Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework - Ting Ge; Guixin He; Qian Cui; Shuangcui Wang; Zekun Wang; Yingying Xie; Yuanyuan Tian; Juyue Zhou; Jianchun Yu; Jinmin Hu; Wentao Li (Discover Oncology)
Abstract Background Lung adenocarcinoma (LUAD) is a prevalent malignant tumor of the respiratory system, with high incidence and mortality rates. Cellular senescence (CS) widely affects the tumor microenvironment (TME) and tumor growth, and is related to the invasion and immune escape of tumor cells. This study aims to develop a robust CS-related signature of LUAD. Methods Using the GSE140797, GSE42458, GSE75037, and GSE85841 datasets, in combination with cellular senescence databases, 75 LUAD CS-related differentially expressed genes (LUAD-CSDEGs) were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequently, we developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 113 combinations to construct a LUAD CS-related signature (LUAD-CSRS), which were assessed in both training and validation cohorts. A LUAD-CSRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Finally, the difference of immune infiltration and response to immunotherapy in patients with high and low risk of LUAD were evaluated. Results Based on a 113-combination machine learning framework, we finally identified a LUAD-CSRS containing eight genes: RECQL4, TIMP1, ANLN, SFN, MDK, KIF2C, AGR2, ITGB4. We also confirmed that it was significantly associated with survival, immune cell infiltration, prognosis, and response to immunotherapy in LUAD patients. Additionally, we found it is related to the activation of immune responses and may be involved in regulating the balance between immune cells in the TME. Conclusion In summary, our study constructed a novel LUAD-CSRS, which is not only expected to be a powerful tool for assisting diagnosis and prognosis evaluation of LUAD, but also may provide guidance for personalized immunotherapy programs.