Department of Laboratory Medicine, Shanghai Eastern Hepatobiliary Surgery Hospitalgrid.414375.0, Shanghai, China.
Clinical Laboratory Medicine Center, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Microbiol Spectr. 2022 Aug 31;10(4):e0140122. doi: 10.1128/spectrum.01401-22. Epub 2022 Jun 23.
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 ( < 0.05). In conclusion, our study provides evidence for the first time that HPV16 E6 sequences contain vital mutation features in predicting HSIL. Moreover, the D32E and H85Y variants of E6 exhibited a significantly higher ability to degrade p53, which may play a vital role in the development of CC. The study provides evidence for the first time that HPV16 E6 sequences contain vital mutation features in predicting the high-grade squamous intraepithelial lesion and can reduce even more unneeded colposcopies without a loss of sensitivity to detect cervical cancer. Moreover, the D32E and H85Y variants of E6 exhibited a significantly higher ability to degrade p53, which may play a vital role in the development of cervical cancer.
人类乳头瘤病毒（HPV）16 型在宫颈细胞和组织中的遗传多样性通常与持续性病毒感染和癌前病变有关。为了探索导致宫颈癌（CC）进展的 HPV16 突变模式，共收集了 199 份 HPV16 阳性宫颈标本的 DNA 样本，并将其分为高级别鳞状上皮内病变（HSIL）和非 HSIL（NHSIL）组。使用下一代测序对 HPV16 E6 区（nt7125-7566）进行测序。基于 Lasso 算法选择的 HPV16 E6 氨基酸突变特征，使用四种机器学习方法建立了 HSIL 预测模型。使用接收者操作特征曲线（ROC）在训练和验证队列中评估模型性能。使用 Western blot 检测 E6 变异体对 p53 的降解。基于 13 个显著突变特征，逻辑回归（LR）模型在训练队列中表现出最佳的预测性能（AUC=0.944，95%CI：0.913-0.976），在独立验证队列中也具有较高的鉴别能力（AUC=0.802，95%CI：0.601-1.000）。在这些特征中，E6 D32E 和 H85Y 变体对 p53 的降解能力明显高于 E6 野生型（<0.05）。综上所述，本研究首次提供了证据，表明 HPV16 E6 序列中包含预测 HSIL 的重要突变特征。此外，E6 的 D32E 和 H85Y 变体对 p53 的降解能力明显更强，这可能在宫颈癌的发展中起着重要作用。本研究首次提供了证据，表明 HPV16 E6 序列中包含预测 HSIL 的重要突变特征，在不降低检测宫颈癌敏感性的情况下，还可以减少更多不必要的阴道镜检查。此外，E6 的 D32E 和 H85Y 变体对 p53 的降解能力明显更强，这可能在宫颈癌的发展中起着重要作用。
