Abstract
Lysosome-dependent cell death (LCD) plays a significant role in overcoming cancer apoptosis and drug resistance. However, the relationship between LCD-associated genes (LCDGs) and glioma, including glioblastoma (GBM) and low-grade glioma (LGG), remains unclear. In this study, an LCDGs risk signature was constructed for glioma patients by utilizing 4 algorithms (Extreme Gradient Boosting, Support Vector Machine, Random Forest, and Generalized Linear Models) to identify core LCDGs. Their correlation with clinical features and the immune microenvironment was also determined in glioma, GBM, and LGG. Additionally, the role of hub LCDGs in various cancers was elucidated via pan-cancer analyses. Validation of the core gene in glioma was performed using qRT-qPCR and immunofluorescence staining analysis. The results showed that the LCDGs risk signature was strongly associated with the prognosis, cancer grades, histological types, and primary therapy outcomes of glioma patients. Furthermore, it was closely linked to the overall survival of LGG patients. Mechanistic analyses revealed a significant association between the risk signature and the immune microenvironment in glioma. Based on differential expression analysis, receiver operating characteristic analysis, and interacted model algorithms, LAPTM4A was identified as a hub LCDG in glioma. It exhibited significant upregulation in glioma, GBM, and LGG samples. Moreover, LAPTM4A expression correlated with the prognosis of glioma and LGG patients, as well as age, grades, histological types, and primary therapy outcomes in glioma. Pan-cancer analysis confirmed that LAPTM4A expression was modulated in the majority of cancers and was associated with the prognosis of various cancers. Mechanistic analyses suggested a strong relationship between LAPTM4A and immune cell infiltration, as well as several drug sensitivities. In conclusion, our findings suggest that LAPTM4A may serve as a potential oncogene associated with LCD in pan-cancer, particularly in glioma, GBM, and LGG. These findings provide important insights for individualized treatment of glioma.