Machine-Learning Screening of Early Cervical Lesions Using HPV Genotyping and Exploratory Fusion-Gene Analysis

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However, conventional screening methods such as cytology and HPV DNA testing remain limited in accuracy and scalability, particularly for early or precancerous lesions. Methods We analyzed HPV infection patterns, genotype distribution, and lesion grades in 5,452 women from Shenzhen, China. Among them, 76 HPV16- or HPV52-positive cases underwent exploratory PCR-based fusion-gene detection. Six feature-selection strategies and thirteen machine-learning classifiers were trained using stratified five-fold cross-validation, with SMOTE for class balancing and grid search for hyperparameter tuning. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP). Results The overall HPV infection rate was 30.3%. HPV52 was the most prevalent genotype (6.1%) in the general population, whereas HPV16 predominated in high-grade lesions and cancer. The number of fusion loci increased with lesion severity, but fusion data alone showed limited predictive value (ROC AUC < 0.60). Integrating HPV genotyping with epidemiological features markedly improved performance: Random Forest achieved ROC AUC and PR AUC of 0.95 in cross-validation and 0.86 in the independent test set. SHAP analysis identified infection burden and high-risk HPV status as dominant predictors, jointly explaining over half of the model variance. Conclusions This study establishes a region-specific epidemiological profile of HPV and introduces an explainable, low-cost machine-learning framework based solely on HPV genotyping. The model demonstrates high accuracy and clinical scalability, providing a practical approach for early screening of cervical lesions. Trial registration ChiCTR, ChiCTR2400089277. Registered 5 September 2024, https//www.chictr.org.cn/showproj.html?proj=240825 human papillomavirus (HPV) cervical lesions early diagnosis genotyping fusion gene machine learning Figures Figure 1 Figure 2 Figure 3 1. Introduction Cervical cancer is the fourth most common malignancy and a leading cause of cancer-related mortality among women worldwide 1 , 2 , with 604,100 new cases and 341,800 deaths reported in 2020 3 . The burden is disproportionately high in low- and middle-income countries, where it ranks second only to breast cancer, underscoring its major public health impact 4 . Therefore, early screening for cervical cancer is of paramount importance to reduce incidence and mortality. Persistent infection with high-risk human papillomavirus (HPV) is the established cause of cervical cancer and its precancerous lesions, detected in nearly all cases 5 , 6 . Cofactors such as Chlamydia trachomatis infection, early sexual activity, multiple partners, multiparity, smoking, and partner-related factors further promote carcinogenesis 7 . HPV genotypes are stratified into high-, medium-, and low-risk groups, with high-risk types such as HPV16 and HPV18 driving most cancers 8 , 9 , though some evidence suggests that medium- and low-risk types may occasionally contribute to malignant progression 10 – 12 . Importantly, genotype distribution varies across regions, influencing infection prevalence and oncogenic potential 13 , 14 , and highlighting the need for region-specific epidemiological studies. Current screening relies on cytology, HPV DNA testing, and colposcopy with biopsy 15 . Cytology has limited sensitivity, HPV DNA testing suffers from low specificity, and colposcopy with biopsy, though the diagnostic gold standard, is invasive and unsuitable for population-level screening 16 – 19 . These limitations emphasize the need for accurate, noninvasive, and scalable molecular strategies, including HPV genotyping and fusion gene detection, to improve early detection. In this study, we investigated HPV infection status, genotype distribution, and lesion grading in 5,452 women from Shenzhen, China, and introduced several innovations: (i) a large-scale cohort to ensure representativeness; (ii) comprehensive profiling of 37 HPV genotypes; (iii) fusion of PCR-based HPV fusion gene detection with genotyping 20 ; (iv) application of multiple feature selection methods and machine learning classifiers to build predictive models 21 – 26 ; and (v) model interpretability analyses using SHapley Additive exPlanations (SHAP) to quantify the contribution of HPV-related features 27 . Together, these efforts aim to advance precision screening and improve diagnostic accuracy for cervical cancer and its precancerous lesions. 2. Materials and Methods 2.1 Study Population This study was approved by the Ethics Committee of the Nanshan District People’s Hospital in Shenzhen (approval number: ky-2024-012801). Clinical records of female patients who visited the gynecology outpatient department at Nanshan Hospital between January 2024 and June 2024 and underwent HPV genotyping were collected for data compilation. Personal information such as name, sex, age, and treatment card number was used to determine whether multiple clinical records belonged to the same individual. If multiple records were identified as belonging to the same patient, only the first diagnostic examination result was included in the study sample. After a rigorous screening process, 5,467 patients with complete clinical data who met the inclusion criteria for this study were selected. Among them, 5,252 patients had abnormal HPV screening results or exhibited clear clinical symptoms, prompting follow-up diagnostic procedures, including ThinPrep cytologic test (TCT), colposcopy, and cervical biopsy, to confirm their condition. The inclusion criteria included female sex; history of sexual activity; no sexual activity, vaginal medication, or douching within 72 h prior to sampling; visiting the gynecology department of our hospital for HPV DNA / TCT / colposcopy / pathology / pathology tests; and no prior HPV vaccination. The exclusion criteria included the presence of tumors other than cervical cancer, no history of sexual activity prior to consultation, treatment for reproductive tract infections (e.g., vaginitis, HPV infection, gonorrhea, or mycoplasma infection) within the last 3 months, and treatment with antibiotics, hormones, or vaginal probiotics within the last month. Of the patients meeting the inclusion criteria, 76 with confirmed HPV16(+) or HPV52(+) status and complete clinical data were included. All underwent TCT, colposcopy, and histopathological examination. For downstream analyses, the HPV16 subgroup comprised 10 cases each of NILM, ASC-US, CIN1, and CIN2/3, whereas the HPV52 subgroup included 10 cases each of NILM, ASC-US, and CIN1, along with 6 cases of CIN2/3. 2.2 Cytology (TCT) The experimental staff performed TCT (cytological preparation) experiments on the collected cervical exfoliative cytology cells. Upon completion, the remaining liquid-based samples were subjected to DNA extraction for HPV genotyping. 2.3 HPV DNA Extraction and Genotyping The cervical sample storage solution was shaken using an oscillator to fully detach the cells from the brush. The solution was then transferred to enzyme-free sterile reagent tubes. HPV DNA extraction was performed following the guidelines provided by the extraction kit. Next, the quality of the experimental samples was tested according to the standard instructions of the HPV genotyping kit. After the quality test was passed, HPV genotyping was conducted. The remaining DNA samples were stored at − 80°C for future use. 2.4 Colposcopy and Histopathology Patients who met the following criteria underwent further colposcopic examination: HR-HPV positive with cytology results of ASC-US or higher; two consecutive cytology results of ASC-US or higher (at least 6 months apart); cytology negative but HR-HPV positive for 1 year; cytology negative and HPV16 or 18 positive; and visible cervical ulcers, masses (tumors), growths, or other suspicious signs of cancer. If the colposcopic examination revealed abnormal findings and the patient met the indications for pathological biopsy, a biopsy was performed under colposcopy. The biopsy diagnosis followed the standards outlined in the “Cervical Cancer Screening and Clinical Management: China Expert Consensus (2020)” and the “WHO Classification of Tumors of Female Reproductive Organs (2020).” 2.5 Primer Design Pérot et al. successfully identified HPV-human fusion sequences in female patients in France using HPV RNA-Seq technology. They developed and organized publicly available primers based on the fusion gene series presented in the literature (Tables S1 and S2). 2.6 PCR Amplification, Electrophoresis, and Imaging Using the primers described above, HPV DNA extracted from exfoliated cervical cell samples of eligible patients was amplified according to the amplification system and program design specified in the Thermo Fisher Scientific Dream Taq Green PCR Master Mix (2x) instruction manual. Following amplification, the PCR products were subjected to electrophoresis, and the gel was imaged. Subsequently, the band positions of the DNA marker and sample bands were compared and analyzed to determine the size and concentration of DNA fragments in the samples. 2.7 Statistical Analysis Data analysis was performed using Python (version 3.11.5) and R (version 4.3.1). Statistical tests included the chi-square test, Fisher’s exact test, or Fisher’s exact test with Monte Carlo simulation, as appropriate, depending on data distribution and expected cell counts. Continuous variables were analyzed using the independent-samples t -test, Welch t -test, or Mann–Whitney U test, according to normality. All tests were two-sided, and a p value < 0.05 was considered statistically significant. 2.8 Feature Selection Six feature selection strategies were applied: Random Forest Recursive Feature Elimination (RF-RFE), Random Forest–based Feature Importance (RFF), Least Absolute Shrinkage and Selection Operator (LASSO), Boruta algorithm (Boruta), Wilcoxon rank-sum test (Wilcoxon), and Spearman correlation analysis (Spearman). 2.9 Machine Learning Models Thirteen classifiers were evaluated: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extremely Randomized Trees (ExtraTrees, ET), Gradient Boosting (GB), AdaBoost (AB), XGBoost (XGB), LightGBM (LGBM), CatBoost (CB), k-Nearest Neighbors (KNN), Gaussian Process Classifier (GP), and Multilayer Perceptron (MLP). 2.10 Model Evaluation Models were trained and tuned using stratified 5-fold cross-validation with grid search; a fixed random seed (42) ensured reproducibility. Class imbalance was handled with the Synthetic Minority Oversampling Technique (SMOTE) 28 . Performance was quantified using the area under the receiver operating characteristic curve (ROC AUC), area under the precision–recall curve (PR AUC), Matthews correlation coefficient (MCC), harmonic mean of precision and recall (F1 score), accuracy, sensitivity, and specificity. An independent test set, not used for training or tuning, was reserved for final evaluation. 2.11 Model Interpretability The optimal model, identified through independent validation, was further subjected to interpretability analysis using SHapley Additive exPlanations (SHAP). SHAP assigns each feature a contribution score to individual predictions by computing the marginal contribution of that feature across all possible feature subsets, thereby quantifying the relative importance and directionality of each variable. 3. Results 3.1 Prevalence of HPV Infection Across Cervical Lesion Grades and Age Groups Among the 5,467 eligible women, 15 HPV-positive cases lacked complete TCT, colposcopy, or biopsy data and were excluded, leaving 5,452 for the final analysis. Of these, 1,651 tested positive for HPV, yielding an overall infection rate of 30.3%. The prevalence was 20.8% in women with normal cytology and 92.6% in those with lesions, including 90.9% in ASC-US, 95.3% in CIN1, 97.4% in CIN2/3, and 100% in cervical cancer (Table 1 , p value < 0.001). Table 1 Distribution of HPV Infection Across Cervical Diagnostic Categories Diagnostic category All cases Infection cases(n,%) Non-infection cases(n,%) P value NILM 4733 985(20.8%) 3748(79.2%) < 0.001 ASC-US 462 420(90.9%) 42(9.1%) CIN1 214 204(95.3%) 10(4.7%) CIN2/3 38 37(97.4%) 1(2.6%) Cervical cancer 5 5(100%) 0 Total 5452 1651(30.3%) 3801(69.7%) Single-type infections accounted for 72.7% of cases, comprising 41.7% with high-risk HPV, 3.4% with medium-risk HPV, and 27.6% with low-risk HPV. Mixed infections comprised 27.3%, most frequently combinations of high-risk and low-risk types (21.2%) (Table 2 , p value < 0.001). In all groups involving high-risk HPV infection, the prevalence of infection in lesion tissues was more than fivefold higher than that in normal tissues (Fig. 1 A). Table 2 .Distribution of HPV Infection by Risk Group Across Cervical Diagnostic Categories Risk group NILM (n = 4733) ASC-US (n = 462) CIN1 (n = 214) CIN2/3 (n = 38) Cervical cancer (n = 5) P value Simple High-risk, n(%) 377(7.97%) 186(40.26%) 98(45.79%) 24(63.16%) 4(80.00%) < 0.001 Simple Medium-risk, n(%) 36(0.76%) 15(3.25%) 5(2.34%) 0 0 Simple Low-risk, n(%) 355(7.50%) 75(16.23%) 25(11.68%) 0 0 High-risk and Medium-risk, n(%) 14(0.30%) 10(2.16%) 9(4.21%) 2(5.26%) 0 High-risk and Low-risk, n(%) 168(3.55%) 116(25.11%) 54(25.23%) 11(28.95%) 1(20.00%) Medium-risk and Low-risk, n(%) 15(0.32%) 6(1.30%) 4(1.87%) 0 0 High-risk and Medium-risk and Low-risk, n(%) 20(0.42%) 12(2.60%) 9(4.21%) 0 0 A total of 37 HPV genotypes were identified. HPV52 was the most prevalent overall (6.1%), while HPV52 (23.4%) and HPV16 (13.2%) were the dominant types in lesion tissues. HPV16 was most frequently detected in CIN2/3 and cervical cancer. Except for HPV26, HPV57, HPV72, and HPV73, most genotypes showed significant distributional differences between normal and lesion tissues (Table 3 , Fig. 1 B, p value < 0.001). Table 3 Distribution of HPV Genotypes Across Histological Diagnoses HPV type NILM (n = 4733) ASC-US (n = 462) CIN1 (n = 214) CIN2/3 (n = 38) Cervical cancer (n = 5) P value HPV-16 41 (0.87%) 40 (8.66%) 39 (18.22%) 13 (34.21%) 3 (60.00%) < 0.001 HPV-18 31 (0.66%) 17 (3.68%) 9 (4.21%) 3 (7.89%) 1 (20.00%) HPV-31 21 (0.44%) 14 (3.03%) 8 (3.74%) 2 (5.26%) 0 HPV-33 10 (0.21%) 10 (2.16%) 7 (3.27%) 4 (10.53%) 0 HPV-35 15 (0.32%) 5 (1.08%) 2 (0.93%) 0 1 (20.00%) HPV-39 84 (1.78%) 46 (9.96%) 21 (9.81%) 6 (15.79%) 1 (20.00%) HPV-45 12 (0.25%) 5 (1.08%) 3 (1.40%) 1 (2.63%) 0 HPV-51 78 (1.65%) 42 (9.09%) 27 (12.62%) 5 (13.16%) 0 HPV-52 166 (3.51%) 109 (23.59%) 48 (22.43%) 10 (26.32%) 1 (20.00%) HPV-56 46 (0.97%) 30 (6.49%) 14 (6.54%) 2 (5.26%) 0 HPV-58 65 (1.37%) 52 (11.26%) 35 (16.36%) 6 (15.79%) 1 (20.00%) HPV-59 43 (0.91%) 14 (3.03%) 3 (1.40%) 0 1 (20.00%) HPV-66 32 (0.68%) 20 (4.33%) 12 (5.61%) 0 0 HPV-68 63 (1.33%) 16 (3.46%) 15 (7.01%) 2 (5.26%) 0 HPV-26 2 (0.04%) 0 1 (0.47%) 0 0 HPV-53 62 (1.31%) 34 (7.36%) 22 (10.28%) 1 (2.63%) 0 HPV-73 10 (0.21%) 2 (0.43%) 2 (0.93%) 0 0 HPV-82 12 (0.25%) 8 (1.73%) 2 (0.93%) 1 (2.63%) 0 HPV-6 36 (0.76%) 10 (2.16%) 11 (5.14%) 1 (2.63%) 0 HPV-11 13 (0.27%) 5 (1.08%) 4 (1.87%) 1 (2.63%) 0 HPV-34 14 (0.30%) 7 (1.52%) 4 (1.87%) 0 0 HPV-40 37 (0.78%) 13 (2.81%) 2 (0.93%) 2 (5.26%) 0 HPV-42 83 (1.75%) 38 (8.23%) 15 (7.01%) 1 (2.63%) 0 HPV-43 46 (0.97%) 22 (4.76%) 12 (5.61%) 0 0 HPV-44 43 (0.91%) 20 (4.33%) 5 (2.34%) 2 (5.26%) 0 HPV-54 87 (1.84%) 26 (5.63%) 17 (7.94%) 3 (7.89%) 0 HPV-55 35 (0.74%) 16 (3.46%) 6 (2.80%) 0 0 HPV-57 0 0 0 0 0 HPV-61 98 (2.07%) 34 (7.36%) 19 (8.88%) 1 (2.63%) 1 (20.00%) HPV-67 27 (0.57%) 13 (2.81%) 4 (1.87%) 0 0 HPV-69 3 (0.06%) 1 (0.22%) 3 (1.40%) 0 0 HPV-70 41 (0.87%) 10 (2.16%) 6 (2.80%) 1 (2.63%) 0 HPV-71 26 (0.55%) 9 (1.95%) 0 0 0 HPV-72 5 (0.11%) 1 (0.22%) 1 (0.47%) 0 0 HPV-81 85 (1.80%) 31 (6.71%) 9 (4.21%) 4 (10.53%) 1 (20.00%) HPV-83 2 (0.04%) 2 (0.43%) 1 (0.47%) 0 0 HPV-84 52 (1.10%) 23 (4.98%) 6 (2.80%) 1 (2.63%) 0 Age distribution also varied significantly across histological categories (Table 4 , p value < 0.01). The highest infection rate was observed in women younger than 25 years (39.3%). Across all age groups, infection rates were consistently higher in lesion tissues compared with normal cytology (Fig. 1 C). Genotype prevalence also differed by age group, with HPV52 showing the highest prevalence in all groups and peaking at 9.7% among women under 25 years (Table 5 , p value < 0.01). Table 4 Distribution of age groups across histological diagnoses Age NILM (n = 4733) ASC-US (n = 462) CIN1 (n = 214) CIN2/3 (n = 38) Cervical cancer (n = 5) P value ≤ 24 353 (7.46%) 47 (10.17%) 19 (8.88%) 3 (7.89%) 0 < 0.01 25–34 1994 (42.13%) 211 (45.67%) 97 (45.33%) 15 (39.47%) 1 (20.00%) 35–44 1284 (27.13%) 100 (21.65%) 52 (24.30%) 4 (10.53%) 2 (40.00%) 45–54 838 (17.71%) 72 (15.58%) 37 (17.29%) 8 (21.05%) 1 (20.00%) ≥ 55 264 (5.58%) 32 (6.93%) 9 (4.21%) 8 (21.05%) 1 (20.00%) Table 5. Distribution of HPV Genotypes Across across Age groups HPV type ≤ 24 (n=422) 25-34 (n=2318) 35-44 (n=1442) 45-54 (n=956) ≥ 55 (n=314) P value HPV-16 21 (4.98%) 61 (2.63%) 28 (1.94%) 16 (1.67%) 10 (3.18%) <0.01 HPV-18 13 (3.08%) 23 (0.99%) 13 (0.90%) 10 (1.05%) 2 (0.64%) HPV-31 6 (1.42%) 22 (0.95%) 7 (0.49%) 6 (0.63%) 4 (1.27%) HPV-33 0 (0.00%) 19 (0.82%) 5 (0.35%) 5 (0.52%) 2 (0.64%) HPV-35 2 (0.47%) 8 (0.35%) 6 (0.42%) 4 (0.42%) 3 (0.96%) HPV-39 17 (4.03%) 76 (3.28%) 34 (2.36%) 22 (2.30%) 9 (2.87%) HPV-45 2 (0.47%) 11 (0.47%) 3 (0.21%) 3 (0.31%) 2 (0.64%) HPV-51 20 (4.74%) 77 (3.32%) 25 (1.73%) 20 (2.09%) 10 (3.18%) HPV-52 41 (9.72%) 137 (5.91%) 70 (4.85%) 61 (6.38%) 25 (7.96%) HPV-56 14 (3.32%) 47 (2.03%) 12 (0.83%) 9 (0.94%) 10 (3.18%) HPV-58 15 (3.55%) 66 (2.85%) 40 (2.77%) 23 (2.41%) 15 (4.78%) HPV-59 10 (2.37%) 29 (1.25%) 14 (0.97%) 7 (0.73%) 1 (0.32%) HPV-66 7 (1.66%) 33 (1.42%) 13 (0.90%) 8 (0.84%) 3 (0.96%) HPV-68 10 (2.37%) 57 (2.46%) 18 (1.25%) 11 (1.15%) 0 (0.00%) HPV-26 2 (0.47%) 1 (0.04%) 0 (0.00%) 0 (0.00%) 0 (0.00%) HPV-53 8 (1.90%) 52 (2.24%) 28 (1.94%) 23 (2.41%) 8 (2.55%) HPV-73 3 (0.71%) 9 (0.39%) 1 (0.07%) 1 (0.10%) 0 (0.00%) HPV-82 5 (1.18%) 9 (0.39%) 6 (0.42%) 2 (0.21%) 1 (0.32%) HPV-6 12 (2.84%) 27 (1.16%) 10 (0.69%) 7 (0.73%) 2 (0.64%) HPV-11 7 (1.66%) 8 (0.35%) 5 (0.35%) 2 (0.21%) 1 (0.32%) HPV-34 5 (1.18%) 10 (0.43%) 7 (0.49%) 1 (0.10%) 2 (0.64%) HPV-40 6 (1.42%) 33 (1.42%) 11 (0.76%) 3 (0.31%) 1 (0.32%) HPV-42 15 (3.55%) 66 (2.85%) 31 (2.15%) 17 (1.78%) 8 (2.55%) HPV-43 10 (2.37%) 35 (1.51%) 19 (1.32%) 11 (1.15%) 5 (1.59%) HPV-44 2 (0.47%) 30 (1.29%) 15 (1.04%) 16 (1.67%) 7 (2.23%) HPV-54 15 (3.55%) 57 (2.46%) 30 (2.08%) 26 (2.72%) 5 (1.59%) HPV-55 8 (1.90%) 23 (0.99%) 13 (0.90%) 10 (1.05%) 3 (0.96%) HPV-57 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) HPV-61 18 (4.27%) 74 (3.19%) 30 (2.08%) 20 (2.09%) 11 (3.50%) HPV-67 10 (2.37%) 16 (0.69%) 8 (0.55%) 6 (0.63%) 4 (1.27%) HPV-69 0 (0.00%) 4 (0.17%) 2 (0.14%) 1 (0.10%) 0 (0.00%) HPV-70 5 (1.18%) 22 (0.95%) 10 (0.69%) 15 (1.57%) 6 (1.91%) HPV-71 2 (0.47%) 10 (0.43%) 8 (0.55%) 7 (0.73%) 8 (2.55%) HPV-72 0 (0.00%) 3 (0.13%) 1 (0.07%) 2 (0.21%) 1 (0.32%) HPV-81 10 (2.37%) 52 (2.24%) 39 (2.70%) 21 (2.20%) 8 (2.55%) HPV-83 1 (0.24%) 0 (0.00%) 1 (0.07%) 1 (0.10%) 2 (0.64%) HPV-84 15 (3.55%) 44 (1.90%) 8 (0.55%) 8 (0.84%) 7 (2.23%) Note : P value represents the overall comparison of HPV genotype distributions across age groups (≤ 24, 25–34, 35–44, 45–54, and ≥ 55 years). The association was evaluated using a chi-square test with Monte Carlo resampling owing to expected cell counts < 5 in multiple subgroups. 3.2 Fusion Gene Characteristics Across Cervical Lesions Given their high prevalence and pathogenic relevance, HPV16 and HPV52 were selected for fusion gene analysis. The mean number of fusion sites for both genotypes increased with lesion severity and was highest in CIN2/3 (Fig. 2 A). In HPV16-positive patients, 32 loci were detected, with locus 13, 24, 15, and 31 showing the highest frequencies. In HPV52-positive patients, 34 loci were identified, with locus 7, 29, 1, and 26 showing the highest frequencies (Fig. 2 A). Among HPV16 loci, locus 30 showed significant differences across clinical grades ( p value < 0.05). Among HPV52 loci, locus 4, 10, 12, 18, and 19 differed significantly ( p value < 0.05), with higher frequencies in CIN1 and above (Fig. 2 B). Random Forest modeling indicated an AUC of 0.72 for HPV16 in CIN2/3, which decreased to 0.60 after removal of locus 30. For HPV52, predictive performance was higher overall, but exclusion of the five significant loci substantially reduced AUC values (Fig. 2 C). These findings suggest that specific fusion loci may serve as potential markers for early lesion classification. 3.3 Machine Learning–Based Predictive Model for Early Screening Fusion gene–based prediction alone produced ROC AUC values below 0.60. To enhance performance, data were processed through six feature selection strategies, yielding 5, 41, 1, 6, 14, and 37 features, respectively. Eight composite feature groups were then generated based on feature overlap (Fig. 3 A–B). The original dataset was randomly divided into a training cohort (n = 4,361) and a validation cohort (n = 1,091) at a 4:1 ratio. Baseline feature distributions were well balanced between the two subsets, with no significant differences observed except for HPV6, HPV44, HPV56 infection (Table S3, p value < 0.05). Thirteen classifiers were trained and evaluated. Across all groups and strategies, ROC AUC ranged from 0.87 to 0.95, PR AUC from 0.85 to 0.95, F1 scores from 0.86 to 0.89, MCC values from 0.72 to 0.78, and accuracy from 0.86 to 0.89 (Table S4, Fig. 3 C–D). Sensitivity reached up to 0.94 and specificity up to 0.93. Ensemble tree-based methods (ExtraTrees, RandomForest, CatBoost, LightGBM) achieved the highest performance, with ROC AUC and PR AUC values of 0.94–0.95, F1 scores of 0.88–0.89, and MCC values of 0.77–0.78. Logistic Regression, SVM, Gaussian Process, and MLP achieved ROC AUC values of 0.90–0.93 and MCC values of 0.74–0.75, whereas XGBoost and AdaBoost achieved ROC AUC values of 0.88–0.90 and MCC values of 0.72–0.73. Within Group 1, Random Forest achieved the best performance with ROC AUC of 0.95, PR AUC of 0.95, accuracy of 0.89, F1 score of 0.88, MCC of 0.77, sensitivity of 0.86, specificity of 0.91, and precision of 0.90 (Table 6). Validation on the independent test set showed consistent results, with ROC AUC of 0.856 (Fig. 3 E). SHAP analysis indicated that the number of HPV infections contributed 30.3% of the total mean absolute SHAP value, high-risk HPV status 24.3%, low-risk HPV 7.2%, age 6.8%, HPV52 infection 5.3%, and HPV16 infection 4.9%, together accounting for 78.8% (Fig. 3 F). SHAP dependence plots further illustrated that an increasing number of HPV infections was associated with higher predicted probabilities of cervical lesions (Fig. 3 G). 4. Discussion This study systematically analyzed the epidemiology of HPV infection in a large cohort of women from Shenzhen, China, and integrated fusion gene detection with machine learning modeling to explore new approaches for the early diagnosis of cervical cancer and precancerous lesions. We found that HPV52 was the most prevalent genotype in the general population, whereas HPV16 was more concentrated in high-grade lesions and cervical cancer. This distribution pattern is consistent with findings from East Asia, highlighting that although HPV52 is the most frequent genotype in the general population, HPV16 plays a stronger oncogenic role in disease progression 29 , 30 . In addition, the highest infection rates were observed among women aged ≤ 25 years and > 55 years, emphasizing the importance of targeted prevention and screening in both young and high-risk populations. Fusion gene analysis further demonstrated an association between viral fusion events and lesion progression. The number of fusion loci increased with disease severity, and HPV16 locus 30 and HPV52 locus 4, 10, 12, 18, and 19 were more frequently detected in high-grade lesions. These findings suggest that fusion site patterns may serve as molecular indicators of progression. However, predictive performance based solely on fusion loci was limited, underscoring the need to combine these markers with additional features for clinical application. When HPV genotyping and epidemiological variables were incorporated into machine learning models, predictive performance improved substantially. Ensemble tree-based classifiers achieved ROC AUC values exceeding 0.94 across multiple feature groups, with Random Forest performing best (AUC 0.95 in the development set and 0.856 in the independent test set). These results demonstrate that multidimensional feature fusion and algorithmic optimization can surpass the sensitivity–specificity trade-offs of conventional cytology and HPV DNA testing, thereby enhancing early detection of cervical lesions. Interpretability analysis further reinforced the reliability of the models. SHAP analysis identified infection burden and high-risk HPV status as the most influential predictors, together accounting for more than half of the model’s variance. Dependence plots demonstrated a monotonic increase in risk with increasing infection burden. High-risk HPV infection emerged as the key determinant, with prevalence in lesion tissues substantially higher than in normal tissues. Low-risk HPV infection also contributed 7.2%, indicating that it should not be disregarded in predictive modeling. These findings show that the models not only exhibit strong predictive performance but also align with biological plausibility 31 , 32 , thereby enhancing their clinical credibility. Compared with studies that require collecting a wide range of predictive factors, including demographic information, cytology, and colposcopy 21 – 26 , 33 , 34 , our approach relies solely on comprehensive HPV genotyping data obtained from standard kits. This makes the method simple, cost-effective, and highly practical. Built on a large-scale cohort covering 37 HPV genotypes, the study provides a robust characterization of regional epidemiology. We further assessed the added value of fusion gene detection by directly comparing it with genotyping and epidemiological features, clarifying its relative contribution. By integrating multiple machine learning models with interpretability analysis, the framework achieved both strong predictive performance and transparency, addressing the “black-box” limitations common to artificial intelligence. Collectively, these advances move cervical cancer screening toward greater precision, efficiency, and clinical applicability. Several limitations should be acknowledged. This was a single-center study with a limited time frame, which may restrict generalizability. The number of high-grade lesions and cancer cases was relatively small, limiting model performance in rare categories. Fusion gene detection relied on previously reported primer sets, which may not cover all fusion events. Although an independent test set was included, external multicenter and cross-platform validation are still lacking. In addition, clinical variables such as viral load, methylation status, E6/E7 mRNA expression were not incorporated 35 , 36 , which may have constrained predictive accuracy. Future research should validate these findings in multicenter, prospective cohorts, optimize fusion detection methods, integrate additional molecular and clinical markers, and assess real-world clinical utility, including impacts on screening, referral, and healthcare resource allocation. Conclusion This study, based on a large-scale population cohort, identified HPV52 and HPV16 as key genotypes associated with lesion progression and proposed an explainable machine learning framework built on HPV genotyping and epidemiological features. This strategy provides a new tool for early diagnosis of cervical cancer and its precancerous lesions and has the potential to enhance screening accuracy and support personalized prevention strategies. Abbreviations HPV: Human papillomavirus; HR-HPV: High-risk HPV; LR-HPV: Low-risk HPV; TCT: ThinPrep cytologic test; NILM: Negative for intraepithelial lesion or malignancy; ASC-US: Atypical squamous cells of undetermined significance; LSIL: Low-grade squamous intraepithelial lesion; HSIL: High-grade squamous intraepithelial lesion; CIN: Cervical intraepithelial neoplasia; NGS: next-generation sequencing. Declarations Ethics approval and consent to participate: This study was conducted in accordance with the principles of the Declaration of Helsinki. The research protocol was reviewed and approved by the Research Ethics Committee of Shenzhen Nanshan People's Hospital (approval number: ky-2024-012801). Written informed consent was obtained from all participants prior to sample collection and data analysis. Consent for publication: Not applicable. Competing the interests: The authors declare that they have no competing interests. Funding: This study was supported by the Shenzhen International Science and Technology Cooperation Program under the Collaborative Innovation Science and Technology Plan (Grant No. 2021308816). Authors' contributions: H. Liu and X. Cao collected the data, performed statistical analyses, and drafted the initial manuscript. H. Liu, Z. Lai, X. Wen, Y. Huang, Y. Liu, and S. Chen carried out the fusion gene experiments. X. Cao, Y. Zheng, W. Kong, and Y. Wang conducted data analyses and constructed the machine learning models. D. Luo supervised the experimental and analytical work, and was responsible for drafting and finalizing the manuscript. All authors read, revised, and approved the final version. Acknowledgements: The authors gratefully acknowledge the financial support provided by the Shenzhen International Science and Technology Cooperation Program under the Collaborative Innovation Science and Technology Plan (Grant No. 2021308816). The authors also sincerely thank all members of the research team for their collaborative efforts. Availability of data and materials: All data used in this study are contained within the manuscript. References Taniue K, Akimitsu N. Fusion genes and RNAs in cancer development. Non-coding RNA. 2021;7(1):10. Jareemit N, Horthongkham N, Therasakvichya S, et al. Human papillomavirus genotype distribution in low-grade squamous intraepithelial lesion cytology, and its immediate risk for high-grade cervical lesion or cancer: a single-center, cross-sectional study. Obstet Gynecol Sci. 2022;65(4):335–45. Yue H, Li X, You J, et al. Acute hematologic toxicity prediction using dosimetric and radiomics features in patients with cervical cancer: does the treatment regimen matter? Front Oncol. 2024;14:1365897. Wadler BM, Judge CM, Prout M, Allen JD, Geller AC. Improving breast cancer control via the use of community health workers in South Africa: a critical review. J Oncol. 2011;2011(1):150423. Shi W, Zhu H, Yuan L, et al. Vaginal microbiota and HPV clearance: A longitudinal study. Front Oncol. 2022;12:955150. Tian S, Zhang L, Li Y, et al. Human papillomavirus E7 oncoprotein promotes proliferation and migration through the transcription factor E2F1 in cervical cancer cells. Anti-Cancer Agents Med Chem (Formerly Curr Med Chemistry-Anti-Cancer Agents). 2021;21(13):1689–96. Musonda JS, Sodo PP, Ayo-Yusuf O, et al. Cervical cancer screening in a population of black South African women with high HIV prevalence: a cross-sectional study. PLOS global public health. 2022;2(11):e0001249. Chatfield-Reed K, Roche VP, Pan Q. cfDNA detection for HPV + squamous cell carcinomas. Oral Oncol. 2021;115:104958. Peng N, Xiao J, He L, Xie L. Association Between Vaginal Microecological Alterations and High-Risk Human Papillomavirus Infection: A Cross-Sectional Study. Front Cell Infect Microbiol. 2025;15:1618846. Herbster S, Paladino A, de Freitas S, Boccardo E. Alterations in the expression and activity of extracellular matrix components in HPV-associated infections and diseases. Clinics. 2018;73:e551s. Desai KT, Adepiti CA, Schiffman M, et al. Redesign of a rapid, low-cost HPV typing assay to support risk‐based cervical screening and management. Int J Cancer. 2022;151(7):1142–9. Ray A. Human Papillomavirus and Other Relevant Issues in Cervical Cancer Pathogenesis. Int J Mol Sci. 2025;26(12):5549. Liao G, Jiang X, She B, et al. Multi-infection patterns and co-infection preference of 27 human papillomavirus types among 137,943 gynecological outpatients across China. Front Oncol. 2020;10:449. Luo Q, Zhang H, Zeng X, Han N, Ma Z, Luo H. HPV specificity and multiple infections and association with cervical cytology in Chongqing, China: a cross-sectional study. BMC Infect Dis. 2024;24(1):804. Ren C, Zeng X, Shi Z, et al. Multi-center clinical study using optical coherence tomography for evaluation of cervical lesions in-vivo. Sci Rep. 2021;11(1):7507. Rijkaart D, Berkhof J, Van Kemenade F, et al. HPV DNA testing in population-based cervical screening (VUSA-Screen study): results and implications. Br J Cancer. 2012;106(5):975–81. Kabir A, Bukar M, Nggada HA, Rann HB, Gidado A, Musa AB. Prevalence of human papillomavirus genotypes in cervical cancer in Maiduguri, Nigeria. Pan Afr Med J 2019;33(1). Wang H-y, Kim H, Park KH. Diagnostic performance of the E6/E7 mRNA-based Optimygene HR-HPV RT-qDx assay for cervical cancer screening. Int J Infect Dis. 2019;78:22–30. Xia C, He L, Sun Y. Expression and prognostic role of CXCL1 gene in colorectal adenocarcinoma. Comput Intell Neurosci. 2022;2022(1):5504731. Pérot P, Biton A, Marchetta J, et al. Broad-range papillomavirus transcriptome as a biomarker of papillomavirus-associated cervical high-grade cytology. J Mol Diagn. 2019;21(5):768–81. Kahng J, Kim E-H, Kim H-G, Lee W. Development of a cervical cancer progress prediction tool for human papillomavirus-positive Koreans: A support vector machine-based approach. J Int Med Res. 2015;43(4):518–25. Choudhury A, Wesabi Y, Won D. Classification of cervical cancer dataset. arXiv preprint arXiv:181210383. 2018. Ahmed M, Kabir MMJ, Kabir M, Hasan MM. Identification of the risk factors of cervical cancer applying feature selection approaches. Paper presented at: 2019 3rd international conference on electrical, computer & telecommunication engineering (ICECTE)2019. Alam TM, Khan MMA, Iqbal MA, Abdul W, Mushtaq M. Cervical cancer prediction through different screening methods using data mining. IJACSA) Int J Adv Comput Sci Appl 2019;10(2). Geetha R, Sivasubramanian S, Kaliappan M, Vimal S, Annamalai S. Cervical cancer identification with synthetic minority oversampling technique and PCA analysis using random forest classifier. J Med Syst. 2019;43(9):286. Asadi F, Salehnasab C, Ajori L. Supervised algorithms of machine learning for the prediction of cervical cancer. J biomedical Phys Eng. 2020;10(4):513. Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 2017;30. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57. Leon-Gomez P, Romero VI. Human papillomavirus, vaginal microbiota and metagenomics: the interplay between development and progression of cervical cancer. Front Microbiol. 2025;15:1515258. Boonkate S, Karnchanabanyong W, Ruengkhachorn I, et al. Significance of Genotype-Specific High‐Risk Human Papillomavirus Testing in Cervical Cancer Screening: A Hospital‐Based Study. J Med Virol. 2025;97(8):e70561. Sun L, Li L, Xu W, Ma C. The immunomodulation role of vaginal microenvironment on human papillomavirus infection. Galen Med J. 2023;12:e2991. Vieira Alves M, Oliveira Pereira G, Alves dos Santos Silva L, et al. Intratype variants and high genotypic diversity of human papillomavirus with polymorphisms in the antigenic hypervariable loops of the L1 protein from women living with human immunodeficiency virus in Northeastern Brazil. J Med Microbiol. 2025;74(3):001981. Garg SK, Kapil M. A Cervical Cancer Prediction Model Using REPTree Classifier. J Comput Theor Nanosci. 2019;16(10):4438–42. Al Mudawi N, Alazeb A. A model for predicting cervical cancer using machine learning algorithms. Sensors. 2022;22(11):4132. Hamers FF, Poullié A-I, Arbyn M. Updated evidence-based recommendations for cervical cancer screening in France. Eur J Cancer Prev. 2022;31(3):279–86. Fertey J, Hagmann J, Ruscheweyh HJ, et al. Methylation of CpG 5962 in L1 of the human papillomavirus 16 genome as a potential predictive marker for viral persistence: A prospective large cohort study using cervical swab samples. Cancer Med. 2020;9(3):1058–68. Additional Declarations No competing interests reported. Supplementary Files TableS13.docx TableS4.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 11 Nov, 2025 Editor assigned by journal 04 Nov, 2025 Editor invited by journal 16 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 16 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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18:05:20","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147805,"visible":true,"origin":"","legend":"","description":"","filename":"9bfdf091c8d04132821380905a58bfeb1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7859112/v1/b9dd7a6a041cc79a54036f6a.xml"},{"id":96492067,"identity":"0effd725-cdb7-4a2f-bdf4-c46ad0f61b08","added_by":"auto","created_at":"2025-11-21 18:05:20","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158367,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7859112/v1/868594910d3107d540e4f19d.html"},{"id":96492054,"identity":"5428169b-5ea2-486a-80b7-dce634ebdc35","added_by":"auto","created_at":"2025-11-21 18:05:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":144781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEpidemiology of HPV infection in women with and without cervical lesions.\u003c/strong\u003e(A) Distribution of single- and mixed-type HPV infections in normal cervical tissues (n = 4,733) and lesion tissues (n = 719). High-risk HPV infections were markedly enriched in lesions (p \u0026lt; 0.001). (B) Genotype-specific distribution of 37 HPV types across histological categories. Most genotypes showed significant differences between normal and lesion tissues (p \u0026lt; 0.001). (C) Age-specific infection counts and rates by histological diagnosis. Infection rates peaked in women aged ≤ 24 years and ≥ 55 years, with significant differences across all age groups (p \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7859112/v1/12b9de249a57c7e6144cbf32.png"},{"id":96492055,"identity":"98a6592a-7f11-4c80-a996-fe4f94f41d99","added_by":"auto","created_at":"2025-11-21 18:05:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":261724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFusion gene characteristics in HPV16 and HPV52 across cervical lesion grades. \u003c/strong\u003e(A) Number of fusion loci detected in HPV16- and HPV52-positive cases stratified by histological grade (NILM, ASC-US, CIN1, CIN2/3). The mean number of Fusion counts increased with lesion severity. (B) Distribution of individual fusion loci. In HPV16-positive cases (32 loci detected), locus 30 was significantly enriched in high-grade lesions (p = 0.021). In HPV52-positive cases (34 loci detected), locus 4, 10, 12, 18, and 19 were more frequent in CIN1 and above (p \u0026lt; 0.05, Fisher’s exact test with Monte Carlo simulation). (C) Random Forest classification performance based on fusion loci. For HPV16, the model achieved an AUC of 0.72 in CIN2/3, which declined to 0.60 after excluding locus 30. For HPV52, removal of the five significant loci substantially reduced predictive accuracy across lesion categories.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7859112/v1/038c48c0f80db576a9d81adf.png"},{"id":96492053,"identity":"71de2f24-ac95-4780-a134-e9e7848d1b8e","added_by":"auto","created_at":"2025-11-21 18:05:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMachine learning framework and predictive performance for early detection of cervical lesions.\u003c/strong\u003e (A) Workflow of model development, including data collection, feature selection, model training, evaluation, independent validation, and interpretability analysis. (B) Feature groups constructed from six selection strategies (RFF, RF-RFE, LASSO, Boruta, Wilcoxon, Spearman) and their overlap. (C, D) Receiver operating characteristic (ROC) and precision–recall (PR) curves of 13 classifiers in Group1. Ensemble tree-based models (Random Forest, ExtraTrees, CatBoost, LightGBM) achieved the highest performance (ROC AUC and PR AUC up to 0.95). (E) Independent test set validation across all models and feature groups.Random Forest in Group 1 achieved ROC AUC = 0.856.(F) SHAP summary plot showing global feature importance. HPV infection count and high-risk HPV status were the strongest predictors, together explaining more than half of model variance. (G) SHAP dependence plot for HPV infection count, showing a monotonic increase in predicted lesion probability with infection burden.\u003c/p\u003e","description":"","filename":"placeholderimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7859112/v1/7b9bfa2c8016faf3817f04f0.png"},{"id":96913041,"identity":"e4b489ef-de9c-4b1f-8d3e-bf2f5380aa09","added_by":"auto","created_at":"2025-11-27 13:50:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1667149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7859112/v1/c70dcb90-bc5f-4e25-a701-49b7b42e3174.pdf"},{"id":96492057,"identity":"916628e9-6def-4558-983f-e4221450b4b3","added_by":"auto","created_at":"2025-11-21 18:05:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23413,"visible":true,"origin":"","legend":"","description":"","filename":"TableS13.docx","url":"https://assets-eu.researchsquare.com/files/rs-7859112/v1/fe98076a463ba3f5d029c815.docx"},{"id":96492056,"identity":"8ee3bd9c-5793-4761-8bb0-dc255c181fb8","added_by":"auto","created_at":"2025-11-21 18:05:20","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24577,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7859112/v1/4b12aa3f407b2a1f338053c0.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine-Learning Screening of Early Cervical Lesions Using HPV Genotyping and Exploratory Fusion-Gene Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCervical cancer is the fourth most common malignancy and a leading cause of cancer-related mortality among women worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, with 604,100 new cases and 341,800 deaths reported in 2020\u003csup\u003e3\u003c/sup\u003e. The burden is disproportionately high in low- and middle-income countries, where it ranks second only to breast cancer, underscoring its major public health impact\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Therefore, early screening for cervical cancer is of paramount importance to reduce incidence and mortality.\u003c/p\u003e\u003cp\u003ePersistent infection with high-risk human papillomavirus (HPV) is the established cause of cervical cancer and its precancerous lesions, detected in nearly all cases\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Cofactors such as Chlamydia trachomatis infection, early sexual activity, multiple partners, multiparity, smoking, and partner-related factors further promote carcinogenesis\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. HPV genotypes are stratified into high-, medium-, and low-risk groups, with high-risk types such as HPV16 and HPV18 driving most cancers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, though some evidence suggests that medium- and low-risk types may occasionally contribute to malignant progression\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Importantly, genotype distribution varies across regions, influencing infection prevalence and oncogenic potential\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and highlighting the need for region-specific epidemiological studies.\u003c/p\u003e\u003cp\u003eCurrent screening relies on cytology, HPV DNA testing, and colposcopy with biopsy\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Cytology has limited sensitivity, HPV DNA testing suffers from low specificity, and colposcopy with biopsy, though the diagnostic gold standard, is invasive and unsuitable for population-level screening\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These limitations emphasize the need for accurate, noninvasive, and scalable molecular strategies, including HPV genotyping and fusion gene detection, to improve early detection.\u003c/p\u003e\u003cp\u003eIn this study, we investigated HPV infection status, genotype distribution, and lesion grading in 5,452 women from Shenzhen, China, and introduced several innovations: (i) a large-scale cohort to ensure representativeness; (ii) comprehensive profiling of 37 HPV genotypes; (iii) fusion of PCR-based HPV fusion gene detection with genotyping\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e; (iv) application of multiple feature selection methods and machine learning classifiers to build predictive models\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e; and (v) model interpretability analyses using SHapley Additive exPlanations (SHAP) to quantify the contribution of HPV-related features\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Together, these efforts aim to advance precision screening and improve diagnostic accuracy for cervical cancer and its precancerous lesions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Population\u003c/h2\u003e\u003cp\u003eThis study was approved by the Ethics Committee of the Nanshan District People\u0026rsquo;s Hospital in Shenzhen (approval number: ky-2024-012801). Clinical records of female patients who visited the gynecology outpatient department at Nanshan Hospital between January 2024 and June 2024 and underwent HPV genotyping were collected for data compilation. Personal information such as name, sex, age, and treatment card number was used to determine whether multiple clinical records belonged to the same individual. If multiple records were identified as belonging to the same patient, only the first diagnostic examination result was included in the study sample. After a rigorous screening process, 5,467 patients with complete clinical data who met the inclusion criteria for this study were selected. Among them, 5,252 patients had abnormal HPV screening results or exhibited clear clinical symptoms, prompting follow-up diagnostic procedures, including ThinPrep cytologic test (TCT), colposcopy, and cervical biopsy, to confirm their condition. The inclusion criteria included female sex; history of sexual activity; no sexual activity, vaginal medication, or douching within 72 h prior to sampling; visiting the gynecology department of our hospital for HPV DNA / TCT / colposcopy / pathology / pathology tests; and no prior HPV vaccination. The exclusion criteria included the presence of tumors other than cervical cancer, no history of sexual activity prior to consultation, treatment for reproductive tract infections (e.g., vaginitis, HPV infection, gonorrhea, or mycoplasma infection) within the last 3 months, and treatment with antibiotics, hormones, or vaginal probiotics within the last month.\u003c/p\u003e\u003cp\u003eOf the patients meeting the inclusion criteria, 76 with confirmed HPV16(+) or HPV52(+) status and complete clinical data were included. All underwent TCT, colposcopy, and histopathological examination. For downstream analyses, the HPV16 subgroup comprised 10 cases each of NILM, ASC-US, CIN1, and CIN2/3, whereas the HPV52 subgroup included 10 cases each of NILM, ASC-US, and CIN1, along with 6 cases of CIN2/3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Cytology (TCT)\u003c/h2\u003e\u003cp\u003eThe experimental staff performed TCT (cytological preparation) experiments on the collected cervical exfoliative cytology cells. Upon completion, the remaining liquid-based samples were subjected to DNA extraction for HPV genotyping.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 HPV DNA Extraction and Genotyping\u003c/h2\u003e\u003cp\u003eThe cervical sample storage solution was shaken using an oscillator to fully detach the cells from the brush. The solution was then transferred to enzyme-free sterile reagent tubes. HPV DNA extraction was performed following the guidelines provided by the extraction kit. Next, the quality of the experimental samples was tested according to the standard instructions of the HPV genotyping kit. After the quality test was passed, HPV genotyping was conducted. The remaining DNA samples were stored at \u0026minus;\u0026thinsp;80\u0026deg;C for future use.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Colposcopy and Histopathology\u003c/h2\u003e\u003cp\u003ePatients who met the following criteria underwent further colposcopic examination: HR-HPV positive with cytology results of ASC-US or higher; two consecutive cytology results of ASC-US or higher (at least 6 months apart); cytology negative but HR-HPV positive for 1 year; cytology negative and HPV16 or 18 positive; and visible cervical ulcers, masses (tumors), growths, or other suspicious signs of cancer. If the colposcopic examination revealed abnormal findings and the patient met the indications for pathological biopsy, a biopsy was performed under colposcopy. The biopsy diagnosis followed the standards outlined in the \u0026ldquo;Cervical Cancer Screening and Clinical Management: China Expert Consensus (2020)\u0026rdquo; and the \u0026ldquo;WHO Classification of Tumors of Female Reproductive Organs (2020).\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Primer Design\u003c/h2\u003e\u003cp\u003eP\u0026eacute;rot et al. successfully identified HPV-human fusion sequences in female patients in France using HPV RNA-Seq technology. They developed and organized publicly available primers based on the fusion gene series presented in the literature (Tables S1 and S2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 PCR Amplification, Electrophoresis, and Imaging\u003c/h2\u003e\u003cp\u003eUsing the primers described above, HPV DNA extracted from exfoliated cervical cell samples of eligible patients was amplified according to the amplification system and program design specified in the Thermo Fisher Scientific Dream Taq Green PCR Master Mix (2x) instruction manual. Following amplification, the PCR products were subjected to electrophoresis, and the gel was imaged. Subsequently, the band positions of the DNA marker and sample bands were compared and analyzed to determine the size and concentration of DNA fragments in the samples.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e\u003cp\u003eData analysis was performed using Python (version 3.11.5) and R (version 4.3.1). Statistical tests included the chi-square test, Fisher\u0026rsquo;s exact test, or Fisher\u0026rsquo;s exact test with Monte Carlo simulation, as appropriate, depending on data distribution and expected cell counts. Continuous variables were analyzed using the independent-samples \u003cem\u003et\u003c/em\u003e-test, Welch \u003cem\u003et\u003c/em\u003e-test, or Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test, according to normality. All tests were two-sided, and a \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Feature Selection\u003c/h2\u003e\u003cp\u003eSix feature selection strategies were applied: Random Forest Recursive Feature Elimination (RF-RFE), Random Forest\u0026ndash;based Feature Importance (RFF), Least Absolute Shrinkage and Selection Operator (LASSO), Boruta algorithm (Boruta), Wilcoxon rank-sum test (Wilcoxon), and Spearman correlation analysis (Spearman).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Machine Learning Models\u003c/h2\u003e\u003cp\u003eThirteen classifiers were evaluated: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extremely Randomized Trees (ExtraTrees, ET), Gradient Boosting (GB), AdaBoost (AB), XGBoost (XGB), LightGBM (LGBM), CatBoost (CB), k-Nearest Neighbors (KNN), Gaussian Process Classifier (GP), and Multilayer Perceptron (MLP).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Model Evaluation\u003c/h2\u003e\u003cp\u003eModels were trained and tuned using stratified 5-fold cross-validation with grid search; a fixed random seed (42) ensured reproducibility. Class imbalance was handled with the Synthetic Minority Oversampling Technique (SMOTE)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Performance was quantified using the area under the receiver operating characteristic curve (ROC AUC), area under the precision\u0026ndash;recall curve (PR AUC), Matthews correlation coefficient (MCC), harmonic mean of precision and recall (F1 score), accuracy, sensitivity, and specificity. An independent test set, not used for training or tuning, was reserved for final evaluation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Model Interpretability\u003c/h2\u003e\u003cp\u003eThe optimal model, identified through independent validation, was further subjected to interpretability analysis using SHapley Additive exPlanations (SHAP). SHAP assigns each feature a contribution score to individual predictions by computing the marginal contribution of that feature across all possible feature subsets, thereby quantifying the relative importance and directionality of each variable.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Prevalence of HPV Infection Across Cervical Lesion Grades and Age Groups\u003c/h2\u003e\u003cp\u003eAmong the 5,467 eligible women, 15 HPV-positive cases lacked complete TCT, colposcopy, or biopsy data and were excluded, leaving 5,452 for the final analysis. Of these, 1,651 tested positive for HPV, yielding an overall infection rate of 30.3%. The prevalence was 20.8% in women with normal cytology and 92.6% in those with lesions, including 90.9% in ASC-US, 95.3% in CIN1, 97.4% in CIN2/3, and 100% in cervical cancer (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of HPV Infection Across Cervical Diagnostic Categories\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnostic category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfection cases(n,%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-infection cases(n,%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNILM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e985(20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3748(79.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASC-US\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e420(90.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42(9.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e204(95.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10(4.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIN2/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37(97.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(2.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCervical cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e5452\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1651(30.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3801(69.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSingle-type infections accounted for 72.7% of cases, comprising 41.7% with high-risk HPV, 3.4% with medium-risk HPV, and 27.6% with low-risk HPV. Mixed infections comprised 27.3%, most frequently combinations of high-risk and low-risk types (21.2%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In all groups involving high-risk HPV infection, the prevalence of infection in lesion tissues was more than fivefold higher than that in normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e.Distribution of HPV Infection by Risk Group Across Cervical Diagnostic Categories\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNILM\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4733)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASC-US\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;462)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCIN1\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCIN2/3\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCervical cancer\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimple High-risk, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e377(7.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e186(40.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98(45.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24(63.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4(80.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimple Medium-risk, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36(0.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15(3.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5(2.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimple Low-risk, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e355(7.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75(16.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25(11.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-risk and Medium-risk, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14(0.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10(2.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9(4.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(5.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-risk and Low-risk, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e168(3.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116(25.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54(25.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11(28.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium-risk and Low-risk, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15(0.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6(1.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4(1.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-risk and Medium-risk and Low-risk, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20(0.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12(2.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9(4.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA total of 37 HPV genotypes were identified. HPV52 was the most prevalent overall (6.1%), while HPV52 (23.4%) and HPV16 (13.2%) were the dominant types in lesion tissues. HPV16 was most frequently detected in CIN2/3 and cervical cancer. Except for HPV26, HPV57, HPV72, and HPV73, most genotypes showed significant distributional differences between normal and lesion tissues (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of HPV Genotypes Across Histological Diagnoses\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV\u0026nbsp;type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNILM\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4733)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASC-US\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;462)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCIN1\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCIN2/3\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCervical cancer\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (0.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (8.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (18.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13 (34.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (60.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"36\" rowspan=\"37\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (0.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (3.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (4.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (7.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (0.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (3.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (3.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (5.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (0.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (2.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (3.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (10.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (0.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (1.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (1.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (9.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (9.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (15.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (0.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (1.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (1.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (9.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (12.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (13.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166 (3.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (23.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (22.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (26.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (0.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (6.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (6.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (5.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (1.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (11.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (16.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (15.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (0.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (3.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (0.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (4.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (5.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (1.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (3.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (7.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (5.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (1.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (7.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (10.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (0.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (0.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (1.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (0.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (2.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (5.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (0.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (1.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (1.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (0.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (1.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (1.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (0.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (2.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (5.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (1.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (8.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (7.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (0.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (4.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (5.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (0.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (4.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (2.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (5.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87 (1.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (5.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (7.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (7.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (0.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (3.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (2.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (2.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (7.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (8.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (0.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (2.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (1.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (0.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (0.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (2.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (2.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (0.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (1.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (0.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (1.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (6.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (4.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (10.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHPV-84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (1.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (4.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (2.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (2.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAge distribution also varied significantly across histological categories (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The highest infection rate was observed in women younger than 25 years (39.3%). Across all age groups, infection rates were consistently higher in lesion tissues compared with normal cytology (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Genotype prevalence also differed by age group, with HPV52 showing the highest prevalence in all groups and peaking at 9.7% among women under 25 years (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of age groups across histological diagnoses\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNILM\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4733)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASC-US\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;462)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCIN1\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCIN2/3\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCervical cancer\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e353 (7.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47 (10.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19 (8.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (7.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1994 (42.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e211 (45.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97 (45.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15 (39.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1284 (27.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100 (21.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52 (24.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4 (10.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (40.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e838 (17.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72 (15.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37 (17.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8 (21.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e264 (5.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32 (6.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (4.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8 (21.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 5. Distribution of HPV Genotypes Across across Age groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.5698%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHPV type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le; 24\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=422)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25-34\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=2318)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35-44\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(n=1442)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45-54\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=956)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge; 55\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(n=314)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e21 (4.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e61 (2.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e28 (1.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e16 (1.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10 (3.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"37\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e13 (3.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e23 (0.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e13 (0.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10 (1.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (0.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e6 (1.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e22 (0.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7 (0.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6 (0.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4 (1.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e19 (0.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5 (0.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5 (0.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (0.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e2 (0.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (0.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6 (0.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4 (0.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3 (0.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e17 (4.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e76 (3.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e34 (2.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e22 (2.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9 (2.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e2 (0.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e11 (0.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3 (0.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3 (0.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (0.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e20 (4.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e77 (3.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e25 (1.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e20 (2.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10 (3.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e41 (9.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e137 (5.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e70 (4.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e61 (6.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e25 (7.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e14 (3.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e47 (2.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e12 (0.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9 (0.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10 (3.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e15 (3.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e66 (2.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e40 (2.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e23 (2.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e15 (4.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e10 (2.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e29 (1.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e14 (0.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7 (0.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e7 (1.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e33 (1.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e13 (0.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (0.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3 (0.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e10 (2.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e57 (2.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e18 (1.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e11 (1.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e2 (0.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e8 (1.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e52 (2.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e28 (1.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e23 (2.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (2.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e3 (0.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9 (0.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e5 (1.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9 (0.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6 (0.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (0.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e12 (2.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e27 (1.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10 (0.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7 (0.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (0.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e7 (1.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (0.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5 (0.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (0.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e5 (1.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10 (0.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7 (0.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (0.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e6 (1.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e33 (1.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e11 (0.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3 (0.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e15 (3.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e66 (2.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e31 (2.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e17 (1.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (2.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e10 (2.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e35 (1.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e19 (1.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e11 (1.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5 (1.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e2 (0.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e30 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e15 (1.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e16 (1.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7 (2.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e15 (3.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e57 (2.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e30 (2.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e26 (2.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5 (1.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e8 (1.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e23 (0.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e13 (0.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10 (1.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3 (0.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e18 (4.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e74 (3.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e30 (2.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e20 (2.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e11 (3.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e10 (2.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e16 (0.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (0.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6 (0.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4 (1.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4 (0.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (0.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e5 (1.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e22 (0.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10 (0.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e15 (1.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6 (1.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e2 (0.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e10 (0.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (0.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7 (0.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (2.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3 (0.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (0.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e10 (2.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e52 (2.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e39 (2.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e21 (2.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (2.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e1 (0.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1 (0.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (0.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5698%;\"\u003e\n \u003cp\u003eHPV-84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7383%;\"\u003e\n \u003cp\u003e15 (3.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e44 (1.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (0.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (0.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7 (2.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003cem\u003eP value represents the overall comparison of HPV genotype distributions across age groups (\u0026le; 24, 25\u0026ndash;34, 35\u0026ndash;44, 45\u0026ndash;54, and \u0026ge; 55 years). The association was evaluated using a chi-square test with Monte Carlo resampling owing to expected cell counts \u0026lt; 5 in multiple subgroups.\u003c/em\u003e\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Fusion Gene Characteristics Across Cervical Lesions\u003c/h2\u003e\u003cp\u003eGiven their high prevalence and pathogenic relevance, HPV16 and HPV52 were selected for fusion gene analysis. The mean number of fusion sites for both genotypes increased with lesion severity and was highest in CIN2/3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn HPV16-positive patients, 32 loci were detected, with locus 13, 24, 15, and 31 showing the highest frequencies. In HPV52-positive patients, 34 loci were identified, with locus 7, 29, 1, and 26 showing the highest frequencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eAmong HPV16 loci, locus 30 showed significant differences across clinical grades (\u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among HPV52 loci, locus 4, 10, 12, 18, and 19 differed significantly (\u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with higher frequencies in CIN1 and above (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eRandom Forest modeling indicated an AUC of 0.72 for HPV16 in CIN2/3, which decreased to 0.60 after removal of locus 30. For HPV52, predictive performance was higher overall, but exclusion of the five significant loci substantially reduced AUC values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). These findings suggest that specific fusion loci may serve as potential markers for early lesion classification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Machine Learning\u0026ndash;Based Predictive Model for Early Screening\u003c/h2\u003e\u003cp\u003eFusion gene\u0026ndash;based prediction alone produced ROC AUC values below 0.60. To enhance performance, data were processed through six feature selection strategies, yielding 5, 41, 1, 6, 14, and 37 features, respectively. Eight composite feature groups were then generated based on feature overlap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe original dataset was randomly divided into a training cohort (n\u0026thinsp;=\u0026thinsp;4,361) and a validation cohort (n\u0026thinsp;=\u0026thinsp;1,091) at a 4:1 ratio. Baseline feature distributions were well balanced between the two subsets, with no significant differences observed except for HPV6, HPV44, HPV56 infection (Table S3, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eThirteen classifiers were trained and evaluated. Across all groups and strategies, ROC AUC ranged from 0.87 to 0.95, PR AUC from 0.85 to 0.95, F1 scores from 0.86 to 0.89, MCC values from 0.72 to 0.78, and accuracy from 0.86 to 0.89 (Table S4, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u0026ndash;D). Sensitivity reached up to 0.94 and specificity up to 0.93.\u003c/p\u003e\u003cp\u003eEnsemble tree-based methods (ExtraTrees, RandomForest, CatBoost, LightGBM) achieved the highest performance, with ROC AUC and PR AUC values of 0.94\u0026ndash;0.95, F1 scores of 0.88\u0026ndash;0.89, and MCC values of 0.77\u0026ndash;0.78. Logistic Regression, SVM, Gaussian Process, and MLP achieved ROC AUC values of 0.90\u0026ndash;0.93 and MCC values of 0.74\u0026ndash;0.75, whereas XGBoost and AdaBoost achieved ROC AUC values of 0.88\u0026ndash;0.90 and MCC values of 0.72\u0026ndash;0.73.\u003c/p\u003e\u003cp\u003eWithin Group 1, Random Forest achieved the best performance with ROC AUC of 0.95, PR AUC of 0.95, accuracy of 0.89, F1 score of 0.88, MCC of 0.77, sensitivity of 0.86, specificity of 0.91, and precision of 0.90 (Table\u0026nbsp;6). Validation on the independent test set showed consistent results, with ROC AUC of 0.856 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eSHAP analysis indicated that the number of HPV infections contributed 30.3% of the total mean absolute SHAP value, high-risk HPV status 24.3%, low-risk HPV 7.2%, age 6.8%, HPV52 infection 5.3%, and HPV16 infection 4.9%, together accounting for 78.8% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). SHAP dependence plots further illustrated that an increasing number of HPV infections was associated with higher predicted probabilities of cervical lesions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study systematically analyzed the epidemiology of HPV infection in a large cohort of women from Shenzhen, China, and integrated fusion gene detection with machine learning modeling to explore new approaches for the early diagnosis of cervical cancer and precancerous lesions. We found that HPV52 was the most prevalent genotype in the general population, whereas HPV16 was more concentrated in high-grade lesions and cervical cancer. This distribution pattern is consistent with findings from East Asia, highlighting that although HPV52 is the most frequent genotype in the general population, HPV16 plays a stronger oncogenic role in disease progression\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In addition, the highest infection rates were observed among women aged\u0026thinsp;\u0026le;\u0026thinsp;25 years and \u0026gt;\u0026thinsp;55 years, emphasizing the importance of targeted prevention and screening in both young and high-risk populations.\u003c/p\u003e\u003cp\u003eFusion gene analysis further demonstrated an association between viral fusion events and lesion progression. The number of fusion loci increased with disease severity, and HPV16 locus 30 and HPV52 locus 4, 10, 12, 18, and 19 were more frequently detected in high-grade lesions. These findings suggest that fusion site patterns may serve as molecular indicators of progression. However, predictive performance based solely on fusion loci was limited, underscoring the need to combine these markers with additional features for clinical application.\u003c/p\u003e\u003cp\u003eWhen HPV genotyping and epidemiological variables were incorporated into machine learning models, predictive performance improved substantially. Ensemble tree-based classifiers achieved ROC AUC values exceeding 0.94 across multiple feature groups, with Random Forest performing best (AUC 0.95 in the development set and 0.856 in the independent test set). These results demonstrate that multidimensional feature fusion and algorithmic optimization can surpass the sensitivity\u0026ndash;specificity trade-offs of conventional cytology and HPV DNA testing, thereby enhancing early detection of cervical lesions.\u003c/p\u003e\u003cp\u003eInterpretability analysis further reinforced the reliability of the models. SHAP analysis identified infection burden and high-risk HPV status as the most influential predictors, together accounting for more than half of the model\u0026rsquo;s variance. Dependence plots demonstrated a monotonic increase in risk with increasing infection burden. High-risk HPV infection emerged as the key determinant, with prevalence in lesion tissues substantially higher than in normal tissues. Low-risk HPV infection also contributed 7.2%, indicating that it should not be disregarded in predictive modeling. These findings show that the models not only exhibit strong predictive performance but also align with biological plausibility\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, thereby enhancing their clinical credibility.\u003c/p\u003e\u003cp\u003eCompared with studies that require collecting a wide range of predictive factors, including demographic information, cytology, and colposcopy\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, our approach relies solely on comprehensive HPV genotyping data obtained from standard kits. This makes the method simple, cost-effective, and highly practical. Built on a large-scale cohort covering 37 HPV genotypes, the study provides a robust characterization of regional epidemiology. We further assessed the added value of fusion gene detection by directly comparing it with genotyping and epidemiological features, clarifying its relative contribution. By integrating multiple machine learning models with interpretability analysis, the framework achieved both strong predictive performance and transparency, addressing the \u0026ldquo;black-box\u0026rdquo; limitations common to artificial intelligence. Collectively, these advances move cervical cancer screening toward greater precision, efficiency, and clinical applicability.\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. This was a single-center study with a limited time frame, which may restrict generalizability. The number of high-grade lesions and cancer cases was relatively small, limiting model performance in rare categories. Fusion gene detection relied on previously reported primer sets, which may not cover all fusion events. Although an independent test set was included, external multicenter and cross-platform validation are still lacking. In addition, clinical variables such as viral load, methylation status, E6/E7 mRNA expression were not incorporated\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, which may have constrained predictive accuracy. Future research should validate these findings in multicenter, prospective cohorts, optimize fusion detection methods, integrate additional molecular and clinical markers, and assess real-world clinical utility, including impacts on screening, referral, and healthcare resource allocation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study, based on a large-scale population cohort, identified HPV52 and HPV16 as key genotypes associated with lesion progression and proposed an explainable machine learning framework built on HPV genotyping and epidemiological features. This strategy provides a new tool for early diagnosis of cervical cancer and its precancerous lesions and has the potential to enhance screening accuracy and support personalized prevention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHPV: Human papillomavirus; HR-HPV: High-risk HPV; LR-HPV: Low-risk HPV; TCT: ThinPrep cytologic test; NILM: Negative for intraepithelial lesion or malignancy; ASC-US: Atypical squamous cells of undetermined significance; LSIL: Low-grade squamous intraepithelial lesion; HSIL: High-grade squamous intraepithelial lesion; CIN: Cervical intraepithelial neoplasia; NGS: next-generation sequencing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. The research protocol was reviewed and approved by the Research Ethics Committee of Shenzhen Nanshan People\u0026apos;s Hospital (approval number: ky-2024-012801). Written informed consent was obtained from all participants prior to sample collection and data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting the interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by the Shenzhen International Science and Technology Cooperation Program under the Collaborative Innovation Science and Technology Plan (Grant No. 2021308816).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eH. Liu and X. Cao collected the data, performed statistical analyses, and drafted the initial manuscript. H. Liu, Z. Lai, X. Wen, Y. Huang, Y. Liu, and S. Chen carried out the fusion gene experiments. X. Cao, Y. Zheng, W. Kong, and Y. Wang conducted data analyses and constructed the machine learning models. D. Luo supervised the experimental and analytical work, and was responsible for drafting and finalizing the manuscript. All authors read, revised, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors gratefully acknowledge the financial support provided by the Shenzhen International Science and Technology Cooperation Program under the Collaborative Innovation Science and Technology Plan (Grant No. 2021308816). The authors also sincerely thank all members of the research team for their collaborative efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eAll data used in this study are contained within the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTaniue K, Akimitsu N. Fusion genes and RNAs in cancer development. Non-coding RNA. 2021;7(1):10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJareemit N, Horthongkham N, Therasakvichya S, et al. Human papillomavirus genotype distribution in low-grade squamous intraepithelial lesion cytology, and its immediate risk for high-grade cervical lesion or cancer: a single-center, cross-sectional study. Obstet Gynecol Sci. 2022;65(4):335\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYue H, Li X, You J, et al. Acute hematologic toxicity prediction using dosimetric and radiomics features in patients with cervical cancer: does the treatment regimen matter? Front Oncol. 2024;14:1365897.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWadler BM, Judge CM, Prout M, Allen JD, Geller AC. Improving breast cancer control via the use of community health workers in South Africa: a critical review. J Oncol. 2011;2011(1):150423.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi W, Zhu H, Yuan L, et al. Vaginal microbiota and HPV clearance: A longitudinal study. Front Oncol. 2022;12:955150.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTian S, Zhang L, Li Y, et al. Human papillomavirus E7 oncoprotein promotes proliferation and migration through the transcription factor E2F1 in cervical cancer cells. Anti-Cancer Agents Med Chem (Formerly Curr Med Chemistry-Anti-Cancer Agents). 2021;21(13):1689\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMusonda JS, Sodo PP, Ayo-Yusuf O, et al. Cervical cancer screening in a population of black South African women with high HIV prevalence: a cross-sectional study. PLOS global public health. 2022;2(11):e0001249.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChatfield-Reed K, Roche VP, Pan Q. cfDNA detection for HPV\u0026thinsp;+\u0026thinsp;squamous cell carcinomas. Oral Oncol. 2021;115:104958.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng N, Xiao J, He L, Xie L. Association Between Vaginal Microecological Alterations and High-Risk Human Papillomavirus Infection: A Cross-Sectional Study. Front Cell Infect Microbiol. 2025;15:1618846.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerbster S, Paladino A, de Freitas S, Boccardo E. Alterations in the expression and activity of extracellular matrix components in HPV-associated infections and diseases. Clinics. 2018;73:e551s.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesai KT, Adepiti CA, Schiffman M, et al. Redesign of a rapid, low-cost HPV typing assay to support risk‐based cervical screening and management. Int J Cancer. 2022;151(7):1142\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRay A. Human Papillomavirus and Other Relevant Issues in Cervical Cancer Pathogenesis. Int J Mol Sci. 2025;26(12):5549.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiao G, Jiang X, She B, et al. Multi-infection patterns and co-infection preference of 27 human papillomavirus types among 137,943 gynecological outpatients across China. Front Oncol. 2020;10:449.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo Q, Zhang H, Zeng X, Han N, Ma Z, Luo H. HPV specificity and multiple infections and association with cervical cytology in Chongqing, China: a cross-sectional study. BMC Infect Dis. 2024;24(1):804.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen C, Zeng X, Shi Z, et al. Multi-center clinical study using optical coherence tomography for evaluation of cervical lesions in-vivo. Sci Rep. 2021;11(1):7507.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRijkaart D, Berkhof J, Van Kemenade F, et al. HPV DNA testing in population-based cervical screening (VUSA-Screen study): results and implications. Br J Cancer. 2012;106(5):975\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKabir A, Bukar M, Nggada HA, Rann HB, Gidado A, Musa AB. Prevalence of human papillomavirus genotypes in cervical cancer in Maiduguri, Nigeria. Pan Afr Med J 2019;33(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang H-y, Kim H, Park KH. Diagnostic performance of the E6/E7 mRNA-based Optimygene HR-HPV RT-qDx assay for cervical cancer screening. Int J Infect Dis. 2019;78:22\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia C, He L, Sun Y. Expression and prognostic role of CXCL1 gene in colorectal adenocarcinoma. Comput Intell Neurosci. 2022;2022(1):5504731.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eP\u0026eacute;rot P, Biton A, Marchetta J, et al. Broad-range papillomavirus transcriptome as a biomarker of papillomavirus-associated cervical high-grade cytology. J Mol Diagn. 2019;21(5):768\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKahng J, Kim E-H, Kim H-G, Lee W. Development of a cervical cancer progress prediction tool for human papillomavirus-positive Koreans: A support vector machine-based approach. J Int Med Res. 2015;43(4):518\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoudhury A, Wesabi Y, Won D. Classification of cervical cancer dataset. arXiv preprint arXiv:181210383. 2018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmed M, Kabir MMJ, Kabir M, Hasan MM. Identification of the risk factors of cervical cancer applying feature selection approaches. Paper presented at: 2019 3rd international conference on electrical, computer \u0026amp; telecommunication engineering (ICECTE)2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlam TM, Khan MMA, Iqbal MA, Abdul W, Mushtaq M. Cervical cancer prediction through different screening methods using data mining. IJACSA) Int J Adv Comput Sci Appl 2019;10(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeetha R, Sivasubramanian S, Kaliappan M, Vimal S, Annamalai S. Cervical cancer identification with synthetic minority oversampling technique and PCA analysis using random forest classifier. J Med Syst. 2019;43(9):286.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsadi F, Salehnasab C, Ajori L. Supervised algorithms of machine learning for the prediction of cervical cancer. J biomedical Phys Eng. 2020;10(4):513.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 2017;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeon-Gomez P, Romero VI. Human papillomavirus, vaginal microbiota and metagenomics: the interplay between development and progression of cervical cancer. Front Microbiol. 2025;15:1515258.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoonkate S, Karnchanabanyong W, Ruengkhachorn I, et al. Significance of Genotype-Specific High‐Risk Human Papillomavirus Testing in Cervical Cancer Screening: A Hospital‐Based Study. J Med Virol. 2025;97(8):e70561.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun L, Li L, Xu W, Ma C. The immunomodulation role of vaginal microenvironment on human papillomavirus infection. Galen Med J. 2023;12:e2991.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVieira Alves M, Oliveira Pereira G, Alves dos Santos Silva L, et al. Intratype variants and high genotypic diversity of human papillomavirus with polymorphisms in the antigenic hypervariable loops of the L1 protein from women living with human immunodeficiency virus in Northeastern Brazil. J Med Microbiol. 2025;74(3):001981.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarg SK, Kapil M. A Cervical Cancer Prediction Model Using REPTree Classifier. J Comput Theor Nanosci. 2019;16(10):4438\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl Mudawi N, Alazeb A. A model for predicting cervical cancer using machine learning algorithms. Sensors. 2022;22(11):4132.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamers FF, Poulli\u0026eacute; A-I, Arbyn M. Updated evidence-based recommendations for cervical cancer screening in France. Eur J Cancer Prev. 2022;31(3):279\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFertey J, Hagmann J, Ruscheweyh HJ, et al. Methylation of CpG 5962 in L1 of the human papillomavirus 16 genome as a potential predictive marker for viral persistence: A prospective large cohort study using cervical swab samples. Cancer Med. 2020;9(3):1058\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"human papillomavirus (HPV), cervical lesions, early diagnosis, genotyping, fusion gene, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7859112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7859112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCervical cancer is the fourth most common malignancy in women worldwide, primarily driven by persistent high-risk human papillomavirus (HPV) infection. However, conventional screening methods such as cytology and HPV DNA testing remain limited in accuracy and scalability, particularly for early or precancerous lesions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed HPV infection patterns, genotype distribution, and lesion grades in 5,452 women from Shenzhen, China. Among them, 76 HPV16- or HPV52-positive cases underwent exploratory PCR-based fusion-gene detection. Six feature-selection strategies and thirteen machine-learning classifiers were trained using stratified five-fold cross-validation, with SMOTE for class balancing and grid search for hyperparameter tuning. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe overall HPV infection rate was 30.3%. HPV52 was the most prevalent genotype (6.1%) in the general population, whereas HPV16 predominated in high-grade lesions and cancer. The number of fusion loci increased with lesion severity, but fusion data alone showed limited predictive value (ROC AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.60). Integrating HPV genotyping with epidemiological features markedly improved performance: Random Forest achieved ROC AUC and PR AUC of 0.95 in cross-validation and 0.86 in the independent test set. SHAP analysis identified infection burden and high-risk HPV status as dominant predictors, jointly explaining over half of the model variance.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study establishes a region-specific epidemiological profile of HPV and introduces an explainable, low-cost machine-learning framework based solely on HPV genotyping. The model demonstrates high accuracy and clinical scalability, providing a practical approach for early screening of cervical lesions.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eChiCTR, ChiCTR2400089277. 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