Identification of thyroid cancer biomarkers using WGCNA and machine learning

Abstract Objective: The incidence of thyroid cancer (TC) is increasing in China, largely due to overdiagnosis from widespread screening and improved ultrasound technology. Identifying precise TC biomarkers is crucial for accurate diagnosis and effective treatment. Methods: TC patient data were obtained from TCGA. DEGs were analyzed using DESeq2, and WGCNA identified gene modules associated with TC. Machine learning algorithms (XGBoost, LASSO, RF) identified key biomarkers, with ROC and AUC > 0.95 indicating strong diagnostic performance. Immune cell infiltration and biomarker