The Evolving Biomarkers in Cervical Cancer: Integrating Multi-Omic and Emerging Approaches for Precision Cervical Cancer Diagnostics

Abstract

Cervical cancer remains a major global health burden despite being one of the most preventable malignancies. The transition from conventional morphology-based screening to human papillomavirus (HPV)-based testing has improved detection sensitivity; however, HPV positivity alone cannot reliably distinguish transient infection from biologically significant transformation. Cervical carcinogenesis is a multistep process involving persistent high-risk HPV infection, viral oncogene activity, host-cell cycle deregulation, epigenetic modification, genomic evolution, immune modulation and metabolic reprogramming. This review examines the evolving landscape of cervical cancer biomarkers and the transition from individual molecular markers toward integrated multi-omic and emerging approaches for improved detection, risk stratification, disease characterization and precision management.
HPV genotyping and viral persistence provide the initial framework for molecular risk assessment, while E6/E7 mRNA expression and p16/Ki-67 dual staining offer insights into transforming infection and host-cell deregulation. DNA methylation, non-coding RNAs, and genomic, transcriptomic, proteomic and metabolomic profiling further reveal the biological heterogeneity and functional consequences of cervical carcinogenesis. Immune biomarkers provide information on the tumour microenvironment and therapeutic responsiveness, whereas liquid-biopsy approaches, including circulating HPV DNA, circulating tumour DNA and extracellular vesicles, offer opportunities for minimally invasive disease monitoring.
Collectively, these biomarker classes interrogate complementary dimensions of cervical cancer biology. No single biomarker can adequately capture its biological complexity or fulfil every clinical requirement. The future of cervical cancer diagnostics and management therefore lies in integrated biomarker architectures combining viral, molecular, multi-omic and circulating tumour-derived information to improve diagnostic precision, risk stratification, longitudinal monitoring and personalized clinical management.