Potential biomarkers and therapeutic targets in cervical cancer: Insights from the meta-analysis of transcriptomics data within network biomedicine perspective

Abstract

The malignant neoplasm of the cervix, cervical cancer, has effects on the reproductive tract. Although infection with oncogenic human papillomavirus is essential for cervical cancer development, it alone is insufficient to explain the development of cervical cancer. Therefore, other risk factors such as host genetic factors should be identified, and their importance in cervical cancer induction should be determined. Although gene expression profiling studies in the last decade have made significant molecular findings about cervical cancer, adequate screening and effective treatment strategies have yet to be achieved. In the current study, meta-analysis was performed on cervical cancer-associated transcriptome data and reporter biomolecules were identified at RNA (mRNA, miRNA), protein (receptor, transcription factor, etc.), and metabolite levels by the integration of gene expression profiles with genome-scale biomolecular networks. This approach revealed already-known biomarkers, tumor suppressors and oncogenes in cervical cancer as well as various receptors (e.g. ephrin receptors EPHA4, EPHA5, and EPHB2; endothelin receptors EDNRA and EDNRB; nuclear receptors NCOA3, NR2C1, and NR2C2), miRNAs (e.g., miR-192-5p, miR-193b-3p, and miR-215-5p), transcription factors (particularly E2F4, ETS1, and CUTL1), other proteins (e.g., KAT2B, PARP1, CDK1, GSK3B, WNK1, and CRYAB), and metabolites (particularly, arachidonic acids) as novel biomarker candidates and potential therapeutic targets. The differential expression profiles of all reporter biomolecules were cross-validated in independent RNA-Seq and miRNA-Seq datasets, and the prognostic power of several reporter biomolecules, including KAT2B, PCNA, CD86, miR-192-5p and miR-215-5p was also demonstrated. In this study, we reported valuable data for further experimental and clinical efforts, because the proposed biomolecules have significant potential as systems biomarkers for screening or therapeutic purposes in cervical carcinoma.

Fig 1. Meta-analysis of the transcriptome datasets associated with cervical cancer.

(A) Venn diagram representing the distribution of the down-regulated transcripts in the datasets, where 113 transcripts were mutually down-regulated in all datasets (i.e., down-regulated core genes). (B) Venn diagram representing the distribution of the up-regulated transcripts in datasets, where 199 transcripts were mutually up-regulated in all datasets (i.e., up-regulated core genes). (C) The clustering of the proteins encoded by the down-regulated core genes of cervical cancer according to their molecular activities. (D) The clustering of the proteins encoded by the up-regulated core genes of cervical cancer according to their molecular activities (DEGs: differentially expressed genes).The gene set overrepresentation analysis of the core genes based on the annotations stored in KEGG and GAD databases resulted in (particularly cancers), p53 signaling, and pyrimidine metabolism (Fig 2). Periodontitis, hypospadias, and arterial blood pressure pathways were down-regulated, whereas up-regulated core genes were enriched in those associated with the cell cycle, DNA replication, oocyte meiosis, several cancers (colorectal, bladder, breast, ovarian, lung, stomach, and prostate), autoimmune disorders (including rheumatoid arthritis and systemic lupus erythematosus), Alzheimer's disease, p53 signaling pathway, and pyrimidine metabolism.

Fig 2. Gene set enrichment analysis of the core genes of cervical cancer.

(A) Significantly enriched disease pathways based on the gene-disease associations presented by the Genetic Association Database (GAD). (B) Significantly enriched biological processes based on the gene-process associations of the Kyoto Encyclopedia of Genes and Genome (KEGG) database. The white bar represents down-regulation of the pathway or process, whereas the black bars represents up-regulation.

Fig 3. Protein-protein interaction (PPI) sub-networks in cervical cancer.

(A) PPI sub-network around the proteins encoded by the down-regulated core genes. (B) PPI sub-network around the proteins encoded by the up-regulated core genes. (C) Hub proteins of the down-regulated PPI sub-network and their topological metrics. (D) Hub proteins of the up-regulated PPI sub-network and their topological metrics.

Fig 4. The cross-validation results for reporter biomolecules.

Box-plots representing the expression levels of (A) KAT2B, (B) PCNA, and (C) CD86 between the low- and high-risk groups. The Kaplan-Meier curves demonstrating the prognostic power of (D) KAT2B, (E) PCNA, (F) CD86, (G) miR-192-5p, and (H) miR-215-5p. The total size of each group is shown at the top right corner, and the number of censored samples is marked with +.

Fig 5. A conceptual summary of reporter biomolecules (receptors, hub proteins, transcription factors, and metabolites) highlighted as potential molecular signatures in cervical cancer.