An innovative predictive model for cervical cancer constructed around a gene profile associated with cholesterol metabolism

Abstract Cholesterol metabolism is crucial for cell survival and cancer progression. The prognostic patterns of genes linked to cholesterol metabolism (CMAGs) in CESC, however, have received very little attention in research. From public databases, TCGA‐CESC cohorts with mRNA expression patterns and the accompanying clinical information of patients were gathered. Consensus clustering was used to find the molecular subtype connected to cholesterol metabolism. In the TCGA‐CESC cohort, a predictive risk model with 28 CMAGs was created using Lasso‐Cox regression. The function enrichment analysis between groups with high‐and low‐risk were investigated by employing GO, KEGG, and GSVA software. The immune cell infiltration was analyzed using ESTIMATE, CIBERSORT, and MCPCOUNTER methods. Finally, we select 7 genes in risk model for further multivariate Cox analysis, and ultimately a hub gene, CHIT1, was identified. Meanwhile, the function of CHIT1 was preliminarily verified in cell and mice tumor model. In conclusion, the abundance of the CHIT1 gene might be beneficial for forecasting the prognosis of CESC, demonstrating that cholesterol metabolism could be a promising treatment target for CESC.