Machine learning based immune evasion signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma

作者：Wenwu Xue, Guanglin Zhang, Cui Yang, Tingting Tan, Weichun Zhang, Hongcai Chen · 发表于：Frontiers in Cell and Developmental Biology · 年份：2025 · DOI：10.3389/fcell.2025.1656367 · 被引用次数：4 · 研究领域：Cancer Immunotherapy and Biomarkers、Ferroptosis and cancer prognosis、Cancer Cells and Metastasis

Background Stomach adenocarcinoma (STAD) remains a major contributor to cancer-related mortality worldwide. Despite advances in immunotherapy, only a subset of STAD patients benefits from immune checkpoint inhibitors, largely due to tumor-intrinsic immune evasion mechanisms. Therefore, robust predictive biomarkers are urgently needed to guide prognosis assessment and therapeutic decision-making. Methods An integrative machine learning framework incorporating 10 algorithms was applied to construct an immune evasion signature (IES) using 101 model combinations. The optimal model was selected based on concordance index (C-index) across validation datasets. The prognostic and immunological relevance of the IES was assessed via survival analyses, immune infiltration deconvolution, and multiple immunotherapy response metrics. Key genes were further validated using qPCR, immunohistochemistry, and in vitro functional assays. Results A four-gene IES developed via the LASSO method demonstrated robust prognostic power across TCGA and multiple external cohorts. High IES score were associated with poor survival, reduced immune cell infiltration (e.g., CD8 + T cells, dendritic cells), elevated M2 macrophage abundance, and an immunosuppressive tumor microenvironment. Patients in the low IES score group exhibited favorable immunotherapy-associated features, including higher TMB, lower TIDE scores, and increased response rates in three independent immunotherapy datasets. Additionally, the IES...