Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

Md. Ali Hossain, Tania Akter Asa, Md. Mijanur Rahman, Shahadat Uddin, Ahmed A. Moustafa, Julian M. W. Quinn and Mohammad Ali Moni
Additional contact information
Md. Ali Hossain: Department of Computer Science & Engineering, Manarat International University, Khagan, Dhaka 1343, Bangladesh
Tania Akter Asa: Electrical and Electronic Engineering, Islamic University, Kushtia 7005, Bangladesh
Md. Mijanur Rahman: Computer Science & Engineering, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh 2205, Bangladesh
Shahadat Uddin: Complex Systems Research Group & Project Management Program, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia
Ahmed A. Moustafa: Marcs Institute for Brain and Behaviour and School of Psychology, Western Sydney University, Sydney, NSW 2751, Australia
Julian M. W. Quinn: Bone Biology Divisions, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
Mohammad Ali Moni: Bone Biology Divisions, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia

IJERPH, 2020, vol. 17, issue 4, 1-21

Abstract: Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

Keywords: thyroid cancer; carcinoma; protein-protein interaction; reporter transcription factors; reporter microRNAs; molecular pathways (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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