):493–502. pmid:34002083
- 12. Ahmmed R, Hossen MB, Ajadee A, Mahmud S, Ali MA, Mollah MMH, et al. Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications. Sci Rep. 2024;14(1):19133. pmid:39160196
- 13. Li J, Gu A, Tang N, Zengin G, Li M, Liu Y. Patient‐derived xenograft models in pan‐cancer: From bench to clinic. Interdiscip Med. 2025.
- 14. Junker K, Ficarra V, Kwon ED, Leibovich BC, Thompson RH, Oosterwijk E. Potential role of genetic markers in the management of kidney cancer. Eur Urol. 2013;63(2):333–40. pmid:23040205
- 15. Rahman MR, Islam T, Shahjaman M, Islam MR, Lombardo SD, Bramanti P, et al. Discovering common pathogenetic processes between COVID-19 and diabetes mellitus by differential gene expression pattern analysis. Brief Bioinform. 2021;22(6):bbab262. pmid:34260684
- 16. Duan X, Wang W, Pan Q, Guo L. Type 2 Diabetes Mellitus Intersects With Pancreatic Cancer Diagnosis and Development. Front Oncol. 2021;11:730038. pmid:34485159
- 17. Liang T, Liang Z, Wu S, Ding Y, Wu Q, Gu B. Global prevalence of Staphylococcus aureus in food products and its relationship with the occurrence and development of diabetes mellitus. Med Adv. 2023;1(1):53–78.
- 18. Tseng C-H. Type 2 Diabetes Mellitus and Kidney Cancer Risk: A Retrospective Cohort Analysis of the National Health Insurance. PLoS One. 2015;10(11):e0142480. pmid:26559055
- 19. Joh H-K, Willett WC, Cho E. Type 2 diabetes and the risk of renal cell cancer in women. Diabetes Care. 2011;34(7):1552–6. pmid:21602426
- 20. Tseng C-H. Use of metformin and risk of kidney cancer in patients with type 2 diabetes. Eur J Cancer. 2016;52:19–25. pmid:26630530
- 21. Psutka SP, Stewart SB, Boorjian SA, Lohse CM, Tollefson MK, Cheville JC, et al. Diabetes mellitus is independently associated with an increased risk of mortality in patients with clear cell renal cell carcinoma. J Urol. 2014;192(6):1620–7. pmid:24931804
- 22. Cheng X, Hou Y. Importance of metabolic and immune profile as a prognostic indicator in patients with diabetic clear cell renal cell carcinoma. Front Oncol. 2023;13:1280618. pmid:37927470
- 23. Gallo M, Adinolfi V, Morviducci L, Acquati S, Tuveri E, Ferrari P, et al. Early prediction of pancreatic cancer from new-onset diabetes: an Associazione Italiana Oncologia Medica (AIOM)/Associazione Medici Diabetologi (AMD)/Società Italiana Endocrinologia (SIE)/Società Italiana Farmacologia (SIF) multidisciplinary consensus position paper. ESMO Open. 2021;6(3):100155. pmid:34020401
- 24. Vavallo A, Simone S, Lucarelli G, Rutigliano M, Galleggiante V, Grandaliano G, et al. Pre-existing type 2 diabetes mellitus is an independent risk factor for mortality and progression in patients with renal cell carcinoma. Medicine (United States). 2014;93:e183. Available: http://journals.lww.com/md-journal%5Cnhttp://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=emed12&NEWS=N&AN=2014611737
- 25. Feng XS, Farej R, Dean BB, Xia F, Gaiser A, Kong SX, et al. CKD Prevalence Among Patients With and Without Type 2 Diabetes: Regional Differences in the United States. Kidney Med. 2022;4: 100385.
- 26.
Lucke T, Herrera R, Wacker M, Holle R, Biertz F, Nowak D, et al. Disease-specific medication, patient-reported diagnoses and their relation to COPD severity for common comorbidities in COPD. 2017;PA698. https://doi.org/10.1183/1393003.congress-2017.pa698
- 27. Zhang P, Wang F, Hu J, Sorrentino R. Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects. Sci Rep. 2015;5:12339. pmid:26196247
- 28. Sommer J, Seeling A, Rupprecht H. Adverse Drug Events in Patients with Chronic Kidney Disease Associated with Multiple Drug Interactions and Polypharmacy. Drugs Aging. 2020;37(5):359–72. pmid:32056163
- 29. Merel SE, Paauw DS. Common Drug Side Effects and Drug-Drug Interactions in Elderly Adults in Primary Care. J Am Geriatr Soc. 2017;65(7):1578–85. pmid:28326532
- 30. Borchelt M. Potential side-effects and interactions in multiple medication in elderly patients: methodology and results of the Berlin Study of Aging. Z Gerontol Geriatr. 1995;28(6):420–8. pmid:8581761
- 31. Qu G, Wang H, Tang C, Yang G, Xu Y. Bioinformatics Study Identified EGF as a Crucial Gene in Papillary Renal Cell Cancer. Dis Markers. 2022;2022:4761803. pmid:35655917
- 32. Hayashi Y, Kamimura-Aoyagi Y, Nishikawa S, Noka R, Iwata R, Iwabuchi A, et al. IL36G-producing neutrophil-like monocytes promote cachexia in cancer. Nat Commun. 2024;15(1):7662. pmid:39266531
- 33. Yang T, Yan C, Yang L, Tan J, Jiang S, Hu J, et al. Identification and validation of core genes for type 2 diabetes mellitus by integrated analysis of single-cell and bulk RNA-sequencing. Eur J Med Res. 2023;28(1):340. pmid:37700362
- 34. Zhong M, Wu Y, Ou W, Huang L, Yang L. Identification of key genes involved in type 2 diabetic islet dysfunction: a bioinformatics study. Biosci Rep. 2019;39(5):BSR20182172. pmid:31088900
- 35. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3. pmid:16646809
- 36. Alam MS, Sultana A, Reza MS, Amanullah M, Kabir SR, Mollah MNH. Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies. PLoS One. 2022;17(5):e0268967. pmid:35617355
- 37. Ben-Hur A, Noble WS. Kernel methods for predicting protein-protein interactions. Bioinformatics. 2005;21 Suppl 1:i38-46. pmid:15961482
- 38. Hong Y, Li J, Xu N, Shu W, Chen F, Mi Y, et al. Integrative genomic and single-cell framework identifies druggable targets for colorectal cancer precision therapy. Front Immunol. 2025;16:1604154. pmid:40496862
- 39. Hong Y, Wang D, Liu Z, Chen Y, Wang Y, Li J. Decoding per- and polyfluoroalkyl substances (PFAS) in hepatocellular carcinoma: a multi-omics and computational toxicology approach. J Transl Med. 2025;23(1):504. pmid:40317014
- 40. Hong Y, Wang D, Lin Y, Yang Q, Wang Y, Xie Y, et al. Environmental triggers and future risk of developing autoimmune diseases: Molecular mechanism and network toxicology analysis of bisphenol A. Ecotoxicol Environ Saf. 2024;288:117352. pmid:39550874
- 41. Cao M, Zhang H, Park J, Daniels NM, Crovella ME, Cowen LJ, et al. Going the distance for protein function prediction: a new distance metric for protein interaction networks. PLoS One. 2013;8(10):e76339. pmid:24194834
- 42. von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003;31(1):258–61. pmid:12519996
- 43. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011;39(Database issue):D561-8. pmid:21045058
- 44. Ajadee A, Mahmud S, Sarkar A, Noor T, Ahmmed R, Haque Mollah MN. Screening of common genomic biomarkers to explore common drugs for the treatment of pancreatic and kidney cancers with type-2 diabetes through bioinformatics analysis. Sci Rep. 2025;15(1):7363. pmid:40025145
- 45. Li J, Wang D, Song S, Wang Y, Wu X, Du Z, et al. Analyzing the potential targets and mechanism of per- and polyfluoroalkyl substances (PFAS) on breast cancer by integrating network toxicology, single-cell sequencing, spatial transcriptomics, and molecular simulation. Funct Integr Genomics. 2025;25(1):119. pmid:40465018
- 46. Hong Y, Li J, Qiu Y, Wang Y, Du Z, Liu Z, et al. Integrative in silico analysis of per- and polyfluorinated alkyl substances (PFAS)-associated molecular alterations in human cancers: a multi-cancer framework for predicting toxicogenomic disruption. Int J Surg. 2025:10.1097/JS9.0000000000002632. pmid:40474796
- 47. Chin C-H, Chen S-H, Wu H-H, Ho C-W, Ko M-T, Lin C-Y. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11. pmid:25521941
- 48. 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
- 49. Chandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18–27. pmid:35078134
- 50. Zhang N, Chen W, Gan Z, Abudurexiti A, Hu X, Sang W. Identification of biomarkers of clear cell renal cell carcinoma by bioinformatics analysis. Medicine (Baltimore). 2020;99(21):e20470. pmid:32481352
- 51. Ding L, Fan L, Xu X, Fu J, Xue Y. Identification of core genes and pathways in type 2 diabetes mellitus by bioinformatics analysis. Mol Med Rep. 2019;20(3):2597–608. pmid:31524257
- 52. Xia J, Gill EE, Hancock REW. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10(6):823–44. pmid:25950236
- 53. Mahmud S, Ajadee A, Hossen MB, Islam MS, Ahmmed R, Ali MA, et al. Gene-expression profile analysis to disclose diagnostics and therapeutics biomarkers for thyroid carcinoma. Comput Biol Chem. 2024;113:108245. pmid:39454454
- 54. Liu Z-P, Wu C, Miao H, Wu H. RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database (Oxford). 2015;2015:bav095. pmid:26424082
- 55. Shannon P, Markiel A, Ozier O, Baliga N, Wang J, Ramage D, et al. Cytoscape: A software environment for integrated models. Genome Res. 1971;13:426.
- 56. Freeman LC. A Set of Measures of Centrality Based on Betweenness. Sociometry. 1977;40(1):35.
- 57. Urban W, Rogowska P. The Case Study of Bottlenecks Identification for Practical Implementation to the Theory of Constraints. Multidiscip Asp Prod Eng. 2018;1(1):399–405.
- 58. Saxena A, Gera R, Iyengar SRS. A Faster Method to Estimate Closeness Centrality Ranking. 2017; 1–25. Available from: http://arxiv.org/abs/1706.02083
- 59. McDonnell A, Lavelle J, Gunnigle P, Collings DG. Management research on multinational corporations: A methodological critique. Econ Soc Rev. 2007;38:235–58.
- 60. Lavorato M, Franco JF, Rider MJ, Romero R. Imposing Radiality Constraints in Distribution System Optimization Problems. IEEE Trans Power Syst. 2012;27(1):172–80.
- 61. Chaudhary R, Balhara M, Jangir DK, Dangi M, Dangi M, Chhillar AK. In Silico Protein Interaction Network Analysis of Virulence Proteins Associated with Invasive Aspergillosis for Drug Discovery. Curr Top Med Chem. 2019;19(2):146–55. pmid:30465504
- 62. Franssen J, Cowez B, Gernay T. Effective stress method to be used in beam finite elements to take local instabilities into account. Fire Saf Sci. 2014;11:544–57.
- 63. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. pmid:10592173
- 64. Kanehisa M, Furumichi M, Sato Y, Matsuura Y, Ishiguro-Watanabe M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 2025;53(D1):D672–7. pmid:39417505
- 65. Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28(11):1947–51. pmid:31441146
- 66. Sarker A, Uddin B, Ahmmed R, Mahmud S, Ajadee A, Pappu MAA, et al. Discovery of mutated oncodriver genes associated with glioblastoma originated from stem cells of subventricular zone through whole exome sequence profile analysis, and drug repurposing. Heliyon. 2025;11(2):e42052. pmid:39906820
- 67. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4(9).
- 68.
Fury W, Batliwalla F, Gregersen PK, Li W. Overlapping Probabilities of Top Ranking Gene Lists, Hypergeometric Distribution, and Stringency of Gene Selection Criterion. 2007;5531–5534. https://doi.org/10.1109/iembs.2006.4398708
- 69. Modhukur V, Iljasenko T, Metsalu T, Lokk K, Laisk-Podar T, Vilo J. MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data. Epigenomics. 2018;10(3):277–88. pmid:29264942
- 70. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia. 2017;19(8):649–58. pmid:28732212
- 71. Burley SK, Berman HM, Kleywegt GJ, Markley JL, Nakamura H, Velankar S. Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive. Methods Mol Biol. 2017;1607:627–41. pmid:28573592
- 72. Bateman A, Martin MJ, O’Donovan C, Magrane M, Apweiler R, Alpi E, et al., UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43(Database issue):D204-12. pmid:25348405
- 73. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res. 2003;31:3381–5.
- 74. Studer G, Rempfer C, Waterhouse AM, Gumienny R, Haas J, Schwede T. QMEANDisCo-distance constraints applied on model quality estimation. Bioinformatics. 2020;36(8):2647. pmid:32048708
- 75.
Laskowski RA, MacArthur MW, Thornton JM. PROCHECK: validation of protein-structure coordinates. 2012;684–687. https://doi.org/10.1107/97809553602060000882
- 76. Saur IML, Panstruga R, Schulze-Lefert P. NOD-like receptor-mediated plant immunity: from structure to cell death. Nat Rev Immunol. 2021;21(5):305–18. pmid:33293618
- 77. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46(W1):W296–303. pmid:29788355
- 78. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019;47(D1):D1102–9. pmid:30371825
- 79. Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform. 2012;4(1):17. pmid:22889332
- 80. Halgren TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem. 1996;17(5–6):490–519.
- 81. Dallakyan S, Olson A. Participation in global governance: Coordinating “the voices of those most affected by food insecurity.” Glob Food Secur Gov. 2015;1263:1–11.
- 82. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–61. pmid:19499576
- 83. Ajadee A, Mahmud S, Ali MA, Mollah MMH, Ahmmed R, Mollah MNH. In-silico discovery of type-2 diabetes-causing host key genes that are associated with the complexity of monkeypox and repurposing common drugs. Brief Bioinform. 2025;26(3):bbaf215. pmid:40370100
- 84. Zhu H, Zhu X, Liu Y, Jiang F, Chen M, Cheng L, et al. Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis. Comput Math Methods Med. 2020;2020:9602016. pmid:33149760
- 85. Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. 2004;1(4):337–41. pmid:24981612
- 86. Pires B, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic properties using graph-based signatures (Theory- How to Interpret pkCSM Result). pKCSM. 2015;5.
- 87. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717. pmid:28256516
- 88. Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacology. 2013;38(1):23–38. pmid:22781841
- 89. Sarker A, Aziz MA, Hossen MB, Mollah MMH, Al-Amin, Mollah MNH. Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches. Sci Rep. 2024;14(1):27545. pmid:39528802
- 90. Lakshminarasimhan R, Liang G. The Role of DNA Methylation in Cancer. Adv Exp Med Biol. 2016;945:151–72. pmid:27826838
- 91. Galamb O, Kalmár A, Sebestyén A, Dankó T, Kriston C, Fűri I, et al. Promoter Hypomethylation and Increased Expression of the Long Non-coding RNA LINC00152 Support Colorectal Carcinogenesis. Pathol Oncol Res. 2020;26(4):2209–23. pmid:32307642
- 92. Chen M, Zhang J, Li N, Qian Z, Zhu M, Li Q, et al. Promoter hypermethylation mediated downregulation of FBP1 in human hepatocellular carcinoma and colon cancer. PLoS One. 2011;6(10):e25564. pmid:22039417
- 93.
Metal coordination by L-amino acid oxidase derived from flounder Platichthys stellatus is structurally essential and regulates antibacterial activity.
- 94. Wessel MD, Jurs PC, Tolan JW, Muskal SM. Prediction of human intestinal absorption of drug compounds from molecular structure. J Chem Inf Comput Sci. 1998;38(4):726–35. pmid:9691477
- 95. Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. J Med Chem. 2015;58(9):4066–72. pmid:25860834
- 96. Lebovitz HE. Type 2 Diabetes: An Overview. Clin Chem. 1999;45(8):1339–45.
- 97. Graff RE, Sanchez A, Tobias DK, Rodríguez D, Barrisford GW, Blute ML, et al. Type 2 Diabetes in Relation to the Risk of Renal Cell Carcinoma Among Men and Women in Two Large Prospective Cohort Studies. Diabetes Care. 2018;41(7):1432–7. pmid:29678810
- 98. Mokhtari D, Welsh N. Potential utility of small tyrosine kinase inhibitors in the treatment of diabetes. Clin Sci (Lond). 2009;118(4):241–7. pmid:19886867
- 99. Li J-S, Chen X, Luo A, Chen D. TFRC-RNA interactions show the regulation of gene expression and alternative splicing associated with IgAN in human renal tubule mesangial cells. Front Genet. 2023;14:1176118. pmid:37547464
- 100. Gilyazova IR, Beeraka NM, Klimentova EA, Bulygin KV, Nikolenko VN, Izmailov AA, et al. Novel MicroRNA Binding Site SNPs and the Risk of Clear Cell Renal Cell Carcinoma (ccRCC): A Case-Control Study. Curr Cancer Drug Targets. 2020;21:203–12. pmid:33222672
- 101. Fernández-Real JM, Mercader JM, Ortega FJ, Moreno-Navarrete JM, López-Romero P, Ricart W. Transferrin receptor-1 gene polymorphisms are associated with type 2 diabetes. Eur J Clin Invest. 2010;40(7):600–7. pmid:20497464
- 102. Yin M, Zhou L, Ji Y, Lu R, Ji W, Jiang G, et al. In silico identification and verification of ferroptosis-related genes in type 2 diabetic islets. Front Endocrinol (Lausanne). 2022;13:946492. pmid:35992146
- 103. Wu Q, Yang F, Yang Z, Fang Z, Fu W, Chen W, et al. Long noncoding RNA PVT1 inhibits renal cancer cell apoptosis by up-regulating Mcl-1. Oncotarget. 2017;8(60):101865–75. pmid:29254209
- 104. Xia HB, Cui HW, Su L, Zhang ZH, Yang XY, Ning SQ, et al. Clinical significance and expression of PUMA, MCL-1, and p53 in human renal cell carcinoma and para-carcinoma tissues. Genet Mol Res. 2017;16(3):10.4238/gmr16039278. pmid:28692117
- 105. Allagnat F, Cunha D, Moore F, Vanderwinden JM, Eizirik DL, Cardozo AK. Mcl-1 downregulation by pro-inflammatory cytokines and palmitate is an early event contributing to β-cell apoptosis. Cell Death Differ. 2011;18(2):328–37. pmid:20798690
- 106. Riscal R, Bull CJ, Mesaros C, Finan JM, Carens M, Ho ES, et al. Cholesterol Auxotrophy as a Targetable Vulnerability in Clear Cell Renal Cell Carcinoma. Cancer Discov. 2021;11(12):3106–25. pmid:34244212
- 107. Song L, Wang S, Zhang X, Song N, Lu Y, Qin C. Bridging the gap between clear cell renal cell carcinoma and cutaneous melanoma: the role of SCARB1 in dysregulated cholesterol metabolism. Aging (Albany NY). 2023;15(19):10370–88. pmid:37801479
- 108. Pérez-Martínez P, Pérez-Jiménez F, Bellido C, Ordovás JM, Moreno JA, Marín C, et al. A polymorphism exon 1 variant at the locus of the scavenger receptor class B type I (SCARB1) gene is associated with differences in insulin sensitivity in healthy people during the consumption of an olive oil-rich diet. J Clin Endocrinol Metab. 2005;90(4):2297–300. pmid:15671101
- 109. Roberts CGP, Shen H, Mitchell BD, Damcott CM, Shuldiner AR, Rodriguez A. Variants in scavenger receptor class B type I gene are associated with HDL cholesterol levels in younger women. Hum Hered. 2007;64(2):107–13. pmid:17476110
- 110. Yang R, Trevillyan JM. c-Jun N-terminal kinase pathways in diabetes. Int J Biochem Cell Biol. 2008;40(12):2702–6. pmid:18678273
- 111. Oya M, Mikami S, Mizuno R, Marumo K, Mukai M, Murai M. C-jun activation in acquired cystic kidney disease and renal cell carcinoma. J Urol. 2005;174(2):726. pmid:16006965
- 112. Ezaki A, Yano H, Pan C, Fujiwara Y, Anami T, Ibe Y, et al. Potential protumor function of CD74 in clear cell renal cell carcinoma. Hum Cell. 2024;37(5):1535–43. pmid:39080216
- 113. Valiño-Rivas L, Baeza-Bermejillo C, Gonzalez-Lafuente L, Sanz AB, Ortiz A, Sanchez-Niño MD. CD74 in Kidney Disease. Front Immunol. 2015;6:483. pmid:26441987
- 114. Chen L, Yin Z, Qin X, Zhu X, Chen X, Ding G, et al. CD74 ablation rescues type 2 diabetes mellitus-induced cardiac remodeling and contractile dysfunction through pyroptosis-evoked regulation of ferroptosis. Pharmacol Res. 2022;176:106086. pmid:35033649
- 115. Li Y, Chen D, Li Y, Jin L, Liu J, Su Z, et al. Oncogenic cAMP responsive element binding protein 1 is overexpressed upon loss of tumor suppressive miR-10b-5p and miR-363-3p in renal cancer. Oncol Rep. 2016;35(4):1967–78. pmid:26796749
- 116. Zhang Z, Guan B, Li Y, He Q, Li X, Zhou L. Increased phosphorylated CREB1 protein correlates with poor prognosis in clear cell renal cell carcinoma. Transl Androl Urol. 2021;10(8):3348–57. pmid:34532259
- 117. Xu Y, Song R, Long W, Guo H, Shi W, Yuan S, et al. CREB1 functional polymorphisms modulating promoter transcriptional activity are associated with type 2 diabetes mellitus risk in Chinese population. Gene. 2018;665:133–40. pmid:29729382
- 118. Shan Q, Zheng G, Zhu A, Cao L, Lu J, Wu D, et al. Epigenetic modification of miR-10a regulates renal damage by targeting CREB1 in type 2 diabetes mellitus. Toxicol Appl Pharmacol. 2016;306:134–43. pmid:27292126
- 119. Stafim da Cunha R, Gregório PC, Maciel RAP, Favretto G, Franco CRC, Gonçalves JP, et al. Uremic toxins activate CREB/ATF1 in endothelial cells related to chronic kidney disease. Biochem Pharmacol. 2022;198:114984. pmid:35245485
- 120. Khalil BD, Sanchez R, Rahman T, Rodriguez-Tirado C, Moritsch S, Martinez AR, et al. An NR2F1-specific agonist suppresses metastasis by inducing cancer cell dormancy. J Exp Med. 2022;219(1):e20210836. pmid:34812843
- 121. Coppola U, Waxman JS. Origin and evolutionary landscape of Nr2f transcription factors across Metazoa. PLoS One. 2021;16(11):e0254282. pmid:34807940
- 122. Fu J, Imani S, Wu M-Y, Wu R-C. MicroRNA-34 Family in Cancers: Role, Mechanism, and Therapeutic Potential. Cancers (Basel). 2023;15(19):4723. pmid:37835417
- 123. Lee A-T, Yang M-Y, Lee Y-J, Yang T-W, Wang C-C, Wang C-J. Gallic Acid Improves Diabetic Steatosis by Downregulating MicroRNA-34a-5p through Targeting NFE2L2 Expression in High-Fat Diet-Fed db/db Mice. Antioxidants (Basel). 2021;11(1):92. pmid:35052597
- 124. Sanchez-Niño MD, Sanz AB, Ihalmo P, Lassila M, Holthofer H, Mezzano S, et al. The MIF receptor CD74 in diabetic podocyte injury. J Am Soc Nephrol. 2009;20(2):353–62. pmid:18842989
- 125. Aguirre V, Uchida T, Yenush L, Davis R, White MF. The c-Jun NH(2)-terminal kinase promotes insulin resistance during association with insulin receptor substrate-1 and phosphorylation of Ser(307). J Biol Chem. 2000;275(12):9047–54. pmid:10722755
- 126. Hirosumi J, Tuncman G, Chang L, Görgün CZ, Uysal KT, Maeda K, et al. A central role for JNK in obesity and insulin resistance. Nature. 2002;420(6913):333–6.
- 127. Bala Kumar S, Mishra S, Das A, Nag S, Naidu R. Gut microbial metabolites targeting JUN in renal cell carcinoma via IL-17 signaling pathway: network pharmacology approach. Mol Divers. 2025. pmid:40251457
- 128. Meteoglu I, Erdogdu IH, Meydan N, Erkus M, Barutca S. NF-KappaB expression correlates with apoptosis and angiogenesis in clear cell renal cell carcinoma tissues. J Exp Clin Cancer Res. 2008;27(1):53. pmid:18928570
- 129. Xiao P, Takiishi T, Violato NM, Licata G, Dotta F, Sebastiani G, et al. NF-κB-inducing kinase (NIK) is activated in pancreatic β-cells but does not contribute to the development of diabetes. Cell Death Dis. 2022;13(5):476. pmid:35589698
- 130. Boussios S, Devo P, Goodall ICA, Sirlantzis K, Ghose A, Shinde SD, et al. Exosomes in the Diagnosis and Treatment of Renal Cell Cancer. Int J Mol Sci. 2023;24(18):14356. pmid:37762660
- 131. Abdelsaid K, Sudhahar V, Harris RA, Das A, Youn S-W, Liu Y, et al. Exercise improves angiogenic function of circulating exosomes in type 2 diabetes: Role of exosomal SOD3. FASEB J. 2022;36(3):e22177. pmid:35142393
- 132. Prajapati B, Jena PK, Rajput P, Purandhar K, Seshadri S. Understanding and modulating the Toll like Receptors (TLRs) and NOD like Receptors (NLRs) cross talk in type 2 diabetes. Curr Diabetes Rev. 2014;10(3):190–200. pmid:24828062
- 133. Jin J, Zhou T-J, Ren G-L, Cai L, Meng X-M. Novel insights into NOD-like receptors in renal diseases. Acta Pharmacol Sin. 2022;43(11):2789–806. pmid:35365780
- 134. Baker RG, Hayden MS, Ghosh S. NF-κB, inflammation, and metabolic disease. Cell Metab. 2011;13(1):11–22. pmid:21195345
- 135. Saxena M, Yeretssian G. NOD-Like Receptors: Master Regulators of Inflammation and Cancer. Front Immunol. 2014;5:327. pmid:25071785
- 136. Alshammari E, Zhang Y, Sobota J, Yang Z. Aberrant DNA Methylation of Tumor Suppressor Genes and Oncogenes as Cancer Biomarkers. Genomic Epigenomic Biomarkers Toxicol Dis. 2022;251–271.
- 137. Han MS, Chung KW, Cheon HG, Rhee SD, Yoon C-H, Lee M-K, et al. Imatinib mesylate reduces endoplasmic reticulum stress and induces remission of diabetes in db/db mice. Diabetes. 2009;58(2):329–36. pmid:19171749
- 138. Ryan CW, Vuky J, Chan JS, Chen Z, Beer TM, Nauman D. A phase II study of everolimus in combination with imatinib for previously treated advanced renal carcinoma. Invest New Drugs. 2011;29(2):374–9. pmid:20012337
- 139. Keisner SV, Shah SR. Pazopanib: the newest tyrosine kinase inhibitor for the treatment of advanced or metastatic renal cell carcinoma. Drugs. 2011;71(4):443–54. pmid:21395357
- 140. Strumberg D. Sorafenib for the treatment of renal cancer. Expert Opin Pharmacother. 2012;13(3):407–19. pmid:22263843
- 141. Karbownik A, Szkutnik-Fiedler D, Czyrski A, Kostewicz N, Kaczmarska P, Bekier M, et al. Pharmacokinetic Interaction between Sorafenib and Atorvastatin, and Sorafenib and Metformin in Rats. Pharmaceutics. 2020;12(7):600. pmid:32605304
- 142. Rambiritch V, Maharaj B, Naidoo P. Glibenclamide in patients with poorly controlled type 2 diabetes: a 12-week, prospective, single-center, open-label, dose-escalation study. Clin Pharmacol. 2014;6:63–9. pmid:24741335
- 143. Diwan V, Gobe G, Brown L. Glibenclamide improves kidney and heart structure and function in the adenine-diet model of chronic kidney disease. Pharmacol Res. 2014;79:104–10. pmid:24291534