Network-based approach to identify key candidate genes and pathways shared by thyroid cancer and chronic kidney disease

Abstract

Abstract Thyroid cancer (TC) is one of the fastest-growing cancers in the world. Thyroid dysfunction has an influence on chronic kidney disease (CKD), but the possible relations between TC and CDK have not been explored yet. The present study aimed to identify shared candidate genes and pathways between TC and CKD from the transcriptomics datasets to identify important clues to the pathological mechanisms in these diseases. The gene expression datasets of TC and CKD were obtained from Gene Expression Omnibus and analyzed to identify common genes between TC and CKD. We have detected 84 common differentially expressed genes (DEGs) between TC and CKD. We have integrated the DEGs with protein-protein interaction (PPI) network and identified ten significant hub proteins (TLE1, CTNNA1, TEK, TPM2, FGFR2, MMP2, SDC2, NRP1, TCF7L1, GSN) based on topological analysis. The integration of DEGs with biomolecular networks revealed transcription factors (FOXC1, GATA2, FOXL1, HINFP, LIMK2, E2F1, POU2F2, TFAP2A, YY1, RERE) and microRNAs (mir-335–5p, mir-26b-5p, mir-124–3p, mir-181a-5p, mir-98–5p, mir-16–5p, mir-7-5p, mir-129–5p, mir-8485, mir-1827). The present study identified biomolecules shared by TC and CKD at protein levels (hub proteins, transcription factors), and RNA levels (mRNAs, microRNAs). Basic biological experiments will be needed to establish them as biomarkers in TC and CKD.