Bioinformatics analysis and verification of gene targets for benign tracheal stenosis

• Published in: Molecular Genetics & Genomic Medicine Vol. 8; no. 6; pp. e1245 - n/a
• Main Authors: Li, Xu‐ze, Wang, Zi‐chen, Qiu, Yong, Ma, Shu‐xian, Meng, Ling‐bing, Wu, Wen‐hao, Zhang, Pei, Yang, Wei, Song, Wen‐ping, Huang, Lining
• Format: Journal Article
• Language: English
• Published: United States Wiley 01.06.2020; John Wiley & Sons, Inc; John Wiley and Sons Inc
• Subjects: Adult; Aged; Aged, 80 and over; bioinformatic; Bioinformatics; Cell cycle; Cell division; Computational Biology; Computational Biology - methods; Cyclin B1; Cyclin B1 - genetics; Cyclin B2; Cyclin B2 - genetics; Datasets; differentially expressed genes; Encyclopedias; Female; Gene expression; Gene Regulatory Networks; Genes; Genetics; Genomes; Humans; Hyperplasia; Ki-67 Antigen; Ki-67 Antigen - genetics; Male; Mechanical ventilation; Medical research; Middle Aged; Molecular modelling; Nuclear division; Original; Original Articles; Ostomy; p53 Protein; Pathogenesis; Principal components analysis; Proteins; QH426-470; Software; Stenosis; Trachea; tracheal stenosis; Tracheal Stenosis - genetics; Transcriptome; bioinformatic; differentially expressed genes; cell cycle; tracheal stenosis
• ISSN: 2324-9269; 2324-9269
• DOI: 10.1002/mgg3.1245

Background Tracheal 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. Methods The 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. Results There 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. Conclusion Bioinformatics 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. Bioinformatics methods could be useful tools to predict the progression of tracheal stenosis (TS) and to explore the mechanisms of the occurrence and development of TS. In addition, there are differently expressed genes (DEGs) between TS and normal tracheal tissues, especially MKI67, CCNB1, and CCNB2.