Bioinformatic Analysis of Key Genes and Pathways Related to Keloids.
Bioinformatic Analysis of Key Genes and Pathways Related to Keloids.
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DOI：
10.1155/2021/5897907
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发表时间：
2021
影响因子：
--
通讯作者：
Gu J
中科院分区：
文献类型：
--
作者：
Bi S;Liu R;Wu B;He L;Gu J
瘢痕疙瘩的病理生理学是复杂的，并且瘢痕疙瘩的治疗仍然是未满足的医学需求。本研究的目的是寻找瘢痕疙瘩与正常皮肤组织差异表达基因中的枢纽基因，以及瘢痕疙瘩发生发展的关键通路。 我们下载了GSE 92566和GSE 90051微阵列数据，其中包含正常皮肤组织和瘢痕疙瘩基因表达数据。GSE 92566被视为用于总结显著DEG的发现数据集，GSE 90051用作验证数据集。对DEG中富集的关键功能和途径进行了基因本体论、京都基因百科全书和基因组途径、Reactome富集分析、基因集富集分析和基因集变异分析。此外，我们还验证了从蛋白质-蛋白质相互作用网络中识别的枢纽基因，并预测了miRNA-枢纽基因的相互作用。 在GSE 92566中鉴定了117个下调的DEG和204个上调的DEG。细胞外和胶原蛋白相关的途径是突出的上调DEGs，而角质化相关的途径与下调DEGs。枢纽基因包括COL 5A 1、COL 5A 2和SERPINH 1，这些基因也在GSE 90051中得到验证。 本研究通过对两个微阵列数据集的生物信息学分析，确定了几个枢纽基因，并为瘢痕疙瘩发展的潜在途径和miRNA-枢纽基因相互作用提供了见解。此外，我们的研究结果将支持未来治疗策略的发展。
The pathophysiology of keloids is complex, and the treatment for keloids is still an unmet medical need. Our study is aimed at identifying the hub genes among the differentially expressed genes (DEGs) between normal skin tissue and keloids and key pathways in the development of keloids. We downloaded the GSE92566 and GSE90051 microarray data, which contain normal skin tissue and keloid gene expression data. GSE92566 was treated as a discovery dataset for summarizing the significantly DEGs, and GSE90051 served as a validation dataset. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, Reactome enrichment analysis, gene set enrichment analysis, and gene set variation analysis were performed for the key functions and pathways enriched in DEGs. Moreover, we also validated the hub genes identified from the protein-protein interaction network and predicted miRNA-hub gene interactions. 117 downregulated DEGs and 204 upregulated DEGs in GSE92566 were identified. Extracellular and collagen-related pathways were prominent in upregulated DEGs, while the keratinization-related pathway was associated with downregulated DEGs. The hub genes included COL5A1, COL5A2, and SERPINH1, which were also validated in GSE90051. This study identified several hub genes and provided insights for the underlying pathways and miRNA-hub gene interactions for keloid development through bioinformatic analysis of two microarray datasets. Additionally, our results would support the development of future therapeutic strategies.
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影响因子：
48
作者：
Li T;Wernersson R;Hansen RB;Horn H;Mercer J;Slodkowicz G;Workman CT;Rigina O;Rapacki K;Stærfeldt HH;Brunak S;Jensen TS;Lage K
通讯作者：
Lage K
影响因子：
14.9
作者：
Karagkouni D;Paraskevopoulou MD;Chatzopoulos S;Vlachos IS;Tastsoglou S;Kanellos I;Papadimitriou D;Kavakiotis I;Maniou S;Skoufos G;Vergoulis T;Dalamagas T;Hatzigeorgiou AG
通讯作者：
Hatzigeorgiou AG
影响因子：
2.4
作者：
Berman, Brian;Maderal, Andrea;Raphael, Brian
通讯作者：
Raphael, Brian
XGR软件可增强对基因组摘要数据的解释，并通过应用于免疫特征说明。
影响因子：
12.3
作者：
Fang H;Knezevic B;Burnham KL;Knight JC
通讯作者：
Knight JC
影响因子：
3
作者：
Bader, GD;Hogue, CW
通讯作者：
Hogue, CW