Abstract
Cervical cancer is a complex disease caused by both genetic susceptibility and environmental factors. Inherited genomic variance, high-risk human papilloma virus (HPV) infection/integration, genome methylation and somatic mutation could all constitute one machine learning model, laying the ground for molecular classification and the precision medicine of cervical cancer. Therefore, for cervical screening, next generation sequencing (NGS)-based HPV DNA and other molecular tests as well as dynamic machine learning models would accurately predict patients with potential to develop the cancer, thereby reducing the burden of repeated screening. Meantime, genome-editing tools targeting HPV would emerge as the next generation gene therapy for HPV-related cervical lesions. In this article, we review the substantial progress on molecular mechanism of cervical cancer development and suggest the future for precise prevention and early treatment of cervical cancer.