Identification of molecular subgroups and establishment of risk model based on the response to oxidative stress to predict overall survival of patients with lung adenocarcinoma.

OBJECTIVE: Oxidative stress is associated with the occurrence and development of lung cancer. However, the specific association between lung cancer and oxidative stress is unclear. This study aimed to investigate the role of oxidative stress in the progression and prognosis of lung adenocarcinoma (LUAD). METHODS: The gene expression profiles and corresponding clinical information were collected from GEO and TCGA databases. Differentially expressed oxidative stress-related genes (OSRGs) were identified between normal and tumor samples. Consensus clustering was applied to identify oxidative stress-related molecular subgroups. Functional enrichment analysis, GSEA, and GSVA were performed to investigate the potential mechanisms. xCell was used to assess the immune status of the subgroups. A risk model was developed by the LASSO algorithm and validated using TCGA-LUAD, GSE13213, and GSE30219 datasets. RESULTS: A total of 40 differentially expressed OSRGs and two oxidative stress-associated subgroups were identified. Enrichment analysis revealed that cell cycle-, inflammation- and oxidative stress-related pathways varied significantly in the two subgroups. Furthermore, a risk model was developed and validated based on the OSRGs, and findings indicated that the risk model exhibits good prediction and diagnosis values for LUAD patients. CONCLUSION: The risk model based on the oxidative stress could act as an effective prognostic tool for LUAD patients. Our findings provided novel genetic biomarkers for prognosis prediction and personalized clinical treatment for LUAD patients.

Construction of a lung adenocarcinoma prognostic model based on N6-methyl-adenosine-related long noncoding RNA and screening of potential drugs based on this model.

Lung adenocarcinoma (LUAD) has a high mortality rate. N6-methyl-adenosine (m6A)-related long noncoding RNA (lncRNA) is associated with tumor prognosis. Our objective was to construct an m6A-related lncRNA prognostic model and screen potential drugs for the treatment of LUAD. The LUAD sequencing data were randomly divided into Train and Test cohorts. In the Train group, the LASSO Cox regression was used to construct the m6A-related lncRNA prognostic model. The LUAD tumor immune dysfunction and exclusion model was used to evaluate immunotherapy efficacy in LUAD. The 'pRRophetic' package was utilized to screen potential drugs for the treatment of LUAD. Eleven m6A-related lncRNAs were identified by LASSO Cox regression and were used to construct the risk model to calculate sample risk scores. Patients were divided into high- and low-risk groups based on their median risk scores. The LUAD data of The Cancer Genome Atlas database showed that the overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group in both cohorts. Multivariate Cox regression analysis showed that this risk model could serve as an independent prognostic factor of LUAD, and receiver operating characteristic curves suggested that m6A-related lncRNA prognostic signature has a good ability in predicting OS. Finally, nine potential drugs for LUAD treatment were screened based on this prognostic model. The prognostic model constructed based on the m6A-related lncRNAs facilitated prognosis prediction in LUAD patients. The screened therapeutic agents have potential application values and provide a reference for the clinical treatment of LUAD.