Hongning Cai

Prevotella as the hub of the cervicovaginal microbiota affects the occurrence of persistent human papillomavirus infection and cervical lesions in women of childbearing age via host NF‐κB/C‐myc

AbstractThere is evidence that coinfection of cervicovaginal high‐risk human papillomavirus (HR‐HPV) and bacteria is common in women of childbearing age. However, the relationship between bacterial vaginosis (BV) and persistent HR‐HPV infection in women of childbearing age and the underlying mechanisms remain unclear. In this study, we determined whether BV affects persistent HR‐HPV infection in women aged 20–45 years and explored the possible mechanisms of their interactions. From January 1 to April 30, 2020, we recruited women aged 20–45 years with and without BV at a ratio of 1:2 from Fujian Maternity and Child Health Hospital. All women were followed up at 0, 12, and 24 months. A BV assay, HR‐HPV genotyping and cervical cytology were performed at each follow‐up. At 0 months, additional vaginal secretions and cervical exfoliated cells were collected for 16S ribosomal RNA sequencing, bacterial metabolite determination, and POU5F1B, C‐myc, TLR4, NF‐κB, and hTERT quantification. A total of 920 women were included. The abundance of Prevotella (p = 0.016) and Gardnerella (p = 0.027) were higher, whereas the abundance of Lactobacillus was lower (p = 0.001) in women with persistent HR‐HPV infection and high‐grade squamous intraepithelial lesions (HSIL). The abundance of Prevotella (p = 0.025) and Gardnerella (p = 0.018) increased in the vaginas of women with persistent HPV16 infection, whereas only the abundance of Prevotella (p = 0.026) was increased in women with persistent HPV18 infection. The abundance of Prevotella in the vagina was significantly positively correlated with the expression levels of TLR4, NF‐κB, C‐myc, and hTERT in host cervical cells (p < 0.05). Our findings suggest that overgrowth of Prevotella in the vagina may influence the occurrence of persistent HR‐HPV infection‐related cervical lesions through host NF‐κB and C‐myc signaling.

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

Abstract Background Cervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden. Objective This study aimed to identify subgroups with differential cervical precancer or cancer risks using machine learning, validate subgroup predictions across datasets, and propose a computational phenomapping strategy to enhance global CCP efforts. Methods We explored the data-driven CCP subgroups by applying unsupervised machine learning to a deeply phenotyped, population-based discovery cohort. We extracted CCP-specific risks of cervical intraepithelial neoplasia (CIN) and cervical cancer through weighted logistic regression analyses providing odds ratio (OR) estimates and 95% CIs. We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. Results This study included 551,934 women (median age, 49 years) in the discovery cohort and 47,130 women (median age, 37 years) in the external cohort. Phenotyping identified 5 CCP subgroups, with CCP4 showing the highest carcinoma prevalence. CCP2–4 had significantly higher risks of CIN2+ (CCP2: OR 2.07 [95% CI: 2.03‐2.12], CCP3: 3.88 [3.78‐3.97], and CCP4: 4.47 [4.33‐4.63]) and CIN3+ (CCP2: 2.10 [2.05‐2.14], CCP3: 3.92 [3.82‐4.02], and CCP4: 4.45 [4.31‐4.61]) compared to CCP1 (P<.001), consistent with the direction of results observed in the external cohort. The proposed triple strategy was validated as clinically relevant, prioritizing high-risk subgroups (CCP3-4) for colposcopies and scaling human papillomavirus screening for CCP1-2. Conclusions This study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy.