Identification of a Risk Signature and Immune Cell Infiltration Based on Extracellular Matrix-Related lncRNAs in Lung Adenocarcinoma.

Abstract

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths globally, with late diagnoses often resulting in poor prognoses. The extracellular matrix (ECM) plays a crucial role in cancer cell processes. Using big data from RNA-seq of LUAD, we aimed to screen ECM-related lncRNAs (long noncoding RNAs) to determine their prognostic significance. Our study analyzed the LUAD cohort from The Cancer Genome Atlas (TCGA). Univariate Cox analysis identified prognostic lncRNAs, and least absolute shrinkage and selection operator (LASSO) regression analysis, followed by multivariate Cox analysis, was used to construct a prognostic model. Kaplan-Meier and ROC curves evaluated the model's prognostic performance. A nomogram was created to predict 3-year survival. Enrichment analysis identified biological processes and pathways involved in the signature. Correlations with the tumor microenvironment (TME) and tumor mutation burden (TMB) were analyzed, and potential drug sensitivities for LUAD were predicted. We initially identified 218 ECM-associated genes and 427 ECM-associated lncRNAs within the TCGA LUAD cohort. Subsequent univariate Cox regression analysis selected 26 lncRNAs with significant prognostic value, and an overall survival (OS)-based LASSO Cox regression model further narrowed this to 14 lncRNAs. Multiple Cox regression analyses then distilled these down to 8 critical lncRNAs forming our prognostic risk signature. Nomograms accurately predicted survival. Finally, several potential therapeutic drugs, including afatinib and crizotinib, were identified. Big data analysis established a prognostic signature that predicts survival and immunization in LUAD patients, providing new insights into survival and treatment options.