Machine learning-based prognostic modeling of lysosome-related genes for predicting prognosis and immune status of patients with hepatocellular carcinoma - Li, Wenhua; Wang, Qianwen; Lu, Junxia; Zhao, Bin; Geng, Yuqing; Wu, Xiangwei; Chen, Xueling (Frontiers in Immunology, 2023)
Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, and lysosomes play an important role in cancer progression as organelles that break down biomolecules such as proteins, nucleic acids, and polysaccharides; however, the molecular mechanisms of lysosome-related genes in hepatocellular carcinoma are not fully understood. Methods：We downloaded hepatocellular carcinoma datasets from the Cancer Genome Atlas(TCGA) and the Gene Expression Omnibus (GEO) as well as lysosome-related gene sets from AIMGO .After univariate Cox screening of the set of lysosome-associated genes differentially expressed in hepatocellular carcinoma and normal tissues, risk models were built by machine learning. Model effects were then assessed using the concordance index (C-index), Kaplan-Meier (K-M) and receiver operating characteristic curves (ROC), and the “GSVA” package was used to explore the biological function and immune microenvironment between the high- and low-risk groups, and the “IMvigor210CoreBiologies” package was used to analyse the response of the high- and low-risk groups to immunotherapy responsiveness, the “pRRophetic”package was used to explore the sensitivity of the high and low-risk groups to chemotherapeutic agents and finally the function of a key gene (RAMP3) was explored at the cellular level. Results ：univariate Cox yielded 46 differentially and prognostically significant lysosome-related genes and risk models were constructed using eight genes (RAMP3,GPLD1,FABP5,CD68,CSPG4,SORT1,CSPG5,CSF3R) derived from machine learning. The C-index and ROC showed that the risk model was a better predictor of clinical outcomes, with the K-M values indicating that the higher risk group had worse clinical outcomes. There were significant differences in biological function, immune microenvironment and responsiveness to immunotherapy and drug sensitivity between the high and low-risk groups. Finally, we found that RAMP3 inhibited the proliferation, migration and invasion of hepatocellular carcinoma cells and correlated with the sensitivity of hepatocellular carcinoma cells to Idarubicin. Conclusion：Lysosome-associated gene risk models built by machine learning can effectively predict patient prognosis and offer new prospects for chemotherapy and immunotherapy in HCC. In addition, cellular-level experiments suggest that RAMP3 may be a new target for the treatment of hepatocellular carcinoma.