Identification of PDGFRB as a prognostic immune-related biomarker in gastric cancer through bioinformatics and clinical analysis

Introduction

Gastric cancer (GC) stands as one of the prevalent malignancies worldwide, posing a significant threat to human life. Global Cancer Statistics for 2020 reflect GC’s high morbidity ranking of fifth and mortality ranking of fourth (1). The majority of GC cases are detected late due to the non-specific symptoms in the initial stages (2). Despite advancements in diagnosis and treatment, the mortality rate for GC remains largely unchanged (3). Consequently, there is a pressing need for in-depth exploration of molecular mechanisms to pave the way for novel therapeutic approaches in managing GC.

Tumor microenvironment (TME), composed of immune and stromal cells, plays a vital role in tumorigenesis and tumor progression, impacting clinical outcomes in response to immunotherapy (4,5). Immunotherapy has emerged as a novel treatment approach for various malignancies, including breast cancer (6), colorectal cancer (7), lung cancer (8), and melanoma (9). The clinical application of immune checkpoint inhibitors, such as programmed death ligand 1 (PD-L1) and cytotoxic T-lymphocyte antigen 4 (CTLA-4), has shown promise in treating cancer (10-12). However, the effectiveness of immunotherapy in GC patients is hindered by individual variability, highlighting the imperative need for new targets and solutions. Building upon the understanding that TME significantly influences cancer progression and patient outcomes, we sought to identify a potential biomarker that could bridge the gap between immune response and GC prognosis.

In our current study, we extracted transcriptome sequencing data from The Cancer Genome Atlas (TCGA) database to pinpoint a promising immune-related biomarker, platelet-derived growth factor receptor-β (PDGFRB). PDGFRB, a well-known proto-oncogene, encodes receptor tyrosine kinases that are activated by platelet-derived growth factor (PDGF) (13). Its documented roles encompass promoting tumor proliferation (14), fostering angiogenesis (15), facilitating metastasis (16,17), and fostering drug resistance (18). Despite these known functions, there remains a scarcity of literature on the correlation between PDGFRB and tumor immunology. Through our analysis, we successfully identified PDGFRB as a differentially expressed gene with immune-related implications, establishing its prognostic value and association with clinicopathological attributes in GC. We present this article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-859/rc).

Methods

Patients and tissue samples

Tissue specimens were obtained through surgical resection at The First Affiliated Hospital of the University of Science and Technology of China (USTC) from January 2017 to December 2018. The overall survival time and disease-free survival time of patients were followed by telephone, with the cut-off point set at August 18, 2022. All patients did not receive any preoperative radiotherapy or chemotherapy. The overall survival time was defined as the interval commencing from the confirmation of a malignant tumor diagnosis and ending either at the time of death from any cause or at the date of the last follow-up. The disease-free survival time referred to the period starting from the time of surgery until disease recurrence or death from any cause or at the date of the last follow-up. Following formalin fixation and paraffin embedding, the gastric tissues were evaluated by two gastrointestinal pathologists. Subsequently, suitable tissues were selected to construct the tissue microarray (TMA). A total of 209 samples from gastric cancer patients and 47 normal samples were included in the study. Among 209 patients with gastric cancer, 195 were followed up and 14 were lost to follow-up. Comprehensive clinicopathological data was extracted from the medical records. Table 1 presents a summary of patient characteristics. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of University of Science and Technology of China (AF/SC-13/04.1). Patients were consented by an informed consent process.

Data source, preprocessing and differentially expressed analysis

The RNA-sequencing data, consisting of 413 gastric samples, were downloaded from the TCGA database (https://portal.gdc.cancer.gov). The corresponding clinical characteristics, including age, stage, grade, tumor size, and overall survival details, were also sourced from TCGA. Data analysis was conducted using R4.2.0 software (https://www.r-project.org). Perl (https://www.perl.org) was employed to convert Ensemble ID into gene symbol matrices and merge RNA-sequencing data files into a unified matrix. An established list of immune-related genes was obtained from the ImmPort database (http://www.immport.org). The identification of immune-related differentially expressed genes (irDEGs) was carried out using the “limma” package in R for differential expression analysis. Heatmap and volcano plot illustrating irDEGs were generated using the “pheatmap” package in R. The threshold criteria were established as: |log2 fold change (logFC)| >1.0, false discovery rate (FDR) <0.05.

The additional dataset GSE84433 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84433), encompassing gene expression data for 357 gastric tumor samples along with corresponding clinical information, was obtained from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). The gene expression data was acquired utilizing the chip platform GPL6947 (Illumina HumanHT-12 V3.0 expression beadchip). Subsequently, the data underwent normalization and transformation into log₂ scale.

Protein-protein interaction (PPI) networking and gene analysis

The Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/) was utilized to build the PPI network of irDEGs sourced from TCGA. Through a combined score ≥0.7, genes demonstrating significant interactions were pinpointed, while those not exhibiting relations with other genes were eliminated. The screening outcomes were further scrutinized using Cytoscape software 4.0.3 to visualize the network. These genes, referred to as hub-irDEGs, served as the basis for subsequent analysis.

Filtration of prognosis-related irDEGs

By integrating the expression profiles of irDEGs with survival data from TCGA, potential prognostic irDEGs associated with GC were identified through Kaplan-Meier analysis and univariate Cox analysis. Furthermore, a venn diagram was constructed to illustrate the overlap between hub-irDEGs and potential prognostic irDEGs cohorts, with the exclusive selection of PDGFRB.

Difference analysis of clinicopathological characteristics

By utilizing the TCGA and GEO datasets, the distinction in PDGFRB expression between GC and normal tissues was assessed through the Wilcoxon test, implemented via the “limma” package. Kaplan-Meier log-rank analysis was employed to compare the survival differences related to PDGFRB between the high- and low-expression group using the “survival” package. Univariate and multivariate Cox analyses were used to determine independent prognostic factors of PDGFRB using “survival” and “survminer” packages. The Wilcoxon signed-rank test was utilized to examine variations between groups with distinct clinicopathological characteristics.

Tumor-infiltrating immune cells analysis

The CIBERSORT computational method (19) was utilized to determine the abundance profile of tumor-infiltrating immune cells in GC specimens from TCGA and GEO datasets. This method was selected for calculating the scores of immuno-infiltrating cells in each sample based on the expression profiles acquired through the R package. Samples with a significance level of P<0.05 were chosen for further evaluation. The Wilcoxon signed-rank test was employed to compare the variations in tumor-infiltrating immune cell content between the high- and low-expression groups of PDGFRB. The outcomes were analyzed using the R “ggplot2” package, with a threshold of P<0.05 considered as statistically significant.

Gene set enrichment analysis (GSEA)

GSEA was employed to assess the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets according to high and low PDGFRB expression levels. A total of 1,000 permutations were carried out for this investigation. The significance threshold for our GSEA analysis was established at FDR <0.05.

Immunohistochemistry (IHC)

The sections were first deparaffinized using xylene and dehydrated in a series of ethanol solutions, followed by washing in tap water. Antigen retrieval was performed by treating the slides with citrate buffer (pH 6.0) using the wet autoclaving method. After cooling to room temperature, the sections were immersed in peroxidase inhibitor for 10 minutes to block endogenous peroxidase activity. Subsequently, the sections were incubated overnight at 4 ℃ with the primary antibody PDGFRB (1:250, Cat: TA506230, clone ID: OTI1E8, Origene Technologies, USA). The slides were then incubated for 20 minutes with horseradish peroxidase secondary antibody (Cat: Kit5020, Maixin, Fuzhou, China), followed by incubation with 3,3’-diaminobenzidine tetrahydrochloride (Zhongshan, Beijing, China) and staining with hematoxylin. Positive and negative controls (with primary antibody replaced by PBS) were included in the experiment.

Scoring of IHC

Each slide was evaluated by two pathologists in a blinded manner using a semi-quantitative scoring system for PDGFRB expression determination. The scoring system considered the percentage of positive tumor cells within epithelioid components (0, absence of staining; 1, <25%; 2, 25–50%; 3, 51–75%; 4, >75%) and the intensity of staining (0, negative; 1, weak; 2, moderate; 3, strong). The final IHC scores were calculated by multiplying the staining intensity by the percentage scores (ranging from 0 to 12). High expression was indicated when the score was 6 or higher, while low expression was determined for scores below 6.

Statistical analysis

All statistical analyses were performed utilizing R (version 4.2.0). The correlation between clinical-pathologic characteristics and PDGFRB was assessed using the Wilcoxon signed-rank test. The relationship between PDGFRB expression and overall survival, disease-free survival was examined through Log-rank method and presented by Kaplan-Meier curve.

Results

Data downloading and immune-related differentially expressed genes screening

A total of 381 GC and 32 normal gastric samples data were retrieved from TCGA database. The expression of all immune-related genes obtained from the ImmPort database was extracted. The irDEGs were identified between GC patients and normal gastric samples (Figure 1A, available online: https://cdn.amegroups.cn/static/public/10.21037tcr-2025-859-1.xlsx). Out of the 403 irDEGs, 159 were upregulated, while 244 were downregulated (Figure 1B).

Figure 1 Identifying PDGFRB as an immune-related, differentially expressed, and prognostic gene in GC. (A) Heatmap depicting the irDEGs. (B) Volcano plot showing irDEGs; red dots indicate upregulated genes, and green dots indicate downregulated genes. (C) Top 30 nodes in the PPI network. (D) Forest plots presenting the HR values of irDEGs from multivariate Cox and Kaplan-Meier regression analysis. (E) Visual representation of the intersection between hub and prognostic irDEGs. fdr, false discovery rate; irDEGs, immune-related differentially expressed genes; GC, gastric cancer; PDGFRB, platelet-derived growth factor receptor-β; PPI, protein-protein interaction.

Intersection analysis of PPI network and univariate COX regression

All irDEGs were analyzed using the PPI network, and the most significant module was identified using Cytoscape. The PPI network included a total of 262 nodes (Figure S1), with the top 30 targets highlighted in Figure 1C. Furthermore, integrating the expression data of irDEGs with survival information led to the identification of 12 prognostic irDEGs through univariate Cox and Kaplan-Meier regression analysis (Figure 1D). Subsequently, the intersection analysis between the top 30 nodes in the PPI network and the 12 prognostic irDEGs revealed only one gene, PDGFRB, overlapped in the afore mentioned analysis (Figure 1E).

Correlation of PDGFRB expression with survival and clinicopathological characteristics in GC patients from TCGA and GEO cohorts

PDGFRB, platelet-derived growth factor receptor, is a cell surface tyrosine kinase receptor for members of the platelet-derived growth factor family (13). Analysis of the data downloaded from the TCGA database indicated a significant increase in PDGFRB expression in GC samples compared to normal samples (Figure 2A). Similar findings were noted in the paired comparison between normal and tumor tissues from the same individual (Figure 2B). Subsequently, an assessment of PDGFRB in conjunction with clinical characteristics was conducted, demonstrating an association between PDGFRB expression and Stage (Figure 2C), T stage (Figure 2D). Notably, when comparing the low T stage with the high T stage (T1 vs. T2–4, Figure 2E), a significant difference was observed (P<0.05). However, there was no significance with N stage (Figure 2F), M stage (Figure 2G), grade (Figure 2H), age (Figure 2I), and gender (Figure 2J). In this study, all samples were categorized into PDGFRB high- and low-expression groups based on the median expression. Survival analysis revealed that GC patients with low PDGFRB expression exhibited prolonged overall survival compared to those with high PDGFRB expression (Figure 2K). Univariate COX analyses indicated that there was a significant positive correlation between PDGFRB and overall survival (Table 2). Regrettably, the multivariate Cox proportional hazards analysis did not reveal a significant correlation between PDGFRB and prognosis (Table 2).

Figure 2 PDGFRB expression correlated with clinicopathologic features and survival in GC from TCGA database. (A,B) Expression levels of PDGFRB in normal versus tumor gastric tissues. PDGFRB expression was associated with clinical and pathological features such as (C) stage, (D,E) T stage, (F) N stage, (G) M stage, (H) grade, (I) age, and (J) gender. (K) Relationship of PDGFRB expression with overall survival in GC. GC, gastric cancer; M, metastasis; N, node; PDGFRB, platelet-derived growth factor receptor-β; T, tumor; TCGA, The Cancer Genome Atlas.

Simultaneously, the data from GSE84433 was used to analyze the relationship between PDGFRB and the clinicopathological characteristics of GC patients. Limited by the clinical characteristics, the analysis of PDGFRB was integrated with T stage, N stage, age, and gender. The results revealed a positive correlation between PDGFRB expression and T stage (T1 vs. T2–3, or T1–2 vs. T3–4; Figure 3A-3C) and N stage (N0 vs. N1–3; Figure 3D,3E), while no significant associations were observed with age (Figure 3F) and gender (Figure 3G). Survival analysis also indicated that the low PDGFRB expression group had an extended overall survival period (Figure 3H), which was consistent with the findings from TCGA. Univariate analyses also showed a positive correlation between PDGFRB and overall survival (Table 3). Nevertheless, such an association was not corroborated by the multivariate Cox proportional hazards analysis (Table 3).

Figure 3 PDGFRB expression correlated with clinicopathologic features and survival in GC from GEO database. Associations between PDGFRB expression and clinicopathologic characteristics, including (A-C) T stage, (D,E) N stage, (F) age, and (G) gender. (H) Overall survival between high and low PDGFRB expression levels in GC. GC, gastric cancer; GEO, Gene Expression Omnibus; M, metastasis; N, node; PDGFRB, platelet-derived growth factor receptor-β; T, tumor.

Validation of the prognostic effect of PDGFRB in GC patients from The First Affiliated Hospital of USTC

The expression of PDGFRB protein was assessed through IHC to examine the distinction between normal gastric mucosa and cancer tissues in clinical patients’ tissues from the First Affiliated Hospital of USTC. Our analysis revealed a significantly higher expression of PDGFRB in GC tissues (156/209, 74.64%) compared to normal gastric mucosa tissues (20/47, 42.55%, P<0.001). Representative staining of GC and mucosa specimens was depicted in Figure 4A. Furthermore, the subcellular localization of PDGFRB showcased predominant expression in the chief cells and parietal cells of the stomach, rather than in cervical mucous cells (Figure 4B).

Figure 4 PDGFRB protein expression correlated with clinicopathologic features and survival in GC from clinical patients’ tissues. (A) PDGFRB protein expression in GC was higher than in normal tissues, as observed in clinical cases (IHC, ×200). (B) PDGFRB protein was predominantly expressed in chief and parietal cells of the stomach, not in cervical mucous cells (IHC, left: ×400, middle: ×200, right: ×400). PDGFRB expression correlated with clinicopathologic characteristics including (C) stage, (D) T stage, (E,F) N stage, (G) M stage, (H) grade, (I) age, and (J) gender in GC patients from clinical cases. Kaplan-Meier survival analysis indicated that high PDGFRB protein expression was associated with poor overall survival (K) and disease-free survival (L) in GC. GC, gastric cancer; IHC, immunohistochemistry; PDGFRB, platelet-derived growth factor receptor-β.

In terms of clinicopathological characteristics, PDGFRB expression was significantly associated with tumor stage, particularly distinguishing between early and late stages (stage I vs. II, III, or IV, P<0.05; Figure 4C). Consistent results were observed for T stage (Figure 4D), N stage (Figure 4E,4F), and M stage (Figure 4G). However, no correlations were found between PDGFRB expression and grade (Figure 4H), age (Figure 4I), or gender (Figure 4J). Kaplan-Meier survival analysis indicated that GC patients with high PDGFRB expression exhibited a poorer prognosis, including overall survival (Figure 4K) and disease-free survival (Figure 4L), compared to those with low PDGFRB expression (both P<0.05). Moreover, univariate Cox analysis demonstrated that PDGFRB was significantly and positively correlated with overall survival (Table 4). Upon incorporation of PDGFRB into the multivariate Cox proportional hazards analysis, no correlation with overall survival was demonstrated (Table 4).

Correlation of PDGFRB with tumor-infiltrating immune cells and immune checkpoints

To delve deeper into the connection between PDGFRB expression and the immune microenvironment, the proportions of tumor-infiltrating immune cells were analyzed using the CIBERSORT algorithm, resulting in the construction of 22 different immune cell profiles in GC samples from TCGA database (Figure S2A,S2B).

A comparison between the high and low expression groups of PDGFRB revealed an abundance of M2 macrophages, resting dendritic cells, and resting mast cells in the high expression group (Figure 5A). Additionally, an analysis of the correlation between PDGFRB expression and immune cell content demonstrated significant associations with 7 immune cells, positively correlated including M2 macrophages (Figure 5B), resting mast cells (Figure 5C), resting dendritic cells (Figure 5D), and regulatory T cells (Figure S2C), as well as memory B cells, plasma cells, and follicular helper T cells (negatively correlated; Figure S2C). The overlapping immune cells identified by both methods included M2 macrophages, resting mast cells, and resting dendritic cells (Figure 5E). Moreover, an examination of tumor-infiltrating immune cells using the GSE84433 dataset displayed significant correlations between PDGFRB and M2 macrophages, memory B cells, monocytes, resting mast cells, regulatory T cells, and activated memory CD4 T cells (Figure S3).

Figure 5 Correlation between PDGFRB expression and immune cell infiltration in the TCGA database. (A) The violin plot indicated the differential infiltration of immune cells between the high and low PDGFRB expression groups in GC from the TCGA cohort. The green color represented the low expression group, while the red color represented the high expression group. (B-D) Positive correlations were observed between PDGFRB expression and several immune cell types in GC patients from the TCGA cohort. (E) Three types of immune cells were included by two correlation analysis methods. GC, gastric cancer; PDGFRB, platelet-derived growth factor receptor-β; TCGA, The Cancer Genome Atlas.

In the field of cancer immunotherapy, immune checkpoints have emerged as a pivotal development, offering potential alternatives for prognostic prediction and adjuvant therapies in GC patients (20). In our study, we conducted an analysis of the correlation between PDGFRB expression and 20 immune checkpoints (21). Among these immune checkpoints, 14 were found to exhibit positive correlations with PDGFRB (R>0, P<0.05), most notably TLR4, PDCD1LG2, HAVCR2, and SIGLEC6 with R values exceeding 0.45, while only HMGB1 displayed a negative correlation (R=−0.21, P<0.05; Figure 6).

Figure 6 PDGFRB was correlated with the immune checkpoints in GC samples of TCGA cohort. GC, gastric cancer; PDGFRB, platelet-derived growth factor receptor-β; TCGA, The Cancer Genome Atlas.

Identification of PDGFRB-related signaling pathways using GSEA

To shed light on the PDGFRB-associated KEGG pathways and to better understand the differential molecular mechanisms activated in GC, GSEA analysis was performed. A total of 83 gene sets were significantly enriched in the high-expression group, while 16 gene sets were significantly enriched in the low-expression group (available online: https://cdn.amegroups.cn/static/public/10.21037tcr-2025-859-2.xlsx). The top 10 results of the GSEA analysis for the high-expression group included pathways such as “FOCAL ADHESION”, “ECM RECEPTOR INTERACTION”, “REGULATION OF ACTIN CYTOSKELETON”, “CYTOKINE-CYTOKINE RECEPTOR INTERACTION”, “HEDGEHOG SIGNALING PATHWAY”, “CELL ADHESION MOLECULES CAMS”, “GAP JUNCTION”, “NEUROACTIVE LIGAND RECEPTOR INTERACTION”, “MAPK SIGNALING PATHWAY” and “DILATED CARDIOMYOPATHY”, indicating a strong association with cancer-related signaling pathways (Figure 7A). Conversely, the top 10 results of the GSEA analysis for the low-expression group included pathways such as “HUNTINGTON’s DISEASE”, “OXIDATIVE PHOSPHORYLATION”, “PARKINSON’s DISEASE”, “ALZHEIMER’s DISEASE”, “PROTEASOME”, “CARDIAC MUSCLE CONTRACTION”, “RIBOSOME”, “TERPENOID BACKBONE BIOSYNTHESIS”, “SPLICEOSOME” and “BASE EXCISION REPAIR” (Figure 7B).

Figure 7 The potential molecular mechanisms of PDGFRB in GC. (A) The KEGG enrichment results based on the genes in the PDGFRB high expression group. (B) The KEGG enrichment results based on the genes in the PDGFRB low expression group. GC, gastric cancer; KEGG, Kyoto Encyclopedia of Genes and Genomes; PDGFRB, platelet-derived growth factor receptor-β.

Discussion

TCGA website is a publicly accessible database containing a vast collection of tumor cases and normal samples, serving as a valuable resource for researchers in conducting analytical studies. In the present investigation, data from TCGA was acquired for a comprehensive analysis. Following an in-depth exploration of immune-related hub genes and prognostic genes, the target gene PDGFRB emerged as the focal point of interest.

PDGFRB encodes platelet-derived growth factor receptor beta, overseeing a multitude of biological processes encompassing angiogenesis, cell proliferation, and differentiation (22-24). Previous studies have linked PDGFRB to tumor malignancy, positioning it as a potential prognostic biomarker for GC patients (25,26). Nevertheless, these findings were solely derived from database information and lacked clinical validation. To address this gap, our study not only utilized data from the TCGA and GEO databases to assess the clinicopathological characteristics of PDGFRB but also incorporated clinical specimens for a new dataset validation.

The correlation between PDGFRB expression and clinicopathological features, as well as survival outcomes, was subsequently assessed. In the present study, the outcomes of the univariate Cox analysis demonstrated a significant association between PDGFRB and the overall survival of patients with GC. This finding implies that PDGFRB could serve as a crucial determinant influencing the prognosis of GC, which is consistent with previous research (26,27). However, multivariate Cox analysis indicated that after adjusting for multiple confounding factors, this correlation lost statistical significance. This could be due to confounding variables that were unaccounted for in univariate analysis and masked PDGFRB’s independent effect on overall survival in the multivariate model. Additionally, the relatively dispersed sample size in the multivariate analysis may have reduced test power, contributing to the failure to detect a significant association. Collinearity among some variables in the multivariate analysis might also have compromised model stability and coefficient accuracy, affecting the interpretation between PDGFRB and overall survival.

Regarding tumor characteristics, Guo et al. highlighted a positive association between PDGFRB expression and tumor invasion depth, lymph node metastasis, and TNM stage in GC (28). Additionally, Suzuki et al. demonstrated that PDGFRB activation correlated with tumor invasion depth (29), whereas Akio et al. observed no association between PDGFRB mRNA levels and clinicopathological features in GC (30). In our current investigation, we observed a positive correlation between PDGFRB and tumor size across three cohorts, while its association with lymph node metastasis was evident in the GEO database and clinical specimens.

The investigation of PDGFRB protein expression and localization in GC through immunohistochemical staining has been limited. Prior research by Raja et al. indicated a higher PDGFRB expression in GC compared to apparently normal and paired normal tissues (31), aligning with our findings. A previous study suggested elevated PDGFRB expression in diffuse type GC compared to intestinal type (32), whereas our data revealed higher expression in intestinal type GC rather than diffuse type (t=2.342, P=0.020; data not shown). In a separate analysis of PDGFB and microvessel density in GC, the specific expression pattern of PDGFRB was not specified, but images revealed PDGFRB expression in pericytes rather than tumor cells (29). Conversely, our study demonstrated predominant PDGFRB expression in GC cells. Moreover, PDGFRB subcellular localization predominantly occurred in chief cells and parietal cells rather than cervical mucous cells, consistent with our study’s expression pattern. Differences in primary antibody clones may account for these discrepancies. Future investigations will delve into the correlation between PDGFRB expression and GC subtypes.

Due to the high mortality rate associated with GC, researchers have shifted their focus towards developing novel treatments beyond traditional approaches such as surgery, radiotherapy, and chemotherapy. The emergence of immune cells as crucial players in the TME has led to the rise of immunotherapy as a promising therapeutic strategy. In our study, we identified PDGFRB from immune-related genes and explored its interactions with immune cells, an area that has been relatively understudied. Through two different assays, we observed a correlation between PDGFRB and M2 macrophages, resting mast cells, and resting dendritic cells. These findings were consistent with data from the GEO database, which also highlighted the association of PDGFRB with M2 macrophages and resting mast cells.

M2 macrophages are known to play a pivotal role in tumor progression by promoting pro-angiogenic and immunosuppressive signals within the TME. They have been identified as independent prognostic factors in various cancers including gastric cancer (33), colorectal cancer (34,35), and hepatocellular carcinoma (36,37). The positive correlation between PDGFRB and M2 macrophages suggests that PDGFRB may serve as a potential role in promoting TME and progression. However, further experimental validation is necessary, and this will be a key focus of our future research endeavors.

GSEA was conducted to investigate the mechanistic pathways linked to PDGFRB. The genes within the PDGFRB high expression group showed significant enrichment in adhesion-related pathways such as “FOCAL ADHESION”, “CELL ADHESION MOLECULES CAMS”, and “GAP JUNCTION”. Cell adhesion plays a critical role in the invasive metastasis of tumors, and there is evidence suggesting that PDGFRB is involved in tumor metastasis. Existing literature has reported the role of PDGFRB in promoting the progression of endometrial cancer (38), osteosarcoma (39), cholangiocarcinoma (40) and so on. Further studies are required to explore the specific contribution of PDGFRB within these pathways.

Conclusions

The immune-related gene PDGFRB demonstrated significant prognostic value in GC and may serve as a potential biomarker for immune response modulation. Our findings highlight its association with tumor progression and TME characteristics, supporting its role in GC prognosis evaluation.

Acknowledgments

We acknowledge TCGA and GEO database for providing data.

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-859/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-859/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-859/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-859/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of University of Science and Technology of China (AF/SC-13/04.1). Patients were consented by an informed consent process.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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