Prognostic value analysis of cholesterol and cholesterol homeostasis related genes in breast cancer by Mendelian randomization and multi-omics machine learning

• DOI: 10.3389/fonc.2023.1246880
• Publication Date: 2023-11-07T09:58:07Z
• Authors: Haodong Wu; Zhixuan Wu; Daijiao Ye; Hongfeng Li; Yinwei Dai; Ziqiong Wang; Jingxia Bao; Yiying Xu; Xiaofei He; Xiaowu Wang; Xuanxuan Dai
• Keywords: Mendelian Randomization; Omics; Value (mathematics)

The high incidence of breast cancer (BC) prompted us to explore more factors that might affect its occurrence, development, treatment, and also recurrence. Dysregulation cholesterol metabolism has been widely observed in BC; however, the detailed role how affects chemo-sensitivity, immune response, as well clinical outcome BC is unknown.With Mendelian randomization (MR) analysis, potential causal relationship between genetic variants risk was assessed first. Then we analyzed 73 homeostasis-related genes (CHGs) samples their expression patterns TCGA cohort with consensus clustering aiming figure out homeostasis prognosis. Based on CHG established a CAG_score used for predicting therapeutic response overall survival (OS) patients. Furthermore, machine learning method adopted accurately predict prognosis patients by comparing multi-omics differences different groups.We alterations plasma appear be correlative venture (MR Egger, OR: 0.54, 95% CI: 0.35-0.84, p<0.006). CHGs were classified into two distinct groups(C1 C2). Notably, C1 group exhibited favorable characterized suppressed enhanced comparison C2 group. In addition, score accompanied performance tumor angiogenesis genes. Interestingly, vascular (CDH5, CLDN5, TIE1, JAM2, TEK) lower CHGs, which means these have poorer stability. exhibits robust predictive capability microenvironment characteristics patients(AUC=0.79). It can optimize administration various first-line drugs, including AKT inhibitors VIII Imatinib, Crizotinib, Saracatinib, Erlotinib, Dasatinib, Rapamycin, Roscovitine Shikonin Finally, employed techniques construct prediction model(Risklight),with an area under feature curve (AUC) up 0.89.With help Risklight, reveal signature angiogenesis, responses, cancer, contributes precision medicine improved BC.