BinHua Dong

Microbiome-driven resistance in cervical cancer therapy: from mechanistic dissection to clinical translation

Abstract Cervical cancer remains a major global health burden. Despite standard-of-care therapies, 30–50% of locally advanced-stage patients develop treatment resistance, leading to recurrence and mortality. While tumour-intrinsic mechanisms (e.g., DNA methylation, cancer-associated fibroblasts) explain only partial resistance heterogeneity, emerging evidence identifies the microbiome as a critical modulator of therapeutic efficacy. This review synthesizes recent advances demonstrating that vaginal microbial dysbiosis, characterized by Lactobacillus iners enrichment and L. crispatus depletion, drives resistance through lactate-mediated metabolic rewiring, immune checkpoint stabilization and drug metabolism alteration. Longitudinal studies reveal dynamic microbiome trajectories during therapy, with geographic variations (notably HIV co-infection in sub-Saharan Africa) further modulating treatment responses. We critically evaluate microbiome-based interventions, from probiotics to engineered bacteria, including synthetic biology-driven precision microbiome therapies, and establishing standardized multi-centre trial protocols. Bridging mechanistic insights with clinical application represents a paradigm shift towards microbiome-informed cervical cancer management.

Concordance analysis between Human Papillomavirus genotyping assay and PCR-reverse spot hybridization for the detection of Human Papillomavirus infection

Introduction. Human papillomavirus (HPV), the predominant viral infection affecting the anogenital tract, is closely linked to the development of intraepithelial neoplasia and malignancies in the cervix and other anal regions. Currently, 15 high-risk HPVs (HR-HPVs) and 3 potential HR-HPV types have been recognized as contributors to cervical cancer. Consequently, it is imperative to conduct HR-HPV screening using suitable tests in order to identify precancerous lesions and prevent the development of cancer. Hypothesis. The Human papillomavirus genotyping (type 23) detection kit (PCR-reverse point hybridization method) is reliable for clinical testing. Aims. The objective of this research was to assess the concordance between the Human papillomavirus genotyping (type 23) detection kit (PCR-reverse point hybridization method) and the approved HPV test. Methodology. A sample of 781 women who received HPV genotype testing during cervical cancer screening consultations at the Department of Gynecology, Fujian Maternity and Child Health Hospital, was examined. Thirty-two cases were excluded for lacking histological results or showing signs of vulvar intraepithelial rheology, leaving 749 valid histological samples. Only 181 valid pathological specimens were available after excluding those without cervical biopsy or total hysterectomy. The consistency of the test results was assessed using the kappa (K) statistic, with CIN2+ serving as the benchmark for determining sensitivity and specificity. Statistical significance was defined as differences with P values <0.05 (two-tailed). Results. The human papillomavirus genotyping (type 23) detection kit (PCR-reverse point hybridization method) and the approved HPV test demonstrated a high level of concordance with a total kappa value of 0.969 (P<0.05). The overall concordance rate was found to be 98.720%. Using cervical intraepithelial neoplasia grade 2+ (CIN2+) as the reference standard, the human papillomavirus genotyping (type 23) detection kit (PCR-reverse point hybridization method) and the approved HPV test both showed 89.655% sensitivity (P>0.05), while the specificity values were 40.590 and 40.309%, respectively (P>0.05). Conclusion. The evaluated HPV test demonstrates comparable performance to other assays available during the same time frame and exhibits strong concordance in detecting the majority of HPV genotypes.

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.

HPV positivity status in males is related to the acquisition of HPV infection in females in heterosexual couples

Abstract Purpose Few studies have focused on the impact of human papillomavirus (HPV) positivity in male partners on female HPV infection and cervical lesions. The purpose of this study was to evaluate the impact of the HPV infection status of husbands on wives’ cervical HPV infection and lesions. Methods We surveyed 251 monogamous couples who attended the outpatient department of Fujian Maternity and Child Health Hospital from 2013 to 2021. HPV type analysis was performed on exfoliated cells of the females’ cervix and males’ urethra by the PCR-reverse dot blot method. We analyzed the prevalence and consistency of HPV types in 251 couples. Subsequently, the risk of HPV infection in females with HPV-positive male partners was analyzed. SPSS version 26 (IBM, Chicago, USA) was used for statistical analysis. Results In 251 couples, the most commonly detected high-risk HPV (HR-HPV) genotypes were 52, 51, 16, and 58 for males and 16, 52, 18, and 58 for females. Wives with HPV-positive husbands had higher infection rates for most HR-HPV genotypes. HR-HPV positivity in husbands was a risk factor for the development of cervical lesions in wives (OR = 2.250, P = 0.014). Both single-type (OR = 2.085, P = 0.040) and multiple-type (OR = 2.751, P = 0.036) infection in husbands will contributed to an increased risk of non-HR-HPV infection and cervical lesions in wives. Conclusion Husbands’ HPV positivity increases the burden of non-HR-HPV infection and increases the risk of cervical lesions developing in wives. It is hoped to provide a reference value for cervical cancer prevention in females and HPV vaccination in males.

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.