ArticleMicrobiology spectrum2022
Deep Sequencing of HPV16 E6 Region Reveals Unique Mutation Pattern of HPV16 and Predicts Cervical Cancer.
Article in Microbiology spectrum, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 32 citations in OpenAlex.
- Article
- A Novel Method for Predicting Oncogenic Types of Human Papillomavirus.Diagnostics (Basel, Switzerland) · 2025Article
- An automatic NGS feature extraction algorithm for predicting EBV-associated nasopharyngeal cancer and high-risk mutation.Virology journal · 2025Article
- Alterations in genomic features and the tumour immune microenvironment predict immunotherapy outcomes in advanced biliary tract cancer patients.British journal of cancer · 2025Article
- HPV16 Phylogenetic Variants in Anogenital and Head and Neck Cancers: State of the Art and Perspectives.Viruses · 2024Review
- Phylogenetic analysis and antigenic epitope prediction for E6 and E7 of Alpha-papillomavirus 9 in Taizhou, China.BMC genomics · 2024Article
- Lightweight Low-Rank Adaptation Vision Transformer Framework for Cervical Cancer Detection and Cervix Type Classification.Bioengineering (Basel, Switzerland) · 2024Article
- Genetic variation of E6 and E7 genes of human papillomavirus type 16 from central China.Virology journal · 2023Article
- A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples.Frontiers in microbiology · 2023Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The genetic diversity of human papillomavirus (HPV) 16 within cervical cells and tissue is usually associated with persistent virus infection and precancerous lesions. To explore the HPV16 mutation patterns contributing to the cervical cancer (CC) progression, a total of 199 DNA samples from HPV16-positive cervical specimens were collected and divided into high-grade squamous intraepithelial lesion (HSIL) and the non-HSIL(NHSIL) groups. The HPV16 E6 region (nt 7125-7566) was sequenced using next-generation sequencing. Based on HPV16 E6 amino acid mutation features selected by Lasso algorithm, four machine learning approaches were used to establish HSIL prediction models. The receiver operating characteristic was used to evaluate the model performance in both training and validation cohorts. Western blot was used to detect the degradation of p53 by the E6 variants. Based on the 13 significant mutation features, the logistic regression (LR) model demonstrated the best predictive performance in the training cohort (AUC = 0.944, 95% CI: 0.913-0.976), and also achieved a high discriminative ability in the independent validation cohort (AUC = 0.802, 95% CI: 0.601-1.000). Among these features, the E6 D32E and H85Y variants have higher ability to degrade p53 compared to the E6 wildtype (
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