Prognostic value analysis of cholesterol and cholesterol homeostasis related genes in breast cancer by Mendelian randomization and multi-omics machine learning - Wu, Haodong; Wu, Zhixuan; Ye, Daijiao; Li, Hongfeng; Dai, Yinwei; Wang, Ziqiong; Bao, Jingxia; Xu, Yiying; He, Xiaofei; Wang, Xiaowu; Dai, Xuanxuan (Frontiers in Oncology, 2023)
The high incidence of breast cancer (BC) prompted us to explore more factors that might affect its occurrence, development, treatment, and also recurrence. Dysregulation of cholesterol metabolism has been widely observed in BC; however, the detailed role of how cholesterol metabolism affects chemo-sensitivity, and immune response, as well as the clinical outcome of BC is unknown.With Mendelian randomization (MR) analysis, the potential causal relationship between genetic variants of cholesterol and BC risk was assessed first. We observed that the alterations in plasma cholesterol appear to be correlative with the venture of BC (MR Egger, OR: 0.54, 95% CI: 0.35-0.84, p<0.006). Then we analyzed 73 cholesterol homeostasis-related genes (CHGs) in BC samples and their expression patterns in the TCGA cohort with consensus clustering analysis, aiming to figure out the relationship between cholesterol homeostasis and BC prognosis. As a result, there were two different expression patterns of CHGs (C1 and C2). We found that the C1 group had a better prognosis with a low immune response and high cholesterol metabolism compared to the C2 group. In addition, high CHG score were accompanied by high performance of tumor angiogenesis genes. Interestingly, the expression of vascular genes (CDH5, CLDN5, TIE1, JAM2, TEK) is lower in patients with high CHG score, which means that these patients have poorer vascular stability. Based on the CHG analysis, we established a CAG_score used for predicting therapeutic response and overall survival (OS) of BC patients, which has high reliability (AUC=0.77) confirmed by clinical data. CAG_score can also optimize the administration of various first-line drugs, i.e. AKT inhibitors Ⅷ, Imatinib, Crizotinib, Saracatinib, Erlotinib, Dasatinib, Rapamycin, Roscovitine and Shikonin for BC patients. Furthermore, a machine learning method (Risklight) was adopted to accurately predict the prognosis of BC patients by comparing multi-omics differences of different risk groups (AUC=0.89).In conclusion, with the help of CAG_score and Risklight, we reveal the signature of cholesterol homeostasis-related genes for angiogenesis, immune responses, and the therapeutic response in breast cancer, which contributes to precision medicine and improved prognosis of BC.