The quantitative proteomics analysis of multi-stage gastric mucosal lesions associated with Helicobacter pylori infection

Xiaoxi Huang; Jiamei Ma; Yening Xiao; Jing Pan; Qin Xu; Xifan Cao; Yongchao Liu; Xiang Yi; Fang Tian

doi:10.1371/journal.pone.0353347

Abstract

Background

Helicobacter pylori (H. pylori) infection is the principal etiological factor for gastric cancer (GC), which ranks as the fourth leading cause of cancer-related mortality globally. Although the Correa cascade describes the stepwise progression from chronic gastritis to intestinal-type GC, the molecular events underlying this “inflammation-to-cancer” transition remain incompletely characterized.

Objective

This study aimed to delineate stage-specific proteomic alterations across H. pylori-associated gastric mucosal lesions and to identify candidate progression-associated biomarkers in GC.

Methods

Label-free quantitative proteomic analysis (LC-MS/MS) was performed on gastric tissue specimens from 22 patients stratified into four histopathological stages: H. pylori-negative chronic non-atrophic gastritis (HpN-CG), H. pylori-positive chronic non-atrophic gastritis (HpC-CG), H. pylori-positive chronic atrophic gastritis (HpC-CAG), and intestinal-type GC. Functional enrichment (GO, KEGG), temporal clustering (Mfuzz), and protein-protein interaction (PPI) network analyses were conducted. Key hub genes were externally assessed using GEPIA RNA-seq data from TCGA and GTEx.

Results

A total of 6,019 proteins were quantified. Differential expression analysis identified 771 DEPs between HpC-CG and HpN-CG, 101 DEPs between HpC-CAG and HpC-CG, and 535 DEPs between GC and HpC-CAG. Immune response pathways were up-regulated upon H. pylori infection, whereas oxidative phosphorylation was progressively suppressed throughout disease progression. Mfuzz clustering revealed that Cluster 4 ribosome-biogenesis proteins, including NIP7 and PDCD11 identified in the proteomic/PPI analysis, exhibited continuous up-regulation from precancerous stages to GC. External RNA-expression validation supported significant up-regulation of seven Cluster 4 hub genes, whereas Cluster 6 proteins did not show concordant transcript-level down-regulation.

Conclusion

This study provides a comprehensive proteomic landscape of H. pylori-driven gastric carcinogenesis. Cluster 4 ribosome-biogenesis proteins represent candidate progression-associated protein markers requiring further protein-level validation, while the progressive decline in oxidative phosphorylation underscores mitochondrial dysfunction as a hallmark of disease progression.

Citation: Huang X, Ma J, Xiao Y, Pan J, Xu Q, Cao X, et al. (2026) The quantitative proteomics analysis of multi-stage gastric mucosal lesions associated with Helicobacter pylori infection. PLoS One 21(7): e0353347. https://doi.org/10.1371/journal.pone.0353347

Editor: Khalid Raza, Jamia Millia Islamia Central University: Jamia Millia Islamia, INDIA

Received: October 10, 2025; Accepted: June 22, 2026; Published: July 31, 2026

Copyright: © 2026 Huang 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 paper and its Supporting Information file.

Funding: This work was supported by the Health Industry Research Projects of Hainan Province (grant numbers 20A200456 and 22A200319), the Natural Science Foundation of Hainan Province (grant number 823RC603), and the Health Science and Technology Innovation Joint Project of Hainan Province (grant number WSJK2024MS230), all awarded to X.H. 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

H. pylori is a recognized pathogen and class I carcinogen [1]. Although infection rates in China have declined from 58.3% to approximately 40% in recent decades, likely due to socioeconomic and healthcare improvements, the substantial population base means H. pylori infection remains a significant public health burden [2]. Notably, only 1–3% of infected individuals develop gastric cancer (GC) [3]. Once established, H. pylori infection persists without spontaneous eradication and can drive a continuum of gastric mucosal inflammation leading to cancer.

Globally, GC ranks fifth in incidence and fourth in cancer-related mortality [1]. In China, it is the second most common cancer and the second leading cause of cancer death, imposing substantial health and economic burdens [4,5]. According to the Lauren classification, GC comprises intestinal and diffuse types, with the intestinal type generally having a more favorable prognosis [6]. The Correa cascade model, proposed in 1992, describes the progression of intestinal-type GC initiated by H. pylori infection. This model outlines sequential pathological stages: superficial gastritis, chronic non-atrophic gastritis (CG), chronic atrophic gastritis (CAG), intestinal metaplasia (IM), dysplasia (DYS), and invasive adenocarcinoma [7]. Prospective studies confirm that long-term H. pylori infection promotes the development of these precancerous lesions [8,9]. However, while the Correa hypothesis frames CG, CAG, IM, DYS, and GC as a continuum, it does not quantify the progression risk at each stage, and the molecular mechanisms driving this “inflammation-to-cancer” transition remain poorly understood.

Proteomics, the large-scale analysis of proteins, offers a powerful approach to identify novel biomarkers and elucidate disease mechanisms [10,11]. While proteomic studies in GC have identified numerous proteins associated with invasion, metastasis, tumorigenesis, and prognosis [12–14], few have systematically examined the proteomic changes across the series of precancerous lesions preceding intestinal GC, particularly in the context of concurrent H. pylori infection and potential diffuse GC components. Applying proteomic technology to profile gastric mucosal tissues at distinct histological stages associated with H. pylori infection is therefore crucial. This approach can pinpoint key proteins and molecular events in H. pylori-induced gastric lesion development, unravel early biological events in GC pathogenesis, and provide a foundation for exploring H. pylori virulence mechanisms and developing diagnostic and individualized therapeutic strategies for GC.

Materials and methods

Study population and sample collection

This single-center, cross-sectional study was conducted at Haikou People’s Hospital between March and December 2023. The study protocol was approved by the Institutional Ethics Committee of Haikou People’s Hospital (Approval No. 2023−065) in accordance with the Declaration of Helsinki. All participants provided written informed consent. Adult patients aged 18–80 years were enrolled based on the following inclusion criteria: (1) endoscopic evidence of gastric mucosal lesions (single erosion, masses, ulcers, atrophy, intestinal metaplasia, or suspected malignancy) with histopathological confirmation of chronic non-atrophic gastritis, chronic atrophic gastritis, or intestinal-type GC; (2) H. pylori infection status determined by ¹³C-urea breath test (UBT) combined with clinical history; and (3) provision of written informed consent.

Exclusion criteria comprised: (1) prior H. pylori eradication therapy; (2) history of gastric surgery or anticancer treatment (chemotherapy, radiotherapy, or targeted therapy) of severe chronic diseases or other malignancies; (3) concurrent severe systemic diseases or other malignancies; (4) heavy smoking (>10 cigarettes/day), excessive alcohol consumption (>30 g ethanol/day), or obesity (BMI ≥ 30 kg/m²); and (5) diffuse-type or mixed-type GC according to the Lauren classification.

Histopathological evaluation

Diagnoses were established through integrated endoscopic and histopathological assessment. Five biopsy specimens were obtained from the most severely affected region of the gastric antrum in each patient. One specimen was immediately fixed in 10% neutral-buffered formalin, paraffin-embedded, and sectioned at 5 μm thickness. Sections were stained with hematoxylin and eosin (H&E) for morphological evaluation and with Giemsa stain for H. pylori detection. Histopathological parameters assessed included: chronic inflammatory infiltration, glandular atrophy, intestinal metaplasia, and H. pylori colonization density. GC cases were classified according to the Lauren criteria. All specimens were independently reviewed by a board-certified gastrointestinal pathologist blinded to clinical data.

The remaining four biopsy specimens were snap-frozen in liquid nitrogen within 30 minutes of collection and stored at −80°C until proteomic analysis. For surgical GC specimens, tumor tissue (0.5 × 0.5 cm) was dissected from the central region within 30 minutes of resection, rinsed with ice-cold sterile saline, flash-frozen in liquid nitrogen, and maintained at −80°C.

Protein extraction

Frozen tissue specimens were pulverized in liquid nitrogen-chilled mortars. Tissue powder was lysed in buffer containing 1% SDS (Amresco), 1% protease inhibitor cocktail (Merck Millipore), 3 μM trichostatin A (MedChemExpress), and 50 mM nicotinamide (Sigma-Aldrich). Following precipitation with 10% trichloroacetic acid/acetone (Sigma-Aldrich), samples underwent pulsed ultrasonication (35% amplitude, 3 s on/5 s off, 3 min total duration). Lysates were centrifuged at 18,000 × g for 10 min at 4°C, and supernatants were collected. Protein concentrations were determined using a bicinchoninic acid (BCA) assay kit (Beyotime).

Trypsin digestion

Protein aliquots (200 μg per sample) were precipitated with ice-cold acetone (1:4, v/v) at −20°C for 2 h. Pellets were washed with acetone, resuspended in 200 mM tetraethylammonium bromide (TEAB; Sigma-Aldrich), and sonicated for 5 min. Reduction was performed with 5 mM dithiothreitol (DTT) at 56°C for 30 min, followed by alkylation with 11 mM iodoacetamide (IAA) at room temperature for 15 min in dark. Proteins were digested with sequencing-grade trypsin (1:50, w/w; Promega) at 37°C overnight. Peptides were acidified to pH 2–3 with 10% trifluoroacetic acid (TFA; Sigma-Aldrich), centrifuged at 18,000 × g for 10 min, and desalted using C18 solid-phase extraction cartridges (Thermo Fisher Scientific). Peptides were eluted with 80% acetonitrile/0.1% TFA and quantified by BCA assay.

LC-MS/MS analysis

Peptides were dissolved in mobile phase A (2% acetonitrile, 0.1% formic acid in water) and separated using a NanoElute ultra-high-performance liquid chromatography system (Bruker, Karlsruhe, Germany). Mobile phase B consisted of 100% acetonitrile with 0.1% formic acid. The chromatographic gradient was as follows: 0–70 min, 6–24% B; 70–84 min, 24–35% B; 84–87 min, 35–80% B; 87–90 min, 80% B. The flow rate was maintained at 450 nL/min.

Separated peptides were analyzed on a trapped ion mobility spectrometry-time of flight (TIMS-TOF) Pro mass spectrometer (Bruker, Billerica, USA) equipped with a CaptiveSpray nano-electrospray ion source. The capillary voltage was set at 1.8 kV. Data acquisition was performed in parallel accumulation-serial fragmentation (PASEF) mode with a scan range of m/z 100–1,700. Ten PASEF MS/MS scans were acquired per cycle for precursor ions with charge states 2–5. Dynamic exclusion was set at 30 s to minimize redundant fragmentation. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the iProX partner repository [15, 16] with the dataset identifier PXD053524.

Proteomic data processing

Raw data were analyzed by Maxquant software (version 1.6.15.0). (1) The human UniProt/Swiss-Prot database (Homo_sapiens_9606_SP_20230103.fasta; 20,389 sequence) was used for protein identification. A decoy database and common contaminant sequences were included for false discovery rate (FDR) estimation. FDR thresholds for peptide-spectrum matches and protein identification were set at 1%. (2) Trypsin/P was specified as the proteolytic enzyme, allowing up to two missed cleavages. Minimum peptide length was set at seven amino acid residues. A maximum of five variable modifications per peptide was permitted. First search and main search mass tolerances for precursor ions were 20 ppm and 20 ppm, respectively. Fragment ion mass tolerance was 20 ppm. (3) In the database search engine, cysteine alkylation was set as a fixed modification. Oxidation of methionine and N-terminal acetylation were specified as variable modifications. Proteins with |log₂ fold change| > 0.585 (corresponding to fold change > 1.5) and p < 0.05 (Student’s t-test) were considered differentially expressed.

Functional enrichment analysis

Gene Ontology (GO) annotation was performed to classify differentially expressed proteins (DEPs) into biological process (BP), cellular component (CC), and molecular function (MF) categories. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to identify enriched signaling pathways. Enrichment analyses were performed using the clusterProfiler package in R, with visualization using ggplot2. Fisher’s exact test was applied for enrichment analysis against the background of all quantified proteins. Pathways with p < 0.05 were considered significantly enriched.

Temporal clustering analysis

To characterize dynamic protein expression patterns across disease stages, Mfuzz soft clustering was performed. Protein abundance values were log2 logarithmic conversion and standardized. Proteins with standard deviation > 0.6 across groups were retained for clustering analysis. The Mfuzz package in R was employed with the following parameters: fuzzification coefficient (m) = 2; number of clusters (k) = 6.

Protein-protein interaction (PPI) network analysis

DEPs from selected clusters were imported into STRING database (version 11.5) to construct PPI networks under moderate confidence settings (interaction score > 0.4). The resulting networks were visualized and analyzed in Cytoscape (version 10.0). Hub proteins were identified using the CytoHubba plugin with the maximal clique centrality (MCC) algorithm, extracting the top 10 hub genes with highest network connectivity.

Validation of hub gene expression

Expression levels of the identified hub genes in GC versus normal gastric tissue were externally assessed using the Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/), which integrates RNA-seq data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects.

Statistical analysis

Statistical analyses were conducted using SPSS 26.0 and Python 3.7.6. Continuous variables with normal distribution were expressed as mean ± standard deviation (SD) and compared using one-way ANOVA. Non-normally distributed variables were presented as median (interquartile range, IQR) and analyzed using Kruskal-Wallis test. Categorical variables were described by frequency and percentage and compared using Fisher’s exact test or chi-square test as appropriate. For proteomic differential analysis, homogeneity of variance was assessed using Levene’s test. Independent samples t-test was applied when variance homogeneity was satisfied; otherwise, Welch’s t-test was used. Statistical significance was defined as p < 0.05.

Results

Patient characteristics

Twenty-two patients meeting inclusion criteria were enrolled and categorized into four groups: H. pylori-negative chronic non-atrophic gastritis (HpN-CG, n = 6), H. pylori-positive chronic non-atrophic gastritis (HpC-CG, n = 6), H. pylori-positive chronic atrophic gastritis (HpC-CAG, n = 6), and intestinal-type GC (n = 4). No significant differences were observed among groups in baseline demographic characteristics, including age, sex distribution, smoking status, and alcohol consumption (Table 1). Representative histopathological images of each group are shown in Fig 1.

Download:

Table 1. Baseline characteristics of study participants.

https://doi.org/10.1371/journal.pone.0353347.t001

Download:

Fig 1. Representative histopathological images of gastric mucosal lesions (H&E staining, × 100).

(A) Chronic non-atrophic gastritis with no H. pylori infection (HpN-CG); (B) chronic non-atrophic gastritis with current H. pylori infection (HpC-CG); (C) chronic atrophic gastritis with current H. pylori infection (HpC-CAG); and (D) intestinal type gastric cancer (GC).

https://doi.org/10.1371/journal.pone.0353347.g001

Protein identification and quantification

Label-free quantitative proteomic analysis was performed on gastric mucosal tissue specimens from these 22 patients. MaxQuant processing of raw data yielded 3,636,872 MS/MS spectra, of which 1,133,120 (31.16%) were successfully matched to peptide sequences. In total, 83,182 peptides corresponding to 6,019 proteins were identified, including 80,034 unique peptides.

To address the robustness of protein identification, we reviewed the peptide-level evidence available in S1 Table. NIP7 (Q9Y221), a key Cluster 4 protein, was supported by 32.8% sequence coverage, 3 total peptides, 3 unique peptides, and 3 razor + unique peptides. Other Cluster 4 hub proteins represented in S1 Table were also supported by multiple unique peptides, including DDX27 (17), EBNA1 BP2 (10), MAK16 (6), WDR12 (9), PNO1 (4), and WDR3 (10). These data support the reliability of the Cluster 4 ribosome-biogenesis signature, although independent protein-level validation in larger cohorts remains warranted.

Principal component analysis (PCA) based on relative quantitative values of all samples was visualized (Fig 2). Comparative analysis showed 771 DEPs between HpC-CG and HpN-CG groups (436 up-regulated and 335 down-regulated). The HpC-CAG vs. HpC-CG comparison revealed 101 DEPs (51 down-regulated and 50 up-regulated), while GC vs. HpC-CAG analysis identified 535 DEPs (274 down-regulated and 261 up-regulated) (Fig 3 and S1 Table). We generated expression heatmaps for the combined DEPs across all comparison groups, requiring protein quantification in at least two-thirds of samples (Fig 4).

Download:

Fig 2. Principal component analysis (PCA) of proteomic profiles.

PCA plot based on normalized protein abundance values across 22 samples. Each point represents an individual sample, with colors indicating pathological groups. The horizontal and vertical axes represent PC1 and PC2, respectively.

https://doi.org/10.1371/journal.pone.0353347.g002

Download:

Fig 3. Volcano plots of differentially expressed proteins (DEPs) across comparison groups.

(A) HpC-CG vs. HpN-CG groups. (B) HpC-CAG vs. HpC-CG groups. (C) GC vs. HpC-CAG groups. The x-axis represents log2-transformed fold change, and the y-axis represents -log10-transformed p value. Dots indicate differentially expressed proteins. Red and blue dots represent up- and down-regulated proteins, respectively. The top five DEPs in each direction are labeled.

https://doi.org/10.1371/journal.pone.0353347.g003

Download:

Fig 4. Hierarchical clustering heatmap of differentially expressed proteins (DEPs).

Rows represent proteins and columns represent samples. Red means high expression, blue means low expression, and grey means not quantifiable in the corresponding sample.

https://doi.org/10.1371/journal.pone.0353347.g004

GO enrichment analysis

For GC vs. HpC-CAG, up-regulated DEPs localized to extracellular regions (p = 1.75e-35) and demonstrated serine-type peptidase activities (p = 8.60e-12 to 4.52e-10), influencing protein activation cascades (p = 2.33e-23) and acute inflammatory responses (p = 1.72e-21). Down-regulated proteins formed endoplasmic reticulum chaperone complexes (p = 8.77e-11) with metabolic enzyme activities, particularly in oxidative phosphorylation and sterol biosynthesis (Fig 5A, 5B).

Download:

Fig 5. Functional enrichment analysis of differentially expressed proteins (DEPs).

(A–F) Gene Ontology (GO) enrichment analysis. (A) Up-regulated and (B) down-regulated DEPs in GC vs. HpC-CAG. (C) Up-regulated and (D) down-regulated DEPs in HpC-CAG vs. HpC-CG. (E) Up-regulated and (F) down-regulated DEPs in HpC-CG vs. HpN-CG. (G–L) KEGG pathway enrichment analysis. (G) Up-regulated and (H) down-regulated DEPs in HpC-CG vs. HpN-CG. (I) Up-regulated and (J) down-regulated DEPs in HpC-CAG vs. HpC-CG. (K) Up-regulated and (L) down-regulated DEPs in GC vs. HpC-CAG. BP, biological process; CC, cellular component; MF, molecular function. Bubble size indicates gene count; color represents adjusted p-value.

https://doi.org/10.1371/journal.pone.0353347.g005

In the HpC-CAG vs. HpC-CG comparison, up-regulated DEPs participated in cell junction formation (bicellular tight junctions, p = 0.0039; apicolateral plasma membranes, p = 0.0055) and exhibited molecular functions including frizzled binding (p = 0.0021) and GTPase activator activity (p = 0.0099). Biological processes such as convergent extension during axis elongation (p = 0.0003) were identified. Down-regulated DEPs were associated with specific granule formation (p = 0.0006), ferric-chelate reductase activity (p = 0.0001), and pattern recognition receptor activity (p = 0.0008) (Fig 5C, 5D).

GO enrichment analysis of DEPs between HpC-CG and HpN-CG groups revealed distinct functional patterns. Up-regulated proteins were predominantly associated with immune-related processes, including major histocompatibility complex (MHC) protein complex assembly (p = 5.90e-11), antigen processing and presentation (p = 3.33e-8), and leukocyte activation (p = 6.02e-19), particularly involving monocyte, lymphocyte, and leukocyte proliferation and differentiation. Conversely, down-regulated proteins were primarily involved in mitochondrial components and oxidative phosphorylation (p = 1.34e-22) (Fig 5E, 5F).

KEGG pathway analysis

HpC-CG vs. HpN-CG up-regulated DEPs enriched immune-related pathways including antigen presentation (p = 3.45e-11) and NK cell-mediated cytotoxicity (p = 4.99e-7), while down-regulated DEPs participated in oxidative phosphorylation (p = 5.73e-26) and metabolic pathways. HpC-CAG vs. HpC-CG comparisons revealed Wnt signaling (p = 0.0037) and pancreatic secretion pathways for up-regulated DEPs, with down-regulated DEPs involved in cancer metabolism. GC vs. HpC-CAG showed complement/coagulation cascades (p = 4.45e-38) as the most significant pathway for up-regulated DEPs, while down-regulated DEPs affected endoplasmic reticulum protein processing (p = 2.10e-7) (Fig 5G-5L).

Temporal clustering of protein expression

Mfuzz soft clustering analysis identified six distinct protein expression patterns across disease progression (Fig 6 and S2 Table). Cluster 1 (319 proteins) displayed a GC-specific up-regulation pattern, with proteins remaining at relatively low levels in HpN-CG, HpC-CG, and HpC-CAG stages but showing marked up-regulation in GC; this cluster was enriched in focal adhesion, ECM-receptor interaction, and PI3K-Akt signaling pathways. Cluster 2 (113 proteins) showed an early up-regulation pattern, with proteins increasing upon H. pylori infection (HpC-CG) and remaining elevated thereafter, and was enriched in antigen processing and presentation and phagosome pathways. Cluster 3 (80 proteins) exhibited a biphasic pattern with an initial increase followed by decrease in GC and was enriched in cell adhesion molecules and tight junction pathways. Cluster 4 (157 proteins) showed a progressive up-regulation pattern with continuous increase from HpN-CG through GC and was enriched in ribosome biogenesis in eukaryotes, spliceosome, and RNA transport pathways. Cluster 5 (141 proteins) displayed transient down-regulation in HpC-CAG with recovery in GC and was enriched in protein export and N-glycan biosynthesis pathways. Cluster 6 (161 proteins) exhibited a progressive down-regulation pattern with continuous decrease across disease stages and was enriched in oxidative phosphorylation, thermogenesis, and metabolic pathways.

Download:

Fig 6. Mfuzz temporal clustering analysis of protein expression patterns.

Six clusters identified based on protein expression trajectories across the four pathological stages. Y-axis: standardized expression level; X-axis: disease stage. Color intensity indicates cluster membership value.

https://doi.org/10.1371/journal.pone.0353347.g006

PPI network analysis and hub gene identification

Given the biological relevance of continuously altered protein expression, we focused on Clusters 4 and 6 for hub gene identification. For Cluster 4, PPI network analysis revealed a highly interconnected module centered on ribosome biogenesis; the top 10 hub genes identified by the MCC algorithm were NIP7, DDX27, EBNA1 BP2, WDR3, UTP14A, PDCD11, UTP18, MAK16, WDR12, and PNO1 (Fig 7A). For Cluster 6, the PPI network identified hub genes predominantly involved in oxidative phosphorylation, including MT-CO2, NDUFA6, NDUFB9, COX7C, COX4I1, COX6C, NDUFA4, MT-CO1, COX7A2, and NDUFS5 (Fig 7B).

Download:

Fig 7. Protein-protein interaction (PPI) network and hub gene identification.

(A) PPI network of Cluster 4 proteins with top 10 hub genes identified by MCC algorithm highlighted. (B) PPI network of Cluster 6 proteins with hub genes related to oxidative phosphorylation. Node color intensity indicates MCC score; node size indicates degree.

https://doi.org/10.1371/journal.pone.0353347.g007

Validation of hub gene expression in external datasets

GEPIA RNA-seq analysis comparing TCGA stomach adenocarcinoma (STAD) samples with GTEx normal gastric tissue revealed that seven of ten Cluster 4 hub genes--DDX27, EBNA1 BP2, MAK16, NIP7, PNO1, WDR3, and WDR12--were significantly up-regulated in GC (p < 0.01; Fig 8); PDCD11, UTP14A, and UTP18 did not reach statistical significance at the RNA level. This finding provided external RNA-level support for the proteomic results and the biological relevance of ribosome biogenesis pathway activation in gastric carcinogenesis. For Cluster 6 hub genes, GEPIA RNA-seq analysis revealed no statistically significant differences between GC and normal tissue (S1 Fig), indicating that RNA expression did not substitute for the protein-level decrease observed in Cluster 6.

Download:

Fig 8. GEPIA RNA-expression validation of Cluster 4 hub genes in gastric cancer.

Box plots compare RNA expression levels of the 10 hub genes between TCGA stomach adenocarcinoma (STAD) and GTEx normal gastric tissue. (*p < 0.01).

https://doi.org/10.1371/journal.pone.0353347.g008

Discussion

Gastric carcinogenesis typically follows a well-defined sequence from chronic gastritis to adenocarcinoma, with H. pylori infection being the predominant etiological factor [7,17,18]. Our proteomic analysis of gastric mucosa across Correa’s cascade stages revealed distinct molecular signatures associated with disease progression.

The initial H. pylori infection phase (HpC-CG vs. HpN-CG) showed up-regulation of immune-related proteins involved in antigen presentation and lymphocyte activation, consistent with established mechanisms of pathogen recognition and inflammatory response initiation [19–21]. These findings align with the paradigm that H. pylori infection triggers robust innate and adaptive immune responses through pattern recognition receptors and MHC mediated antigen presentation. This persistent inflammatory microenvironment likely contributes to subsequent malignant transformation through cumulative genetic and epigenetic alterations [22–24].

During progression to atrophic gastritis, we observed significant dysregulation of cell junction proteins and cytoskeletal components, suggesting early structural changes in the gastric epithelium. These findings align with previous reports linking cellular adhesion alterations to pre-neoplastic changes [25].

The most dramatic proteomic changes occurred in the transition to GC, particularly in complement and coagulation pathways. Notably, tissue factor overexpression correlated with disease progression and metastatic potential, supporting its role as a potential biomarker [26,27]. The observed downregulation of mitochondrial respiratory chain components in Cluster 6 proteins further highlights metabolic reprogramming as a key feature of gastric carcinogenesis [28–30]. This observation is consistent with the Warburg effect, wherein cancer cells preferentially utilize glycolysis even in the presence of adequate oxygen. The declining oxidative phosphorylation capacity may represent an adaptive response to hypoxic tumor microenvironments and/or a consequence of mitochondrial DNA mutations accumulated during chronic inflammation.

Our cluster analysis identified the 10 Cluster 4 hub proteins--NIP7, DDX27, EBNA1 BP2, WDR3, UTP14A, PDCD11, UTP18, MAK16, WDR12, and PNO1--as a coordinated ribosome-biogenesis module that increased progressively from HpN-CG through GC. NIP7 is required for accurate pre-rRNA processing and ribosomal subunit maturation [31]. PDCD11, also known as ALG-4/RRP5, participates in early pre-rRNA processing and small-subunit maturation [32,33]. DDX27 is a DEAD-box RNA helicase, whereas EBNA1 BP2, UTP14A, UTP18, MAK16, WDR3, WDR12, and PNO1 are nucleolar or rRNA-processing factors involved in pre-ribosomal particle assembly, small- or large-subunit maturation, and ribosome biogenesis. The shared direction of change among these proteins suggests activation of nucleolar ribosome-production machinery during gastric carcinogenesis rather than isolated dysregulation of a single marker. Because ribosome biogenesis is closely linked to cell growth and malignant proliferation, this Cluster 4 module may reflect increased biosynthetic demand during progression to GC [34].

Our findings have several potential clinical implications. First, the stage-specific proteomic signatures could inform development of multi-marker panels for early GC detection. Second, the coordinated increase of Cluster 4 ribosome-biogenesis proteins may help prioritize candidates for future protein-level validation in larger independent cohorts. Third, the progressive up-regulation of ribosome biogenesis pathways suggests a potential biological vulnerability, although functional and clinical validation remains necessary before these proteins can be used as prognostic or therapeutic markers.

Several limitations of this study should be acknowledged. First, the relatively small cohort, particularly the GC group, limits statistical power and generalizability of our findings. Second, the study compared different patients at different disease stages rather than following individual patients over time, which cannot capture the dynamic molecular changes within individual patients during disease progression. Third, the HpN-CG group, while H. pylori negative, represents patients with chronic gastritis rather than truly healthy individuals, which may affect the interpretation of H. pylori-specific effects. Fourth, the key findings were externally assessed using public RNA-seq databases, but RNA expression cannot substitute for direct protein-level validation, particularly for proteins such as those in Cluster 6 where RNA and protein patterns may diverge. Fifth, biopsy specimens from gastritis groups and surgical specimens from GC patients represent different tissue procurement methods that may introduce systematic bias. Sixth, the mechanistic roles of identified proteins, particularly the Cluster 4 ribosome-biogenesis module, require experimental confirmation through immunohistochemistry, immunoblotting, cell-line studies, and animal models. Seventh, our study did not include intestinal metaplasia or dysplasia stages, which are important intermediates in the Correa cascade. Future studies should address these limitations through larger, multi-center cohorts with longitudinal follow-up and functional validation experiments.

Conclusion

Through integrated proteomic and bioinformatic analysis, we have systematically characterized the dynamic molecular changes occurring during H. pylori-associated gastric carcinogenesis across distinct pathological stages. Our study identified stage-specific protein expression patterns, including immune pathway activation upon H. pylori infection, progressive up-regulation of Cluster 4 ribosome-biogenesis proteins, and progressive down-regulation of oxidative phosphorylation proteins. These findings suggest candidate progression-associated protein modules that require further protein-level and functional validation. The concurrent decline in oxidative phosphorylation machinery underscores the importance of metabolic reprogramming in gastric carcinogenesis. These findings provide a molecular framework for understanding GC pathogenesis and highlight promising directions for future biomarker validation and therapeutic investigation.

Supporting information

S1 Table. Differentially expressed proteins across consecutive stages of Helicobacter pylori-associated gastric mucosal lesion progression.

https://doi.org/10.1371/journal.pone.0353347.s001

(XLSX)

S2 Table. Mfuzz soft-clustering assignments and temporal protein-expression profiles across Helicobacter pylori-associated gastric mucosal lesion stages.

https://doi.org/10.1371/journal.pone.0353347.s002

(XLS)

S1 Fig. External RNA-expression assessment of Cluster 6 hub genes in gastric cancer using GEPIA.

https://doi.org/10.1371/journal.pone.0353347.s003

(TIF)

References

1. Sung H, Ferlay J, Siegel RL. 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.
- View Article
- Google Scholar
2. Ren S, Cai P, Liu Y, Wang T, Zhang Y, Li Q, et al. Prevalence of Helicobacter pylori infection in China: A systematic review and meta-analysis. J Gastroenterol Hepatol. 2022;37(3):464–70. pmid:34862656
- View Article
- PubMed/NCBI
- Google Scholar
3. Wang F, Meng W, Wang B, Qiao L. Helicobacter pylori-induced gastric inflammation and gastric cancer. Cancer Lett. 2014;345(2):196–202. pmid:23981572
- View Article
- PubMed/NCBI
- Google Scholar
4. He Y, Wang Y, Luan F, Yu Z, Feng H, Chen B, et al. Chinese and global burdens of gastric cancer from 1990 to 2019. Cancer Med. 2021;10(10):3461–73. pmid:33931958
- View Article
- PubMed/NCBI
- Google Scholar
5. Global burden of 369 diseases and injuries in 204 countries and territories. 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22.
- View Article
- Google Scholar
6. Qiu M, Cai M, Zhang D, Wang Z, Wang D, Li Y, et al. Clinicopathological characteristics and prognostic analysis of Lauren classification in gastric adenocarcinoma in China. J Transl Med. 2013;11:58. pmid:23497313
- View Article
- PubMed/NCBI
- Google Scholar
7. Correa P, Piazuelo MB. The gastric precancerous cascade. J Dig Dis. 2012;13(1):2–9. pmid:22188910
- View Article
- PubMed/NCBI
- Google Scholar
8. Aumpan N, Vilaichone R-K, Pornthisarn B, Chonprasertsuk S, Siramolpiwat S, Bhanthumkomol P, et al. Predictors for regression and progression of intestinal metaplasia (IM): A large population-based study from low prevalence area of gastric cancer (IM-predictor trial). PLoS One. 2021;16(8):e0255601. pmid:34379655
- View Article
- PubMed/NCBI
- Google Scholar
9. Mera RM, Bravo LE, Camargo MC, Bravo JC, Delgado AG, Romero-Gallo J, et al. Dynamics of Helicobacter pylori infection as a determinant of progression of gastric precancerous lesions: 16-year follow-up of an eradication trial. Gut. 2018;67(7):1239–46. pmid:28647684
- View Article
- PubMed/NCBI
- Google Scholar
10. Gheybi E, Hosseinzadeh P, Tayebi-Khorrami V, Rostami M, Soukhtanloo M. Proteomics in decoding cancer: A review. Clin Chim Acta. 2025;574:120302. pmid:40220985
- View Article
- PubMed/NCBI
- Google Scholar
11. Xiao Q, Zhang F, Xu L, Yue L, Kon OL, Zhu Y, et al. High-throughput proteomics and AI for cancer biomarker discovery. Adv Drug Deliv Rev. 2021;176:113844. pmid:34182017
- View Article
- PubMed/NCBI
- Google Scholar
12. Fu H, Yang H, Zhang X, Wang B, Mao J, Li X, et al. Exosomal TRIM3 is a novel marker and therapy target for gastric cancer. J Exp Clin Cancer Res. 2018;37(1):162. pmid:30031392
- View Article
- PubMed/NCBI
- Google Scholar
13. Zhou B, Zhou Z, Chen Y, Deng H, Cai Y, Rao X, et al. Plasma proteomics-based identification of novel biomarkers in early gastric cancer. Clin Biochem. 2020;76:5–10. pmid:31765635
- View Article
- PubMed/NCBI
- Google Scholar
14. Fan H, Li X, Li Z-W, Zheng N-R, Cao L-H, Liu Z-C, et al. Urine proteomic signatures predicting the progression from premalignancy to malignant gastric cancer. EBioMedicine. 2022;86:104340. pmid:36356476
- View Article
- PubMed/NCBI
- Google Scholar
15. Chen T, Ma J, Liu Y, Chen Z, Xiao N, Lu Y, et al. iProX in 2021: connecting proteomics data sharing with big data. Nucleic Acids Res. 2022;50(D1):D1522–7. pmid:34871441
- View Article
- PubMed/NCBI
- Google Scholar
16. Ma J, Chen T, Wu S, Yang C, Bai M, Shu K, et al. iProX: an integrated proteome resource. Nucleic Acids Res. 2019;47(D1):D1211–7. pmid:30252093
- View Article
- PubMed/NCBI
- Google Scholar
17. Salvatori S, Marafini I, Laudisi F, Monteleone G, Stolfi C. Helicobacter pylori and Gastric Cancer: Pathogenetic Mechanisms. Int J Mol Sci. 2023;24(3):2895. pmid:36769214
- View Article
- PubMed/NCBI
- Google Scholar
18. Moss SF. The Clinical Evidence Linking Helicobacter pylori to Gastric Cancer. Cell Mol Gastroenterol Hepatol. 2016;3(2):183–91. pmid:28275685
- View Article
- PubMed/NCBI
- Google Scholar
19. Yang H, Hu B. Immunological perspective: Helicobacter pylori infection and gastritis. Mediators Inflamm. 2022;2022:2944156.
- View Article
- Google Scholar
20. Wang X, Zhao G, Shao S, Yao Y. Helicobacter pylori triggers inflammation and oncogenic transformation by perturbing the immune microenvironment. Biochim Biophys Acta Rev Cancer. 2024;1879(5):189139. pmid:38897421
- View Article
- PubMed/NCBI
- Google Scholar
21. Gobert AP, Wilson KT. Induction and Regulation of the Innate Immune Response in Helicobacter pylori Infection. Cell Mol Gastroenterol Hepatol. 2022;13(5):1347–63. pmid:35124288
- View Article
- PubMed/NCBI
- Google Scholar
22. He J, Hu W, Ouyang Q, Zhang S, He L, Chen W, et al. Helicobacter pylori infection induces stem cell-like properties in Correa cascade of gastric cancer. Cancer Lett. 2022;542:215764. pmid:35654291
- View Article
- PubMed/NCBI
- Google Scholar
23. Valenzuela MA, Canales J, Corvalán AH, Quest AFG. Helicobacter pylori-induced inflammation and epigenetic changes during gastric carcinogenesis. World J Gastroenterol. 2015;21(45):12742–56. pmid:26668499
- View Article
- PubMed/NCBI
- Google Scholar
24. Tan SYX, Zhang J, Tee W-W. Epigenetic regulation of inflammatory signaling and inflammation-induced cancer. Front Cell Dev Biol. 2022;10:931493. pmid:35757000
- View Article
- PubMed/NCBI
- Google Scholar
25. Teng YS, Chen WY, Yan ZB. L-plastin promotes gastric cancer growth and metastasis in a Helicobacter pylori cagA-ERK-SP1-dependent manner. Molecular Cancer Research. 2021;19(6):968–78.
- View Article
- Google Scholar
26. Garnier D, Milsom C, Magnus N, Meehan B, Weitz J, Yu J, et al. Role of the tissue factor pathway in the biology of tumor initiating cells. Thromb Res. 2010;125 Suppl 2:S44-50. pmid:20434004
- View Article
- PubMed/NCBI
- Google Scholar
27. Unruh D, Horbinski C. Beyond thrombosis: the impact of tissue factor signaling in cancer. J Hematol Oncol. 2020;13(1):93. pmid:32665005
- View Article
- PubMed/NCBI
- Google Scholar
28. Su F, Zhou F-F, Zhang T, Wang D-W, Zhao D, Hou X-M, et al. Quantitative proteomics identified 3 oxidative phosphorylation genes with clinical prognostic significance in gastric cancer. J Cell Mol Med. 2020;24(18):10842–54. pmid:32757436
- View Article
- PubMed/NCBI
- Google Scholar
29. Zhang L-H, Zhuo H-Q, Hou J-J, Zhou Y, Cheng J, Cai J-C. Proteomic signatures of infiltrative gastric cancer by proteomic and bioinformatic analysis. World J Gastrointest Oncol. 2022;14(11):2097–107. pmid:36438703
- View Article
- PubMed/NCBI
- Google Scholar
30. Chen H, Jing C, Shang L, Zhu X, Zhang R, Liu Y, et al. Molecular characterization and clinical relevance of metabolic signature subtypes in gastric cancer. Cell Rep. 2024;43(7):114424. pmid:38959111
- View Article
- PubMed/NCBI
- Google Scholar
31. Morello LG, Hesling C, Coltri PP, Castilho BA, Rimokh R, Zanchin NIT. The NIP7 protein is required for accurate pre-rRNA processing in human cells. Nucleic Acids Res. 2011;39(2):648–65. pmid:20798176
- View Article
- PubMed/NCBI
- Google Scholar
32. PDCD11 - Programmed cell death 11. https://www.ncbi.nlm.nih.gov/gene/22984. 2026.
- View Article
- Google Scholar
33. UniProt. RRP5 - Ribosome biogenesis protein RRP5. UniProt Consortium. https://www.uniprot.org/uniprotkb/A0A3B3IUD7. 2026.
- View Article
- Google Scholar
34. Pelletier J, Thomas G, Volarević S. Corrigendum: Ribosome biogenesis in cancer: new players and therapeutic avenues. Nat Rev Cancer. 2018;18(2):134. pmid:29368746
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Sung H, Ferlay J, Siegel RL. 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.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Ren S, Cai P, Liu Y, Wang T, Zhang Y, Li Q, et al. Prevalence of Helicobacter pylori infection in China: A systematic review and meta-analysis. J Gastroenterol Hepatol. 2022;37(3):464–70. pmid:34862656
View Article
PubMed/NCBI
Google Scholar

[5] View Article

[6] PubMed/NCBI

[7] Google Scholar

[ref3] 3. Wang F, Meng W, Wang B, Qiao L. Helicobacter pylori-induced gastric inflammation and gastric cancer. Cancer Lett. 2014;345(2):196–202. pmid:23981572
View Article
PubMed/NCBI
Google Scholar

[9] View Article

[10] PubMed/NCBI

[11] Google Scholar

[ref4] 4. He Y, Wang Y, Luan F, Yu Z, Feng H, Chen B, et al. Chinese and global burdens of gastric cancer from 1990 to 2019. Cancer Med. 2021;10(10):3461–73. pmid:33931958
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref5] 5. Global burden of 369 diseases and injuries in 204 countries and territories. 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22.
View Article
Google Scholar

[17] View Article

[18] Google Scholar

[ref6] 6. Qiu M, Cai M, Zhang D, Wang Z, Wang D, Li Y, et al. Clinicopathological characteristics and prognostic analysis of Lauren classification in gastric adenocarcinoma in China. J Transl Med. 2013;11:58. pmid:23497313
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref7] 7. Correa P, Piazuelo MB. The gastric precancerous cascade. J Dig Dis. 2012;13(1):2–9. pmid:22188910
View Article
PubMed/NCBI
Google Scholar

[24] View Article

[25] PubMed/NCBI

[26] Google Scholar

[ref8] 8. Aumpan N, Vilaichone R-K, Pornthisarn B, Chonprasertsuk S, Siramolpiwat S, Bhanthumkomol P, et al. Predictors for regression and progression of intestinal metaplasia (IM): A large population-based study from low prevalence area of gastric cancer (IM-predictor trial). PLoS One. 2021;16(8):e0255601. pmid:34379655
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref9] 9. Mera RM, Bravo LE, Camargo MC, Bravo JC, Delgado AG, Romero-Gallo J, et al. Dynamics of Helicobacter pylori infection as a determinant of progression of gastric precancerous lesions: 16-year follow-up of an eradication trial. Gut. 2018;67(7):1239–46. pmid:28647684
View Article
PubMed/NCBI
Google Scholar

[32] View Article

[33] PubMed/NCBI

[34] Google Scholar

[ref10] 10. Gheybi E, Hosseinzadeh P, Tayebi-Khorrami V, Rostami M, Soukhtanloo M. Proteomics in decoding cancer: A review. Clin Chim Acta. 2025;574:120302. pmid:40220985
View Article
PubMed/NCBI
Google Scholar

[36] View Article

[37] PubMed/NCBI

[38] Google Scholar

[ref11] 11. Xiao Q, Zhang F, Xu L, Yue L, Kon OL, Zhu Y, et al. High-throughput proteomics and AI for cancer biomarker discovery. Adv Drug Deliv Rev. 2021;176:113844. pmid:34182017
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref12] 12. Fu H, Yang H, Zhang X, Wang B, Mao J, Li X, et al. Exosomal TRIM3 is a novel marker and therapy target for gastric cancer. J Exp Clin Cancer Res. 2018;37(1):162. pmid:30031392
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref13] 13. Zhou B, Zhou Z, Chen Y, Deng H, Cai Y, Rao X, et al. Plasma proteomics-based identification of novel biomarkers in early gastric cancer. Clin Biochem. 2020;76:5–10. pmid:31765635
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref14] 14. Fan H, Li X, Li Z-W, Zheng N-R, Cao L-H, Liu Z-C, et al. Urine proteomic signatures predicting the progression from premalignancy to malignant gastric cancer. EBioMedicine. 2022;86:104340. pmid:36356476
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. Chen T, Ma J, Liu Y, Chen Z, Xiao N, Lu Y, et al. iProX in 2021: connecting proteomics data sharing with big data. Nucleic Acids Res. 2022;50(D1):D1522–7. pmid:34871441
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Ma J, Chen T, Wu S, Yang C, Bai M, Shu K, et al. iProX: an integrated proteome resource. Nucleic Acids Res. 2019;47(D1):D1211–7. pmid:30252093
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref17] 17. Salvatori S, Marafini I, Laudisi F, Monteleone G, Stolfi C. Helicobacter pylori and Gastric Cancer: Pathogenetic Mechanisms. Int J Mol Sci. 2023;24(3):2895. pmid:36769214
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref18] 18. Moss SF. The Clinical Evidence Linking Helicobacter pylori to Gastric Cancer. Cell Mol Gastroenterol Hepatol. 2016;3(2):183–91. pmid:28275685
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref19] 19. Yang H, Hu B. Immunological perspective: Helicobacter pylori infection and gastritis. Mediators Inflamm. 2022;2022:2944156.
View Article
Google Scholar

[72] View Article

[73] Google Scholar

[ref20] 20. Wang X, Zhao G, Shao S, Yao Y. Helicobacter pylori triggers inflammation and oncogenic transformation by perturbing the immune microenvironment. Biochim Biophys Acta Rev Cancer. 2024;1879(5):189139. pmid:38897421
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref21] 21. Gobert AP, Wilson KT. Induction and Regulation of the Innate Immune Response in Helicobacter pylori Infection. Cell Mol Gastroenterol Hepatol. 2022;13(5):1347–63. pmid:35124288
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref22] 22. He J, Hu W, Ouyang Q, Zhang S, He L, Chen W, et al. Helicobacter pylori infection induces stem cell-like properties in Correa cascade of gastric cancer. Cancer Lett. 2022;542:215764. pmid:35654291
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref23] 23. Valenzuela MA, Canales J, Corvalán AH, Quest AFG. Helicobacter pylori-induced inflammation and epigenetic changes during gastric carcinogenesis. World J Gastroenterol. 2015;21(45):12742–56. pmid:26668499
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref24] 24. Tan SYX, Zhang J, Tee W-W. Epigenetic regulation of inflammatory signaling and inflammation-induced cancer. Front Cell Dev Biol. 2022;10:931493. pmid:35757000
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref25] 25. Teng YS, Chen WY, Yan ZB. L-plastin promotes gastric cancer growth and metastasis in a Helicobacter pylori cagA-ERK-SP1-dependent manner. Molecular Cancer Research. 2021;19(6):968–78.
View Article
Google Scholar

[95] View Article

[96] Google Scholar

[ref26] 26. Garnier D, Milsom C, Magnus N, Meehan B, Weitz J, Yu J, et al. Role of the tissue factor pathway in the biology of tumor initiating cells. Thromb Res. 2010;125 Suppl 2:S44-50. pmid:20434004
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref27] 27. Unruh D, Horbinski C. Beyond thrombosis: the impact of tissue factor signaling in cancer. J Hematol Oncol. 2020;13(1):93. pmid:32665005
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref28] 28. Su F, Zhou F-F, Zhang T, Wang D-W, Zhao D, Hou X-M, et al. Quantitative proteomics identified 3 oxidative phosphorylation genes with clinical prognostic significance in gastric cancer. J Cell Mol Med. 2020;24(18):10842–54. pmid:32757436
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref29] 29. Zhang L-H, Zhuo H-Q, Hou J-J, Zhou Y, Cheng J, Cai J-C. Proteomic signatures of infiltrative gastric cancer by proteomic and bioinformatic analysis. World J Gastrointest Oncol. 2022;14(11):2097–107. pmid:36438703
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref30] 30. Chen H, Jing C, Shang L, Zhu X, Zhang R, Liu Y, et al. Molecular characterization and clinical relevance of metabolic signature subtypes in gastric cancer. Cell Rep. 2024;43(7):114424. pmid:38959111
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref31] 31. Morello LG, Hesling C, Coltri PP, Castilho BA, Rimokh R, Zanchin NIT. The NIP7 protein is required for accurate pre-rRNA processing in human cells. Nucleic Acids Res. 2011;39(2):648–65. pmid:20798176
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref32] 32. PDCD11 - Programmed cell death 11. https://www.ncbi.nlm.nih.gov/gene/22984. 2026.
View Article
Google Scholar

[122] View Article

[123] Google Scholar

[ref33] 33. UniProt. RRP5 - Ribosome biogenesis protein RRP5. UniProt Consortium. https://www.uniprot.org/uniprotkb/A0A3B3IUD7. 2026.
View Article
Google Scholar

[125] View Article

[126] Google Scholar

[ref34] 34. Pelletier J, Thomas G, Volarević S. Corrigendum: Ribosome biogenesis in cancer: new players and therapeutic avenues. Nat Rev Cancer. 2018;18(2):134. pmid:29368746
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

Figures

Abstract

Background

Objective

Methods

Results

Conclusion

Introduction

Materials and methods

Study population and sample collection

Histopathological evaluation

Protein extraction

Trypsin digestion

LC-MS/MS analysis

Proteomic data processing

Functional enrichment analysis

Temporal clustering analysis

Protein-protein interaction (PPI) network analysis

Validation of hub gene expression

Statistical analysis

Results

Patient characteristics

Protein identification and quantification

GO enrichment analysis

KEGG pathway analysis

Temporal clustering of protein expression

PPI network analysis and hub gene identification

Validation of hub gene expression in external datasets

Discussion

Conclusion

Supporting information

S1 Table. Differentially expressed proteins across consecutive stages of Helicobacter pylori-associated gastric mucosal lesion progression.

S2 Table. Mfuzz soft-clustering assignments and temporal protein-expression profiles across Helicobacter pylori-associated gastric mucosal lesion stages.

S1 Fig. External RNA-expression assessment of Cluster 6 hub genes in gastric cancer using GEPIA.

References