Characteristic of molecular subtype based on lysosome-associated genes reveals clinical prognosis and immune infiltration of gastric cancer - Hu, Maodong; Chong, Ruifeng; Liu, Weilin; Liu, Shuangyong; Liu, Xiaolei (Frontiers in Oncology, 2023)
Background: Lysosome are involved in nutrient sensing, cell signaling, cell death, immune responses and cell metabolism, which play an important role in the initiation and development of multiple tumors. However, the biological function of lysosome in gastric cancer (GC) has not been revealed. Here, we aim to screen lysosome-associated genes and established a corresponding prognostic risk signature for GC, then explore the role and underlying mechanisms. Methods: The lysosome-associated genes (LYAGs) were obtained from MSigDB database. Differentially expressed lysosome-associated genes (DE-LYAGs) of GC were acquired based on the TCGA database and GEO database. According to the expression level of DE-LYAGs, we divided the GC patients into different subgroups and then explored tumor microenvironment (TME) landscape and immunotherapy response of in LYAG subtypes using GSVA, ESTIMATE and ssGSEA algorithms. Univariate Cox regression analysis, LASSO algorithm and multivariate Cox regression analysis were adopted to identify the prognostic LYAGs and then establish a risk model for patients with GC. The Kaplan-Meier analysis, Cox regression analysis and ROC analysis were utilized to evaluate the performance of the prognostic risk model. We then investigated the biological role and underlying mechanism of lysosome in GC by TME landscape and immunotherapy response analysis, somatic mutation landscape and MSI analysis, drug sensitivity analysis. Results: Thirteen DE-LYAGs were obtained and utilized to distinguish three subtypes in GC samples. The subtypes were correlated with prognosis, tumor-related immunological abnormalities and pathway dysregulation. Furthermore, we constructed a prognostic risk model for GC based on DEG in the three subtypes. The Kaplan-Meier analysis suggested that higher risk score related to short OS rate. The Cox regression analysis and ROC analysis indicated that risk model had independent and excellent ability in predicting prognosis of GC patients. Mechanically, a remarkable difference was observed in immune infiltration, immunotherapy response, somatic mutation landscape and drug sensitivity. Conclusions: We established a novel signature based on lysosome-associated genes, which could be served as a prognostic biomarker for GC. Our study might provide new insights into individualized prognostication and precision treatment for GC. Keywords: gastric cancer, lysosome-associated genes, molecular subtype, risk model, prognostic prediction