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Identification of key candidate genes and small molecule drugs in cervical cancer by bioinformatics strategy

Authors Tang X, Xu YC, Lu L, Jiao Y, Liu JJ, Wang LL, Zhao HB

Received 29 April 2018

Accepted for publication 5 June 2018

Published 13 September 2018 Volume 2018:10 Pages 3533—3549


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Dr Antonella D'Anneo

Xin Tang,1 Yicong Xu,2,3 Lin Lu,2,3 Yang Jiao,2,3 Jianjun Liu,2,3 Linlin Wang,2,3 Hongbo Zhao2,3

1School of Rehabilitation, Kunming Medical University, Kunming, China; 2Institute of Molecular and Clinical Medicine, Kunming Medical University, Kunming, China; 3Yunnan Key Laboratory of Stem Cell and Regenerative Medicine, Kunming, China

Purpose: Cervical cancer (CC) is one of the most common malignant tumors among women. The present study aimed at integrating two expression profile datasets to identify critical genes and potential drugs in CC.
Materials and methods: Expression profiles, GSE7803 and GSE9750, were integrated using bioinformatics methods, including differentially expressed genes analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and protein–protein interaction (PPI) network construction. Subsequently, survival analysis was performed among the key genes using Gene Expression Profiling Interactive Analysis websites. Connectivity Map (CMap) was used to query potential drugs for CC.
Results: A total of 145 upregulated genes and 135 downregulated genes in CC were identified. The functional changes of these differentially expressed genes related to CC were mainly associated with cell cycle, DNA replication, p53 signaling pathway, and oocyte meiosis. A PPI network was identified by STRING with 220 nodes and 2,111 edges. Thirteen key genes were identified as the intersecting genes of the enrichment pathways and the top 20 nodes in PPI network. Survival analysis revealed that high mRNA expression of MCM2, PCNA, and RFC4 was significantly associated with longer overall survival, and the survival was significantly better in the low-expression RRM2 group. Moreover, CMap predicted nine small molecules as possible adjuvant drugs to treat CC.
Conclusion: Our study found key dysregulated genes involved in CC and potential drugs to combat it, which might provide insights into CC pathogenesis and might shed light on potential CC treatments.

Keywords: cervical cancer, bioinformatics, cell cycle, biomarker, drug

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