Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

Abstract

Background: This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis.

Methods: Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein-protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets.

Results: A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained.

Conclusions: These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

Keywords: Bioinformatics analysis; Cervical cancer; Cervical intraepithelial neoplasia; Differentially expressed genes; Functional enrichments.

Fig. 1

Flowchart of the integrated analysis

Fig. 2

Correlations of all DEGs from three different expression profiling microarrays. A ~ C: GSE63514, D ~ F: GSE64217, G ~ I: GSE138080

Fig. 3

Screening DEGs via intersection analysis. Upregulated genes (A) and downregulated genes (B) in the N-CIN group. Upregulated genes (C) and downregulated genes (D) in the CIN-CC group. Upregulated genes (E) and downregulated genes (F) in the N-CC group

Fig. 4

GSEA snapshots of KEGG pathway enrichment analysis: DNA mismatch repair (N-CIN)

Fig. 5

GSEA snapshots of KEGG pathway enrichment analysis: Small cell lung cancer (CIN-CC)

Fig. 6

GSEA snapshots of KEGG pathway enrichment analysis: Cell cycle (N-CC)

Fig. 7

PPI network of the DEGs

Fig. 8

Functional enrichment in the PPI network of DEGs

Fig. 9

Discovering high scoring hub genes in cervical cancer development (cytoHubba). Topological analysis: (A) MCC, (B) DMNC, (C) MNC, (D) Degree, (E) EPC, (F) Bottleneck, (G) Eccentricity, (H) Closeness, (I) Radiality, (J) Betweenness, (K) Stress, and (I) Clustering Coefficient

Fig. 10

Expression boxplots of hub genes by GEPIA2. NUSAP1, TOP2A, KIF2C, NDC80, ASPM, KIF20A, CDK1, KIF11, BIRC5, MCM2, and CHEK1 were significantly upregulated in cervical cancer tissues compared with normal tissues (P < 0.01)

Fig. 11

The expression of MCM2, TOP2A, BLM, RMI2, EXO1, RFC4, PSCA, KNTC1, CDC45 and GINS2 was significantly related to the overall survival of patients with CESC

Fig. 12

The expression of CXCL8, TNFAIP6, CXCL5 and CDA was significantly related to the overall survival of patients with CESC

Fig. 13

Multiple gene comparison and dimensionality reduction for prognosis. A Interactive heat map of tissue-specific expression in different cancer types. B Dimensionality reduction

Fig. 14

Key prognostic molecular interaction network model and enrichment analysis. A PPI network of key prognostic genes (PPI highest confidence 0.900). B Biological processes. C Cellular components. D Molecular functions. E KEGG pathways. F Reactome pathways