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Original Research

Open Access

Bioinformatic analysis identifies potential key genes in the pathogenesis of uterine leiomyoma

  • Yi-Chao Jin1,†
  • Tong-Hui Ji1,†
  • Xiong Yuan1
  • Ying Sun1,†
  • Yu-Jie Sun2
  • Jie Wu1,*,

1Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029 Jiangsu, P. R. China

2Key laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, 211126 Jiangsu, P. R. China

DOI: 10.31083/j.ejgo.2021.01.2151 Vol.42,Issue 1,February 2021 pp.50-65

Submitted: 25 May 2020 Accepted: 28 August 2020

Published: 15 February 2021

*Corresponding Author(s): Jie Wu E-mail: wujiemd@126.com

† These authors contributed equally.

Abstract

Objective: The present study aimed to screen hub genes for pathology of uterine leiomyoma. Methods: The microarray data of GSE31699, containing 16 uterine leiomyoma tissue samples and 16 matched normal myometrium samples, were downloaded from the Gene Expression Omnibus database (GEO). The “limma” R language package was used to identify differently-expressed genes (DEGs) between uterine leiomyoma and myometrium. Gene Ontology (GO) and pathway enrichment analyses were performed by using clusterprofiler, the DEGs were mostly enriched in post-synapse assembly, response to glucocorticoid, extracellular matrix receptor interaction and coagulation cascades. Subsequently, a protein-protein interaction (PPI) network of DEGs was constructed by Search Tool for the Retrieval of Interacting Genes Database (STRING) and visualized by utilizing Cytoscape software. We screened hub clusters of PPI network by the plug-in Molecular Complex Detection (MCODE) in Cytoscape, then clusterprofiler was also utilized to analyze functions and pathways enrichment of the genes in the hub clusters. Furthermore, we employed the “WGCNA” package in R to conduct co-expression network for all genes in GSE31699. Ultimately, we selected the overlapped genes in hub clusters of DEGs’ PPI network and WGCNA. Results: Five genes (COL5A2, ALDH1A1, GNG11, EFEMP1, ANXA1) were finally validated in other GEO datasets (GSE64763, GSE764, GSE593) and Oncomine database. Gene set enrichment analysis (GSEA) was also performed for the hub genes. The expression of COL5A2 was significantly higher in uterine leiomyoma compared with that in myometrium, while the expression of the other hub genes was significantly lower in uterine leiomyoma. Conclusion: The results indicated that COL5A2, ALDH1A1, GNG11, EFEMP1 and ANXA1 may be the key pathological genes in uterine leiomyoma.


Keywords

Bioinformatics analysis; Uterine leiomyoma; PPI; WGCNA


Cite and Share

Yi-Chao Jin,Tong-Hui Ji,Xiong Yuan,Ying Sun,Yu-Jie Sun,Jie Wu. Bioinformatic analysis identifies potential key genes in the pathogenesis of uterine leiomyoma. European Journal of Gynaecological Oncology. 2021. 42(1);50-65.

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