ReviewCellular and molecular life sciences : CMLS2026
From HPV infection to cervical precancer: Molecular mechanisms, immune microenvironment and emerging technologies.
Review in Cellular and molecular life sciences : CMLS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
5 authors.
Funding
Abstract
Cervical cancer remains a leading cause of cancer-related mortality among women globally, with persistent high-risk human papillomavirus (HPV) infection as the primary etiological factor. Cervical precancerous lesions, including high-grade squamous intraepithelial lesions and adenocarcinoma in situ, represent the critical window for intervention prior to invasive cancer development. However, additional factors such as immunosuppression, smoking, and microbiome composition also play crucial roles in the development of cervical precancerous lesions. While HPV vaccination, screening, and treatment have reduced cancer incidence, these must be complemented by personalized approaches that target the molecular mechanisms driving carcinogenesis. Key dysregulated tumor suppressors (e.g., p16) and signaling pathways (PI3K/AKT/mTOR, Wnt/β-catenin) highlight the need for advanced therapeutic strategies. The immune microenvironment also plays a critical role in cancer progression, suggesting potential for immunotherapy. Promising biomarkers like p16, Ki67, and microRNAs require integration with technologies such as single-cell RNA sequencing and spatial transcriptomics for deeper insights into tumor heterogeneity and microenvironment dynamics. Additionally, while deep learning models hold promise in improving diagnostic accuracy for cervical intraepithelial neoplasia (CIN), challenges such as data variability and interpretability need addressing. In conclusion, incorporating genomics, transcriptomics, immunotherapy, and artificial intelligence will enable more personalized and effective interventions, ultimately reducing the global burden of cervical cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.