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Identification of prognostic biomarkers and immunotherapy response predictors in lung adenocarcinoma: integrative
Chenlin Cao1, Wan Qin1, Ruoxuan Gong1
1Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background:
Lung adenocarcinoma (LUAD), the most prevalent and clinically heterogeneous form of lung cancer, lacks robust biomarkers for prognosis and immunotherapy response prediction. This study aimed to identify key prognostic genes and develop a predictive framework to guide personalized treatment strategies.
Methods:
We integrated summary data-based Mendelian randomization (SMR) with machine learning to identify LUAD-associated genes. RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed. A prognostic model was constructed with 101 combinations of 10 machine learning algorithms and validated via receiver operating characteristic (ROC) curve and concordance index analyses. Single-cell RNA-sequencing and spatial transcriptomic data were further examined to clarify the therapeutic implications of the identified genes.
Results:
SMR identified 316 LUAD-associated genes, among which 33 were associated with prognosis according to univariate Cox regression. The optimal model combining random survival forest and ridge regression achieved superior predictive accuracy, with the area under the curves for 1-, 3-, and 5-year survival being 0.757, 0.707, and 0.686, respectively. High-risk patients exhibited distinct immune microenvironment suppression, reduced tumor mutation burden, and diminished immunotherapy responsiveness as compared to their low-risk counterparts. Single-cell and spatial transcriptomic analyses identified interactions between cancer cells and stromal cells as potential therapeutic targets.
Conclusions:
The integrative approach employed in this study identified prognostic biomarkers and immunotherapy response predictors for patients with LUAD, offering a machine learning-driven framework for risk stratification and precision oncology. The findings underscore the clinical utility of combining genetic epidemiology with advanced analytics to optimize therapeutic strategies.