{"title":"Machine learning based immune evasion signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.","authors":"Wenwu Xue, Guanglin Zhang, Cui Yang, Tingting Tan, Weichun Zhang, Hongcai Chen","doi":"10.3389/fcell.2025.1656367","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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 <i>in vitro</i> functional assays.</p><p><strong>Results: </strong>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<sup>+</sup> 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 stratified patients by sensitivity to chemotherapy and targeted therapies. KLF16, one of the signature genes, was upregulated in STAD and promoted cancer cell proliferation <i>in vitro</i>.</p><p><strong>Conclusion: </strong>We established a novel IES with strong potential to predict prognosis and immunotherapy response in STAD. This IES may serve as a valuable tool for risk stratification and individualized treatment planning in clinical practice.</p>","PeriodicalId":12448,"journal":{"name":"Frontiers in Cell and Developmental Biology","volume":"13 ","pages":"1656367"},"PeriodicalIF":4.6000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12507746/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Cell and Developmental Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.3389/fcell.2025.1656367","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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 stratified patients by sensitivity to chemotherapy and targeted therapies. KLF16, one of the signature genes, was upregulated in STAD and promoted cancer cell proliferation in vitro.
Conclusion: We established a novel IES with strong potential to predict prognosis and immunotherapy response in STAD. This IES may serve as a valuable tool for risk stratification and individualized treatment planning in clinical practice.
期刊介绍:
Frontiers in Cell and Developmental Biology is a broad-scope, interdisciplinary open-access journal, focusing on the fundamental processes of life, led by Prof Amanda Fisher and supported by a geographically diverse, high-quality editorial board.
The journal welcomes submissions on a wide spectrum of cell and developmental biology, covering intracellular and extracellular dynamics, with sections focusing on signaling, adhesion, migration, cell death and survival and membrane trafficking. Additionally, the journal offers sections dedicated to the cutting edge of fundamental and translational research in molecular medicine and stem cell biology.
With a collaborative, rigorous and transparent peer-review, the journal produces the highest scientific quality in both fundamental and applied research, and advanced article level metrics measure the real-time impact and influence of each publication.