Clinical Significance of SDC2 in Gastric Cancer: Insights From a Multi-Center Cohort Study

Abstract

Objectives:

Gastric Cancer (GC) poses a significant global health challenge, necessitating effective biomarkers for early detection and prognosis. This study investigates the relationship between SDC2 expression and GC patient outcomes.

Methods:

We analyzed SDC2 expression in GC and its association with patient outcomes using Kaplan-Meier survival and Cox regression analyses. A pan-cancer analysis was performed to assess cross-tumor SDC2 expression patterns. Validation included immunohistochemistry and single-cell data analyses to confirm SDC2 expression in GC tissues and cell types, with findings supported by independent cohort studies.

Results:

Elevated SDC2 expression correlates with poor prognosis in GC patients, marked by lower survival rates, enhanced tumor microenvironment heterogeneity, decreased tumor mutational burden, and reduced immunotherapy efficacy. Kaplan-Meier and Cox regression analyses confirm that higher SDC2 expression is associated with shorter overall survival, establishing it as an independent prognostic risk factor. Pan-cancer analysis reveals consistent SDC2 expression patterns across multiple cancers, indicating broad clinical relevance. Validation through immunohistochemistry and single-cell data analysis confirms SDC2 expression in GC tissues and cell types. Independent cohort studies further support these findings.

Conclusion:

In summary, this study underscores the potential of SDC2 as a promising target for early diagnosis and therapeutic intervention in GC, with implications for other malignancies.

Keywords

gastric cancer SDC2 cancer biomarker tumor microenvironment immunotherapy

Introduction

Gastric cancer (GC) represents a significant global health challenge, particularly prevalent in East Asia.¹ In 2022, the incidence of GC approached about 1 million new cases, with nearly 660 000 fatalities, establishing it as the fifth most frequently diagnosed cancer and the fifth leading cause of cancer mortality worldwide.² The insidious nature of GC, characterized by a paucity of overt symptoms during its initial phases, often results in delayed diagnosis. This delay is a critical factor contributing to the poor prognosis of GC patients, as evidenced by the persistently low average 5-year survival rate, which remains below 20%.³ Currently, the primary treatment for metastatic GC is systemic chemotherapy. However, the median survival time for patients receiving standard chemotherapy regimens is approximately 12 months.⁴ This underscores the critical need for early screening and accurate diagnosis to alleviate the disease burden and improve patient outcomes. Presently, upper gastrointestinal endoscopy serves as the gold standard for GC screening. Despite its efficacy, this method has notable limitations, including suboptimal sensitivity and specificity, as well as significant financial and human resource demands. Consequently, there is a pressing need for a more practical and efficient screening approach. With advancements in genetic analysis technologies, cancer biomarkers, also known as tumor markers, that indicate the presence of cancer are poised to play a crucial role in the diagnosis and treatment of GC and other malignancies. The emergence of liquid biopsy technology has enabled the detection of specific molecular information from solid tumors through the analysis of body fluids.⁵ However, many of the biomarkers identified for GC screening are predominantly expressed in advanced stages of the disease. Therefore, developing biomarkers that can facilitate early detection of GC and predict disease recurrence holds significant promise and is of paramount importance.

Syndecans are a family of multifunctional proteoglycans involved in cell-cell interactions, cell-matrix interactions, signaling pathways, and the modulation of the tumor microenvironment (TME). Comprising 4 members-SDC1, SDC2, SDC3 and SDC4, the syndecan family has been associated with the diagnosis and prognosis of multiple cancer types.⁶ For instance, SDC1 has been shown to indicate a poor prognosis in head and neck squamous cell carcinoma and thyroid cancer by enhancing tumor cell invasion and metastasis.^7,8 In GC, SDC4 acts as a key regulator of invasive behavior, with its expression strongly correlating with poor overall survival (OS) in patients.⁹ Similarly, in colon cancer, SDC2 amplifies carcinogenic activity through the promotion of intercellular crosstalk and alteration of the TME.¹⁰ Given these findings, the syndecan family holds potential as diagnostic and therapeutic markers for tumors.

SDC2, a member of the syndecan family, interacts with adhesion molecules, growth factors, and a variety of other systems. It plays a crucial role in the processes of organism formation, maintenance, and repair.¹¹ Research has demonstrated that SDC2 influences the invasiveness of cells across various cancers, suggesting its potential as a valuable biomarker.¹² Notably, several studies have indicated that the presence of methylated SDC2 in fecal samples can serve as an effective tool for the early diagnosis of colorectal cancer.^13,14 Moreover, circular RNA has been shown to contribute to the progression of cervical cancer by increasing the expression of SDC2.¹⁵ In lung cancer, SDC2 is also implicated in the invasion and metastasis of cancer cells, highlighting the inhibition of SDC2 as a promising therapeutic target.¹⁶ However, the precise mechanisms by which SDC2 functions in GC remain to be elucidated.

In this study, we examined the association between SDC2 expression and the clinical prognosis of GC patients. We then explored and validated the prognostic significance and underlying mechanisms of SDC2 in GC through analyses of gene expression, gene mutations, the immune microenvironment, and immunotherapy effectiveness. Additionally, we examined the expression patterns of SDC2 across various cancers to further validate our findings and explore its potential as a diagnostic marker in other malignancies. Our results indicate that the upregulation of SDC2 expression is significantly associated with a poor prognosis in GC, positioning SDC2 as a potential key target for both diagnosis and treatment. These findings offer a new perspective on the management of GC.

Materials and Methods

Data Collection

We retrieved clinical and gene expression data from 348 GC samples from The Cancer Genome Atlas (TCGA)¹⁷ using the R package “CuratedCancerPrognosisData”¹⁸(Version 1.0), as well as relevant datasets from the Gene Expression Omnibus (GEO) platform, specifically GSE15459, GSE62254, and GSE84437.^19-21 Additionally, we downloaded somatic mutation data for GC from the Genomic Data Commons (GDC) official website (https://portal.gdc.cancer.gov/) and processed it into Mutation Annotation Format (MAF).

Exploring the Relationship Between SDC2 Expression and Patient Clinical Information

Based on the median SDC2 expression level, we divided patients into high and low SDC2 expression groups and compared differences in gender, age, tumor grade, and tumor stage between the 2 groups. These comparisons were organized and analyzed using the R package “table1” (Version 1.4.3). To assess survival outcomes, we used the Kaplan-Meier method (Kaplan and Meier, 1958) to plot survival curves for the high and low SDC2 expression groups and estimated the restricted mean survival time (RMST) using the R package “survRM2” (Version 1.0-4). To identify independent prognostic factors, we first performed univariate Cox analysis to screen for prognostic variables among SDC2 expression levels and a range of clinicopathological parameters. Multivariate Cox proportional hazards regression models (CoxPHs) were then constructed using a stepwise regression method to identify independent prognostic factors. Subsequently, we utilized the R package “survivalROC” (Version 1.0.3) to organize the relevant data and construct time-dependent ROC curves at 1, 5, 9, and 13 years. Furthermore, we utilized bootstrap resampling (500 iterations, 80% ratio) to compare the prognostic time-dependent AUC of SDC2 against those of TP53, CD274 (PD-L1), and ERBB2 (HER2), in the TCGA-STAD and GSE84437 cohorts, with statistical significance evaluated by the Wilcoxon signed-rank test.

Investigating the Correlation Between SDC2 Expression Level and Cancer Biomarkers

We utilized the scoring section of the R package “IOBR”²² (Version 0.99.8) to investigate the correlations between SDC2 expression levels and various tumor-related clinical markers. After annotating genes and removing duplicates, we applied principal components analysis (PCA) algorithms to derive feature scores. We then identified survival-associated features based on phenotype data and visualized the correlation between SDC2 expression and these feature scores using heatmaps.

Investigating the Correlation Between SDC2 and Tumor Mutations in GC

As previously described, we obtained somatic mutation data related to GC and examined the correlations between mutations in specific oncogenes and tumor suppressor genes with SDC2 expression levels. Subsequently, we calculated the tumor mutational burden (TMB) for all samples using the R package “maftools”²³ (Version 2.14.0).

Investigating the Potential Function of SDC2 in GC

We employed the R package “DESeq2” (Version 1.38.1) to identify differentially expressed genes in the GSE62254 cohort based on high and low SDC2 expression. Differentially expressed genes were screened using a criterion of |log₂((Fold Change)| >2 and a corrected P-value <.05. Next, we utilized the R package “clusterProfiler” (Version 4.6.0)²⁴ to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses on the set of differentially expressed genes. Additionally, Gene Set Enrichment Analysis (GSEA)²⁵ was conducted using the MSigDB c5.all.v7.0.entrez.gmt dataset. Enrichment was considered significant if the Fisher’s exact test P-value was less than .05.

Investigating the Correlation Between SDC2 and Immune Therapy Efficacy

We obtained standardized pan-cancer datasets from UCSC xena (https://xenabrowser.net/), specifically the TCGA Pan-Cancer dataset. From these datasets, we extracted SDC2 gene expression data for each sample and downloaded level 4 simple nucleotide variation datasets for all TCGA samples, which were processed by MuTect2 software.²⁶ We used the R package “maftools” (Version 2.14.0) to calculate the TMB for each sample. Additionally, we evaluated the response of TCGA-STAD samples to immune checkpoint inhibitors via the Tumor Immune Dysfunction and Exclusion (TIDE) database, thereby deriving TIDE scores for all samples.²⁷

SDC2 and Drug Treatment in GC Patients

We retrieved drug data (DTP NCI-60) and processed RNA expression data from the CellMiner database²⁸. Then, we computed the Spearman’s correlation coefficient between SDC2 expression and various drugs. Furthermore, we employed the R package “pRRophetic”²⁹ (Version 0.5) to construct ridge regression models predicting drug IC50 values. These models were built using transcriptome expression profiles from 2 cohorts and cell line gene expression profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) database.

SDC2 Expression in Single Cells

We analyzed cell clustering in GC single-cell datasets GSE134520³⁰ and GSE167297³¹ using the TISCH2³² database. Using this analysis, we evaluated the expression levels of SDC2 across different cell types.

Immunohistochemistry (IHC)

We collected tissue samples from 94 patients with newly diagnosed gastric cancer (GC) admitted to Zhongnan Hospital of Wuhan University between January 1, 2018, and December 31, 2020, and constructed tissue microarrays. The inclusion criteria were as follows: age ⩾18 years; pathologically confirmed primary gastric cancer; no prior neoadjuvant therapy; sufficient tissue available for RNA extraction or immunohistochemistry (IHC); complete clinical data; and available follow-up information. Exclusion criteria included: administration of neoadjuvant therapy prior to surgery; inadequate tissue quality or quantity; perioperative mortality (within 30 days of surgery); missing TNM staging or survival data; and incomplete follow-up or loss to follow-up. This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement³³ (Supplemental Table 10). The study protocol was approved by the Ethics Committee of Zhongnan Hospital of Wuhan University (Approval No. 2020133). For IHC staining, we performed dewaxing and rehydration, antigen retrieval, blocking non-specific binding sites, incubation with primary and secondary antibodies, color development, counterstaining, and sealing. The primary antibody used was an anti-SDC2 antibody diluted at 1:150. The detailed experimental procedure followed our previous publication.³⁴ Additionally, QuPath (Version 0.4.3)³⁵ software was employed to quantify the proportion of SDC2-positive cells in tumor tissues, which served as an indicator of SDC2 expression levels in tumor cells. All of the aforementioned procedures were conducted independently by 2 experienced pathologists.

Investigating the Pan-Cancer Characteristics of SDC2

We obtained SDC2 expression data across pan-cancer using the Sangerbox,³⁶ which utilizes the uniformly standardized datasets from the UCSC Xena browser (https://xenabrowser.net/) and applies a log₂(x + 1) transformation. This processed data was then integrated with previously acquired TCGA prognosis data. We extracted gene expression profiles for each tumor type and mapped them to gene symbols. Using the R packages “IOBR” (Version 0.99.8) and methods such as xCell,³⁷ Timer,³⁸ CIBERSORT,³⁹ MCPcounter,⁴⁰ and QUANTISEQ,⁴¹ we evaluated the infiltration scores of various immune cells in each sample based on gene expression. Adjusted P values (−log₁₀[x]) were calculated, and the results were visualized using bubble plots created with the R package “ggplot2” (Version 3.4.3). Additionally, we integrated pan-cancer SDC2 expression data and performed a log₂(x + 1) transformation. Using the R package “TCGAplot”⁴² (Version 4.0.0), we integrated microsatellite instability (MSI),⁴³ purity,⁴⁴ and TMB data for the samples and calculated the Spearman’s correlation between SDC2 expression and these 3 scores across different cancer types. Building on this, we further utilized the R package “ESTIMATE”⁴⁵ (Version 1.0.13) to obtain the stroma scores, immune scores, and estimate scores for each sample and calculated the Spearman correlations of these scores with SDC2 expression levels.⁴⁵

Results

SDC2 Indicates Poor Prognosis in GC Patients

As shown in Supplemental Tables 1-4, Chi-square analysis revealed that the expression levels of SDC2 were significantly correlated with the subtypes of GC (invasive, metabolic, proliferative, and unstable), Lauren classification, and histological type, while no correlation was observed between SDC2 expression and age, gender, or perineural invasion. Additionally, Kaplan-Meier (KM) curves revealed that GC patients with high SDC2 expression had shorter OS in the GSE15459, GSE62254, GSE84437, and TCGA-STAD cohorts (Figure 1A, C, E and G), a finding confirmed by RMST analysis (Supplemental Tables 6-9). Building on these results, we conducted univariate and multivariate CoxPH analyses, which confirmed that SDC2 expression was an independent prognostic risk factor for GC patients (Figure 1B, D, F and H). Furthermore, the results of the time-dependent ROC curves supported this conclusion (Supplemental Figures 1A-D). The further analysis revealed that SDC2 exhibited significantly higher time-dependent AUCs compared to established biomarkers such as TP53, PD-L1, and HER2 (Supplemental Figures 2A and B).

Figure 1.

The prognostic features and functional enrichment analyses of SDC2 in GC: (A) KM curves for high and low SDC2 expression groups in GSE15459, (B) univariate and multivariate CoxPHs were used to calculate the HR values of SDC2 expression levels and other clinicopathologic indicators in GSE15459, (C) KM curves for high and low SDC2 expression groups in GSE62254, (D) univariate and multivariate CoxPHs were used to calculate the HR values of SDC2 expression levels and other clinicopathologic indicators in GSE62254, (E) KM curves for high and low SDC2 expression groups in GSE84437, (F) univariate and multivariate CoxPHs were used to calculate the HR values of SDC2 expression levels and other clinicopathologic indicators in GSE84437, (G) KM curves for high and low SDC2 expression groups in TCGA-STAD, and (H) univariate and multivariate CoxPHs were used to calculate the HR values of SDC2 expression levels and other clinicopathologic indicators in TCGA-STAD.

SDC2 is Significantly Associated With Metabolic Pathways, TME, Extracellular Vesicles, Immune Response, and Signaling and Cytokine Biomarkers

Biomarkers are measurable substances, molecules, genes, or characteristics that indicate a physiological state or disease change in an organism, playing a crucial role in early screening, diagnosis, staging, and prognosis assessment.⁴⁶ Therefore, we organized biomarkers related to metabolic pathways, the TME, extracellular vesicles, and immune responses, as well as signaling and cytokines. Using PCA for feature scoring, we found that the high SDC2 expression group had higher scores in pathways such as TGF-beta family member receptor, cytokines, normal fibroblast, and pan-F TBRS, while showing lower scores in the release of tumor antigens and glutathione metabolism (Figure 2A). These findings suggested that SDC2 might play significant roles in tumor progression, immune evasion, and microenvironment remodeling.

Figure 2.

Correlation between SDC2 expression and tumor markers in GC and functional enrichment analysis of SDC2 in GC: (A) heat maps of multiple cancer biomarker scores in GSE62254 and TCGA-STAD, dividing the samples into 2 groups based on median expression levels of SDC2, (B) mutant landscapes of TCGA-STAD, in which the samples were divided into 2 groups according to the median expression level of SDC2, (C) functional enrichment analysis of GSE62254 using the KEGG database, and (D) functional enrichment analysis using GO analysis in GSE62254 using BP, CC, and MF classes.

High SDC2 Expression Predicts Lower TMB and Poorer Immune Therapy Response

To investigate the potential relationship between SDC2 and immunotherapy response, we first compared the gene mutation status between high and low SDC2 expression groups (Figure 2B). Based on this comparison, we analyzed the TMB for each SDC2 expression group and found that patients with high SDC2 expression had lower TMB, suggesting they might have poorer responses to immunotherapy (Figure 3A). To further validate this idea, we used the TIDE database for analysis. The TIDE algorithm simulates tumor immune escape, and a higher TIDE score predicts a poorer immunotherapy response. According to our study, patients with high SDC2 expression had higher TIDE scores, suggesting that patients in the high SDC2 expression group had a poorer response to immunotherapy compared to those in the low SDC2 expression group (Figure 3B).

Figure 3.

The correlations between SDC2 expression and TMB, and immunotherapy efficacy in GC: (A) TMB comparison between high and low SDC2 expression groups and (B) comparison of TIDE scores between high and low SDC2 expression groups.

SDC2 Expression Levels Are Associated With Multiple Biological Functions in GC

To elucidate the functional roles of SDC2 in GC, we conducted KEGG enrichment analysis, GO enrichment analysis, and GSEA based on the MSigDB database. In KEGG analysis, SDC2 was primarily enriched in pathways such as cancer pathways, DNA replication, and mTOR signaling pathway (Figure 2C). In GO enrichment analysis, we explored the molecular function (MF), cellular component (CC), and biological process (BP) that SDC2 might be involved in. The results indicated that SDC2 was associated with extracellular matrix (ECM) organization, collagen-containing ECM, and ECM structural constituent (Figure 2D). Additionally, GSEA results showed significant enrichment of SDC2 in collagen binding and extracellular structure organization (Supplemental Figure 3A). Taken together, these findings suggest that SDC2 is implicated in cellular metabolism, growth, proliferation, and metastasis, with a strong association with the ECM. Then, we employed various computational methods to analyze the association between SDC2 and the GC TME. Using CIBERSORT analysis, we determined the proportion of immune cells within GC cells and found that the high SDC2 expression group had significantly lower levels of plasma cells, activated memory CD4⁺ T-cells, and T follicular helper cells (Supplemental Figure 3B). Additionally, ESTIMATE analysis indicated that the high SDC2 expression group had significantly higher stromal scores, immune scores, and estimate scores (Supplemental Figure 3C).

Screening Potential Anti-GC Drugs Based on SDC2

Using the CellMiner database, we screened relevant drugs from NCI-60 cancer cell lines and found that SDC2 expression was positively correlated with the activity levels of Telatinib and M2698, while it was negatively correlated with Crizotinib and LEE-011 (Figure 4A). Existing studies have explored the potential of these drugs in the treatment and diagnosis of GC.^47-50 Furthermore, analysis using the GDSC database revealed that SDC2 expression was positively correlated with drugs such as GW843682X and XMD8-85, while negatively correlated with Pazopanib and Ruxolitinib (Figure 4B and C).

Figure 4.

Screening anti-GC drugs based on SDC2 expression: (A) Spearman’s correlations between SDC2 expression and drugs in CellMiner database, (B) Spearman’s correlations between SDC2 expression and IC50 values in GSE62254, and (C) Spearman’s correlations between SDC2 expression and IC50 values in GSE84437.

Single-Cell Level Analysis of GC Reveals High SDC2 Expression in Fibroblasts

We utilized the TISCH2 database to analyze the single-cell datasets GSE134520 and GSE167297 to explore the expression characteristics of SDC2 across different cell populations in GC tissue. The results indicated that SDC2 was predominantly expressed in fibroblasts within GC tissues in both datasets (Figure 5A-D).

Figure 5.

The expression characteristics of SDC2 at the single-cell level: (A) single-cell clustering for GSE134520, (B) single-cell expression profiles of APOD in cell clusters for GSE134520, (C) single-cell clustering from GSE167297, and (D) single-cell expression profiles of APOD in cell clusters from GSE167297.

Validation of the Relationship Between SDC2 Expression Levels and Clinical Prognosis in GC Patients From Independent Cohorts

To validate our findings, we collected clinical information from 94 human GC samples at Zhongnan Hospital of Wuhan University, and formed the ZN-STAD cohort. We performed immunohistochemical staining on these samples (Figure 6A) and estimated SDC2 protein levels. KM curves demonstrated that patients with high SDC2 expression had shorter OS (Figure 6B). Univariate and multivariate CoxPH analyses further confirmed that SDC2 expression level remained an independent prognostic factor for GC patients (Figure 6C). The clinical characteristics of patients in the ZN-STAD cohort are summarized in Supplemental Table 5.

Figure 6.

Independent validation of the prognostic roles of SDC2 in GC: (A) representative IHC staining images in high SDC2 expression group and low SDC2 expression group, (B) KM curves of high and low SDC2 expression groups in an independent cohort, and (C) univariate and multivariate CoxPHs for SDC2 expression levels and other clinicopathological markers in an independent cohort, with N representing N stage.

Prognosis, Immunology, Tumor Heterogeneity, and Mutation Characteristics of SDC2 in Pan-Cancer

By analyzing the characteristics of SDC2 in pan-cancer, we further evaluated its potential implications in cancer diagnosis and treatment. We examined the relationship between SDC2 and patient prognosis across various cancers using a univariate CoxPH model. The results indicated that high SDC2 expression was associated with poor prognosis in 7 tumor types, including Glioma (GBMLGG), Brain Lower Grade Glioma (LGG), Uveal Melanoma (UVM), Stomach adenocarcinoma (STAD), Glioblastoma multiforme (GBM), Stomach and Esophageal carcinoma (STES), and Mesothelioma (MESO). Conversely, low SDC2 expression was associated with poor outcomes in 2 tumor types, Kidney renal clear cell carcinoma (KIRC) and Pan-kidney cohort (KIPAN; Supplemental Figure 4A). Based on these observations, we also explored the relationship between SDC2 expression and tumor staging, discovering that SDC2 expression significantly correlated with tumor stages in various cancers. For example, in STAD, SDC2 expression increased progressively with T staging (Supplemental Figure 4D). Additionally, we investigated the relationship between SDC2 and immune regulation by analyzing 150 genes involved in 5 categories of immune pathways, including chemokines, receptors, MHC, immuno-inhibitors, and immuno-stimulators.

We found that SDC2 was associated with various immune regulatory genes in a pan-cancer manner (Supplemental Figure 4B). To assess the immune infiltration scores of SDC2 across pan-cancer, we used 5 algorithms (xCELL, Timer, CIBERSORT, MCPcounter and QUANTISEQ). The results showed negative correlations between SDC2 expression and various immune cells such as plasma cells, Th1 cells, and basophils, while positive correlations were observed with fibroblasts, macrophages, and endothelial cells. These findings indicate a potential role for SDC2 in the immune infiltration process to some extent (Figure 7A-E). Subsequently, using the ESTIMATE algorithm, we evaluated the relationship between SDC2 and the TME in a pan-cancer manner. We found that SDC2 expression was significantly positively correlated with the stromal scores, immune scores, and estimate scores in most cancers (Supplemental Figure 5A). Given the important role of tumor heterogeneity in tumor growth, invasion, and drug sensitivity, we assessed 3 tumor heterogeneity indicators: MSI, purity, and TMB. While the trends of SDC2 in various cancers were not entirely consistent, in STAD, SDC2 showed significant negative correlations with all 3 indicators (Supplemental Figure 5B). Lastly, we analyzed mutation information and protein domains and found that SDC2 frequently undergoes missense mutations at pan-cancer level, particularly at amino acid position 202 within the Syndecan domain (Supplemental Figure 4C).

Figure 7.

The pan-cancer analyses of SDC2: (A) Correlation analysis between SDC2 expression and multiple immune cells in pan-cancer using xCELL algorithm score, (B) correlation analysis between SDC2 expression and multiple immune cells in pan-cancer using Timer algorithm score, (C) correlation analysis between SDC2 expression and multiple immune cells in pan-cancer using CIBERSORT algorithm score, (D) correlation analysis between SDC2 expression and multiple immune cells in pan-cancer using MCPcounter algorithm score, and (E) correlation analysis between SDC2 expression and multiple immune cells in pan-cancer using QUANTISEQ algorithm score.

Discussion

GC remains one of the leading causes of cancer-related deaths globally, and most patients are often diagnosed at advanced stages, at which point prognosis is poor and treatment options are limited.³ Current diagnostic methods primarily rely on invasive procedures such as upper gastrointestinal endoscopy. Commonly used biomarkers, including carbohydrate antigen (CA), alpha-fetoprotein (AFP), and carcinoembryonic antigen (CEA),^51,52 are not specific to GC and demonstrate suboptimal specificity and sensitivity.⁵³ Therefore, there is a pressing need to identify new diagnostic and therapeutic targets.

It is widely recognized that during cancer development, changes in cell-cell and cell-ECM adhesions lead to ECM degradation and enhanced motility of cancer cells.⁵⁴ Focusing on cell-cell and cell-matrix adhesion can provide insights into the metabolic behavior of cancer cells and aid in identifying suitable therapeutic targets. Syndecans, a type of heparan sulfate proteoglycan (HSPG), play a crucial role in regulating various biological processes, including cell adhesion to the ECM and growth factor signaling.⁵⁵ These functions suggest that syndecans have the potential to serve as biomarkers for cancer diagnosis and prognosis.

SDC2, a member of the syndecan family, also known as fibroglycan, exerts multiple functional roles in diverse cell types.⁵⁶ Studies indicate that SDC2 regulates adhesion-dependent signaling pathways by binding to ECM ligands and is involved in cell growth, adhesion, migration, and differentiation.^57,58 Additionally, SDC2 is involved in dendritic spine formation via the EphB2 receptor tyrosine kinase⁵⁹ and promotes stress fiber formation in conjunction with integrin alpha5beta1.⁶⁰ These findings indicate that SDC2 has a complex regulatory mechanism and may be involved in multiple biological processes. However, the specific role of SDC2 in GC remains unclear. Therefore, this study systematically analyzes the characteristics of SDC2 in GC using various bioinformatics methods combined with corresponding experiments.

We first examined the characteristics of SDC2 in GC prognosis. Chi-square analysis and CoxPH models indicated that SDC2 is an independent risk factor for GC prognosis. The results of KM curves, RMST analysis, and time-dependent ROC curves further supported this conclusion. Subsequently, we explored the characteristics of SDC2 in GC. Using PCA algorithms, we found that SDC2 expression in GC was significantly associated with the TGF-beta pathway, cytokines, tumor antigens and glutathione metabolism. Previous studies have demonstrated that TGF-beta promotes tumor immune evasion by inhibiting T cell function and impairing antigen presentation.^61,62 This mechanism may represent an important pathway through which SDC2 influences the progression of GC. Enrichment analysis revealed that SDC2 might be involved in focal adhesions, ECM receptor interactions, cell adhesion molecules and mTOR signaling pathways in GC. As is well established, the mTOR pathway is closely interconnected with the AKT signaling pathway. According to existing research, SDC2 can interact with the PDK1-PH domain, promoting PDK1 membrane translocation and ultimately leading to AKT pathway activation.⁶³ Previous studies have also highlighted the PI3K/Akt/mTOR signaling pathway as a key player in metabolic reprogramming. This pathway can be modulated by tumor-derived extracellular vesicles (TDEVs) to suppress immune responses, thereby facilitating tumor cell proliferation.⁶⁴ Such immune-modulatory activity may represent another potential mechanism through which SDC2 regulates the development of GC. Furthermore, when we correlated SDC2 with immunotherapy response, we found that high SDC2 expression was associated with poorer responses to immunotherapy. Following this, we screened potential anti-cancer drugs based on SDC2 expression and utilized single-cell sequencing databases, finding that SDC2 is predominantly expressed in fibroblasts. Based on these findings, we explored the potential of SDC2 at pan-cancer level, primarily analyzing its prognostic, immunological, tumor heterogeneity, and mutation characteristics across various cancers. Our analysis revealed significant correlations with multiple indicators, suggesting the potential of SDC2 as a biomarker. Specifically, in GC, we noted that SDC2 expression was significantly negatively correlated with MSI, purity, and TMB.

According to existing research, MSI tumor cells exhibit hypermutant phenotypes and can express large amounts of peptides that function as neoantigens, thereby stimulating T cell recruitment and activation.⁶⁵ This suggests that higher MSI often correlates with better survival rates⁶⁶ and improved responses to immunotherapy.⁶⁷ Similarly, TMB is generally associated with enhanced responses to immunotherapy; numerous studies have demonstrated that higher levels of TMB often indicate a better prognosis. Our research found a significant negative correlation between SDC2 expression and both MSI and TMB in GC, indicating the potential of SDC2 as a predictor of immunetherapy response and patient prognosis.

The TME refers to the complex ecosystem composed of surrounding cells, matrices, and other factors that play crucial roles at various stages of tumorigenesis, metastasis, and treatment. Various molecules can influence tumor growth by modulating the TME.^68-70 It primarily consists of cancer-associated fibroblasts (CAFs), the ECM, tumor vasculature, and non-tumor cells.⁷¹ Numerous studies have demonstrated that targeting CAFs is a significant strategy for regulating the TME, which provides a key research direction for our study.^72,73 In our study, many signaling pathways enriched with SDC2 were associated with fibroblasts and the ECM. We also found significant correlations between SDC2 and various TME algorithms, along with a notable negative correlation with tumor purity. In addition, we observed a notable association between SDC2 expression and immune infiltration in GC. Specifically, SDC2 expression was significantly negatively correlated with the presence of plasma cells. Previous studies have shown that plasma cells can enhance anti-tumor immunity by producing large amounts of cytokines⁷⁴ and antibodies that drive phagocytosis and antibody-dependent cellular cytotoxicity (ADCC).^75,76 Conversely, SDC2 expression showed a significant positive correlation with fibroblast infiltration. Recent studies have demonstrated that CAFs secrete cytokines such as IL-6, TGF-beta, CXCL12, and CXCL1, which inhibit the activity of cytotoxic T cells and promote the recruitment of immunosuppressive cells like myeloid-derived suppressor cells (MDSCs).⁷⁷ These processes collectively contribute to the establishment of an immunosuppressive TME that supports the survival of tumor cells. These findings suggest that SDC2 may play an important role in the TME of GC and may indicate the mechanism by which SDC2 affects the TME.

Single-cell level analyses similarly indicated that SDC2 is predominantly expressed in fibroblasts. Existing studies have shown that high expression of SDC2 in CAFs is associated with poor survival and invasive phenotypes, and overexpression of SDC2 within CAFs promotes tumor growth.⁷⁸ In combination with the previously discussed roles of CAFs in shaping the TME, these findings further support the notion that SDC2 may contribute to GC progression by modulating the TME. Furthermore, studies have demonstrated that CAFs can significantly contribute to immune evasion and therapeutic resistance through mechanisms such as secreting specific molecules and remodeling the extracellular matrix.^79,80 This may help explain why high SDC2 expression is associated with a poorer prognosis and reduced response to immunotherapy. Notably, this observation aligns with our previous findings regarding TMB and TIDE scores, thereby reinforcing the potential role of SDC2 in influencing treatment response.

Our drug screening analysis identified a substantial number of compounds significantly associated with SDC2 expression, several of which show promising potential for GC treatment. For example, the Phase II study (TEL0805 trial) demonstrated that the combination of telatinib with chemotherapy achieved an objective response rate (ORR) of 67% in treatment-naive patients with advanced GC or gastroesophageal junction cancer,⁸¹ highlighting its promising therapeutic potential in this patient population. Preclinical studies have demonstrated that AP24534(ponatinib) exhibits potent anti-tumor activity in GC models, effectively inhibiting tumor cell proliferation and inducing regression in a dose-dependent manner, with therapeutic efficacy positively correlating with drug exposure levels.⁸² Additionally, T0901317 has been shown to significantly suppress the proliferation and colony-forming ability of GC cell lines.⁸³ Although crizotinib has not yet been investigated for the treatment of GC, a phase I cohort study has identified that a small proportion of highly invasive gastroesophageal cancers (GEC) exhibit transient sensitivity to the MET-targeted inhibitor crizotinib.⁸⁴ These findings indicate that SDC2 expression could serve as a potentially informative biomarker to guide treatment decisions. For instance, the significant positive correlation between SDC2 expression and telatinib activity suggests enhanced therapeutic sensitivity, whereas significant negative correlations with ponatinib, T0901317, and crizotinib imply reduced drug responsiveness. These associations suggest that targeting SDC2-related pathways may represent a viable therapeutic strategy and provide meaningful insights for guiding future drug discovery and personalized treatment approaches.

This series of studies highlights that SDC2 is closely related to various aspects, including prognosis, biomarkers, biological functions, immunotherapy, and the TME in GC, making it a promising therapeutic target. Additionally, our pan-cancer analyses of SDC2 not only confirmed its potential in GC but also suggested broader applications across other cancer types.

As previously mentioned, current diagnostic approaches for GC largely depend on invasive surgical procedures, and the commonly used biomarkers often exhibit limited sensitivity and specificity. Therefore, the development of novel diagnostic and therapeutic targets is urgently needed. Liquid biopsy has gained increasing attention due to its non-invasive nature⁸⁵ and potential for early cancer detection,⁸⁶ positioning it as a promising avenue for future cancer screening strategies.⁸⁷ Compared to existing GC biomarkers such as TP53, PD-L1, and HER2, SDC2 demonstrates a higher AUC, indicating superior diagnostic accuracy. This suggests that SDC2 has the potential to serve as a more effective biomarker in GC, offering greater applicability and promise for clinical translation. Given the potential role of SDC2 as a biomarker in GC detection, integrating SDC2 analysis with liquid biopsy technology represents a feasible and innovative direction that warrants further exploration.

Limitations

To further elucidate the underlying mechanisms of SDC2 in GC, additional experimental evidence is required. Besides, in vitro cell experiments and in vivo animal models are necessary to further validate the functional roles of SDC2 and to evaluate the efficacy and safety of potential drug candidates targeting GC.

Conclusions

In summary, our research demonstrates that SDC2 is a promising therapeutic target for GC. We identified a significant correlation between high SDC2 expression and poor prognosis in GC patients and found strong associations between SDC2 and the TME as well as various cancer biomarkers. Furthermore, we observed that high SDC2 expression is indicative of lower TMB and MSI, which corresponds to poorer immune therapy responses. Based on these findings, we screened potential anti-GC drugs according to SDC2 expression levels, providing a clear research direction for subsequent treatment strategies. Overall, our study highlights the key role of SDC2 in the progression of GC and underscores its immense potential as a biomarker.

Supplemental Material

sj-pdf-1-cix-10.1177_11769351261452672 – Supplemental material for Clinical Significance of SDC2 in Gastric Cancer: Insights From a Multi-Center Cohort Study

Supplemental material, sj-pdf-1-cix-10.1177_11769351261452672 for Clinical Significance of SDC2 in Gastric Cancer: Insights From a Multi-Center Cohort Study by Yihang Xu, Zisong Wang, Xuanyu Wang, Yuxin Chen, Jian Xu, Fan Wang, Sufang Tian and Xiaoping Liu in Cancer Informatics

Footnotes

Acknowledgements

We sincerely thank all individuals who participated in this work, including the patients. We also wish to express our gratitude to the team that provided access to the GEO database. Their efforts have made it possible for scientific research to break through geographical limitations.

Abbreviations

GC: gastric cancer; TME: tumor microenvironment; OS: overall survival; TCGA: the cancer genome atlas; GEO: gene expression omnibus; GDC: genomic data commons; MAF: mutation annotation format; RMST: restricted mean survival time; CoxPHs: cox proportional hazards regression models; PCA: principal components analysis; TMB: tumor mutational burden; GO: gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; GSEA: gene set enrichment analysis; TIDE: tumor immune dysfunction and exclusion; GDSC: genomics of drug sensitivity in cancer; IHC: immunohistochemistry; MSI: microsatellite instability; KM: Kaplan-Meier; MF: molecular function; CC: cellular component; BP: biological process; ECM: extracellular matrix; GBMLGG: glioma; LGG: brain lower grade glioma; UVM: uveal melanoma; STAD: stomach adenocarcinoma; GBM: glioblastoma multiforme; STES: stomach and esophageal carcinoma; MESO: mesothelioma; KIRC: kidney renal clear cell carcinoma; KIPAN: pan-kidney cohort; CA: carbohydrate antigen; AFP: alpha-fetoprotein; CEA: carcinoembryonic antigen; HSPG: heparan sulfate proteoglycan; TDEVs: tumor-derived extracellular vesicles; CAFs: cancer-associated fibroblasts; ADCC: antibody-dependent cellular cytotoxicity; MDSCs: myeloid-derived suppressor cells; ORR: objective response rate; GEC: gastroesophageal cancers;

ORCID iD

Xiaoping Liu

Ethical Considerations

The collection of tissues for construction of the ZN-STAD cohort was approved by the Institutional Review Board of Zhongnan Hospital of Wuhan University (2020133) .

Consent to Participate

The construction of ZN-STAD cohort was under a waiver of informed consent from the participants.

Consent for Publication

All patients participating in this study provided written informed consent prior to the commencement of the study.

Authors’ Contributions

Yihang Xu: Formal analysis, Data Curation, Visualization, Writing–Original Draft, Writing–Review & Editing; Zisong Wang: Formal analysis, Data Curation, Visualization, Writing–Review & Editing; Xuanyu Wang, Yuxin Chen and Jian Xu: Writing–Review & Editing; Sufang Tian, Fan Wang and Xiaoping Liu: Conceptualization, Resources, Writing–Review & Editing

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

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