Identification of novel cholesterol metabolism-related biomarkers for thyroid cancer to predict the prognosis, immune infiltration, and drug sensitivity

Cholesterol metabolism plays a vital role in tumor proliferation, regulation of tumor immune escape, and drug resistance. This study aimed to investigate the predictive value of cholesterol metabolism-related genes in thyroid cancer (THCA) and the relationship between immune invasion and drug sensitivity. Methods: Cholesterol metabolism-related genes were obtained from the molecular signatures database, and univariate Cox regression and least absolute shrinkage and selection operator(LASSO) were used to construct a predictive model of cholesterol metabolism-related genes based on the TCGA-THCA dataset. The TCGA dataset was randomly divided into a training group and a validation group to verify the model's predictive value and independent prognostic effect. We then constructed a nomogram and performed enrichment analysis, immune cell infiltration, and drug sensitivity analysis. Finally, TCGA-THCA and GSE33630 datasets were used to detect the expression of signature genes, which was further verified by the HPA database. Result: Six CMRGs (FADS1, NPC2, HSD17B7, ACSL4, APOE, HMGCS2) were obtained by univariate Cox and LASSO regression to construct a prognostic model for 155 genes related to cholesterol metabolism. Their prognostic value was confirmed in the validation set, and a perfect stable nomogram was constructed combined with clinical features. We found a significant reduction in immune cell infiltration in the high-risk group and obtained sensitive drugs for different risk groups through drug sensitivity analysis. The GSE33630 dataset verified the expression of six CMRGs, and the HPA database verified the protein expression of the NPC2 gene. Conclusion: Cholesterol metabolism-related features are a promising biomarker for predicting THCA prognosis and can potentially guide immunization and targeted therapy.