Identification and validation of subclusters of papillary thyroid carcinoma based on Human Phenotype Ontology

Abstract

Background The increase in the diagnosis of papillary thyroid carcinoma (PTC) has prompted researchers to establish a diagnostic model and identify functional subclusters. The Human Phenotype Ontology (HPO) platform is widely available for differential diagnostics and phenotype-driven investigations based on next-generation sequence-variation data. However, a systematic and comprehensive study to identify and validate PTC subclusters based on HPO is lacking. Methods We first used the HPO platform to identify the PTC subclusters. An enrichment analysis was then conducted to examine the key biological processes and pathways associated with the subclusters, and a gene mutation analysis of the subclusters was conducted. For each subcluster, the differentially expressed genes (DEGs) were selected and validated. Finally, a single-cell RNA-sequencing data set was used to verify the DEGs. Results In our study, 489 PTC patients from The Cancer Genome Atlas (TCGA) were included. Our analysis demonstrated that distinct subclusters of PTC are associated with different survival times and have different functional enrichment, and that C-C motif chemokine ligand 21 ( CCL21 ) and zinc finger CCHC-type containing 12 ( ZCCHC12 ) were the common down- and upregulated genes, respectively, in the 4 subclusters. Additionally, 20 characteristic genes were identified in the 4 subclusters, some of which have previously been reported to have roles in PTC. Further, we found that these characteristic genes were mainly expressed in thyrocytes, endothelial cells, and fibroblasts, and were rarely expressed in immune cells. Conclusions We first identified subclusters in PTC based on HPO and found that patients with distinct subclusters have different prognoses. We then identified and validated the characteristic genes in the 4 subclusters. These findings are expected to serve as a crucial reference that will improve our understanding of PTC heterogeneity and the use of novel targets.