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As the predominant thyroid cancer, papillary thyroid cancer (PTC) accounts for 75‑85% of thyroid cancer cases. This research aimed to investigate transcriptomic changes and key genes in PTC. Using RNA‑sequencing technology, the transcriptional profiles of 5 thyroid tumor tissues and 5 adjacent normal tissues were obtained. The single nucleotide polymorphisms (SNPs) were identified by SAMtools software and then annotated by ANNOVAR software. After differentially expressed genes (DEGs) were selected by edgR software, they were further investigated by enrichment analysis, protein domain analysis, and protein‑protein interaction (PPI) network analysis. Additionally, the potential gene fusion events were predicted using FusionMap software. A total of 70,172 SNPs and 2,686 DEGs in the tumor tissues, as well as 83,869 SNPs in the normal tissues were identified. In the PPI network, fibronectin 1 (FN1; degree=31) and transforming growth factor β receptor 1 (TGFβR1; degree=22) had higher degrees. A total of 7 PPI pairs containing the non‑synonymous risk SNP loci in the interaction domains were identified. Particularly, the interaction domains involved in the interactions of FN1 and 5 other proteins (such as FN1‑tenascin C, TNC) had non‑synonymous risk SNP loci. Furthermore, 11 and 4 gene fusion events were identified in all of the tumor tissues and normal tissues, respectively. Additionally, the NK2 homeobox 1‑surfactant associated 3 (NKX2‑1‑SFTA3) gene fusion was identified in both tumor and normal tissues. These results indicated that TGFβR1 and the NKX2‑1‑SFTA3 gene fusion may be involved in PTC. Furthermore, FN1 and TNC containing the non‑synonymous risk SNP loci might serve a role in PTC by interacting with each other.

Objective: In this study, crucial genes and microRNAs (miRNAs) associated with the progression, staging, and prognosis of papillary thyroid cancer (PTC) were identified. Methods: Four PTC datasets, including our own mRNA-sequencing (mRNA-seq) dataset and three public datasets downloaded from Gene Expression Omnibus and The Cancer Genome Atlas, were used to analyze differentially expressed genes (DEGs) and miRNAs (DEMs) between PTC tumor tissues and paired normal tissues (control). Gene ontology (GO) terms and pathways associated with these DEGs were identified, and protein-protein interactions (PPIs) were analyzed. Additionally, an miRNA-mRNA regulatory network was constructed and the functions of DEMs were explored. Finally, miRNAs/mRNAs associated with tumor staging and prognosis were identified. The expression levels of several key genes and miRNAs were validated by qRT-PCR. Results: Numerous DEGs and DEMs were identified between tumor and control groups in four datasets. The DEGs were significantly enriched in cell adhesion and cancer-related GO terms and pathways. In the constructed PPI network, ITGA2, FN1, ICAM1, TIMP1 and CDH2 were hub proteins. In the miRNA-mRNA negative regulatory networks, miR-204-5p regulated the largest number of target genes, such as TNFRSF12A. miR-146b, miR-204, miR-7-2, and FN1 were associated with tumor stage in PTC, and TNFRSF12A and CLDN1 were related to prognosis. Conclusions: Our results suggested the important roles of ITGA2, FN1, ICAM1, TIMP1 and CDH2 in the progression of PTC. miR-204-5p, miR-7-2, and miR-146b are potential biomarkers for PTC staging and FN1, CLDN1, and TNFRSF12A may serve as markers of prognosis in PTC.