Abstract

Persistent high-risk human papillomavirus (hrHPV) infection is confirmed as the major cause of cervical cancer. According to the HPV infection status, cervical cancer could be generalized as following three subgroups: HPV-negative, pure HPV-infection, and HPV-integration. Currently, the impact of HPV status on cervical cancer prognosis remains under dispute. Therefore, we explored the potential correlation between HPV status and the clinical outcome of cervical cancer, by establishing a robust prognostic predicting model based on a cervical cancer cohort using The Cancer Genome Atlas (TCGA) database. We performed an iCluster algorithm incorporating DNA copy number variation, SNP, DNA methylation, mRNA expression, and miRNA expression profile together and classified the cohort into three clusters. According to defined clusters, we established an HPV score system by weighing resultant gene alterations through random forest and COX models. This prediction tool could help to identify cervical cancer prognosis through evaluating individual HPV infection status and subsequent genetic modification, which might provide insights into HPV-related gene driven cervical cancer treatment strategies, yet its predictive power and robustness need to be further verified with independent cohorts.

Highlights

  • Cervical cancer (CC) is one of the most perplexing women’s health problems and the commonest gynecological cancers, ranking fifth in the incidence rate and mortality rate of women (Bray et al, 2018)

  • Since it is known that HPV status alone (HPV-negative, pure HPVnegative cervical cancer (HPV-)infection, HPV-int) cannot effectively predict the prognosis of cervical cancer, this study aimed to develop a multi-factor model, called the “HPV-score,” according to virus infection status and resultant gene alterations according to defined clusters

  • It is generally believed that persistent infection of high-risk HPV is the main etiology of cervical cancer (Cohen et al, 2019), a variable proportion of tumors are reported to be negative for high-risk human papillomavirus (hrHPV) (Lei et al, 2018)

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Summary

Introduction

Cervical cancer (CC) is one of the most perplexing women’s health problems and the commonest gynecological cancers, ranking fifth in the incidence rate and mortality rate of women (Bray et al, 2018). Persistent high-risk HPV (hrHPV) infection has been identified as the main event leading to cervical cancer (Cohen et al, 2019). The cumulative lifetime infection rate for HPV can range from 60 to 70%, only a few infections persist and eventually lead to cancer (Qingqing et al, 2020). It is generally believed that the integration of the HPV genome into the host chromosome is the key genetic step in the pathogenesis of cervical carcinomas (Pett and Coleman, 2007). HPV integration usually involves the inactivation of viral E1 and E2 regions, resulting in the upregulation of oncogenes E6 and E7. E6 oncoprotein degrades p53, inhibits cancer cell apoptosis and viral DNA replication. E7 is known for suppressing RB1, which abrogates cell cycle arrest and stimulates proliferation (Hu and Ma, 2018)

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