A New Survival Model Based on Cholesterol Biosynthesis-Related Genes for Prognostic Prediction in Clear Cell Renal Cell Carcinoma

Abstract

In our study, the value of cholesterol biosynthesis is related to clinical analysis in 32 cancer forms in the GSEA database facility. We have a mutation between 25 CBRGs. In The Cancer Genome Atlas database, clear cell renal cell carcinoma (ccRCC, $n=539$ ) was upregulated or downregulated in 22 out of 25 cases ( $n=72$ ) compared with normal kidney tissue. Then, using LASSO regression analysis, the survival model that is based on nine risk-related CBRGs (CYP51A1, HMGCR, HMGCS1, IDI1, FDFT1, SQLE, ACAT2, FDPS, and NSDHL) is established. ROC curves confirmed the good omen of the new survival mode, and the area under the curve is 0.72 (5 years) and 0.709 (10 years). High SQLE and ACAT2 expression and low NSDHL, FDPS, CYP51A1, FDFT1, HMGCS1, HMGCR, and IDI1 expression were closely related to patients with high-risk renal clear cell carcinoma. Two types of Cox regression, uni- and multivariate, were used to determine risk scores, age, staging, and grade as independent risk factors for prognosis in patients with clear cell renal cell carcinoma. The results showed the prediction model established by 9 selected CBRGs could predict the prognosis more accurately.