ArticleGenes2022
Identification of Potential Diagnostic Biomarkers and Biological Pathways in Hypertrophic Cardiomyopathy Based on Bioinformatics Analysis.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- The Relationship Between BigET-1 and Cardiac Remodeling in Patients with Hypertrophic Obstructive Cardiomyopathy.Molecular biotechnology · 2025Article
- Investigating the role of long non-coding RNA in hypertrophic cardiomyopathy.bioRxiv : the preprint server for biology · 2025Article
- Uncovering dendritic cell specific biomarkers for diagnosis and prognosis of cardiomyopathy using single cell RNA sequencing and comprehensive bioinformatics analysis.Scientific reports · 2025Article
- Identification of Diagnostic Biomarkers for Colorectal Polyps Based on Noninvasive Urinary Metabolite Screening and Construction of a Nomogram.Cancer medicine · 2025Article
- Systemic inflammation is associated with myocardial fibrosis in patients with obstructive hypertrophic cardiomyopathy.ESC heart failure · 2025Article
- Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers.Journal of cellular and molecular medicine · 2024Article
- Identification of crosstalk genes and immune characteristics between Alzheimer's disease and atherosclerosis.Frontiers in immunology · 2024Article
- Identification of important modules and biomarkers in diabetic cardiomyopathy based on WGCNA and LASSO analysis.Frontiers in endocrinology · 2024Article
- Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning.The Journal of international medical research · 2023Article
- Spatial Transcriptomic Analysis of Focal and Normal Areas of Myocyte Disarray in Human Hypertrophic Cardiomyopathy.International journal of molecular sciences · 2023Article
- ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network.Hereditas · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypertrophic cardiomyopathy (HCM) is a genetic heterogeneous disorder and the main cause of sudden cardiac death in adolescents and young adults. This study was aimed at identifying potential diagnostic biomarkers and biological pathways to help to diagnose and treat HCM through bioinformatics analysis. We selected the GSE36961 dataset from the Gene Expression Omnibus (GEO) database and identified 893 differentially expressed genes (DEGs). Subsequently, 12 modules were generated through weighted gene coexpression network analysis (WGCNA), and the turquoise module showed the highest negative correlation with HCM (cor = −0.9, p-value = 4 × 10−52). With the filtering standard gene significance (GS) < −0.7 and module membership (MM) > 0.9, 19 genes were then selected to establish the least absolute shrinkage and selection operator (LASSO) model, and LYVE1, MAFB, and MT1M were finally identified as key genes. The expression levels of these genes were additionally verified in the GSE130036 dataset. Gene Set Enrichment Analysis (GSEA) showed oxidative phosphorylation, tumor necrosis factor alpha-nuclear factor-κB (TNFα-NFκB), interferon-gamma (IFNγ) response, and inflammatory response were four pathways possibly related to HCM. In conclusion, LYVE1, MAFB, and MT1M were potential biomarkers of HCM, and oxidative stress, immune response as well as inflammatory response were likely to be associated with the pathogenesis of HCM.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.