ArticleBMC medical genomics2021
Multi-factor regulatory network and different clusters in hypertrophic obstructive cardiomyopathy.
Article in BMC medical genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy.Frontiers in genetics · 2025Article
- Exploration of the common pathogenic link between COVID-19 and diabetic foot ulcers: An in silico approach.Health science reports · 2023Article
- The role of SMAD signaling in hypertrophic obstructive cardiomyopathy: an immunohistopathological study in pediatric and adult patients.Scientific reports · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPractical biosignatures and thorough understanding of regulatory processes of hypertrophic obstructive cardiomyopathy (HOCM) are still lacking.
methodsFirstly, public data from GSE36961 and GSE89714 datasets of Gene Expression Omnibus (GEO), Gene database of NCBI (National Center of Biotechnology Information) and Online Mendelian Inheritance in Man (OMIM) database were merged into a candidate gene set of HOCM. Secondly, weighted gene co-expression network analysis (WGCNA) for the candidate gene set was carried out to determine premier co-expressed genes. Thirdly, significant regulators were found out by virtue of a multi-factor regulatory network of long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and transcription factors (TFs) with molecule interreactions from starBase v2.0 database and TRRUST v2 database. Ultimately, HOCM unsupervised clustering and "tsne" dimensionality reduction was employed to gain hub genes, whose classification performance was evaluated by a multinomial model of lasso logistic regression analysis binded with receiver operating characteristic (ROC) curve.
resultsTwo HOCM remarkably-interrelated modules were from WGCNA, followed by the recognition of 32 crucial co-expressed genes. The multi-factor regulatory network disclosed 7 primary regulatory agents, containing lncRNAs (XIST, MALAT1, and H19), TFs (SPI1 and SP1) and miRNAs (hsa-miR-29b-39 and has-miR-29a-3p). Four clusters of HOCM and 4 hub genes (COMP, FMOD, AEBP1 and SULF1) significantly expressing in preceding four subtypes were obtained, while ROC curve demonstrated satisfactory performance of clustering and 4 genes.
conclusionsOur consequences furnish valuable resource which may bring about prospective mechanistic and therapeutic anatomization in HOCM.
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