The specific regulatory networks between different variants in papillary thyroid carcinoma: A transcriptomics study

Abstract

Abstract Objective: To analyze the molecular pathogenesis or characteristics based on transcriptomics techniques to diagnose and treat papillary thyroid carcinoma (PTC) caused by mutations. Methods: We conducted transcriptome sequencing to identify differentially expressed genes (DEGs) in PTC-derived cell lines TPC-1, BCPAP, IHH4, and CVPTC. Additionally, gene expression profiling was performed using microarray in GEO database to screen out representative dataset chips and analyze DEGs of PTC tissues in the clinic. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on DEGs were performed using DAVID database. We established the protein-protein interaction (PPI) network using STRING database and built the transcription factor (TF) regulation network based on module analysis for identifying master regulators along with gene modules. Results: We identified 4,353 and 3,250 DEGs among cell line (CVPTC/BCPAP/IHH4 or TPC-1/BCPAP/IHH4) and normal thyroid cells independently. After screening GSE27155 dataset, we identified 1,075 DEGs among PTC tissue samples (classic/RET_PTC mutation/high cellular variant) and normal tissue samples. The DEGs in the three groups were enriched in different pathways like cell proliferation, signal dysregulation, immune dysregulation, angiogenesis and cancer pathways. By constructing the PPI network and performing the module analysis, we determined top 10 key genes in the three groups separately. The results showed that 22 and 61 important TFs were significantly enriched in PPI of the two cell groups respectively and only one important TF was significantly enriched in PPI of PTC tissues. Conclusion: We identified the transcriptomic features of PTC cell lines and clinical PTC tissues associated with invasive behavior.