ArticleMolecular genetics & genomic medicine2020
Bioinformatics analysis and verification of gene targets for benign tracheal stenosis.
Article in Molecular genetics & genomic medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Mitomycin C Induces Autophagy in Human Tracheal Fibroblasts and Suppresses Their Growth.Laryngoscope investigative otolaryngology · 2025Article
- Comparison of the difference between post-intubation tracheal stenosis and post-tracheostomy tracheal stenosis: a cross-sectional study.Journal of thoracic disease · 2025Article
- Identifying Molecular Pathophysiology and Potential Therapeutic Options in Iatrogenic Tracheal Stenosis.Biomedicines · 2024Article
- Gene expression profiles in COVID-19-associated tracheal stenosis indicate persistent anti-viral response and dysregulated retinol metabolism.BMC research notes · 2024Article
- Knockdown of SOX9 alleviates tracheal fibrosis through the Wnt/β-catenin signaling pathway.Journal of molecular medicine (Berlin, Germany) · 2022Article
- Bioinformatics analysis and verification of gene targets for benign tracheal stenosis.Molecular genetics & genomic medicine · 2020Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTracheal injury could cause intratracheal scar hyperplasia which in turn causes benign tracheal stenosis (TS). With the increasing use of mechanical ventilation and ventilator, the incidence of TS is increasing. However, the molecular mechanisms of TS have not been elucidated. It is significant to further explore the molecular mechanisms of TS.
methodsThe repeatability of public data was verified. Differently expressed genes (DEGs) and most significant genes were identified between TS and normal samples. Enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed. The comparative toxicogenomics database were analyzed. TS patients were recruited and RT-qPCR were performed to verify the most significant genes.
resultsThere exist strong correlations among samples of TS and normal group. There was a total of 194 DEGs, including 61 downregulated DEGs and 133 upregulated DEGs. GO were significantly enriched in mitotic nuclear division, cell cycle, and cell division. Analysis of KEGG indicated that the top pathways were cell cycle, and p53 pathway. MKI67(OMIM:176741), CCNB1(OMIM:123836), and CCNB2(OMIM:602755) were identified as the most significant genes of TS, and validated by the clinical samples.
conclusionBioinformatics methods might be useful method to explore the mechanisms of TS. In addition, MKI67, CCNB1, and CCNB2 might be the most significant genes of TS.
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