HPV Genotype Distribution and Cervical Lesions in Colposcopy-Referred Women in Western China: A Cross-Sectional Study

HPV Genotype Distribution and Cervical Lesions in Colposcopy-Referred Women in Western China: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article HPV Genotype Distribution and Cervical Lesions in Colposcopy-Referred Women in Western China: A Cross-Sectional Study Jiahan Lai, Siqi Li, Keliya Mai, Lili Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8607078/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background HPV genotype distribution and sociodemographic patterns of cervical lesion severity remain under-characterized for colposcopy-referred cohorts in multiethnic western China. This evidence gap impedes precision triage for clinical populations selected through screening thresholds. Objectives To delineate HPV genotype composition and examine sociodemographic profiles associated with biopsy-confirmed cervical lesion grades within a single-center hospital-based colposcopy referral cohort in Xinjiang. Methods Cross-sectional analysis of 2,934 patients who underwent colposcopy with complete biopsy data at People's Hospital of Xinjiang Uygur Autonomous Region (January 2018–June 2025). Data captured demographics, HPV genotyping, ThinPrep cytology, and histopathological endpoints. Statistical evaluation employed chi-square tests, standardized residual analysis, and ordinal logistic regression. Results Within this referral cohort, high-grade lesions (CIN2+) constituted 44.3% of biopsy specimens. HPV16 accounted for 51.1% of HPV-positive detections, increasing to 77.2% among cervical cancer specimens. HPV52 (10.2%) and HPV58 (9.5%) formed a secondary genotype cluster. Age distribution exhibited a bimodal pattern: high-grade precancerous lesions predominated among patients < 40 years, while invasive cancer specimens were concentrated in those ≥ 50 years. Lower educational attainment and physical-labor occupation corresponded to higher proportions of advanced-stage disease specimens. Conclusion In this Xinjiang colposcopy-referred cohort, HPV16 predominates among high-grade lesions, while HPV52/58 constitute a consequential secondary cluster. The observed sociodemographic distributions underscore the necessity of embedding both virological patterns and social determinants into risk stratification frameworks for referral populations, providing data to inform regionally-tailored triage protocols in secondary care. Human Papillomavirus (HPV) HPV Genotypes Cervical Lesions Colposcopy Xinjiang/China Cross-Sectional Study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Cervical cancer remains a global health priority, prompting WHO's 2020 "Global Strategy to Accelerate the Elimination of Cervical Cancer" with 90–70–90 targets for 2030.[1–3] The etiology of this malignancy is rooted in persistent infection with high-risk human papillomavirus genotypes. Notably, HPV-16 and HPV-18 are implicated in over 70% of global cases.[4–6] As the world's most populous nation, China reported approximately 151,000 new cases and 56,000 deaths in 2022.[7] The disease burden demonstrates pronounced geographic heterogeneity,[8–10] with remote and economically disadvantaged regions exhibiting disproportionately elevated case numbers and mortality[11]. China recently integrated free bivalent HPV vaccination for specified adolescent females into its national immunization program,[12] creating unprecedented opportunities for cancer control.[13] However, the concordance between vaccine-targeted HPV 16/18 and circulating genotypes in multi-ethnic populations of Western China remains inadequately characterized.[14] This knowledge gap constrains accurate assessment of vaccine effectiveness and localization of evidence-based strategies, particularly for patient populations undergoing colposcopic biopsy in tertiary care settings. This challenge is particularly acute in Xinjiang, where vast territory, ethnic diversity, and uneven healthcare distribution limit robust epidemiological data.[15] Optimization of triage protocols within resource-constrained settings constitutes a critical prerequisite for attaining WHO elimination targets. The WHO guideline for screening and treatment of cervical pre-cancer lesions explicitly endorses dual-stain cytology for triaging HPV-positive women.[16] The diagnostic performance of conventional screening modalities across distinct demographic subsets and the role of socioeconomic variables have received limited empirical attention.[2, 17] Furthermore, current evidence predominantly derives from community-based screening initiatives. Consequently, the clinical and virological profile of the colposcopy-referred cohort—a high-risk population directly informing management pathways—remains inadequately characterized for hospital-based settings.[18] Our team previously described HPV infection patterns among Xinjiang's general female population, identifying HPV16 as predominant among rural Uyghur women in Hotan with distinctive local risk factors (early age at first marriage, multiple marriages).[19, 20] providing an initial regional evidence base. However, a substantial evidence deficit persists regarding the colposcopy-referred population—a high-risk cohort triaged following positive screening.[21] Whether genotype distribution in this clinical population diverges from community-based patterns remains unknown. Moreover, the influence of specific virological and socioeconomic determinants on cervical lesion severity has not been systematically investigated. Accordingly, this study systematically analyzes biopsy data from this pivotal colposcopy-referred cohort with two primary objectives: (1) delineate HPV genotype distribution and compare with our general population data; and (2) examine associations between ethnic background, geographical origin, socioeconomic circumstances and high-grade cervical lesions. The resulting evidence will inform integrated control strategies that bridge community screening initiatives with precision clinical management. 2. Methods 2.1 Research Design and Population This single-center retrospective cross-sectional study was conducted at People's Hospital of Xinjiang Uygur Autonomous Region. From January 2018 to June 2025, 6,488 patients undergoing colposcopy with complete cervical histopathological biopsy results were consecutively screened; following predefined inclusion and exclusion criteria, 2,934 eligible patients formed the final analytic cohort. Demographics, HPV testing, and ThinPrep cytology results were systematically collected.The study protocol was approved by the Ethics Committee of People's Hospital of Xinjiang Uygur Autonomous Region ( Approval No.: KY2022080502 ). Given the retrospective design using de-identified medical records, the ethics committee waived the requirement for informed consent. All data were anonymized prior to analysis, with personal identifiers removed and replaced by study-specific codes to ensure patient confidentiality. The study adhered to the Declaration of Helsinki and institutional regulations governing the ethical use of medical data for research purposes. 2.2 Inclusion and Exclusion Criteria Inclusion Criteria (1) Colposcopy with complete cervical biopsy results at People's Hospital of Xinjiang Uygur Autonomous Region; (2) Complete demographics (age, ethnicity, education, residence, occupation); (3) Documented HPV testing (viral load/genotyping) and/or ThinPrep cytology (TCT). Exclusion Criteria (1) Acute genital infection or vaginal interference (sexual activity, intravaginal medication, irrigation within 72 hours) prior to colposcopy; (2) Prior treatment for cervical precancerous lesions (e.g., CIN) or related hysterectomy;(3) Pregnancy or lactation; (4) Missing key demographic data. 2.3 Data Collection and Variable Definition Data were extracted from electronic medical records, encompassing: (1) Sociodemographics: age, ethnicity (Han, Uyghur, Kazakh, Hui, other), residence region (Northern/Southern Xinjiang), education (primary or below, middle school, high school, university, postgraduate), and occupation (physical labor, mental labor, retired); (2) Laboratory findings: high-risk HPV genotyping (with viral load in some cases) and ThinPrep cytology (TCT) classified as NILM, ASC-US, LSIL, or HSIL+; (3) Histopathology (diagnostic reference): colposcopy-directed biopsy results categorized as normal/inflammation, HPV infection alone, CIN1, CIN2, CIN3, or cervical cancer. 2.4 Specimen Sampling Methods HPV Sampling and Detection Cervical exfoliated cells were collected by a gynecologist who first removed excess mucus with a cotton swab, then used an HPV detection brush rotated five times clockwise at the external cervical os to facilitate mucosal cell adherence. The brush tips were placed separately into vials containing transport medium and stored at 2–8°C until HPV DNA extraction and genotyping. HPV DNA extraction, viral load quantification, and genotyping were performed following manufacturer protocols using a PCR assay (QIAGEN Enterprises Management Co., Ltd., Shanghai, China). ThinPrep Cytological Test (TCT) Cellular specimens were collected identically to HPV sampling. Samples were processed by the ThinPrep 2000 system, Papanicolaou-stained, and independently interpreted by two senior cytopathologists blinded to clinical data. Final reports represented consensus diagnoses. Colposcopy Examination and Pathological Diagnosis Experienced gynecologists performed colposcopy with targeted biopsies of acetowhite epithelium, iodine-negative zones, or suspicious lesions. Tissue specimens underwent formalin fixation, paraffin embedding, hematoxylin–eosin staining, and microscopic examination for definitive histopathological diagnosis. 2.5 Statistical Analysis Variables were analyzed by measurement level and distribution. Categorical data are presented as frequencies and percentages—composition ratios within this referral cohort, not population rates. Continuous variables were tested for normality (Shapiro-Wilk test, Q-Q plots); normally distributed data are expressed as mean ± SD, non-normal data as median(IQR). Stacked bar charts visualized relationships among HPV genotype, TCT, and histopathology in patients with complete data. Associations between HPV genotype distribution and categorical TCT/histopathology outcomes were assessed using Pearson's chi-square test, with Cramer's V quantifying effect size and standardized residuals identifying key contributing cells. Significant findings prompted post-hoc pairwise comparisons with Bonferroni correction to control family-wise error rate. HPV genotype distribution across demographics was displayed via stacked bar charts. For grouped demographics, chi-square tests evaluated associations with histopathology, with p-values adjusted using Bonferroni FDR. Standardized residuals and odds ratios quantified category-specific deviations and risks. Ordinal logistic regression assessed ordered associations with pathological severity. Key findings were synthesized and presented graphically as heatmaps. Analyses were performed in R (v4.3.3) using dplyr (data manipulation), survey (complex procedures), ggplot2, and patchwork. Two-sided p < 0.05 indicated significance. This descriptive exploratory analysis identifies potential associations without inferring causality or population-level epidemiology. 3. Results 3.1 Basic Characteristics of the Research Subjects Table 1 summarizes characteristics of the final referral cohort (n = 2,934), selected from 6,488 women undergoing colposcopy at our institution (Jan 2018–June 2025). Mean age was 44.11 ± 11.35 years. Histopathology revealed normal tissue/inflammation in 655 (22.3%) and various cervical lesions in 2,279 (77.7%)—percentages representing composition ratios within this cohort, not population rates. The cohort was predominantly Han (48.3%) and Uyghur (34.1%), with 79.4% residing in Northern Xinjiang. Over half (55.0%) had university education or higher. Occupations were primarily mental workers (47.3%) and manual workers (38.2%). Table 1 Baseline Sociodemographic and Clinical Characteristics of the Study Population. Characteristic Overall (N = 2934) Characteristic Overall (N = 2934) Age (years, Mean ± SD) 44.11 ± 11.35 Region, n (%) Age Group (years), n (%) Northern Xinjiang 2329 (79.4) <30 283 (9.6) Southern Xinjiang 605 (20.6) 30–39 837 (28.5) Occupation, n (%) 40–49 846 (28.8) Physical Labor 1119 (38.2) 50–59 691 (23.6) Mental Labor 1388 (47.3) ≥60 277 (9.4) Retired 427 (14.6) Ethnicity, n (%) TCT Result, n (%) Han 1418 (48.3) NILM 711 (31.2) Uyghur 1000 (34.1) ASC-US 813 (35.7) Kazakh 289 (9.9) LSIL 531 (23.3) Hui 132 (4.5) HSIL+ 225 (9.9) Other Minorities 95 (3.2) Biopsy Result, n (%) Education Level, n (%) Normal/Inflammation 655 (22.3) Primary and Below 358(12.3) HPV Infection Alone 570 (19.4) Middle School 529 (18.0) CIN1 412 (14.0) High School 434 (14.8) CIN2 577 (19.7) University 1534 (52.3) CIN3 437 (14.9) Postgraduate 79 (2.7) Squamous Cell Carcinoma 254 (8.7) Adenocarcinoma 29 (1.0) Note. TCT, ThinPrep Cytologic Test; ASC-US, Atypical Squamous Cells of Undetermined Significance; LSIL, Low-grade Squamous Intraepithelial Lesion; HSIL, High-grade Squamous Intraepithelial Lesion. TCT results were available for 2,280 (77.7%) participants. Due to the retrospective nature of the study, missing TCT data were excluded from this category and from all subsequent analyses involving TCT. 3.2 HPV Genotype Distribution and Cervical Lesion Spectrum in the Colposcopy Cohort HPV genotype distribution and lesion spectrum are illustrated in Fig. 2 . High-grade lesions (CIN2+) comprised 44.3% of biopsies from this colposcopy-referred cohort (Fig. 2 A). HPV 16 predominated (51.1% of HPV-positive cases in this referral cohort), with HPV 52 (10.2%) and 58 (9.5%) detected more frequently than HPV 18 (8.9%) (Fig. 2 B). HPV 16 proportions increased markedly with severity: from 40.3% in normal cytology to 65.2% in HSIL (Fig. 2 C), and histopathologically from 29.3% in HPV-infected tissue to 54.2% in CIN2, 72.1% in CIN3, and 77.2% in squamous cell carcinoma (Fig. 2 D). Among 29 adenocarcinoma cases, HPV 18 detection (36%) exceeded its overall proportion. HPV 16/18 carried higher proportions of high-grade lesions versus HPV 52/58, which more often presented as low-grade (Fig. 2 E). These adenocarcinoma co-infection findings warrant validation in larger studies due to limited sample size. 3.3 Association of HPV Genotype and TCT Findings with Histopathological Outcomes Within the three-step cervical screening framework, HPV Genotype and TCT serve as primary screening modalities, with colposcopy/biopsy providing definitive diagnosis. This study assessed their predictive capacity in a colposcopy-referred cohort. HPV genotype showed significant association with histopathology (χ² = 305.63, P < 0.001; Cramer's V = 0.165) (Fig. 3 A). Standardized residual analysis revealed HPV16's pronounced positive correlation with high-grade lesions: CIN3 (8.11, P < 0.001) and cancer (8.74, P < 0.001) proportions significantly exceeded expected values, while representation in normal/inflammatory tissue was lower (-5.06, P < 0.001). Other genotypes (HPV52, 58, 56) associated primarily with lower-grade lesions (infection, CIN1, CIN2), demonstrating HPV genotyping's imperfect concordance with pathology and the necessity of biopsy confirmation. TCT demonstrated a stronger association (χ² = 258.81, P < 0.001; Cramer's V = 0.242) (Fig. 3 B). A clear gradient corresponded to histological severity: HSIL positively associated with high-grade lesions (CIN3, cancer), LSIL predominantly with CIN1, while ASC-US lacked discriminatory specificity. This discrepancy between cytological inference and histological confirmation constitutes the fundamental rationale for colposcopy and biopsy. Detailed frequency distributions appear in Supplementary Tables 1–2 . In summary, both HPV Genotype and TCT showed significant but imperfect associations with histological outcomes in this referral cohort, underscoring their role as screening rather than diagnostic tools. 3.4 Association between Demographic Characteristics and Distribution of Cervical Lesions This study aimed to evaluate the relationship between demographic factors—specifically age, educational level, and occupation—and the distribution of cervical lesions, as determined by pathological findings from colposcopic biopsies. 3.4.1 General Demographic Characteristics Age : Significant association with lesion severity (χ² = 239.29, P < 0.001; Cramer's V = 0.14). Standardized residual analysis revealed a bimodal pattern (Fig. 4 A): women + 2.45, FDR P 60, had markedly elevated invasive cancer rates (residual = + 9.90, FDR P < 0.001). Ordinal logistic regression indicated increasing severity with age (OR = 3.74, 95% CI: 2.86–4.89, P < 0.001), though proportional odds may not hold uniformly. Education : Significant inverse association (χ² = 269.75, P < 0.001; Cramer's V = 0.15). The lowest education group (primary school or below) showed substantially higher invasive cancer proportions (residual = + 13.66, FDR P < 0.001), while the highest education group (university or above) had lower cancer rates and higher precancerous detection (Fig. 4 B). Higher education was associated with reduced lesion severity (OR = 0.44, 95% CI: 0.34–0.58, P < 0.001). Occupation : Significant disparities (χ² = 253.99, P < 0.001; Cramer's V = 0.21). Manual workers exhibited markedly elevated cervical cancer proportions (residual = + 10.57, FDR P < 0.001), while mental workers showed lower cancer rates with higher early-stage lesion detection (Fig. 4 C). Mental workers had substantially reduced risk of high-grade lesions versus manual workers (OR = 0.40, 95% CI: 0.33–0.48, P < 0.001). Detailed frequency distributions are in Supplementary Table 3 . 3.4.2 Exploratory Analysis of Ethnicity and Regional Characteristics An exploratory analysis stratified by geography (Northern vs Southern Xinjiang, demarcated by Tianshan Mountains' ~42°N latitude, Fig. 5 A) and ethnicity (Fig. 5 C- 5 D) revealed HPV 16 predominance across all subgroups with notable variations. Southern Xinjiang showed a concentrated profile with HPV 16 accounting for 60.32% of cases (Fig. 5 B-b), while Northern Xinjiang exhibited greater diversity where HPV 52 (11.88%) and HPV 58 (10.76%) formed a significant secondary cluster (Fig. 4 B-a). Ethnicity-specific findings: Hui population had the highest HPV 16 proportion (64.84%, Fig. 5 D-d); Han showed relatively high HPV 52 (14.81%) and HPV 58 (12.67%, Fig. 5 D-a); Kazakh presented a distinct profile with prominent HPV 39 (8.16%, Fig. 5 D-c ) . 4. Discussion 4.1 Reinterpreting Oncogenic Dominance: HPV16 Enrichment as a Socially Scaffolded Biological Phenomenon The findings discussed in this study derive fundamentally from a single-center colposcopy referral cohort. The observed predominance of HPV16 (51.1%) reflects the combined influence of clinical selection bias and viral biological properties. Consequently, generalization to the general community is precluded. The following analyses should be interpreted as hypothesis generation within this framework. Causal validation is not pursued.[22–24] HPV16 prevalence in this referral cohort (51.1%) exceeds population-based screening benchmarks threefold. This disproportionate representation reflects the convergence of heterogeneous lesion progression kinetics and colposcopy triage thresholds. HPV16 integrates into host cellular DNA within 12 months post-infection at a 3–5 fold higher frequency relative to HPV52/58, with median progression time to CIN3 + of 5–8 years.[25–27] By contrast, HPV52/58-related lesions at the cohort median age (44.1 years) predominantly exhibit spontaneous regression or indolent persistence, thereby failing to meet referral criteria. This "accelerated triage" mechanism, rather than true prevalence disparities, accounts for the differential distribution of HPV52/58 across histologic grades. Specifically, the genotype constituted 23.3% of CIN1 lesions but merely 8.1% of invasive cancers in this cross-sectional cohort.[28–30] Key omission Viral load was not analyzed, preventing the establishment of a dose-response gradient between HPV16 DNA levels and lesion grade, which limits mechanistic interpretation. 4.2 Triple Projection of Social Determinants: Language-Mobility-Life Course Gaps The associations between age, education, occupation and lesion severity manifested in this cohort constitute non-random residual effects of socioeconomic gradients in the referral population. These effects operate through mechanistic pathways distinctive to Xinjiang's sociodemographic context:[31–33] Education (OR = 0.44, 95% CI: 0.34–0.58) : Lower educational attainment functioned as a composite proxy for systemic information accessibility barriers rather than individual health literacy deficits. Mandarin proficiency limitations among low-education ethnic minority groups (particularly Uyghur and Kazakh populations) constrained therapeutic communication dynamics. The institutional under-provision of multilingual services constituted the fundamental driver. Manifesting as inadequate language-concordant screening materials and insufficient bilingual staffing relative to ethnic demographics, this systemic deficit generated the standardized residual of + 13.66 as a quantifiable clinical signature of linguistic accessibility gaps. Accordingly, intervention priorities must target institutional multilingual health information infrastructure rather than individual competency augmentation. Occupation (standardized residual + 10.57 for invasive cancer among manual laborers) These findings reveal an institutional misalignment between screening protocols and the life course trajectories of mobile populations. Lifestyle mobility patterns encompassing transhumance and seasonal labor migration systematically disrupt screening participation continuity. Among manual laborers, the paradox of low precancer detection concurrent with elevated invasive cancer rates signals a cascading phenomenon. This pattern reflects a "migration-absence-diagnostic delay" mechanism. Such patterns underscore systemic gaps in health governance for mobile populations, especially Kazakh herders and Uyghur farmers. Age (bimodal distribution) These findings reveal an institutional misalignment between screening policy frameworks and life-course trajectories. The CIN2/3 peak occurring before age 40 originates from discordance between screening initiation (ages 25–30) and Xinjiang's earlier marriage patterns.[25, 34] Disproportionate representation among women aged ≥ 60 years derives from compounded structural barriers—remote-area travel costs and cognitive biases that normalize postmenopausal bleeding. These barriers precipitate systematic screening disengagement. Collectively, these phenomena demonstrate coverage gaps at both extremes of the female reproductive life span. 4.3 Hypothetical Cues on Geographic and Ethnic Variations Specific findings—including elevated HPV16 prevalence in southern Xinjiang (60.3%) versus northern Xinjiang (48.1%) and HPV39 enrichment within the Kazakh subgroup (8.16%)—are constrained by the geographic and ethnic skew inherent in single-center referral patterns. Consequently, these observations warrant investigation rather than definitive conclusion. Plausible explanations for these variations include social network closure or host genetic factors such as HLA polymorphisms. However, validation requires integrating viral phylogeography with ethnographic investigation. Critically, our single-center data do not support conclusions regarding true population-level differences.[2, 35] 4.4 Methodological Constraints and a Progressive Precision Pathway Despite inherent single-center referral biases, this study derives from the sole National Clinical Research Center for Gynecologic Diseases in Xinjiang, accounting for amounts of provincial cervical screening referrals. The bias inherent in this study's referral design precisely reveals systemic failure points of the current screening system in Xinjiang: Inability to distinguish incident / persistent infections This indicates the necessity of establishing a prospective Central Asian natural history cohort (currently absent) to quantify type-specific clearance/progression dynamics. Absence of viral load and HLA typing This gap identifies molecular stratification variables that subsequent investigations must incorporate. Without such data, modifiers of HPV16 accelerated progression remain unresolvable. Insufficient sample size in the adenocarcinoma subgroup Adenocarcinoma subgroup analysis included only 29 cases. Although the observed 36% HPV18 proportion aligns with Swedish cohorts (35–52%),[36] limited statistical power precluded detection of significant oncogenic advantage. This limitation necessitates expansion of sample sizes for validation. Policy translation logic Based on the threefold HPV16 enrichment and even higher proportion in southern Xinjiang, immediate piloting of direct HPV16-positive referral is feasible. However, service disruptions for mobile populations, early-married groups, and elderly dropouts require prioritized sequential interventions, not simultaneous rollout.[37–39] The value of single-center data lies in identifying critical failure nodes, not providing universal solutions. 5. Conclusion This colposcopy cohort analysis empirically examines how sociocultural gradients reshape HPV genotype oncogenic patterns within Xinjiang's multiethnic, geographically polarized frontier context. These findings necessitate a paradigm shift from biological universalism toward precision public health frameworks integrating viral phylogenetics, social network theory, and ethnic immunogenetics. The stark disparities we quantify mandate regionally nuanced and ethnically informed cervical cancer elimination strategies in China. These encompass HPV16's threefold enrichment indicating clinical filtering failure and occupational cancer risks encoded in mobility patterns. Rather than construing single-center referral data as a limitation, we reposition it as a sentinel surveillance node at the confluence of infectious disease dynamics and health equity. This vantage point identifies critical system failure nodes. These nodes comprise language-exclusion barriers, mobile population governance gaps, and life-course policy misalignments. These are precisely the junctures where next-generation cancer prevention policies must be forged. Policy implications are immediate pilot direct HPV16-positive colposcopy referral; deploy bilingual mobile screening units for mobile populations; and realign screening initiation to earlier life-course stages in this region. Declarations a. Ethics approval and consent to participate This study was approved by the Ethics Committee of People's Hospital of Xinjiang Uygur Autonomous Region ( Approval No.: KY2022080502 ). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. b. Consent for publication Not applicable. c. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author Prof. Lili Han on reasonable request. Correspondence should be addressed to [email protected] . d. Competing interests The authors declare that they have no competing interests. e. Funding This work was supported by the Autonomous Region Key R&D Program Project ( Grant No.: 2022B03018-1 ). f. Authors' contributions L.J. conceived and designed the study, conducted the investigation, performed the formal analysis, curated the data, and wrote the original draft. L.S. developed the methodology and software, performed validation and visualization, and reviewed & edited the manuscript. M.K. provided resources, assisted in the investigation, and reviewed & edited the manuscript. 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Factors associated with high-risk HPV infection and cervical cancer screening methods among rural Uyghur women aged >30 years in Xinjiang. BMC Cancer. 2018;18(1):1162. doi:10.1186/s12885-018-5076-0 de Waard J, Bhattacharya A, de Boer MT, van Hemel BM, Esajas MD, Vermeulen KM, et al. Clinical validation of a three-marker methylation panel to detect CIN3+ in vaginal self-samples in the dutch population-based screening programme. Clin Epigenetics. 2025;17:95. doi:10.1186/s13148-025-01498-7 Lu Z, Dong B, Yao P, Huang Y, Fu L, Wang B, et al. Characteristics of human papillomavirus prevalence and infection patterns among women aged 35-65 in Fujian province, China: A nine-year retrospective observational study. J Med Virol. 2025;97(2):e70238. doi:10.1002/jmv.70238 Chinese Society of Colposcopy and Cervical Pathology of China Healthy Birth Science Association, Chinese Society of Gynecologic Oncology of Chinese Medical Association, Gynecologic Oncology Committee of China Anti-Cancer Association, Obstetric and Gynecologic Health Branch of China International Exchange and Promotion Association for Medical and Healthcare, National Cervical Cancer Prevention and Treatment Collaborative Group of China Cancer Foundation, Oncology Prevention and Control Committee of China Preventive Medicine Association, et al. Chinese cervical cancer screening guidelines (Part 1). Chin J Clin Obstet Gynecol. 2023;24(4):437-442. Chinese Society of Colposcopy and Cervical Pathology of China Healthy Birth Science Association, Chinese Society of Gynecologic Oncology of Chinese Medical Association, Cervical Cancer Committee of China Anti-Cancer Association, Obstetric and Gynecologic Health Branch of China International Exchange and Promotion Association for Medical and Healthcare, National Cervical Cancer Prevention and Treatment Collaborative Group of China Cancer Foundation, Oncology Prevention and Control Committee of China Preventive Medicine Association, et al. Chinese cervical cancer screening guidelines (Part 2). Chin J Clin Obstet Gynecol. 2025;26(1):88-96. Ren Y, Qin F, Shen L, Li L, Wu Q, Yi P. Triage of women with a positive HPV DNA test: Evaluating a DNA methylation panel for detecting cervical intraepithelial neoplasia grade 3 and cervical cancer in cervical cytology samples. BMC Cancer. 2025;25:1207. doi:10.1186/s12885-025-13996-8 Chen Y, Li X, Chen P, Yin Z, Zhu P, Zhang L, et al. ZNF671 methylation is a potential regression predictor of cervical intraepithelial neoplasia grade 3 in the colposcopy-to-conization interval. Pathol Res Pract. 2025;273:156116. doi:10.1016/j.prp.2024.156116 Paz-Zulueta M, Álvarez-Paredes L, Rodríguez Díaz JC, Parás-Bravo P, Andrada Becerra ME, Rodríguez Ingelmo JM, et al. Prevalence of high-risk HPV genotypes, categorised by their quadrivalent and nine-valent HPV vaccination coverage, and the genotype association with high-grade lesions. BMC Cancer. 2018;18(1):112. doi:10.1186/s12885-018-4492-2 Løvestad AH, Repesa A, Costanzi JM, Lagström S, Christiansen IK, Rounge TB, et al. Differences in integration frequencies and APOBEC3 profiles of five high-risk HPV types adheres to phylogeny. Tumour Virus Res. 2022;14:200247. doi:10.1016/j.tvr.2022.200247 Li J, Li S. From viral infection to genome reshaping: The triggering role of HPV integration in cervical cancer. Int J Mol Sci. 2025;26(18):9214. doi:10.3390/ijms26189214 Solis-Ponce L, Stosic M, Curicó G, Espetia S, Sanchez-Grandez H, Hassan SS, et al. Genotype distribution and molecular characterization of HPV in the Peruvian Amazon: Insights into prevalence, lineage diversity, and viral integration. Sci Rep. 2025;15:32535. doi:10.1038/s41598-025-92049-3 World Health Organization. Social determinants of health. Geneva: World Health Organization. Available from: https://www.who.int/health-topics/social-determinants-of-health [cited 2025 Dec 1]. Rezhake R, Abuduxikuer G, Abudurexiti G, Zhuo Q, Muhetaer K, Abulimiti T, et al. Evaluation of the multiple HPV-based "screen and triage" algorithms in real-world settings of rural China. Cancer Biol Med. 2025;22(9):1053-1067. doi:10.20892/j.issn.2095-3941.2024.0209 Hu J, Li L, Pang L, Chen Y, Yang L, Liu C, et al. HLA-DRB11501 and HLA-DQB10301 alleles are positively associated with HPV16 infection-related kazakh esophageal squamous cell carcinoma in Xinjiang China. Cancer Immunol Immunother. 2012;61(11):2135-2141. doi:10.1007/s00262-012-1241-5 Huang J, Huang K, Xu R, Wang M, Liao Q, Xiong H, et al. The associations of HLA-a02:01 and DRB111:01 with hepatitis C virus spontaneous clearance are independent of IL28B in the Chinese population. Sci Rep. 2016;6:31485. doi:10.1038/srep31485 Dahlström LA, Ylitalo N, Sundström K, Palmgren J, Ploner A, Eloranta S, et al. Prospective study of human papillomavirus and risk of cervical adenocarcinoma. Int J Cancer. 2010;127(8):1923-1930. doi:10.1002/ijc.25426 Winer RL, Lin J, Anderson ML, Tiro JA, Green BB, Gao H, et al. Strategies to increase cervical cancer screening with mailed human papillomavirus self-sampling kits: A randomized clinical trial. JAMA. 2023;330(20):1971-1981. doi:10.1001/jama.2023.21877 Zhao X, Hu S, Zhao S, Rezhake R, Huang L, Duan X, et al. Risk assessment of self-sampling HPV tests based on PCR, signal amplification to guide the appropriate screening intervals: A prospective study in China. J Natl Cancer Cent. 2022;2(4):298-305. doi:10.1016/j.jncc.2022.11.002 Zhang J, Dong Z, Xu L, Han X, Sheng Z, Chen W, et al. An injection molded SlipChip with self-sampling for integrated point-of-care testing of human papilloma virus. Adv Sci (Weinh). 2024;11(43):e2406367. doi:10.1002/advs.202406367 Cervical Cancer Committee of China Anti-Cancer Association, Li LY. Cervical cancer screening standards (2025 edition). Chin J Pract Gynecol Obstet. 2025;41(3):332-337. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers invited by journal 30 Jan, 2026 Editor assigned by journal 29 Jan, 2026 Editor invited by journal 21 Jan, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 19 Jan, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8607078","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583305023,"identity":"5a1585ab-2c45-4b67-8525-fc8b603aa849","order_by":0,"name":"Jiahan Lai","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Jiahan","middleName":"","lastName":"Lai","suffix":""},{"id":583305024,"identity":"0d12fa6f-42ed-455b-a5a2-78e346a6681d","order_by":1,"name":"Siqi Li","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Li","suffix":""},{"id":583305025,"identity":"b57021a6-79e9-44f4-8da3-a180dec88a2e","order_by":2,"name":"Keliya Mai","email":"","orcid":"","institution":"Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Keliya","middleName":"","lastName":"Mai","suffix":""},{"id":583305026,"identity":"4928b942-5dec-41c3-91bd-1ef803140cd1","order_by":3,"name":"Lili Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACgwMMjAcSCiSYGRiYDxz48IM4LQwHEgxAWtgSD87sIVYLgwGIyWN8mIONGC3Hzx448MDAgt3g+JkPhxl4GOT5xQ7g12J/Ji8B7DDJntwNhwssGAxnzk4g5LAcA7AWfgneDYdn8DAkGNwmpOX8G4gWNgmeB4d52IjRcgNuCw8DsVqgtkj2pBkAA1mCCL+czzF8+KOiLtng+OHHHz78sJHnlyagBQaSobQEccpBwI54paNgFIyCUTDiAADkAkd8XzZ5ggAAAABJRU5ErkJggg==","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":true,"prefix":"","firstName":"Lili","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2026-01-15 05:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8607078/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8607078/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101789105,"identity":"0c1dccea-07bd-46cb-9139-d5df1c3689fe","added_by":"auto","created_at":"2026-02-03 15:56:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1122227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEligibility Flowchart for the Single-Center Colposcopy-Referred Cohort\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8607078/v1/ccbeac8966111ddc84b70cb0.png"},{"id":101789175,"identity":"5516d994-ec9d-466a-a88d-e203031935bc","added_by":"auto","created_at":"2026-02-03 15:56:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5027894,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of HPV Genotypes in Relation to Cytological and Histopathological Findings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Proportions of HPV Genotypes Detected in the referral cohort.(B) Histopathological diagnosis profile of cervical biopsy specimens.(C) Relative frequency of the five most common HPV genotypes, stratified by ThinPrep cytologic test (TCT) results (Normal, ASC-US, LSIL, HSIL).(D) Relative frequency of the five most common HPV genotypes, stratified by histopathological diagnosis.(E) Distribution of histopathological diagnoses for each HPV genotype. \u003cstrong\u003eNote.\u003c/strong\u003e Proportions are calculated within each genotype group among HPV-positive cases in this referral cohort.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8607078/v1/15a82545048ee4d46bde3c8e.png"},{"id":101789133,"identity":"6286bccf-eafc-4fd7-b400-00bf00e33488","added_by":"auto","created_at":"2026-02-03 15:56:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1474822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003etandardized Residual Analysis of Associations Between Screening Tests and Histopathological Diagnoses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)Standardized residuals for the association between specific HPV genotypes and cervical histopathological diagnoses.(B) Standardized residuals for the association between ThinPrep cytologic test (TCT) results and cervical histopathological diagnoses. \u003cstrong\u003eNote.\u003c/strong\u003e Absolute residual \u0026gt; |2.0| indicates cell contribution to χ² significance (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8607078/v1/393616acba7d9a5820902c4a.png"},{"id":101789115,"identity":"6d1f5f3d-745c-4630-93cc-af8654e1148e","added_by":"auto","created_at":"2026-02-03 15:56:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":925226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStandardized Residual Analysis of Associations Between Sociodemographic Factors and Histopathological Outcomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Standardized residuals for the association between age groups and histopathological findings.(B) Standardized residuals for the association between educational attainment and histopathological findings.(C) Standardized residuals for the association between occupational categories and histopathological findings. \u003cstrong\u003eNote.\u003c/strong\u003e Absolute residual \u0026gt; |2.0| indicates cell contribution to χ² significance (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8607078/v1/1ec228fe50b1da3f257b20bd.png"},{"id":101789067,"identity":"8502ea12-29b5-45c9-b5f1-f7720a75d371","added_by":"auto","created_at":"2026-02-03 15:56:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2536694,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution Characteristics of HPV Genotypes by Geographic Region and Ethnicity in a Single-Center this referral cohort in Xinjiang.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic map of Xinjiang Uygur Autonomous Region showing geographic division, with Northern Xinjiang indicated in red and Southern Xinjiang in blue.(B) Distribution of the HPV genotype detection by geographic region: (B-a) Detection distribution of HPV genotypes in Northern Xinjiang; (B-b) Detection distribution of the HPV genotypes in Southern Xinjiang.(C) Overall distribution of HPV genotype detection among different ethnic groups in the this referral cohort.(D) Specific distribution of the top five HPV genotypes among major ethnic groups: (D-a) Han population; (D-b) Uyghur population; (D-c) Kazakh population; (D-d) Hui population.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8607078/v1/0593566dec7d4d3c380eb79c.png"},{"id":101789251,"identity":"c20d6b10-dcd9-470b-b997-ba51c02f75ba","added_by":"auto","created_at":"2026-02-03 15:56:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11864247,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8607078/v1/c67f19ab-2f0a-4cdc-85ef-5a9104fc1943.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HPV Genotype Distribution and Cervical Lesions in Colposcopy-Referred Women in Western China: A Cross-Sectional Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCervical cancer remains a global health priority, prompting WHO's 2020 \"Global Strategy to Accelerate the Elimination of Cervical Cancer\" with 90\u0026ndash;70\u0026ndash;90 targets for 2030.[1\u0026ndash;3] The etiology of this malignancy is rooted in persistent infection with high-risk human papillomavirus genotypes. Notably, HPV-16 and HPV-18 are implicated in over 70% of global cases.[4\u0026ndash;6] As the world's most populous nation, China reported approximately 151,000 new cases and 56,000 deaths in 2022.[7] The disease burden demonstrates pronounced geographic heterogeneity,[8\u0026ndash;10] with remote and economically disadvantaged regions exhibiting disproportionately elevated case numbers and mortality[11]. China recently integrated free bivalent HPV vaccination for specified adolescent females into its national immunization program,[12] creating unprecedented opportunities for cancer control.[13] However, the concordance between vaccine-targeted HPV 16/18 and circulating genotypes in multi-ethnic populations of Western China remains inadequately characterized.[14] This knowledge gap constrains accurate assessment of vaccine effectiveness and localization of evidence-based strategies, particularly for patient populations undergoing colposcopic biopsy in tertiary care settings.\u003c/p\u003e \u003cp\u003eThis challenge is particularly acute in Xinjiang, where vast territory, ethnic diversity, and uneven healthcare distribution limit robust epidemiological data.[15] Optimization of triage protocols within resource-constrained settings constitutes a critical prerequisite for attaining WHO elimination targets. The WHO guideline for screening and treatment of cervical pre-cancer lesions explicitly endorses dual-stain cytology for triaging HPV-positive women.[16] The diagnostic performance of conventional screening modalities across distinct demographic subsets and the role of socioeconomic variables have received limited empirical attention.[2, 17] Furthermore, current evidence predominantly derives from community-based screening initiatives. Consequently, the clinical and virological profile of the colposcopy-referred cohort\u0026mdash;a high-risk population directly informing management pathways\u0026mdash;remains inadequately characterized for hospital-based settings.[18]\u003c/p\u003e \u003cp\u003eOur team previously described HPV infection patterns among Xinjiang's general female population, identifying HPV16 as predominant among rural Uyghur women in Hotan with distinctive local risk factors (early age at first marriage, multiple marriages).[19, 20] providing an initial regional evidence base. However, a substantial evidence deficit persists regarding the colposcopy-referred population\u0026mdash;a high-risk cohort triaged following positive screening.[21] Whether genotype distribution in this clinical population diverges from community-based patterns remains unknown. Moreover, the influence of specific virological and socioeconomic determinants on cervical lesion severity has not been systematically investigated.\u003c/p\u003e \u003cp\u003eAccordingly, this study systematically analyzes biopsy data from this pivotal colposcopy-referred cohort with two primary objectives: (1) delineate HPV genotype distribution and compare with our general population data; and (2) examine associations between ethnic background, geographical origin, socioeconomic circumstances and high-grade cervical lesions. The resulting evidence will inform integrated control strategies that bridge community screening initiatives with precision clinical management.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research Design and Population\u003c/h2\u003e \u003cp\u003eThis single-center retrospective cross-sectional study was conducted at People's Hospital of Xinjiang Uygur Autonomous Region. From January 2018 to June 2025, 6,488 patients undergoing colposcopy with complete cervical histopathological biopsy results were consecutively screened; following predefined inclusion and exclusion criteria, 2,934 eligible patients formed the final analytic cohort. Demographics, HPV testing, and ThinPrep cytology results were systematically collected.The study protocol was approved by the Ethics Committee of People's Hospital of Xinjiang Uygur Autonomous Region (\u003cb\u003eApproval No.: KY2022080502\u003c/b\u003e). Given the retrospective design using de-identified medical records, the ethics committee waived the requirement for informed consent. All data were anonymized prior to analysis, with personal identifiers removed and replaced by study-specific codes to ensure patient confidentiality. The study adhered to the Declaration of Helsinki and institutional regulations governing the ethical use of medical data for research purposes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eInclusion Criteria\u003c/strong\u003e \u003cp\u003e(1) Colposcopy with complete cervical biopsy results at People's Hospital of Xinjiang Uygur Autonomous Region; (2) Complete demographics (age, ethnicity, education, residence, occupation); (3) Documented HPV testing (viral load/genotyping) and/or ThinPrep cytology (TCT).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExclusion Criteria\u003c/strong\u003e \u003cp\u003e(1) Acute genital infection or vaginal interference (sexual activity, intravaginal medication, irrigation within 72 hours) prior to colposcopy; (2) Prior treatment for cervical precancerous lesions (e.g., CIN) or related hysterectomy;(3) Pregnancy or lactation; (4) Missing key demographic data.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection and Variable Definition\u003c/h2\u003e \u003cp\u003eData were extracted from electronic medical records, encompassing: (1) Sociodemographics: age, ethnicity (Han, Uyghur, Kazakh, Hui, other), residence region (Northern/Southern Xinjiang), education (primary or below, middle school, high school, university, postgraduate), and occupation (physical labor, mental labor, retired); (2) Laboratory findings: high-risk HPV genotyping (with viral load in some cases) and ThinPrep cytology (TCT) classified as NILM, ASC-US, LSIL, or HSIL+; (3) Histopathology (diagnostic reference): colposcopy-directed biopsy results categorized as normal/inflammation, HPV infection alone, CIN1, CIN2, CIN3, or cervical cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Specimen Sampling Methods\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eHPV Sampling and Detection\u003c/strong\u003e \u003cp\u003eCervical exfoliated cells were collected by a gynecologist who first removed excess mucus with a cotton swab, then used an HPV detection brush rotated five times clockwise at the external cervical os to facilitate mucosal cell adherence. The brush tips were placed separately into vials containing transport medium and stored at 2\u0026ndash;8\u0026deg;C until HPV DNA extraction and genotyping. HPV DNA extraction, viral load quantification, and genotyping were performed following manufacturer protocols using a PCR assay (QIAGEN Enterprises Management Co., Ltd., Shanghai, China).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eThinPrep Cytological Test (TCT)\u003c/strong\u003e \u003cp\u003eCellular specimens were collected identically to HPV sampling. Samples were processed by the ThinPrep 2000 system, Papanicolaou-stained, and independently interpreted by two senior cytopathologists blinded to clinical data. Final reports represented consensus diagnoses.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eColposcopy Examination and Pathological Diagnosis\u003c/strong\u003e \u003cp\u003eExperienced gynecologists performed colposcopy with targeted biopsies of acetowhite epithelium, iodine-negative zones, or suspicious lesions. Tissue specimens underwent formalin fixation, paraffin embedding, hematoxylin\u0026ndash;eosin staining, and microscopic examination for definitive histopathological diagnosis.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eVariables were analyzed by measurement level and distribution. Categorical data are presented as frequencies and percentages\u0026mdash;composition ratios within this referral cohort, not population rates. Continuous variables were tested for normality (Shapiro-Wilk test, Q-Q plots); normally distributed data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, non-normal data as median(IQR).\u003c/p\u003e \u003cp\u003eStacked bar charts visualized relationships among HPV genotype, TCT, and histopathology in patients with complete data. Associations between HPV genotype distribution and categorical TCT/histopathology outcomes were assessed using Pearson's chi-square test, with Cramer's V quantifying effect size and standardized residuals identifying key contributing cells. Significant findings prompted post-hoc pairwise comparisons with Bonferroni correction to control family-wise error rate.\u003c/p\u003e \u003cp\u003eHPV genotype distribution across demographics was displayed via stacked bar charts. For grouped demographics, chi-square tests evaluated associations with histopathology, with p-values adjusted using Bonferroni FDR. Standardized residuals and odds ratios quantified category-specific deviations and risks. Ordinal logistic regression assessed ordered associations with pathological severity. Key findings were synthesized and presented graphically as heatmaps.\u003c/p\u003e \u003cp\u003eAnalyses were performed in R (v4.3.3) using dplyr (data manipulation), survey (complex procedures), ggplot2, and patchwork. Two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated significance. This descriptive exploratory analysis identifies potential associations without inferring causality or population-level epidemiology.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Basic Characteristics of the Research Subjects\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes characteristics of the final referral cohort (n\u0026thinsp;=\u0026thinsp;2,934), selected from 6,488 women undergoing colposcopy at our institution (Jan 2018\u0026ndash;June 2025). Mean age was 44.11\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35 years. Histopathology revealed normal tissue/inflammation in 655 (22.3%) and various cervical lesions in 2,279 (77.7%)\u0026mdash;percentages representing composition ratios within this cohort, not population rates. The cohort was predominantly Han (48.3%) and Uyghur (34.1%), with 79.4% residing in Northern Xinjiang. Over half (55.0%) had university education or higher. Occupations were primarily mental workers (47.3%) and manual workers (38.2%).\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\u003eBaseline Sociodemographic and Clinical Characteristics of the Study Population.\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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;2934)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;2934)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.11\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegion, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group (years), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorthern Xinjiang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2329 (79.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e283 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSouthern Xinjiang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e605 (20.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e837 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eOccupation, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e846 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhysical Labor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1119 (38.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e691 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMental Labor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1388 (47.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e277 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e427 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTCT Result, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1418 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNILM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e711 (31.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUyghur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1000 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASC-US\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e813 (35.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKazakh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e289 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLSIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e531 (23.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHSIL+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e225 (9.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Minorities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eBiopsy Result, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal/Inflammation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e655 (22.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary and Below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e358(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHPV Infection Alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e570 (19.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e529 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCIN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e412 (14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e434 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCIN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e577 (19.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1534 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCIN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e437 (14.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSquamous Cell Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e254 (8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote.\u003c/b\u003e TCT, ThinPrep Cytologic Test; ASC-US, Atypical Squamous Cells of Undetermined Significance; LSIL, Low-grade Squamous Intraepithelial Lesion; HSIL, High-grade Squamous Intraepithelial Lesion. TCT results were available for 2,280 (77.7%) participants. Due to the retrospective nature of the study, missing TCT data were excluded from this category and from all subsequent analyses involving TCT.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 HPV Genotype Distribution and Cervical Lesion Spectrum in the Colposcopy Cohort\u003c/h2\u003e \u003cp\u003eHPV genotype distribution and lesion spectrum are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. High-grade lesions (CIN2+) comprised 44.3% of biopsies from this colposcopy-referred cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). HPV 16 predominated (51.1% of HPV-positive cases in this referral cohort), with HPV 52 (10.2%) and 58 (9.5%) detected more frequently than HPV 18 (8.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). HPV 16 proportions increased markedly with severity: from 40.3% in normal cytology to 65.2% in HSIL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), and histopathologically from 29.3% in HPV-infected tissue to 54.2% in CIN2, 72.1% in CIN3, and 77.2% in squamous cell carcinoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Among 29 adenocarcinoma cases, HPV 18 detection (36%) exceeded its overall proportion. HPV 16/18 carried higher proportions of high-grade lesions versus HPV 52/58, which more often presented as low-grade (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These adenocarcinoma co-infection findings warrant validation in larger studies due to limited sample size.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association of HPV Genotype and TCT Findings with Histopathological Outcomes\u003c/h2\u003e \u003cp\u003eWithin the three-step cervical screening framework, HPV Genotype and TCT serve as primary screening modalities, with colposcopy/biopsy providing definitive diagnosis. This study assessed their predictive capacity in a colposcopy-referred cohort.\u003c/p\u003e \u003cp\u003eHPV genotype showed significant association with histopathology (χ\u0026sup2; = 305.63, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Cramer's V\u0026thinsp;=\u0026thinsp;0.165) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Standardized residual analysis revealed HPV16's pronounced positive correlation with high-grade lesions: CIN3 (8.11, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cancer (8.74, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) proportions significantly exceeded expected values, while representation in normal/inflammatory tissue was lower (-5.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other genotypes (HPV52, 58, 56) associated primarily with lower-grade lesions (infection, CIN1, CIN2), demonstrating HPV genotyping's imperfect concordance with pathology and the necessity of biopsy confirmation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTCT demonstrated a stronger association (χ\u0026sup2; = 258.81, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Cramer's V\u0026thinsp;=\u0026thinsp;0.242) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). A clear gradient corresponded to histological severity: HSIL positively associated with high-grade lesions (CIN3, cancer), LSIL predominantly with CIN1, while ASC-US lacked discriminatory specificity. This discrepancy between cytological inference and histological confirmation constitutes the fundamental rationale for colposcopy and biopsy. Detailed frequency distributions appear in \u003cb\u003eSupplementary Tables\u0026nbsp;1\u0026ndash;2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn summary, both HPV Genotype and TCT showed significant but imperfect associations with histological outcomes in this referral cohort, underscoring their role as screening rather than diagnostic tools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Association between Demographic Characteristics and Distribution of Cervical Lesions\u003c/h2\u003e \u003cp\u003eThis study aimed to evaluate the relationship between demographic factors\u0026mdash;specifically age, educational level, and occupation\u0026mdash;and the distribution of cervical lesions, as determined by pathological findings from colposcopic biopsies.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 General Demographic Characteristics\u003c/h2\u003e \u003cp\u003e \u003cb\u003eAge\u003c/b\u003e: Significant association with lesion severity (χ\u0026sup2; = 239.29, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Cramer's V\u0026thinsp;=\u0026thinsp;0.14). Standardized residual analysis revealed a bimodal pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA): women\u0026thinsp;\u0026lt;\u0026thinsp;40 years showed higher CIN2/3 proportions (residual\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;2.45, FDR P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while those\u0026thinsp;\u0026ge;\u0026thinsp;50 years, especially\u0026thinsp;\u0026gt;\u0026thinsp;60, had markedly elevated invasive cancer rates (residual\u0026thinsp;=\u0026thinsp;+\u0026thinsp;9.90, FDR P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Ordinal logistic regression indicated increasing severity with age (OR\u0026thinsp;=\u0026thinsp;3.74, 95% CI: 2.86\u0026ndash;4.89, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), though proportional odds may not hold uniformly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEducation\u003c/b\u003e: Significant inverse association (χ\u0026sup2; = 269.75, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Cramer's V\u0026thinsp;=\u0026thinsp;0.15). The lowest education group (primary school or below) showed substantially higher invasive cancer proportions (residual\u0026thinsp;=\u0026thinsp;+\u0026thinsp;13.66, FDR P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the highest education group (university or above) had lower cancer rates and higher precancerous detection (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Higher education was associated with reduced lesion severity (OR\u0026thinsp;=\u0026thinsp;0.44, 95% CI: 0.34\u0026ndash;0.58, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eOccupation\u003c/b\u003e: Significant disparities (χ\u0026sup2; = 253.99, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Cramer's V\u0026thinsp;=\u0026thinsp;0.21). Manual workers exhibited markedly elevated cervical cancer proportions (residual\u0026thinsp;=\u0026thinsp;+\u0026thinsp;10.57, FDR P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while mental workers showed lower cancer rates with higher early-stage lesion detection (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Mental workers had substantially reduced risk of high-grade lesions versus manual workers (OR\u0026thinsp;=\u0026thinsp;0.40, 95% CI: 0.33\u0026ndash;0.48, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eDetailed frequency distributions are in \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Exploratory Analysis of Ethnicity and Regional Characteristics\u003c/h2\u003e \u003cp\u003eAn exploratory analysis stratified by geography (Northern vs Southern Xinjiang, demarcated by Tianshan Mountains' ~42\u0026deg;N latitude, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and ethnicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) revealed HPV 16 predominance across all subgroups with notable variations. Southern Xinjiang showed a concentrated profile with HPV 16 accounting for 60.32% of cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-b), while Northern Xinjiang exhibited greater diversity where HPV 52 (11.88%) and HPV 58 (10.76%) formed a significant secondary cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-a).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEthnicity-specific findings: Hui population had the highest HPV 16 proportion (64.84%, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-d); Han showed relatively high HPV 52 (14.81%) and HPV 58 (12.67%, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-a); Kazakh presented a distinct profile with prominent HPV 39 (8.16%, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-c\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Reinterpreting Oncogenic Dominance: HPV16 Enrichment as a Socially Scaffolded Biological Phenomenon\u003c/h2\u003e \u003cp\u003eThe findings discussed in this study derive fundamentally from a single-center colposcopy referral cohort. The observed predominance of HPV16 (51.1%) reflects the combined influence of clinical selection bias and viral biological properties. Consequently, generalization to the general community is precluded. The following analyses should be interpreted as hypothesis generation within this framework. Causal validation is not pursued.[22\u0026ndash;24]\u003c/p\u003e \u003cp\u003eHPV16 prevalence in this referral cohort (51.1%) exceeds population-based screening benchmarks threefold. This disproportionate representation reflects the convergence of heterogeneous lesion progression kinetics and colposcopy triage thresholds. HPV16 integrates into host cellular DNA within 12 months post-infection at a 3\u0026ndash;5 fold higher frequency relative to HPV52/58, with median progression time to CIN3\u0026thinsp;+\u0026thinsp;of 5\u0026ndash;8 years.[25\u0026ndash;27] By contrast, HPV52/58-related lesions at the cohort median age (44.1 years) predominantly exhibit spontaneous regression or indolent persistence, thereby failing to meet referral criteria. This \"accelerated triage\" mechanism, rather than true prevalence disparities, accounts for the differential distribution of HPV52/58 across histologic grades. Specifically, the genotype constituted 23.3% of CIN1 lesions but merely 8.1% of invasive cancers in this cross-sectional cohort.[28\u0026ndash;30]\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eKey omission\u003c/strong\u003e \u003cp\u003eViral load was not analyzed, preventing the establishment of a dose-response gradient between HPV16 DNA levels and lesion grade, which limits mechanistic interpretation.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Triple Projection of Social Determinants: Language-Mobility-Life Course Gaps\u003c/h2\u003e \u003cp\u003eThe associations between age, education, occupation and lesion severity manifested in this cohort constitute non-random residual effects of socioeconomic gradients in the referral population. These effects operate through mechanistic pathways distinctive to Xinjiang's sociodemographic context:[31\u0026ndash;33]\u003c/p\u003e \u003cp\u003e \u003cb\u003eEducation (OR\u0026thinsp;=\u0026thinsp;0.44, 95% CI: 0.34\u0026ndash;0.58)\u003c/b\u003e: Lower educational attainment functioned as a composite proxy for systemic information accessibility barriers rather than individual health literacy deficits. Mandarin proficiency limitations among low-education ethnic minority groups (particularly Uyghur and Kazakh populations) constrained therapeutic communication dynamics. The institutional under-provision of multilingual services constituted the fundamental driver. Manifesting as inadequate language-concordant screening materials and insufficient bilingual staffing relative to ethnic demographics, this systemic deficit generated the standardized residual of +\u0026thinsp;13.66 as a quantifiable clinical signature of linguistic accessibility gaps. Accordingly, intervention priorities must target institutional multilingual health information infrastructure rather than individual competency augmentation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOccupation (standardized residual\u0026thinsp;+\u0026thinsp;10.57 for invasive cancer among manual laborers)\u003c/strong\u003e \u003cp\u003eThese findings reveal an institutional misalignment between screening protocols and the life course trajectories of mobile populations. Lifestyle mobility patterns encompassing transhumance and seasonal labor migration systematically disrupt screening participation continuity. Among manual laborers, the paradox of low precancer detection concurrent with elevated invasive cancer rates signals a cascading phenomenon. This pattern reflects a \"migration-absence-diagnostic delay\" mechanism. Such patterns underscore systemic gaps in health governance for mobile populations, especially Kazakh herders and Uyghur farmers.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAge (bimodal distribution)\u003c/strong\u003e \u003cp\u003eThese findings reveal an institutional misalignment between screening policy frameworks and life-course trajectories. The CIN2/3 peak occurring before age 40 originates from discordance between screening initiation (ages 25\u0026ndash;30) and Xinjiang's earlier marriage patterns.[25, 34] Disproportionate representation among women aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years derives from compounded structural barriers\u0026mdash;remote-area travel costs and cognitive biases that normalize postmenopausal bleeding. These barriers precipitate systematic screening disengagement. Collectively, these phenomena demonstrate coverage gaps at both extremes of the female reproductive life span.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Hypothetical Cues on Geographic and Ethnic Variations\u003c/h2\u003e \u003cp\u003eSpecific findings\u0026mdash;including elevated HPV16 prevalence in southern Xinjiang (60.3%) versus northern Xinjiang (48.1%) and HPV39 enrichment within the Kazakh subgroup (8.16%)\u0026mdash;are constrained by the geographic and ethnic skew inherent in single-center referral patterns. Consequently, these observations warrant investigation rather than definitive conclusion.\u003c/p\u003e \u003cp\u003ePlausible explanations for these variations include social network closure or host genetic factors such as HLA polymorphisms. However, validation requires integrating viral phylogeography with ethnographic investigation. Critically, our single-center data do not support conclusions regarding true population-level differences.[2, 35]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Methodological Constraints and a Progressive Precision Pathway\u003c/h2\u003e \u003cp\u003eDespite inherent single-center referral biases, this study derives from the sole National Clinical Research Center for Gynecologic Diseases in Xinjiang, accounting for amounts of provincial cervical screening referrals. The bias inherent in this study's referral design precisely reveals systemic failure points of the current screening system in Xinjiang:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInability to distinguish incident / persistent infections\u003c/strong\u003e \u003cp\u003eThis indicates the necessity of establishing a prospective Central Asian natural history cohort (currently absent) to quantify type-specific clearance/progression dynamics.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAbsence of viral load and HLA typing\u003c/strong\u003e \u003cp\u003eThis gap identifies molecular stratification variables that subsequent investigations must incorporate. Without such data, modifiers of HPV16 accelerated progression remain unresolvable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInsufficient sample size in the adenocarcinoma subgroup\u003c/strong\u003e \u003cp\u003eAdenocarcinoma subgroup analysis included only 29 cases. Although the observed 36% HPV18 proportion aligns with Swedish cohorts (35\u0026ndash;52%),[36] limited statistical power precluded detection of significant oncogenic advantage. This limitation necessitates expansion of sample sizes for validation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolicy translation logic\u003c/strong\u003e \u003cp\u003eBased on the threefold HPV16 enrichment and even higher proportion in southern Xinjiang, immediate piloting of direct HPV16-positive referral is feasible. However, service disruptions for mobile populations, early-married groups, and elderly dropouts require prioritized sequential interventions, not simultaneous rollout.[37\u0026ndash;39] The value of single-center data lies in identifying critical failure nodes, not providing universal solutions.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis colposcopy cohort analysis empirically examines how sociocultural gradients reshape HPV genotype oncogenic patterns within Xinjiang's multiethnic, geographically polarized frontier context. These findings necessitate a paradigm shift from biological universalism toward precision public health frameworks integrating viral phylogenetics, social network theory, and ethnic immunogenetics. The stark disparities we quantify mandate regionally nuanced and ethnically informed cervical cancer elimination strategies in China. These encompass HPV16's threefold enrichment indicating clinical filtering failure and occupational cancer risks encoded in mobility patterns. Rather than construing single-center referral data as a limitation, we reposition it as a sentinel surveillance node at the confluence of infectious disease dynamics and health equity. This vantage point identifies critical system failure nodes. These nodes comprise language-exclusion barriers, mobile population governance gaps, and life-course policy misalignments. These are precisely the junctures where next-generation cancer prevention policies must be forged.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePolicy implications are immediate\u003c/strong\u003e \u003cp\u003epilot direct HPV16-positive colposcopy referral; deploy bilingual mobile screening units for mobile populations; and realign screening initiation to earlier life-course stages in this region.\u003c/p\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003ea. Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of People\u0026apos;s Hospital of Xinjiang Uygur Autonomous Region (\u003cstrong\u003eApproval No.: KY2022080502\u003c/strong\u003e). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author \u003cstrong\u003eProf. Lili Han\u003c/strong\u003e on reasonable request. Correspondence should be addressed to \u003cstrong\[email protected]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed. Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee. Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Autonomous Region Key R\u0026amp;D Program Project (\u003cstrong\u003eGrant No.: 2022B03018-1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef. Authors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eL.J.\u0026nbsp;\u003c/strong\u003econceived and designed the study, conducted the investigation, performed the formal analysis, curated the data, and wrote the original draft. \u003cstrong\u003eL.S.\u003c/strong\u003e developed the methodology and software, performed validation and visualization, and reviewed \u0026amp; edited the manuscript. \u003cstrong\u003eM.K.\u003c/strong\u003e provided resources, assisted in the investigation, and reviewed \u0026amp; edited the manuscript. \u003cstrong\u003eH.L.\u0026nbsp;\u003c/strong\u003econceived and designed the study, provided supervision and project administration, acquired funding, and reviewed \u0026amp; edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGopalkrishnan K, Karim R. 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BMC Cancer. 2025;25:1207. doi:10.1186/s12885-025-13996-8 \u003c/li\u003e\n\u003cli\u003eChen Y, Li X, Chen P, Yin Z, Zhu P, Zhang L, et al. ZNF671 methylation is a potential regression predictor of cervical intraepithelial neoplasia grade 3 in the colposcopy-to-conization interval. Pathol Res Pract. 2025;273:156116. doi:10.1016/j.prp.2024.156116 \u003c/li\u003e\n\u003cli\u003ePaz-Zulueta M, \u0026Aacute;lvarez-Paredes L, Rodr\u0026iacute;guez D\u0026iacute;az JC, Par\u0026aacute;s-Bravo P, Andrada Becerra ME, Rodr\u0026iacute;guez Ingelmo JM, et al. Prevalence of high-risk HPV genotypes, categorised by their quadrivalent and nine-valent HPV vaccination coverage, and the genotype association with high-grade lesions. BMC Cancer. 2018;18(1):112. doi:10.1186/s12885-018-4492-2 \u003c/li\u003e\n\u003cli\u003eL\u0026oslash;vestad AH, Repesa A, Costanzi JM, Lagstr\u0026ouml;m S, Christiansen IK, Rounge TB, et al. 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Available from: https://www.who.int/health-topics/social-determinants-of-health [cited 2025 Dec 1]. \u003c/li\u003e\n\u003cli\u003eRezhake R, Abuduxikuer G, Abudurexiti G, Zhuo Q, Muhetaer K, Abulimiti T, et al. Evaluation of the multiple HPV-based \u0026quot;screen and triage\u0026quot; algorithms in real-world settings of rural China. Cancer Biol Med. 2025;22(9):1053-1067. doi:10.20892/j.issn.2095-3941.2024.0209 \u003c/li\u003e\n\u003cli\u003eHu J, Li L, Pang L, Chen Y, Yang L, Liu C, et al. HLA-DRB11501 and HLA-DQB10301 alleles are positively associated with HPV16 infection-related kazakh esophageal squamous cell carcinoma in Xinjiang China. Cancer Immunol Immunother. 2012;61(11):2135-2141. doi:10.1007/s00262-012-1241-5 \u003c/li\u003e\n\u003cli\u003eHuang J, Huang K, Xu R, Wang M, Liao Q, Xiong H, et al. The associations of HLA-a02:01 and DRB111:01 with hepatitis C virus spontaneous clearance are independent of IL28B in the Chinese population. Sci Rep. 2016;6:31485. doi:10.1038/srep31485 \u003c/li\u003e\n\u003cli\u003eDahlstr\u0026ouml;m LA, Ylitalo N, Sundstr\u0026ouml;m K, Palmgren J, Ploner A, Eloranta S, et al. Prospective study of human papillomavirus and risk of cervical adenocarcinoma. Int J Cancer. 2010;127(8):1923-1930. doi:10.1002/ijc.25426 \u003c/li\u003e\n\u003cli\u003eWiner RL, Lin J, Anderson ML, Tiro JA, Green BB, Gao H, et al. Strategies to increase cervical cancer screening with mailed human papillomavirus self-sampling kits: A randomized clinical trial. JAMA. 2023;330(20):1971-1981. doi:10.1001/jama.2023.21877 \u003c/li\u003e\n\u003cli\u003eZhao X, Hu S, Zhao S, Rezhake R, Huang L, Duan X, et al. Risk assessment of self-sampling HPV tests based on PCR, signal amplification to guide the appropriate screening intervals: A prospective study in China. J Natl Cancer Cent. 2022;2(4):298-305. doi:10.1016/j.jncc.2022.11.002\u003c/li\u003e\n\u003cli\u003eZhang J, Dong Z, Xu L, Han X, Sheng Z, Chen W, et al. An injection molded SlipChip with self-sampling for integrated point-of-care testing of human papilloma virus. Adv Sci (Weinh). 2024;11(43):e2406367. doi:10.1002/advs.202406367\u003c/li\u003e\n\u003cli\u003eCervical Cancer Committee of China Anti-Cancer Association, Li LY. Cervical cancer screening standards (2025 edition). Chin J Pract Gynecol Obstet. 2025;41(3):332-337.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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), HPV Genotypes, Cervical Lesions, Colposcopy, Xinjiang/China, Cross-Sectional Study","lastPublishedDoi":"10.21203/rs.3.rs-8607078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8607078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHPV genotype distribution and sociodemographic patterns of cervical lesion severity remain under-characterized for colposcopy-referred cohorts in multiethnic western China. This evidence gap impedes precision triage for clinical populations selected through screening thresholds.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo delineate HPV genotype composition and examine sociodemographic profiles associated with biopsy-confirmed cervical lesion grades within a single-center hospital-based colposcopy referral cohort in Xinjiang.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCross-sectional analysis of 2,934 patients who underwent colposcopy with complete biopsy data at People's Hospital of Xinjiang Uygur Autonomous Region (January 2018\u0026ndash;June 2025). Data captured demographics, HPV genotyping, ThinPrep cytology, and histopathological endpoints. Statistical evaluation employed chi-square tests, standardized residual analysis, and ordinal logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWithin this referral cohort, high-grade lesions (CIN2+) constituted 44.3% of biopsy specimens. HPV16 accounted for 51.1% of HPV-positive detections, increasing to 77.2% among cervical cancer specimens. HPV52 (10.2%) and HPV58 (9.5%) formed a secondary genotype cluster. Age distribution exhibited a bimodal pattern: high-grade precancerous lesions predominated among patients\u0026thinsp;\u0026lt;\u0026thinsp;40 years, while invasive cancer specimens were concentrated in those\u0026thinsp;\u0026ge;\u0026thinsp;50 years. Lower educational attainment and physical-labor occupation corresponded to higher proportions of advanced-stage disease specimens.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn this Xinjiang colposcopy-referred cohort, HPV16 predominates among high-grade lesions, while HPV52/58 constitute a consequential secondary cluster. The observed sociodemographic distributions underscore the necessity of embedding both virological patterns and social determinants into risk stratification frameworks for referral populations, providing data to inform regionally-tailored triage protocols in secondary care.\u003c/p\u003e","manuscriptTitle":"HPV Genotype Distribution and Cervical Lesions in Colposcopy-Referred Women in Western China: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:55:09","doi":"10.21203/rs.3.rs-8607078/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-16T11:28:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T14:09:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219779933598448871663900940540683406814","date":"2026-04-13T15:31:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T12:51:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258139481608091783266947277180956368416","date":"2026-03-25T01:39:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259892651512973024989929596171750741820","date":"2026-02-03T08:38:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T06:22:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T07:13:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-21T06:04:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-19T09:59:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-01-19T09:48:04+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"7941a6e1-ce5a-448d-9edf-31b8949e51a5","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T07:08:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:55:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8607078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8607078","identity":"rs-8607078","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}