Recent studies have demonstrated the close relationship between parathyroid adenoma (PA) and thyroid follicular adenoma (FTA). However, the underlying pathogenesis remains unknown. This study focused on exploring common pathogenic genes, as well as the pathogenesis of these two diseases, through bioinformatics methods. This work obtained PA and FTA datasets from the Integrated Gene Expression Database to identify the common differentially expressed genes (DEGs) of two diseases. The functions of the genes were investigated by GO and KEGG enrichment. The program CytoHubba was used to select the hub genes, while receiver operating characteristic curves were plotted to evaluate the predictive significance of the hub genes. The DGIbd database was used to identify gene-targeted drugs. This work detected a total of 77 DEGs. Enrichment analysis demonstrated that DEGs had activities of 3′,5′-cyclic AMP, and nucleotide phosphodiesterases and were associated with cell proliferation. NOS1, VWF, TGFBR2, CAV1, and MAPK1 were identified as hub genes after verification. The area under the curve of PA and FTA was>0.7, and the hub genes participated in the Relaxin Signaling Pathway, focal adhesion, and other pathways. The construction of the mRNA-miRNA interaction network yielded 11 important miRNAs, while gene-targeting drug prediction identified four targeted drugs with possible effects. This bioinformatics study demonstrated that cell proliferation and tumor suppression and the hub genes co-occurring in PA and FTA, have important effects on the occurrence and progression of two diseases, which make them potential diagnostic biomarkers and therapeutic targets.

Received: 23 October 2022

Accepted after revision: 15 December 2022

Accepted Manuscript online:
04 January 2023

Article published online:
27 February 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

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