Identification of stemness subtypes and prognostic modeling in thyroid cancer: the critical role of DPYSL3 in tumor progression and immune microenvironment

Abstract Purpose: The objective of this study was to develop and evaluate a novel classifier and prognostic model based on the stemness characteristics of thyroid cancer patients. Methods: Utilizing transcriptomic data from thyroid carcinoma (THCA) patients in The Cancer Genome Atlas (TCGA) database, we calculated the stemness index (mRNAsi) using the one-class logistic regression (OCLR) method. Patients were subsequently classified into three distinct subtypes through consensus cluster analysis. Results: Subtype III, characterized by its stem-like properties, exhibited significantly lower overall