Comprehensive Analysis and Reinforcement Learning of Hypoxic Genes Based on Four Machine Learning Algorithms for Estimating the Immune Landscape, Clinical Outcomes, and Therapeutic Implications in Patients With Lung Adenocarcinoma - Sun, Zhaoyang; Zeng, Yu; Yuan, Ting; Chen, Xiaoying; Wang, Hua; Ma, Xiaowei (Frontiers in Immunology, 2022)
Background: Patients with lung adenocarcinoma (LUAD) exhibit significant heterogeneity in therapeutic responses and overall survival (OS). In recent years, accumulating research have uncovered the critical roles of hypoxia in a variety of solid tumors, but its role in LUAD is not currently fully elucidated. This study aims to discover novel insights into the mechanistic and therapeutic implications of the hypoxia genes in LUAD cancers by exploring the potential association between hypoxia and LUAD. Methods: Four machine learning approaches were implemented to screen out potential hypoxia-related genes for prognosis of LUAD based on gene expression profile of LUAD samples obtained from TGCA, then validated by six cohorts of validation datasets. The risk score derived from the hypoxia-related genes was proven to be an independent factor by using the univariate Cox, multivariate Cox regression analyses and Kaplan-Meier survival analyses. Hypoxia-related mechanisms based on tumor mutational burden (TMB), the immune activity and therapeutic value were also performed to adequately digging deeper into the clinical value of hypoxia-related genes. Finally, the expression level of hypoxia genes was validated at protein level and clinical samples from LUAD patients at transcript levels. Results: All patients in TCGA and GEO-LUAD group were distinctly stratified into low- and high-risk groups based on the risk score. Survival analyses demonstrated that our risk score could serve as a powerful and independent risk factor for OS, and the nomogram also exhibited high accuracy. LUAD Patients in high-risk group presented worse OS, lower TMB and lower immune activity. We found that the model is highly sensitive to immune features. Moreover, we revealed that the hypoxia-related genes had potential therapeutic value for LUAD patients based on the drug sensitivity and chemotherapeutic response prediction. The protein and gene expression levels of ten selected hypoxia gene also showed significant difference between LUAD tumors tissues and normal tissues. The validation experiment showed the gene transcript levels of most genes of them were consistent with the levels of their translated proteins. Conclusions: Our study will provide a valuable resource that will guide both mechanistic and therapeutic implications of the hypoxia genes in LUAD cancers.