Identification of potential pathogenic candidates or diagnostic biomarkers in papillary thyroid carcinoma using 
expression and methylation profiles

Wei,Songfeng; Yun,Xinwei; Ruan,Xianhui; Wei,Xi; Zheng,Xiangqian; Gao,Ming

doi:10.3892/ol.2019.11059

Identification of potential pathogenic candidates or diagnostic biomarkers in papillary thyroid carcinoma using expression and methylation profiles

Authors:
- Songfeng Wei
- Xinwei Yun
- Xianhui Ruan
- Xi Wei
- Xiangqian Zheng
- Ming Gao
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Affiliations: Department of Thyroid and Neck Tumors, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, P.R. China, Department of Ultrasonic Diagnosis and Treatment, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, P.R. China
Published online on: November 5, 2019 https://doi.org/10.3892/ol.2019.11059
Pages: 6670-6678
Copyright: © Wei et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The mechanisms underlying the pathogenesis of papillary thyroid carcinoma (PTC) have not yet been elucidated. The aim of the current study was to identify potential pathogenic biomarkers in PTC by comprehensively analyzing gene expression and methylation profiles, and to increase the understanding of PTC pathogenesis. The gene expression profiles of the GSE97001 and GSE83520 datasets, the miRNA expression profiles of the GSE73182 dataset, and the DNA methylation profiles of the GSE86961 and GSE97466 datasets were downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) and the differentially expressed microRNAs (DEMs) were identified using the limma package in R, and the differentially methylated sites (DMSs) were identified using the β distribution and two‑sample t‑tests. The Database for Annotation, Visualization and Integrated Discovery, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome were subsequently used to perform functional and pathway enrichment analysis. The miRNA target genes were predicted using the online databases miRWalk. The protein‑protein interactions (PPI) were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins. The regulatory network was constructed, and the gene expression and methylation levels of the key nodes were detected using reverse‑transcription quantitative‑polymerase chain reaction (PCR) and methylation‑specific PCR. A total of 155 overlapping DEGs were identified between the GSE97001 and GSE83520 datasets, and 19 DEMs between PTC tissue and normal tissue samples were identified in the GSE73182 set. In the GSE86961 and GSE97466 datasets, 2,910 overlapping DMSs that were associated with 38 downregulated methylated genes were identified. The overlapping DEGs were enriched in 46 Gene Ontology terms and one KEGG pathway. A total of 60 PPI pairs were identified for the overlapping DEGs and 12 negative miRNA‑gene pairs were identified for the DEMs. The expression levels of hsa‑miR‑199a‑5p and decorin (DCN) were decreased in patients with PTC. C‑X‑C motif chemokine ligand 12 (CXCL12) was hypermethylated and had a decreased expression level in PTC tissues. LDL receptor related protein 4 (LRP4) and carbonic anhydrase 12 (CA12) were hypomethylated and had an increased expression level. The present study revealed that hsa‑miR‑199a‑5p, DCN, CXCL12, LRP4 and CA12 may serve important roles in the pathogenesis of PTC.

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