PPARγ Targets-Derived Diagnostic and Prognostic Index for Papillary Thyroid Cancer

Abstract

Simple Summary Through targeted next-generation sequencing of thyroid cancer-related genes in monozygotic twins with papillary thyroid cancer (PTC), we identified common variants of the gene encoding peroxisome proliferator activated receptor gamma (PPARG). Notably, the expression levels of PPARγ target genes were frequently deregulated in PTC compared to benign tissues and were closely associated with disease-specific survival (DSS) outcomes in a TCGA-PTC cohort. Machine learning-powered personalized scoring index comprising 10 PPARγ targets, termed as PPARGi, achieved a near-perfect accuracy in distinguishing cancers from benign tissues, and further identified a small subpopulation of patients at high-risk across different profiling platforms. Abstract In most cases, papillary thyroid cancer (PTC) is highly curable and associated with an excellent prognosis. Yet, there are several clinicopathological features that lead to a poor prognosis, underscoring the need for a better genomic strategy to refine prognostication and patient management. We hypothesized that PPARγ targets could be potential markers for better diagnosis and prognosis due to the variants found in PPARG in three pairs of monozygotic twins with PTC. Here, we developed a 10-gene personalized prognostic index, designated PPARGi, based on gene expression of 10 PPARγ targets. Through scRNA-seq data analysis of PTC tissues derived from patients, we found that PPARGi genes were predominantly expressed in macrophages and epithelial cells. Machine learning algorithms showed a near-perfect performance of PPARGi in deciding the presence of the disease and in selecting a small subset of patients with poor disease-specific survival in TCGA-THCA and newly developed merged microarray data (MMD) consisting exclusively of thyroid cancers and normal tissues.