A Prognosis and Immunological Study of Cellular Aging Related lncRNAs in Lung Adenocarcinoma

Abstract

Background: Recently, cellular senescence has been recognized as a novel hallmark of cancer. However, the mechanisms that control cellular senescence remain poorly understood. This study seeks to discover long non-coding RNAs (lncRNAs) that are linked to senescence and can predict outcomes in lung adenocarcinoma (LUAD) patients. Methods: Using RNA sequencing data from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and senescence genes from the CellAge database, a group of lncRNAs related to senescence was initially identified. Subsequently, through univariate and multivariate Cox regression analyses, a senescence-related lncRNA signature (SenLncSig) linked to the prognosis of LUAD was developed. LUAD patients were categorized into high-risk and low-risk groups based on the median risk score of SenLncSig. Kaplan-Meier analysis was employed to assess differences in overall survival (OS) between these subgroups. Additionally, disparities in Gene Set Enrichment Analysis (GSEA), immune infiltration, scores from the tumor immune dysfunction and exclusion (TIDE) module, and choices of chemotherapy and targeted therapy were also evaluated between the high-risk and low-risk groups. Results: Based on univariate and multivariate Cox regression analyses, we established a prognostic model composed of seven senescence-related lncRNAs, named SenLncSig, which includes AL031775.2, AC026355.1, AL365181.2, AC090559.1, AL606489.1, AL513550.1, and C20orf197. This model divides patients into high-risk and low-risk groups, with the high-risk group showing significantly shorter overall survival compared to the low-risk group, with a hazard ratio (HR) of 1.326. The predictive accuracy of SenLncSig was validated through ROC curves and principal component analysis (PCA). Additionally, we developed an intuitive survival prediction tool that combines SenLncSig with clinical pathological features to assess patients' overall survival (OS). Enrichment analysis (GSEA) revealed that SenLncSig is involved in multiple pathways related to tumor immune modulation. Risk model analysis indicated significant differences between the high and low-risk groups in terms of immune status and responses to immunotherapy, chemotherapy, and targeted therapy. Conclusion: In this study, a lncRNA signature named as SenLncSig was developed that not only identifies the senescence phenotype and predicts prognosis but also has the capability to forecast the response of LUAD to immunotherapy.