Identification of hub genes in papillary thyroid carcinoma: robust rank aggregation and weighted gene co-expression network analysis

• Published in: Journal of translational medicine 2020-04, Vol.18 (1), p.170-170, Article 170
• Main Authors: Liu, Yang, Chen, Ting-Yu, Yang, Zhi-Yan, Fang, Wei, Wu, Qian, Zhang, Chao
• Format: Article
• Language: English
• Subjects: Analysis; Biomarkers; Cancer genetics; Carcinoma; Datasets; Deoxyribonucleic acid; Development and progression; Diagnosis; DNA; DNA Methylation; DNA Methylation - genetics; Gene expression; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Genes; Genetic aspects; Humans; Kinases; Malignancy; Methylation; Neuroendocrine tumors; Papillary thyroid carcinoma; Robust rank aggregation; Studies; Thyroid; Thyroid cancer; Thyroid Cancer, Papillary - genetics; Thyroid diseases; Thyroid Neoplasms - genetics; Transcriptome; Tumor-infiltrating lymphocytes; Weighted gene co-expression network analysis
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-020-02327-7

Papillary thyroid carcinoma (PTC), which is the most common endocrine malignancy, has been steadily increasing worldwide in incidence over the years, while mechanisms underlying the pathogenesis and diagnostic for PTC are incomplete. The purpose of this study is to identify potential biomarkers for diagnosis of PTC, and provide new insights into pathogenesis of PTC. Based on weighted gene co-expression network analysis, Robust Rank Aggregation, functional annotation, GSEA and DNA methylation, were employed for investigating potential biomarkers for diagnosis of PTC. Black and turquoise modules were identified in the gene co-expression network constructed by 1807 DEGs that from 6 eligible gene expression profiles of Gene Expression Omnibus database based on Robust Rank Aggregation and weighted gene co-expression network analysis. Hub genes were significantly down-regulated and the expression levels of the hub genes were different in different stages in hub gene verification. ROC curves indicated all hub genes had good diagnostic value for PTC (except for ABCA6 AUC = 89.5%, the 15 genes with AUC > 90%). Methylation analysis showed that hub gene verification ABCA6, ACACB, RMDN1 and TFPI were identified as differentially methylated genes, and the decreased expression level of these genes may relate to abnormal DNA methylation. Moreover, the expression levels of 8 top hub genes were correlated with tumor purity and tumor-infiltrating immune cells. These findings, including functional annotations and GSEA provide new insights into pathogenesis of PTC. The hub genes and methylation of hub genes may as potential biomarkers provide new insights for diagnosis of PTC, and all these findings may be the direction to study the mechanisms underlying of PTC in the future.