ArticleHuman genomics2022
Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis.
Article in Human genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 27 citations in OpenAlex.
- Fatty acid degradation-related gene signatures as biomarkers for ocular sarcoidosis.Scientific reports · 2025Article
- Integrated multiple machine learning and Mendelian randomization reveal LTF gene as a prognostic biomarker for nonspecific orbital inflammation.BMC pharmacology & toxicology · 2025Article
- Machine learning unveils hypoxia-immune gene hub for clinical stratification of thyroid-associated ophthalmopathy.Scientific reports · 2025Article
- Gluconeogenesis related gene signatures as biomarkers for nonspecific orbital inflammation.Scientific reports · 2025Article
- Development and validation of potential molecular subtypes and signatures of thyroid eye disease based on angiogenesis-related gene analysis.BMC pharmacology & toxicology · 2025Article
- Molecular insights into ulcerative colitis and orbital inflammation.Scientific reports · 2025Article
- Development and validation of potential molecular subtypes and signatures of ocular sarcoidosis based on autophagy-related gene analysis.Open medicine (Warsaw, Poland) · 2025Article
- Immune Regulation and ECM-Related Pathway Enrichment Reveal ATP2A3 as a Prognostic Biomarker for Nonspecific Orbital Inflammation: An Integrated Machine Learning and Mendelian Randomization Analysis.Mediators of inflammation · 2025Article
- Identification of key extracellular proteins as the potential biomarkers in thyroid eye disease.PloS one · 2025Article
- Integrated machine learning and Mendelian randomization reveal PALMD as a prognostic biomarker for nonspecific orbital inflammation.Scientific reports · 2024Article
- Formerly degenerate seventh zinc finger domain from transcription factor ZNF711 rehabilitated by experimental NMR structure.Protein science : a publication of the Protein Society · 2024Article
- Deciphering the role of HLF in idiopathic orbital inflammation: integrative analysis via bioinformatics and machine learning techniques.Scientific reports · 2024Article
- The shared mechanism and potential diagnostic markers for premature ovarian failure and dry eye disease.Scientific reports · 2024Article
- The roles of IRF8 in nonspecific orbital inflammation: an integrated analysis by bioinformatics and machine learning.Journal of ophthalmic inflammation and infection · 2024Article
- Formerly degenerate seventh zinc finger domain from transcription factor ZNF711 rehabilitated by experimental NMR structure.bioRxiv : the preprint server for biology · 2024Article
- Glutamine metabolism-related genes and immunotherapy in nonspecific orbital inflammation were validated using bioinformatics and machine learning.BMC genomics · 2024Article
- Bioinformatic validation and machine learning-based exploration of purine metabolism-related gene signatures in the context of immunotherapeutic strategies for nonspecific orbital inflammation.Frontiers in immunology · 2024Article
- Elucidating the multifaceted roles of GPR146 in non-specific orbital inflammation: a concerted analytical approach through the prisms of bioinformatics and machine learning.Frontiers in medicine · 2024Article
- Purine metabolism-related genes and immunization in thyroid eye disease were validated using bioinformatics and machine learning.Scientific reports · 2023Article
- Mining Autoimmune-Disorder-Linked Molecular-Mimicry Candidates inMicroorganisms · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThyroid eye disease (TED) is the most common orbital pathology that occurs in up to 50% of patients with Graves' disease. Herein, we aimed at discovering the possible hub genes and pathways involved in TED based on bioinformatical approaches.
resultsThe GSE105149 and GSE58331 datasets were downloaded from the Gene Expression Omnibus (GEO) database and merged for identifying TED-associated modules by weighted gene coexpression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. EdgeR was run to screen differentially expressed genes (DEGs). Transcription factor (TF), microRNA (miR) and drug prediction analyses were performed using ToppGene suite. Function enrichment analysis was used to investigate the biological function of genes. Protein-protein interaction (PPI) analysis was performed based on the intersection between the list of genes obtained by WGCNA, lmQCM and DEGs, and hub genes were identified using the MCODE plugin. Based on the overlap of 497 genes retrieved from the different approaches, a robust TED coexpression network was constructed and 11 genes (ATP6V1A, PTGES3, PSMD12, PSMA4, METAP2, DNAJA1, PSMA1, UBQLN1, CCT2, VBP1 and NAA50) were identified as hub genes. Key TFs regulating genes in the TED-associated coexpression network, including NFRKB, ZNF711, ZNF407 and MORC2, and miRs including hsa-miR-144, hsa-miR-3662, hsa-miR-12136 and hsa-miR-3646, were identified. Genes in the coexpression network were enriched in the biological processes including proteasomal protein catabolic process and proteasome-mediated ubiquitin-dependent protein catabolic process and the pathways of endocytosis and ubiquitin-mediated proteolysis. Drugs perturbing genes in the coexpression network were also predicted and included enzyme inhibitors, chlorodiphenyl and finasteride.
conclusionsFor the first time, TED-associated coexpression network was constructed and key genes and their functions, as well as TFs, miRs and drugs, were predicted. The results of the present work may be relevant in the treatment and diagnosis of TED and may boost molecular studies regarding TED.
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