ArticlePharmacogenomics and personalized medicine2021
Identification of Underlying Hub Genes Associated with Hypertrophic Cardiomyopathy by Integrated Bioinformatics Analysis.
Article in Pharmacogenomics and personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 12 citations in OpenAlex.
- Identification of key genes and signaling pathways associated with acute pancreatitis and acute lung injury by bioinformatics analysis.PloS one · 2026Article
- The microenvironment cell index is a novel indicator for the prognosis and therapeutic regimen selection of cancers.Journal of translational medicine · 2025Article
- Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy.Frontiers in genetics · 2025Article
- Machine learning-driven diagnostic signature provides new insights in clinical management of hypertrophic cardiomyopathy.ESC heart failure · 2024Article
- IdentifyingBiomolecules · 2024Article
- Identification ofFrontiers in molecular biosciences · 2024Article
- FRZB affectsExperimental and therapeutic medicine · 2023Article
- Exploration of Key Immune-Related Transcriptomes Associated with Doxorubicin-Induced Cardiotoxicity in Patients with Breast Cancer.Cardiovascular toxicology · 2023Article
- Article
- GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data.PeerJ. Computer science · 2023Article
- Dysfunctional network of hub genes in hypertrophic cardiomyopathy patients.American journal of translational research · 2022Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundConsidered as one of the major reasons of sudden cardiac death, hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease. However, effective treatment for HCM is still lacking. Identification of hub gene may be a powerful tool for discovering potential therapeutic targets and candidate biomarkers.
methodsWe analysed three gene expression datasets for HCM from the Gene Expression Omnibus. Two of them were merged by "sva" package. The merged dataset was used for analysis while the other dataset was used for validation. Following this, a weighted gene coexpression network analysis (WGCNA) was performed, and the key module most related to HCM was identified. Based on the intramodular connectivity, we identified the potential hub genes. Then, a receiver operating characteristic curve analysis was performed to verify the diagnostic values of hub genes. Finally, we validated changes of hub genes, for genetic transcription and protein expression levels, in datasets of HCM patients and myocardium of transverse aortic constriction (TAC) mice.
resultsIn the merged dataset, a total of 455 differentially expressed genes (DEGs) were identified from normal and hypertrophic myocardium. In WGCNA, the blue module was identified as the key module and the genes in this module showed a high positive correlation with HCM. Functional enrichment analysis of DEGs and key module revealed that the extracellular matrix, fibrosis, and neurohormone pathways played important roles in HCM. FRZB, COL14A1, CRISPLD1, LUM, and sFRP4 were identified as hub genes in the key module. These genes showed a good predictive value for HCM and were significantly up-regulated in HCM patients and TAC mice. We also found protein expression of LUM and sFRP4 increased in myocardium of TAC mice.
conclusionThis study revealed that five hub genes are involved in the occurrence and development of HCM, and they are potentially to be used as therapeutic targets and biomarkers for HCM.
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