Study on the mechanism of lncRNAs in lung adenocarcinoma based on Bioinformatics

Abstract Objective: This study aims to investigate the role of lncRNAs in lung adenocarcinoma using bioinformatics methods. Methods: Gene expression profiles and clinical data of lung adenocarcinoma patients were extracted from the TCGA database. PERL software was used to distinguish between mRNA and lncRNA, and edgeR software was employed to identify differentially expressed lncRNAs. Univariate Cox regression was then applied to further screen for differentially expressed lncRNAs associated with prognosis. A predictive model was constructed using Lasso regression, followed by the generation of risk scores to distinguish between high-risk and low-risk groups. ROC curves were generated to visualize the predictive ability of the current features for the disease. Multivariate Cox regression analysis was used to evaluate risk genes. Results: We identified 119 differentially expressed lncRNAs, of which 97 were upregulated and 22 were downregulated. Seventeen differentially expressed lncRNAs were associated with survival prognosis. We further investigated the role of these prognostic differentially expressed lncRNAs in lung adenocarcinoma. The prognostic differentially expressed lncRNAs were divided into two subgroups, and it was found that the survival of cluster 1 was better than that of cluster 2. A prognostic model was then established, and the results indicated eight risk lncRNAs. Finally, multivariate Cox regression analysis revealed that three risk lncRNAs—AL365181.3, FAM83A-AS1, and AC245041.1—could serve as independent prognostic factors and play important roles in lung adenocarcinoma. Conclusion: Using bioinformatics methods, we preliminarily explored the role of lncRNAs in lung adenocarcinoma and found that FAM83A-AS1 can serve as an independent risk prognostic factor in lung adenocarcinoma, potentially playing a promoting role in tumor development. Additionally, high expression of FAM83A-AS1 may be associated with higher drug sensitivity to gefitinib, afatinib, and savolitinib, providing a potential target for personalized treatment of lung adenocarcinoma.