Identification of MKNK1 and TOP3A as ovarian endometriosis risk-associated genes using integrative genomic analyses and functional experiments

Yizhou Huang; Jie Luo; Yue Zhang; Tao Zhang; Xiangwei Fei; Liqing Chen; Yingfan Zhu; Songyue Li; Caiyun Zhou; Kaihong Xu; Yunlong Ma; Jun Lin; Jianhong Zhou

doi:10.1016/j.csbj.2023.02.001

Identification of MKNK1 and TOP3A as ovarian endometriosis risk-associated genes using integrative genomic analyses and functional experiments

Comput Struct Biotechnol J. 2023 Feb 5:21:1510-1522. doi: 10.1016/j.csbj.2023.02.001. eCollection 2023.

Authors

Yizhou Huang¹, Jie Luo¹, Yue Zhang¹, Tao Zhang¹, Xiangwei Fei², Liqing Chen¹, Yingfan Zhu¹, Songyue Li¹, Caiyun Zhou³, Kaihong Xu¹, Yunlong Ma⁴, Jun Lin¹, Jianhong Zhou¹

Affiliations

¹ Department of Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, Zhejiang Province, PR China.
² Key Laboratory of Women's Reproductive Health of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, Zhejiang Province, PR China.
³ Department of Pathology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, Zhejiang Province, PR China.
⁴ Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University 325027 Wenzhou, Zhejiang Province, PR China.

Abstract

The risk of endometriosis (EM), which is a common complex gynaecological disease, is related to genetic predisposition. However, it is unclear how genetic variants confer the risk of EM. Here, via Sherlock integrative analysis, we combined large-scale genome-wide association studies (GWAS) summary statistics on EM (N = 245,494) with a blood-based eQTL dataset (N = 1490) to identify EM risk-related genes. For validation, we leveraged two independent eQTL datasets (N = 769) for integration with the GWAS data. Thus, we prioritised 14 genes, including GIMAP4, TOP3A, and NMNAT3, which showed significant association with susceptibility to EM. We also utilised two independent methods, Multi-marker Analysis of GenoMic Annotation and S-PrediXcan, to further validate the EM risk-associated genes. Moreover, protein-protein interaction network analysis showed the 14 genes were functionally connected. Functional enrichment analyses further demonstrated that these genes were significantly enriched in metabolic and immune-related pathways. Differential gene expression analysis showed that in peripheral blood samples from patients with ovarian EM, TOP3A, MKNK1, SIPA1L2, and NUCB1 were significantly upregulated, while HOXB2, GIMAP5, and MGMT were significantly downregulated compared with their expression levels in samples from the controls. Immunohistochemistry further confirmed the increased expression levels of MKNK1 and TOP3A in the ectopic and eutopic endometrium compared to normal endometrium, while HOBX2 was downregulated in the endometrium of women with ovarian EM. Finally, in ex vivo functional experiments, MKNK1 knockdown inhibited ectopic endometrial stromal cells (EESCs) migration and invasion. TOP3A knockdown inhibited EESCs proliferation, migration, and invasion, while promoting their apoptosis. Convergent lines of evidence suggested that MKNK1 and TOP3A are novel EM risk-related genes.

Keywords: Endometriosis; Expression quantitative trait loci; Genome-wide association study; Integrative genomics analysis; Risk genes.