Journal of Bioinformatics and Systems BiologyGenetic & Genomic MedicinePeer-reviewed journal article
A Systems Approach Identifies Key Regulators of HPV-Positive Cervical Cancer
Musalula SinkalaiD, Mildred ZuluiD, Panji NkhomaiD, Doris KafitaiD, Ephraim ZuluiD, Rabecca Tembo, Zifa Ngwira, Victor DakaiD, Sody Mweetwa MunsakaiD
Abstract
Cervical cancer has remained the most prevalent and lethal malignancy among women worldwide and accounted for over 250,000 deaths in 2019. Nearly ninety-five per cent of cervical cancer cases are associated with persistent infection with high-risk Human Papillomavirus (HPV), and seventy per cent of these are associated with viral integration in the host genome. HPV-infection imparts specific changes in the regulatory network of infected cancer cells that are of diagnostic, prognostic and importance. Here, we conducted a systems-level analysis of the regulatory network changes, and the associated regulatory proteins thereof, in HPV-positive cervical cancer. We applied functional pathway analysis to show that HPV-positive cancers are characterised by perturbations of numerous cellular processes, predominantly in those linked to the cell cycle, mitosis, cytokine and immune cell signalling. Using computational predictions, we revealed that HPV-positive cervical cancers are regulated by transcription factors including, SOX2, E2F, NANOG, OCT4, and MYC, which control various processes such as the renewal of cancer stem cells, and the proliferation and differentiation of tumour cells. Through the analysis of upstream regulatory kinases, we identified the mitogen-activated protein kinases; among others, MAPK1, MAPK3 and MAPK8, and the cyclin-dependent kinases; among others, CDK1, CDK2 and CDK4, as the key kinases that control the biological processes in HPV-positive cervical cancers. Taken together, we uncover a landscape of the key regulatory pathways and proteins in HPV-positive cervical cancers, all of which may provide attractive drug targets for future therapeutics.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 5
- FWCI
- 0.72
- Field percentile
- top 33%
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