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生物信息学分析和良性气管狭窄相关基因靶点的验证。

Bioinformatics analysis and verification of gene targets for benign tracheal stenosis.

机构信息

Department of Anesthesiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China.

Anesthesiology Department, Beijing Hospital, National Center of Gerontology, Beijing, P. R. China.

出版信息

Mol Genet Genomic Med. 2020 Jun;8(6):e1245. doi: 10.1002/mgg3.1245. Epub 2020 Apr 20.

Abstract

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.

摘要

背景

气管损伤可导致气管内瘢痕增生,进而导致良性气管狭窄(TS)。随着机械通气和呼吸机的使用日益增加,TS 的发病率也在增加。然而,TS 的分子机制尚未阐明。进一步探讨 TS 的分子机制具有重要意义。

方法

验证公共数据的可重复性。鉴定 TS 与正常样本之间的差异表达基因(DEGs)和最重要的基因。对基因本体论(GO)和京都基因与基因组百科全书(KEGG)进行富集分析。分析比较毒理学基因组数据库。招募 TS 患者并进行 RT-qPCR 以验证最重要的基因。

结果

TS 和正常组的样本之间存在很强的相关性。共有 194 个 DEGs,包括 61 个下调 DEGs 和 133 个上调 DEGs。GO 在有丝核分裂、细胞周期和细胞分裂中显著富集。KEGG 分析表明,前 5 条途径是细胞周期和 p53 途径。MKI67(OMIM:176741)、CCNB1(OMIM:123836)和 CCNB2(OMIM:602755)被确定为 TS 最重要的基因,并通过临床样本进行了验证。

结论

生物信息学方法可能是探索 TS 机制的有用方法。此外,MKI67、CCNB1 和 CCNB2 可能是 TS 最重要的基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d07/7284051/79cd1b875921/MGG3-8-e1245-g001.jpg

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