ArticleComputational and mathematical methods in medicine2022
Mining of Potential Biomarkers and Pathway in Valvular Atrial Fibrillation (VAF) via Systematic Screening of Gene Coexpression Network.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers.
What it found
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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.
The trial behind it
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Reviewing Atrial Fibrillation Pathophysiology from a Network Medicine Perspective: The Relevance of Structural Remodeling, Inflammation, and the Immune System.Life (Basel, Switzerland) · 2023Review
- Integrative bioinformatics analysis reveals STAT2 as a novel biomarker of inflammation-related cardiac dysfunction in atrial fibrillation.Open medicine (Warsaw, Poland) · 2023Article
- Retracted: Mining of Potential Biomarkers and Pathway in Valvular Atrial Fibrillation (VAF) via Systematic Screening of Gene Coexpression Network.Computational and mathematical methods in medicine · 2023Article
Corrections and comments
- Retraction · 2023-12-06Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: We apply the bioinformatics method to excavate the potential genes and therapeutic targets associated with valvular atrial fibrillation (VAF). Methods: The downloaded gene expression files from the gene expression omnibus (GEO) included patients with primary severe mitral regurgitation complicated with sinus or atrial fibrillation rhythm. Subsequently, the differential gene expression in left and right atrium was analyzed by R software. Additionally, weighted correlation network analysis (WGCNA), principal component analysis (PCA), and linear model for microarray data (LIMMA) algorithm were used to determine hub genes. Then, Metascape database, DAVID database, and STRING database were used to annotate and visualize the gene ontology (GO) analysis, KEGG pathway enrichment analysis, and PPI network analysis of differentially expressed genes (DEGs). Finally, the TFs and miRNAs were predicted by using online tools, such as PASTAA and miRDB. Results: 20,484 differentially expressed genes related to atrial fibrillation were obtained through the analysis of left and right atrial tissue samples of GSE115574 gene chip, and 1,009 were with statistical significance, including 45 upregulated genes and 964 downregulated genes. And the hub genes implicated in AF of NPC2, ODC1, SNAP29, LAPTM5, ST8SIA5, and FCGR3B were screened. Finally, the main regulators of targeted candidate biomarkers and microRNAs, EIF5A2, HIF1A, ZIC2, ELF1, and STAT2, were found in this study. Conclusion: These hub genes, NPC2, ODC1, SNAP29, LAPTM5, ST8SIA5, and FCGR3B, are important for the development of VAF, and their enrichment pathways and TFs elucidate the involved molecular mechanisms and assist in the validation of drug targets.
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Registered trials
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