Abstract
Lysosomes are critical organelles that act as degradation centers and signaling hubs within cells, playing a significant role in various cellular processes and human diseases, including cancer. However, the extent to which they influence the heterogeneity and clinical outcomes of ovarian cancer (OC) remains inadequately understood. In this study, we used consensus clustering to identify two distinct lysosome-related clusters (LCs) in OC by analyzing the expression profiles of OC patients from The Cancer Genome Atlas (TCGA) database. Further analyses revealed the functional characteristics and immune landscapes of these subgroups, providing valuable insights into the tumor immune microenvironment (TIME) and tumor responses to immunotherapy. Additionally, we developed and validated a prognostic model based on differentially expressed genes (DEGs) between the two LCs, demonstrating its effectiveness in predicting patient prognosis, TIME characteristics, and immunotherapy potential in OC. A further investigation explored the relationship between lysosome-associated risk scores, IC50 values of standard antitumor agents, and the expression levels of prognostic genes. Finally, in vitro experiments showed that inhibiting CRHR1, a lysosome-associated prognostic gene, significantly reduced OC cell proliferation, invasion, and migration. In conclusion, our study establishes a novel lysosome-based classification and prognostic framework for OC, offering a practical tool to predict clinical outcomes and guide personalized immunotherapy strategies.