A stromal-derived five-gene signature predicts gastric cancer recurrence through integrated bioinformatics and...

Gastric cancer (GC) remains a major cause of cancer-related mortality worldwide, with recurrence rates exceeding 40% following curative resection. While molecular profiling has revealed GC heterogeneity, computational approaches integrating tumor microenvironment (TME) characteristics for prognostic assessment remain underexplored. Therefore, this study aimed to develop and validate a stromal-derived gene signature for predicting gastric cancer recurrence and to characterize its tumor-microenvironmental and cell-type specificity through integrated bulk transcriptomic, machine-learning, and single-cell analyses.

Using publicly available datasets, we developed a prognostic signature through bootstrap-based feature selection (1,000 iterations) using 279 GC patients from GSE62254, divided into training (n=196) and validation (n=83) cohorts. Weighted gene co-expression network analysis (WGCNA) identified recurrence-associated modules. Single-cell RNA sequencing (scRNA-seq) of 43,560 cells determined cell-type specificity. Six machine learning algorithms assessed predictive performance. External validation used The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD), including 419 patients with complete risk-score data, and pan-cancer cohorts (n=9,328 across 33 cancer types).

The five-gene signature (FBXL2, MLLT11, NES, HEYL, AKAP12) achieved significant risk stratification [training: hazard ratio (HR) =3.074, 95% confidence interval (CI): 2.283-4.140, P=1.44×10^-13; validation: HR =3.07, 95% CI: 1.56-6.05, P=7.93×10^-4] with area under the curve (AUC) of 0.792 (95% CI: 0.721-0.863) at 12 months. Single-cell analysis revealed cell-type-enriched expression patterns: AKAP12 in fibroblasts (18.3%), NES in myeloid cells (11.3%), HEYL in smooth muscle cells (7.2%), and MLLT11 in secretory cells (4.6%). WGCNA localized 80% of signature genes to extracellular matrix (ECM) remodeling pathways. High-risk patients exhibited elevated predicted chemotherapy resistance across eight agents (all P<0.001) and distinct immune correlation patterns, with MLLT11 correlating with M2 macrophages (r=0.224, P=1.73×10^-4). The signature remained prognostic independent of tumor mutational burden (TMB) (P=0.78). Pan-cancer analysis across 33 TCGA cohorts (n=9,328) demonstrated tissue-specific prognostic value in 4 cancer types after false discovery rate (FDR) correction (FDR <0.05), including the primary GC cohort (STAD, HR =1.6, FDR =0.041), indicating limited cross-cancer applicability.

This stromal-derived five-gene signature suggests that microenvironment composition is strongly associated with GC recurrence, independent of tumor-intrinsic mutational burden. The predominant stromal expression and independence from TMB indicate that prognostic value derives from stromal-tumor interactions rather than tumor cell-autonomous features. While experimental validation is required to establish causality, these findings identify the stromal compartment as a potential therapeutic target and highlight the importance of integrating microenvironment characteristics into prognostic assessment.