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Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis

Authors Dai F, Chen G, Wang Y, Zhang L, Long Y, Yuan M, Yang D, Liu S, Cheng Y, Zhang L

Received 27 December 2018

Accepted for publication 15 April 2019

Published 12 June 2019 Volume 2019:12 Pages 4517—4532


Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Ms Aruna Narula

Peer reviewer comments 2

Editor who approved publication: Dr Takuya Aoki

Fangfang Dai,1,2,* Gantao Chen,3,* Yanqing Wang,1 Li Zhang,1 Youmei Long,1 Mengqin Yuan,1 Dongyong Yang,1 Shiyi Liu,1 Yanxiang Cheng,1 Liping Zhang2

1Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, People’s Republic of China; 2Department of Obstetrics and Gynecology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, People’s Republic of China; 3Department of Gastroenterology, Third People’s Hospital of Xiantao in Hubei Province, Wuhan 430060, People’s Republic of China

*These authors contributed equally to this work

Background: Cervical carcinoma is one of the most common malignant gynecological tumors and is associated with high rates of morbidity and mortality. Early diagnosis and early treatment can reduce the mortality rate of cervical cancer. However, there is still no specific biomarkers for the diagnosis and detection of cervical cancer prognosis. Therefore, it is greatly urgent in searching biomarkers correlated with the diagnosis and prognosis of cervical cancer.
Results: The mRNA and microRNA expression profile datasets (GSE7803, GSE9750, GSE63514, and GSE30656) were downloaded from the Gene Expression Omnibus database (GEO). The three microarray datasets were integrated to one via integrated bioinformatics. Differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained by R software. The protein–protein interaction (PPI) networks of the DEGs were performed from the STRING database and further visualized by Cytoscape software. A total of 83 DEGs and 14 DEMs were screened from the microarray expression profile datasets. The miRNAs validated to be associated with cervical cancer were obtained using HMDD online website and the target genes of DEMs were identified using the miRWalk2.0 online database. ESR1, PPP1R3C, NSG1, and TMPRSS11D were the gene targets of hsa-miR-21; the targets of hsa-miR-16 were GYS2, ENDOU, and KLF4. These targets were all downregulated in cervical cancer. Finally, we verified the expression of those targets in cervical tissues from TCGA and GTEx databases and analyzed their relationship with survival of cervical cancer patients. In the end, the expression of key genes in cervical cancer tissues was verified via experiment method, we found KLF4 and ESR1 were downregulated in tumor tissues.
Conclusion: This study indicates that KLF4 and ESR1 are downregulated by the upregulated miR21 and miRNA16 in cervical cancer, respectively, using bioinformatics analysis, and the lower expression of KLF4 and ESR1 is closely related to the poor prognosis. They might be of clinical significance for the diagnosis and prognosis of cervical cancer, and provide effective targets for the treatment of cervical cancer.

Keywords: cervical cancer, integrated bioinformatics, differentially expressed genes, differentially expressed miRNAs, biomarkers

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