Huifeng Xue

Integrative Analysis of Shared Pathogenic Genes and Potential Mechanisms in Gardnerella vaginalis and Persistent HPV16 Infection

Bacterial vaginosis, often accompanied by Gardnerella vaginalis (GV) overgrowth, is associated with persistent high‐risk human papillomavirus (HR‐HPV) infection, particularly HPV16. This study integrated transcriptomic data from in vitro GV infection experiments and a GEO dataset (GSE75132) of HPV16 persistence to elucidate shared pathogenic mechanisms. Differential expression analysis identified 4115 genes modulated by GV infection and 861 by HPV16 persistence, with 74 common differentially expressed genes (DEGs) displaying consistent trends. Enrichment analyses revealed that these DEGs participate in metabolic pathways, immune defense, host–pathogen interactions, and carcinogenesis. Protein–protein interaction networks and Random Forest (RF) feature selection pinpointed radical S‐adenosyl methionine domain containing 2 (RSAD2) and Interferon‐induced protein with tetratricopeptide repeats 1 (IFIT1) as central hub genes. Upstream transcription analysis identified the homer_AGTTTCAGTTTC_ISRE motif and established a ceRNA network involving hsa‐miR‐654‐5p, IFIT1/RSAD2, and lncRNAs. Mendelian randomization (MR) and colocalization analyses linked RSAD2 downregulation to an increased risk of cervical carcinoma in situ (rs2595163, PPH4 = 0.62), while ROC analysis demonstrated strong diagnostic potential for the combined hub gene expression. Notably, single‐cell transcriptomics revealed distinct RSAD2 and IFIT1 expression patterns in immune and epithelial cells during the progression from HPV infection to cervical cancer. Collectively, these findings support RSAD2 and IFIT1 as promising biomarkers and therapeutic targets for HPV‐related cervical lesions.

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.