ArticleCell journal2023
Integrated Bioinformatic Analysis of Differentially Expressed Genes Associated with Wound Healing.
Article in Cell journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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.
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Who cites it
3 citing papers in PubMed.
- Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm.Genome biology · 2024Article
- DExplore: An Online Tool for Detecting Differentially Expressed Genes from mRNA Microarray Experiments.Biology · 2024Article
- Exploring Co-expression Modules-Traits Correlation through Weighted Gene Co-expression Network Analysis: A Promising Approach in Wound Healing Research.Medical journal of the Islamic Republic of Iran · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWound healing is a complex process involving the coordinated interaction of various genes and molecular<br />pathways. The study aimed to uncover novel therapeutic targets, biomarkers and candidate genes for drug development<br />to improve successful wound repair interventions.<br />Materials and Methods: This study is a network-meta analysis study. Nine wound healing microarray datasets obtained<br />from the Gene Expression Omnibus (GEO) database were used for this study. Differentially expressed genes (DEGs)<br />were described using the Limma package and shared genes were used as input for weighted gene co-expression<br />network analysis. The Gene Ontology analysis was performed using the EnrichR web server, and construction of a<br />protein-protein interaction (PPI) network was achieved by the STRING and Cytoscape.<br />Results: A total of 424 DEGs were determined. A co-expression network was constructed using 7692 shared genes<br />between nine data sets, resulting in the identification of seven modules. Among these modules, those with the top 20<br />genes of up and down-regulation were selected. The top down-regulated genes, including TJP1, SEC61A1, PLEK,<br />ATP5B, PDIA6, PIK3R1, SRGN, SDC2, and RBBP7, and the top up-regulated genes including RPS27A, EEF1A1,<br />HNRNPA1, CTNNB1, POLR2A, CFL1, CSNk1E, HSPD1, FN1, and AURKB, which can potentially serve as therapeutic<br />targets were identified. The KEGG pathway analysis found that the majority of the genes are enriched in the "Wnt<br />signaling pathway".<br />Conclusion: In our study of nine wound healing microarray datasets, we identified DEGs and co-expressed modules<br />using WGCNA. These genes are involved in important cellular processes such as transcription, translation, and posttranslational<br />modifications. We found nine down-regulated genes and ten up-regulated genes, which could serve as<br />potential therapeutic targets for further experimental validation. Targeting pathways related to protein synthesis and cell<br />adhesion and migration may enhance wound healing, but additional experimental validation is needed to confirm the<br />effectiveness and safety of targeted interventions.
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