Tingting Zhang

Characterization of cervical microbiota in cervical intraepithelial neoplasia and cervical cancer using low-coverage whole genome sequencing

ABSTRACT This study characterized compositional shifts in cervical microbiota across disease stages from benign conditions through cervical intraepithelial neoplasia (CIN) to cervical cancer (CC) and investigated interactions with high-risk HPV (hr-HPV) infection using species-resolution profiling to identify severity-associated biomarkers. Cervical exfoliated epithelial cells from 50 patients (eight normal/CIN1, 15 CIN2, 19 CIN3, 5 CC) were analyzed using Low-Coverage Whole Genome Sequencing combined with the Ultrasensitive Chromosomal Aneuploidy Detector (UCAD), a technology featuring a two-step normalization framework that systematically converts raw microbial reads into statistically validated abundance deviations. This enables quantitative identification of pathologically relevant microbiota through cohort-wide Z-score benchmarking. Microbial diversity, differential biomarkers, and HPV-microbiota interactions were assessed using Kruskal-Wallis tests, LEfSe, and Random Forest modeling. Results revealed progressive Lactobacillus depletion (e.g., Lactobacillus crispatus : 32.9% in ≤CIN2 vs. 8.8% in CC) and enrichment of pathobionts like Gardnerella and Bacteroides with lesion severity. CC exhibited the highest microbial diversity (Shannon index: CC vs. CIN2, P =0.045), dominated by HPV16 (11.8%), Bacteroides (55.4%), and Porphyromonas (25.2%). LEfSe identified HPV16, HPV35, Parvimonas micra , and Anaerococcus lactolyticus as CC-specific markers, while Random Forest highlighted Mobiluncus curtisii (importance score=2.0) and HPV16 as key discriminators. CC microbiota showed significant Bacteroidetes enrichment (82% at class level) and reduced Firmicutes abundance. These findings suggest carcinogenesis-associated microbial restructuring, marked by Lactobacillus loss, anaerobic proliferation, and HPV16/35 dominance, potentially modulating disease progression. The identified signatures may inform diagnostic development and microbiome-targeted therapies. IMPORTANCE Our study pioneers an LC-WGS/UCAD approach to characterize microbial across the spectrum from benign lesions through precancerous cervical intraepithelial neoplasia to invasive cervical carcinoma. By identifying lesion-specific microbial biomarkers and HPV-associated cofactors, this work advances mechanistic understanding of microbiota-driven oncogenesis and informs future strategies for microbiota-targeted cervical cancer prevention.

Screening of cervical intraepithelial neoplasia based on multiple features extracted from multi-electrode bioimpedance spectroscopy

Abstract Objective. Bioimpedance spectroscopy (BIS) has emerged as a promising technique for screening cervical intraepithelial neoplasia (CIN) since the electrical properties vary with the pathological status of cervical tissues. In this study, we aimed to evaluate the ability of CIN screening using multiple features extracted from BIS measurements collected with a multi-electrode BIS probe. Approach. This study enrolled 161 patients with gynecological diseases, including 44 with and 117 without cervical dysplasia. Upon the histological diagnosis, the samples were classified as normal, CIN I, and CIN II with p16 positive (p16(+))/CIN III. Complex impedance spectra of in vitro cervical conization tissues were measured using the BIS probe. A Cole–Cole plot was generated from each patient’s data measured on the conized cervix, and various features were extracted. Receiver operating characteristic (ROC) curves were generated, and the area under each ROC curve (AUC) was calculated. Main results. As a result, fifteen features from Cole–Cole plots differed significantly ( p < 0.01) between normal cervices and CIN. The AUCs based on multiple features, as determined by multivariable logistic regression, were 0.93 for normal cervix vs CIN I, 0.99 for normal cervix vs CIN II p16(+)/CIN III, and 0.94 for normal cervix vs CIN. These AUCs were improved by 14.8%, 7.6%, and 8.0%, respectively, compared with the results based on features extracted from only the real part of the impedance spectra. Significance. In conclusion, CIN can be accurately diagnosed using multiple features extracted from the impedance spectrum of in vitro cervical samples. Particularly, this method was highly accurate in classifying CIN II p16(+)/CIN III, which has a higher risk of progression to cancer.

Trends in bacterial resistance among perioperative infections in patients with primary ovarian cancer: A retrospective 20-year study at an affiliated hospital in South China

Background We aimed to analyze the epidemiological and drug-resistance trends among bacterial cultures from perioperative infections in patients with primary ovarian cancer. Methods Medical and bacteriological records for patients with ovarian cancer patients who developed perioperative infections after primary cytoreductive surgery from 1999 to 2018 were reviewed retrospectively. Results The incidence of perioperative infections and the culture-positive percentage among patients in the first 10 years were 20.2% and 29.3%, respectively, and the equivalent rates in the second 10 years were 18.0% and 33.5%. The most commonly isolated pathogens in both year-groups were Escherichia coli and Enterococcus spp., but the respective percentages differed between the groups. Some strains of Staphylococcus aureus and Enterococcus spp. in the second 10-year group were resistant to linezolid and vancomycin, and ciprofloxacin resistance among Gram-negative bacteria isolates also increased in this group. However, resistance of Gram-negative bacteria to imipenem and meropenem was low among in both groups. Conclusion The pathogen distribution in perioperative infections in patients with primary ovarian cancer undergoing cytoreductive changed slightly from 1999 to 2018, and the antibiotic resistance of the main isolated pathogens increased. These results indicate the importance of periodic bacterial surveillance of surgical infections in these patients.

Obesity in children and adolescents and the risk of ovarian cancer: A systematic review and dose‒response meta-analysis

Objective The relationship between obesity in children and adolescents and the risk of ovarian cancer remains controversial. The aim of this meta-analysis was to explore the exact shape of this relationship. Methods We conducted dose‒response meta-analyses of cohort and case‒control studies, including published studies derived from searches in the PubMed, Embase, Web of Science and Cochrane Library databases until October 2022. Pooled effect size estimates are expressed as relative risks (RRs) or odds ratios (ORs) with 95% confidence intervals (CIs) and were evaluated by fixed-effect models. A nonlinear dose‒response meta-analysis was performed by using a restricted cubic spline model. Results After screening 4215 publications, 10 studies were included in the present meta-analysis. Overall analyses revealed statistically significant associations of obesity in children and adolescents with ovarian cancer (adjusted RR = 1.19, 95% CI: 1.11 to 1.28, P < 0.001). Moreover, the association was consistently significant in most subgroup analyses, for example, using geographic stratification, the results remained stable both in the Americas(RR = 1.11; 95% CI: 1.01 to 1.21; P = 0.022) and Europe (RR = 1.46; 95% CI: 1.21 to 1.77; P<0.001). For the dose‒response analyses, the risk of ovarian cancer increased with the degree of obesity, and the trend increased rapidly when body mass index (BMI) was over 25.95 kg/m2. Conclusion Our findings indicate that obesity in children and adolescents is a risk factor for ovarian cancer, and the risk increases with increasing BMI.