Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
A radiotherapy-related fibrosis gene signature-based risk model for predicting prognosis and immunological features in lung adenocarcinoma.
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Qiu Yanyan, Lv Cui, Ding Shubo
Аннотация
BACKGROUND: Lung adenocarcinoma (LUAD) is marked by significant tumor heterogeneity and immune interactions that influence therapeutic response, while radiation-induced fibrosis poses a critical clinical challenge by compromising pulmonary function and complicating post-treatment surveillance.
METHODS: A prognostic risk signature for lung adenocarcinoma was established by integrating transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories. The methodology involved initial screening for differentially expressed radiotherapy-related fibrosis genes, followed by least absolute shrinkage and selection operator (LASSO) regression coupled with multivariate Cox analysis to derive a compact risk model. Rigorous validation-encompassing survival analysis, receiver operating characteristic (ROS) evaluation, and external cohort testing-confirmed its predictive accuracy. Subsequent analyses delved into functional enrichment pathways, the tumor immune microenvironment, mutational characteristics, and potential chemotherapeutic responsiveness.
RESULTS: Following construction and validation, a model based on six genes demonstrated high accuracy in predicting patient outcomes. Low-risk patients exhibited "hot" immune phenotypes with favorable immunotherapy responses, while high-risk patients showed elevated tumor mutational burden (TMB) and differential drug sensitivity. Three molecular subtypes were identified, with Group 3 representing a "cold" tumor phenotype associated with poorest prognosis.
CONCLUSION: This study developed and validated a six-gene-fibrosis-based prognostic model for LUAD. The model stratifies survival risk and correlates with immune features and drug sensitivity, but provides a preliminary framework requiring prospective clinical validation.
METHODS: A prognostic risk signature for lung adenocarcinoma was established by integrating transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories. The methodology involved initial screening for differentially expressed radiotherapy-related fibrosis genes, followed by least absolute shrinkage and selection operator (LASSO) regression coupled with multivariate Cox analysis to derive a compact risk model. Rigorous validation-encompassing survival analysis, receiver operating characteristic (ROS) evaluation, and external cohort testing-confirmed its predictive accuracy. Subsequent analyses delved into functional enrichment pathways, the tumor immune microenvironment, mutational characteristics, and potential chemotherapeutic responsiveness.
RESULTS: Following construction and validation, a model based on six genes demonstrated high accuracy in predicting patient outcomes. Low-risk patients exhibited "hot" immune phenotypes with favorable immunotherapy responses, while high-risk patients showed elevated tumor mutational burden (TMB) and differential drug sensitivity. Three molecular subtypes were identified, with Group 3 representing a "cold" tumor phenotype associated with poorest prognosis.
CONCLUSION: This study developed and validated a six-gene-fibrosis-based prognostic model for LUAD. The model stratifies survival risk and correlates with immune features and drug sensitivity, but provides a preliminary framework requiring prospective clinical validation.