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

Abstract

BackgroundPatients with lung adenocarcinoma (LUAD) exhibit significant heterogeneity in therapeutic responses and overall survival (OS). In recent years, accumulating research has 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.MethodsFour machine learning approaches were implemented to screen out potential hypoxia-related genes for the prognosis of LUAD based on gene expression profile of LUAD samples obtained from The Cancer Genome Atlas (TCGA), 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 and 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 dig 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.ResultsAll 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 10 selected hypoxia gene also showed significant difference between LUAD tumors tissues and normal tissues. The validation experiment showed that the gene transcript levels of most of their genes were consistent with the levels of their translated proteins.ConclusionsOur study might contribute to the optimization of risk stratification for survival and personalized management of LUAD patients by using the hypoxia genes, which will provide a valuable resource that will guide both mechanistic and therapeutic implications of the hypoxia genes in LUAD cancers.

Identification of hypoxia‐related gene signatures based on multi‐omics analysis in lung adenocarcinoma

“…Although the role of hypoxia‐related genes in prognosis prediction of LUAD has received extensive attention in recent years, 48–51 the role of hypoxia in LUAD was not fully elucidated. Several studies have combined hypoxia and immunization to predict the prognosis of patients with LUAD, and the constructed models also have good predictive performances 51,52 . Therefore, it is still necessary to construct more accurate prediction models of LUAD from multi‐omics levels.…”

“…Several studies have combined hypoxia and immunization to predict the prognosis of patients with LUAD, and the constructed models also have good predictive performances. 51 , 52 Therefore, it is still necessary to construct more accurate prediction models of LUAD from multi‐omics levels. Considering that tumour hypoxia is a complex biological process which involves complicated regulation networks and multiple signal pathways among beaucoup genes.…”

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

“…LAUD is a highly heterogeneous tumor in terms of pathology, biology, and clinical behavior, which leads to signifcant challenges to therapy and prognostic prediction [25,26]. In the past, several diferent approaches have been attempted to predict the prognosis of LAUD patients, for example, using features from pathology images, molecule biomarkers based on bioinformatics analysis and laboratory data, and clinical staging [27][28][29]. Here, our research focused on the combination of pathology, bioinformatics, and clinical characteristics of LAUD patients and provided a more accurate assessment of the prognosis of LAUD patients.…”

Prognostic markers of ferroptosis-related long non-coding RNA in lung adenocarcinomas

“…Based on the multivariate Cox regression analysis for risk score and other clinicopathological factors by the rms R package, a clinically adaptable nomogram prediction model was established to predict the survival probability of 489 LUAD individuals in 1-, 3-, and 5-years from the TCGA group. Then, the calibration analysis and time-dependent ROC curve were used to evaluate the prognostic value of the nomogram for LUAD patients (Sun et al, 2022).…”