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. 2020 Jun;8(6):e1200.
doi: 10.1002/mgg3.1200. Epub 2020 Mar 17.

Identification of key genes and pathways of diagnosis and prognosis in cervical cancer by bioinformatics analysis

Hua-Ju Yang  1   2   3 , Jin-Min Xue  1   2   3 , Jie Li  1   2   3 , Ling-Hong Wan  1   2   3 , Yu-Xi Zhu  1   2   3
Affiliations

Identification of key genes and pathways of diagnosis and prognosis in cervical cancer by bioinformatics analysis

Hua-Ju Yang et al. Mol Genet Genomic Med. 2020 Jun.

Abstract

Background: Cervical cancer as one of the most common malignant tumors lead to bad prognosis among women. Some researches already focus on the carcinogenesis and pathogenesis of cervical cancer, but it is still necessary to identify more key genes and pathways.

Methods: Differentially expressed genes were identified by GEO2R from the gene expression omnibus (GEO) website, then gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyzed by DAVID. Meanwhile, protein-protein interaction network was constructed by STRING, and both key genes and modules were found in visualizing network through Cytoscape. Besides, GEPIA did the differential expression of key genes and survival analysis. Finally, the expression of genes related to prognosis was further explored by UNLCAN, oncomine, and the human protein atlas.

Results: Totally 57 differentially expressed genes were founded, not only enriched in G1/S transition of mitotic cell cycle, mitotic nuclear division, and cell division but also participated in cytokine-cytokine receptor interaction, toll-like receptor signaling pathway, and amoebiasis. Additionally, 12 hub genes and 3 key modules were screened in the Cytoscape visualization network. Further survival analysis showed that TYMS (OMIM accession number 188350), MCM2 (OMIM accession number 116945), HELLS (OMIM accession number 603946), TOP2A (OMIM accession number 126430), and CXCL8 (OMIM accession number 146930) were associated with the prognosis of cervical cancer.

Conclusion: This study aim to better understand the characteristics of some genes and signaling pathways about cervical cancer by bioinformatics, and could provide further research ideas to find new mechanism, more prognostic factors, and potential therapeutic targets for cervical cancer.

Keywords: bioinformatics analysis; cervical cancer; diagnosis and prognosis; differentially expressed genes.

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Conflict of interest statement

There are no conflicts of interest of the authors.

Figures

FIGURE 1
FIGURE 1
Venn diagram. Identification of the differentially expressed genes in the GSE63514, GSE6791, and GSE9750 gene expression profile datasets. The three datasets showed an overlap of 57 genes. DEGs, Differentially expressed genes
FIGURE 2
FIGURE 2
Gene ontology enrichment analysis. BP, Biological Process; CC, Cellular Components; MF, Molecular Function
FIGURE 3
FIGURE 3
PPI network of 57 DEGs was constructed in STRING. DEGs, Differentially expressed genes
FIGURE 4
FIGURE 4
PPI network, most significant module of DEGs and Interaction network of the hub genes. (a) PPI network of DEGs was constructed in Cytoscape, red nodes represents upregulated genes, and blue nodes represents downregulated genes. (b–d) There modules were obtained from PPI network using MCODE plug‐in in Cytoscape. (e) Hub genes and their coexpression genes were analyzed by cBioPortal. Nodes with bold black outline mean hub genes. Nodes with thin black outline the coexpression genes. DEGs, differentially expressed genes; MCODE, molecular complex detection; PPI, protein–protein interaction
FIGURE 5
FIGURE 5
Expression boxplots of key genes using GEPIA website. ECT2, WDHD1, TYMS, FANCI, CXCL8, MCM2, HELLS, DTL, CEP55, CDK1, AURKA, and TOP2A were significantly upregulated in cervical cancer compared with normal tissues (p < .01). GEPIA, Gene Expression Profiling Interactive Analysis; ECT2: NC_000003.12; WDHD1: NC_000014.9; TYMS: NC_000018.10; FANCI: NC_000015.10; CXCL8: NC_00 0004.12; MCM2: NC_000003.12; HELLS: NC_000010.11; DTL:NC_000001.11; CEP55: NC_000010.11; CDK1: NC_000010.11; AURKA:NC_000020.11
FIGURE 6
FIGURE 6
Survival analysis. Expression level of CXCL8, HELLS, MCM2, TYMS, and TOP2A was significantly related to the overall survival of patients with cervical squamous cancer (p < .05)
FIGURE 7
FIGURE 7
Expression profiles in human cancers, analysis comparison, and protein expression of CXCL8. (a) Expression profiles in human cancers of CXCL8 were analyzed by UALCAN. (b) The expression of the CXCL8 was compared during the four datasets. Normal tissues as control group. Values above the average were deemed to be overexpressed hub genes (red). (c) Immunohistochemistry results from human protein atlas database showed CXCL8 protein was upregulated
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