Meta single-cell atlas and xQTL post-GWAS analysis revealed the pathogenic features of thyroid cancer for target...

Thyroid cancer (TC) is an endocrine malignancy characterized by metabolic abnormalities, with its incidence continually on the rise. Understanding the pathogenesis of this cancer would help develop better diagnostic and therapeutic methods. We aimed to integrate single-cell transcriptomics, bulk transcriptomics, and GWAS data to identify causal associations with thyroid cancer at the gene level. We intended to utilize single-cell atlases to identify malignant cells and their characteristics, and employed SMR to search for genetic loci causally associated with thyroid cancer. We validated the expression differences of the genes at the single-cell level and bulk level, as well as through immunohistochemistry experimental results. We investigated the tumor immune microenvironment of patients, attempting to find immune subgroups with differential proportions. Based on these subgroups, we conducted multi-machine learning modeling to predict the likelihood of disease and developed a corresponding interactive web application. HMGA2, SDCCAG8, DLG5, MT1E, RABL2B, RERE, and NDUFA12 all demonstrated to varying degrees their roles in promoting or inhibiting the occurrence and development of thyroid cancer, with HMGA2 showing consistency across all analyses. We also identified some immune subtypes significantly associated with TC and chose markers of T_cell_C8_STMN1 to construct patient diagnostic models. Through various combinations of machine learning feature selection and model construction, we ultimately built 178 diagnostic models, with the combination of glmBoost+RF having the best diagnostic performance (Average AUPR: 0.9915). The predictive web pages ( https://zclab-cnp.shinyapps.io/TC-WEB/ ) can provide convenience and reference for clinical personnel.