A non-invasive secreted protein-based gene signature for prognostic stratification and tumor microenvironment assessment in gastric cancer.

Background: Gastric cancer (GC) is a highly heterogeneous malignancy with poor prognosis. Current prognostic models for GC rely on invasive tissue-based high-throughput sequencing. Secreted proteins, detectable non-invasively and involved in tumor microenvironment (TME) remodeling, offer promising biomarkers. We aimed to develop a non-invasive prognostic signature based on secreted protein-coding genes (SPCGs) to stratify GC patients and predict TME characteristics. Methods: We obtained RNA sequencing data and clinical information from 375 GC and 32 paracancerous tissue samples from The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD). Differentially expressed SPCGs were identified by intersecting differentially expressed genes with 731 Human Protein Atlas (HPA) secreted protein genes. An 8-SPCG signature was constructed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. The model's predictive performance was validated through Kaplan-Meier survival curves, time-dependent receiver operating characteristic (ROC) analysis, and multivariable Cox regression. A nomogram integrating risk scores and clinical parameters was developed and validated using calibration curves. Functional annotation was conducted through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Tumor mutational burden (TMB) profiles and immune cell infiltration were compared between risk subgroups. The biological properties and clinical significance of SERPINE1 were validated through Results: An 8-SPCG signature (SERPINE1, C6, GRP, GCG, IL1F10, IGFBP1, ITIH2, and APOD) was identified and validated to predict overall survival in GC patients. The risk score derived from this signature was significantly associated with TME characteristics, including TME scores, immune cell infiltration, and immune checkpoint expression. High-risk patients exhibited an immunosuppressive microenvironment and lower TMB. Functional enrichment analysis indicated that the high-risk group was enriched in extracellular matrix-related pathways, while the low-risk group was associated with cellular metabolism and gene expression pathways. SERPINE1 was overexpressed in GC tissues, peripheral blood, and malignant effusions, and its high expression correlated with poor prognosis. Conclusions: We established a non-invasive 8-SPCG signature that may serve as a potential predictor for GC prognosis and TME features. SERPINE1 was identified as a promising mediator linking GC progression to CAFs interactions, supporting its further investigation as a therapeutic target.