Analysis of transcriptomic data reveals the landscape of cholesterol metabolism in prostate cancer and impact of related signature on survival

• Published in: Discover. Oncology Vol. 15; no. 1; pp. 777 - 24
• Main Authors: Sun, Jian-Xuan, An, Ye, Xu, Meng-Yao, Ma, Si-Yang, Liu, Chen-Qian, Xu, Jin-Zhou, Xia, Qi-Dong, Wang, Shao-Gang
• Format: Journal Article
• Language: English
• Published: New York Springer US 18.12.2024; Springer Nature B.V; Springer
• Subjects: Androgens; Cancer Research; Cancer therapies; Cell growth; Chemotherapy; Cholesterol; Cholesterol metabolism; Genes; Immunotherapy; Internal Medicine; Medical prognosis; Medicine; Medicine & Public Health; Metabolism; Molecular Medicine; Mortality; Oncology; Penicillin; Prostate cancer; Proteins; Radiation therapy; Radiotherapy; Statins; Surgical Oncology; Tumor microenvironment; Tumor mutation burden; United States > US; China; Chemotherapy; Tumor microenvironment; Cholesterol metabolism; Prostate cancer; Immunotherapy; Tumor mutation burden
• ISSN: 2730-6011; 2730-6011
• DOI: 10.1007/s12672-024-01658-x

Background Cholesterol metabolism is essential for the development and progression of prostate cancer (PCa). Our previous study provided a new insight of cholesterol metabolism in the systematic management of PCa. However, the comprehensive role of cholesterol metabolism in PCa remains unclear. Methods Using the cholesterol metabolism related genes (CMRGs) downloaded from the MSigDB database, and gene expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we constructed a cholesterol risk index by the least absolute shrinkage and selection operator (LASSO) model, and correlated the risk index with prognosis, tumor mutation burden (TMB), tumor microenvironment (TME) infiltration and response to chemotherapy and immunotherapy. RT-qPCR, western blot, immunohistochemistry, cell proliferation assays by CCK-8 and EdU assays, and cell apoptosis assays by flow cytometry analysis were also performed. Results We found PCa was tightly correlated with the cholesterol metabolism pathways. The cholesterol risk index was an excellent and independent predictor of prognosis for PCa. A nomogram involving the risk index and other clinical factors (age, T stage) was established to explore the clinical value of risk index. We found high-risk index group was associated with worse prognosis, higher TMB, lower infiltration level of CD8 + T cells and a worse response to chemotherapy and immunotherapy. RT-qPCR, western blot and immunohistochemical staining validated the expression level of important CMRGs in PCa. In vitro experiments revealed downregulation of cholesterol metabolism could inhibit the proliferation of PCa cells and promoted their apoptosis. Conclusions We demonstrated the comprehensive role of cholesterol metabolism in PCa. Using the risk index, we could predict the prognosis, TME infiltration and response to chemotherapy/immunotherapy of PCa. Better understanding and evaluating the cholesterol metabolism could aid in precision medicine and promoting prognosis of PCa.