Identification and validation of eight lysosomes-related genes signatures and correlation with immune cell infiltration in lung adenocarcinoma

Abstract

AbstractLung cancer is the leading cause of cancer-related death. Lysosomes are key degradative compartments that maintain protein homeostasis. In current study, we aimed to construct a lysosomes-related genes signature to predict the overall survival (OS) of patients with Lung Adenocarcinoma (LUAD). Differentially expressed lysosomes-related genes (DELYs) were analyzed using The Cancer Genome Atlas (TCGA-LUAD cohort) database. The prognostic risk signature was identified by Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression and multivariate Cox analysis. The predictive performance of the signature was assessed by Kaplan–Meier curves and Time-dependent receiver operating characteristic (ROC) curves. Gene set variant analysis (GSVA) was performed to explore the potential molecular biological function and signaling pathways. ESTIMATE and single sample gene set enrichment analysis (ssGSEA) were applied to estimate the difference of tumor microenvironment (TME) between the different risk subtypes. An eight prognostic genes (ACAP3, ATP8B3, BTK, CAV2, CDK5R1, GRIA1, PCSK9, and PLA2G3) signature was identified and divided patients into high-risk and low-risk groups. The prognostic signature was an independent prognostic factor for OS (HR > 1, p < 0.001). The molecular function analysis suggested that the signature was significantly correlated with cancer-associated pathways, including angiogenesis, epithelial mesenchymal transition, mTOR signaling, myc-targets. The low-risk patients had higher immune cell infiltration levels than high-risk group. We also evaluated the response to chemotherapeutic, targeted therapy and immunotherapy in high- and low-risk patients with LUAD. Furthermore, we validated the expression of the eight gene expression in LUAD tissues and cell lines by qRT-PCR. LYSscore signature provide a new modality for the accurate diagnosis and targeted treatment of LUAD and will help expand researchers’ understanding of new prognostic models.