Integrative molecular diagnostics for HPV-driven cervical carcinogenesis: a translational review of mechanisms and multimodal risk stratification

Abstract

Despite effective prophylactic vaccines, high-risk human papillomavirus (HPV) infection remains a leading cause of cervical cancer, particularly in regions with limited screening access. Conventional methods, including cytology and HPV DNA testing, lack specificity for identifying lesions at high risk of progression, resulting in overtreatment. This narrative translational review synthesizes current evidence on integrating complementary molecular modalities to improve risk stratification along the biological continuum from HPV infection to malignancy. Persistent high-risk HPV drives carcinogenesis via viral oncoprotein activity, host DNA damage response (DDR) dysregulation, and epigenetic remodeling, yet no single biomarker captures this complexity. Quantitative PCR sensitively detects viral DNA load but not transforming activity; immunocytochemistry for p16INK4a/Ki-67 reflects oncogenic pathway disruption; gene expression and methylation profiling reveal downstream transcriptional changes; and emerging DDR assays (e.g., γH2AX) indicate upstream genomic stress. Multi-omics studies suggest progression risk is best inferred from co-occurring viral activity, host stress responses, and phenotypic dysregulation. A multimodal approach combining viral detection, protein-level transformation markers, and selected molecular signatures, provides a biologically grounded framework to distinguish transient infection from high-risk precancer. Literature was identified through targeted searches of PubMed, Scopus, and Web of Science, emphasizing peer-reviewed studies, meta-analyses, translational investigations, and clinically validated diagnostic platforms published in English. This review proposes a structured, integrative model for cervical cancer risk assessment, offering a tiered, context-appropriate strategy that correlates diagnostic modalities with stages of HPV-mediated transformation. This framework aims to enhance clinical precision, prioritize high-risk individuals, and reduce overtreatment.