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ITLN1 identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial ovarian cancer

Authors Liu JH, Li SY, Liang JY, Jiang Y, Wan YC, Zhou SL, Cheng WJ

Received 4 October 2018

Accepted for publication 24 January 2019

Published 25 March 2019 Volume 2019:11 Pages 2379—2392


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Editor who approved publication: Dr Kenan Onel

JinHui Liu,1 SiYue Li,1 JunYa Liang,2 Yi Jiang,1 YiCong Wan,1 ShuLin Zhou,1 WenJun Cheng1

1Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China; 2Hypertension Research Center, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu, China

Background: Epithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable.
Materials and methods: Gene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the “limma” package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein–protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples.
Results: We analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA.
Conclusion: In this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.

Keywords: bioinformatics analysis, epithelial ovarian cancer, PPI, WGCNA, ITLN1

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