Screening of Therapeutic Candidate Genes of Quercetin for Cervical Cancer and Analysis of Their Regulatory Network
Authors Li Y, Kou J, Wu T, Zheng P, Chao X
Received 20 October 2020
Accepted for publication 14 January 2021
Published 5 February 2021 Volume 2021:14 Pages 857—866
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Prof. Dr. Takuya Aoki
Yuanyuan Li,1,2 Jiushe Kou,3 Tao Wu,4 Pengsheng Zheng,1 Xu Chao2
1Department of Reproductive Medicine, The First Affiliated Hospital, College of Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, People’s Republic of China; 2Scientific Research Department, The Second Affiliated Hospital, Shaanxi University of Chinese Medicine, Xixian New Area, Shaanxi, 712000, People’s Republic of China; 3Pain Department, The Second Affiliated Hospital, Shaanxi University of Chinese Medicine, Xixian New Area, Shaanxi, 712000, People’s Republic of China; 4College of Acupuncture and Massage, Shaanxi University of Chinese Medicine, Xixian New Area, Shaanxi, 712046, People’s Republic of China
Correspondence: Xu Chao
Scientific Research Department, The Second Affiliated Hospital of Shaanxi University of Chinese Medicine, 831 Longtaiguan Road, Xixian New Area, Shaanxi Province, 712000, People’s Republic of China
Department of Reproductive Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, 76 West Yanta Road, Xi’an, Shaanxi Province, 710061, People’s Republic of China
Purpose: To explore the therapeutic targets and regulatory mechanisms of the antitumor drug quercetin in the treatment of cervical cancer.
Methods: Cervical cancer (HeLa) cells were treated with quercetin and subjected to RNA sequencing using the BGISEQ-500 platform. By combined analysis of GEO database and RNA-seq results, the differentially expressed genes (DEGs) (namely, the genes in the GEO database that were upregulated/downregulated in cervical cancer compared with normal cervix and downregulated/upregulated after quercetin treatment) were identified. Functional enrichment and protein–protein interaction analyses were carried out for the DEGs. The candidate genes were identified using the Gentiscape2.2 and MCODE plug-ins for Cytoscape software, and the upstream miRNAs, lncRNAs, and circRNAs of the candidate genes were predicted using the online tools MirDIP, TarBase, and ENCORI. Finally, the regulatory network was constructed using Cytoscape software.
Results: Quercetin significantly inhibited the proliferation of cervical cancer cells. The combined analyses of the GEO database and RNA-seq results obtained 74 DEGs, and the functional enrichment analysis of the DEGs identified 861 biological processes, 32 cellular components, 50 molecular functions, and 56 KEGG pathways. Five therapeutic candidate genes, including EGFR, JUN, AR, CD44, and MUC1, were selected, and 10 miRNAs, 1 lncRNA, and 71 circRNAs upstream of these genes were identified. Finally, a lncRNA/circRNA-miRNA-mRNA-pathway regulatory network was constructed.
Conclusion: In this study, data mining was used to identify candidate genes and their regulatory network for the treatment of cervical cancer to provide a theoretical basis for targeted therapy of cervical cancer.
Keywords: quercetin, GEO, RNA-seq analysis, DEGs, candidate genes, regulatory network, cervical cancer
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