Comparative Clinicopathological Features, HPV Status, and Prognostic Determinants of Squamous Cell Carcinoma and Adenocarcinoma of the Cervix: A Retrospective Cohort Analysis (2020–2024)

Comparative Clinicopathological Features, HPV Status, and Prognostic Determinants of Squamous Cell Carcinoma and Adenocarcinoma of the Cervix: A Retrospective Cohort Analysis (2020–2024) | 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 Comparative Clinicopathological Features, HPV Status, and Prognostic Determinants of Squamous Cell Carcinoma and Adenocarcinoma of the Cervix: A Retrospective Cohort Analysis (2020–2024) Emad Alqassim, Mashael J. Abu-Alola, Ahmad Y. Alqassim, Asma Tulbah, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8259753/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cervical cancer remains a significant global health burden, with squamous cell carcinoma (SCC) and adenocarcinoma representing the two predominant histological subtypes. While human papillomavirus (HPV) infection underlies most cervical cancers, comparative clinicopathological features and prognostic determinants between SCC and ADC remain incompletely characterized. Methods We conducted a retrospective cohort analysis of cervical cancer patients treated between 2020–2024. Patient demographics, clinical characteristics, p16 immunohistochemistry status, histological subtype, differentiation grade, clinical staging, and survival outcomes were analyzed. Comparative statistics, multivariate logistic regression, LASSO regression, temporal trend analysis, and Kaplan-Meier survival analysis were performed to identify prognostic determinants and histotype-specific differences. Results The analysis included 85 patients: 69 with squamous cell carcinoma and 16 with adenocarcinoma. Both subtypes demonstrated similarly high p16 positivity rates (89.8% vs 88.2%, p = 0.45), confirming HPV's predominant role regardless of histological type. Menopausal status emerged as a significant distinguishing factor (p = 0.0495), with SCC patients more likely to be postmenopausal. SCC patients were older on average (52.16 vs 48.2 years, p = 0.0565). Multivariate analysis identified p16 positivity as the strongest predictor of advanced disease (OR = 2.45, p = 0.047), while marital status demonstrated protective effects (OR = 0.87, p = 0.021). Kaplan-Meier analysis revealed significant survival differences by clinical stage (log-rank p = 0.03), with high-stage patients showing progressive decline from 95% to 73% survival over five years, while low-stage patients maintained 100% survival. Conclusion While SCC and ADC share similar molecular characteristics and clinical presentations, SCC preferentially affects older, postmenopausal women. p16 positivity serves as a key molecular predictor of disease severity, and clinical staging remains the most critical prognostic determinant. These findings underscore the importance of early detection strategies and reinforce the prognostic value of molecular markers in cervical cancer management. Cervical cancer squamous cell carcinoma adenocarcinoma p16 survival analysis retrospective study HPV clinical staging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cervical cancer represents the fourth most common malignancy among women worldwide, with approximately 660,000 new cases and deaths reported in 2020 [ 1 ]. Despite advances in HPV vaccination and screening programs implemented over the past decade, significant disparities in outcomes persist globally [ 2 , 3 ]. The two predominant histological subtypes, squamous cell carcinoma (SCC) and adenocarcinoma (ADC), account for approximately 83% and 12% of cervical cancers, respectively [ 4 , 5 ]. The biological and clinical differences between SCC and ADC have been the subject of considerable research interest. While both subtypes are predominantly associated with high-risk HPV infection, with significant regional variation in genotype distribution beyond the traditional HPV 16 and 18 focus, emerging evidence suggests distinct epidemiological patterns, molecular characteristics, and clinical behaviors [ 6 , 7 ]. Adenocarcinoma has been associated with younger patient age at diagnosis, different anatomical distribution within the cervix, and potentially distinct treatment responses compared to squamous cell carcinoma. However, prognostic differences between histological subtypes remain controversial, with some studies demonstrating significantly worse survival for adenocarcinoma in both early-stage (HR = 1.39) and advanced-stage disease (HR = 1.21) [ 8 ], while other contemporary analyses report no significant histotype-specific survival differences. Contemporary treatment standardization following international guidelines minimizes therapeutic confounding variables, enhancing the validity of histological subtype comparisons [ 9 ]. Epidemiological studies have documented significant shifts in cervical cancer patterns that are particularly relevant for comparative clinicopathological analysis. Recent population-based evidence demonstrates that adenocarcinoma now comprises a substantial proportion of cervical cancers in developed countries, with a pronounced predilection for younger women [ 10 , 11 ]. This epidemiological shift reflects differential screening effectiveness, as cytological screening demonstrates superior detection of squamous cell carcinoma precursors compared to adenocarcinoma precursors, resulting in disproportionate reductions in squamous cell carcinoma incidence while adenocarcinoma rates remain stable or increase [ 8 , 12 ]. These changing demographic and screening patterns raise important questions about whether traditional comparative analyses between squamous cell carcinoma and adenocarcinoma remain valid in contemporary patient populations, particularly regarding disease presentation, staging, and prognostic outcomes [ 13 ]. The role of HPV in cervical carcinogenesis is well-established, with p16 immunohistochemistry serving as a reliable surrogate marker for HPV-mediated transformation [ 14 , 15 ]. P16 overexpression, resulting from functional inactivation of the retinoblastoma pathway by HPV E7 oncoprotein, has emerged as both a diagnostic tool and favorable prognostic marker in cervical cancer, with P16-positive tumors demonstrating better treatment response and improved survival outcomes [ 16 , 17 ]. However, the comparative prevalence and prognostic significance of p16 positivity across different histological subtypes requires further elucidation. Clinical staging remains the cornerstone of cervical cancer prognosis and treatment planning, with the International Federation of Gynecology and Obstetrics (FIGO) staging system serving as the primary framework for risk stratification [ 18 , 19 ]. Nevertheless, the identification of additional demographic, molecular, and clinical factors that influence disease presentation and outcomes could enhance prognostic accuracy and inform personalized treatment approaches [ 20 , 21 ]. Although previous studies have examined survival differences between SCC and ADC [ 2 , 22 , 23 ], the comparative prevalence and prognostic significance of molecular markers, particularly P16 positivity, across these histological subtypes in contemporary populations requires further investigation. Furthermore, the prognostic significance of demographic factors, molecular markers, and their interactions with clinical staging requires comprehensive multivariate analysis to identify independent predictors of disease severity and survival outcomes. The primary aim of this study was to evaluate demographic, clinical, and pathological differences between squamous cell carcinoma and adenocarcinoma of the cervix in a contemporary patient cohort. Secondary objectives included exploratory analysis of factors associated with advanced stage presentation and preliminary assessment of survival outcomes, acknowledging that definitive prognostic conclusions would require larger sample sizes and prospective validation. These comparative insights have direct clinical applications: histotype-specific risk factors can enhance patient counseling with more precise prognostic information, demographic and molecular determinants can strengthen risk stratification models for optimized screening and treatment intensity, and identification of predictive markers can guide individualized treatment decisions regarding surgical approaches, adjuvant therapy selection, and surveillance strategies in contemporary personalized cervical cancer care. Through comprehensive statistical analysis, we sought to identify key prognostic determinants and provide insights into the comparative clinical behavior of these two major cervical cancer subtypes to inform patient counseling, contribute to risk stratification models, and guide individualized treatment planning. Methods Study Design and Setting We conducted a retrospective observational cohort study analyzing cervical cancer patients diagnosed and treated at King Faisal Specialist Hospital & Research Centre (KFSHRC), Riyadh, Saudi Arabia, between January 2020 and December 2024. KFSHRC is a tertiary care academic medical center, serving as a major regional referral center for Saudi Arabia and the Middle East [ 24 ]. The institution maintains international accreditation standards including Magnet Recognition Program® and HIMSS Stage 7 certification, ensuring standardized oncology care protocols and comprehensive data collection systems. Study Population The study included all patients with histologically confirmed cervical cancer diagnosed during the study period. Inclusion criteria comprised: (1) histologically confirmed primary cervical cancer; (2) complete clinical staging information; (3) available demographic and clinical data; and (4) confirmed histological subtype (squamous cell carcinoma or adenocarcinoma). Exclusion criteria included: (1) other histological subtypes (neuroendocrine tumors, sarcomas); (2) recurrent disease; (3) incomplete staging information; and (4) patients lost to follow-up within 30 days of diagnosis. Of 90 patients initially screened with cervical cancer diagnosis, 85 met inclusion criteria, yielding a final study population of 85 patients (69 squamous cell carcinoma, 16 adenocarcinoma). All eligible cases meeting inclusion criteria during the study period (2020–2024) were included to maximize statistical power and ensure comprehensive representation of the patient population. Data Collection Patient data were collected from medical records and included the following variables: demographic characteristics (age at diagnosis, body mass index [BMI], marital status, menopausal status), clinical parameters (FIGO stage, degree of differentiation), molecular markers (p16 immunohistochemistry status), histological subtype, and outcome measures (vital status at last follow-up). Clinical staging was performed according to the 2018 FIGO staging system, with stages categorized as low (I-II) versus high (III-IV) for analytical purposes. Data quality was ensured through dual verification by two independent reviewers, with discrepancies resolved by consensus. Missing data patterns were assessed, and cases with > 20% missing critical variables were excluded from analysis. Complete case analysis was performed for multivariable modeling, with sensitivity analyses conducted to assess the impact of missing data on primary outcomes. P16 Immunohistochemistry P16 immunohistochemistry was performed on formalin-fixed, paraffin-embedded tumor tissue using standard protocols [ 25 ]. Primary antibody (E6H4 clone, Ventana, Medical Systems Inc. Tucson, AZ, USA) was used at 1.0µg/ml on VENTANA BenchMark ULTRA, Ventana. Quality control measures included positive and negative controls with each batch. P16 positivity was defined as continuous strong nuclear and cytoplasmic staining of the basal cell layer with extension upward involving at least one-third of the epithelial thickness ("diffuse staining pattern"), consistent with established criteria for HPV-associated cervical cancer [ 26 , 27 ] Focal or patchy nuclear staining patterns were considered negative. Ethical Considerations This study was conducted in accordance with the Declaration of Helsinki and approved by the King Faisal Specialist Hospital and Research Centre (KFSHRC) Institutional Review Board (IRB)/Research Ethics Committee (REC) (IRB # 2251677, approved November 4, 2025, valid for 12 months). Given the retrospective nature of this study using de-identified patient data from medical records, the KFSHRC IRB/REC granted a waiver for informed consent requirements in accordance with institutional guidelines for retrospective chart reviews using secondary data. Patient confidentiality and data privacy were maintained throughout the study period. Statistical Analysis Descriptive statistics were calculated for all variables, with continuous variables presented as means with standard deviations and categorical variables as frequencies and percentages. Comparative analysis between histological subtypes employed Chi-square tests for categorical variables and t-tests for continuous variables. Multivariate logistic regression analysis was performed to identify predictors of high clinical stage presentation, with results presented as odds ratios (OR) with 95% confidence intervals. Variables with p-values < 0.20 in univariate analysis were included in the multivariate model. LASSO (Least Absolute Shrinkage and Selection Operator) regression was implemented for feature selection and regularization to identify the most parsimonious set of predictors for clinical stage determination while minimizing overfitting. The optimal lambda parameter was selected through cross-validation. Temporal trend analysis was conducted using linear regression to assess changes in p16 positivity rates and advanced-stage disease presentation over the study period. Survival analysis was performed using the Kaplan-Meier method, with survival curves compared using the log-rank test. Follow-up time was calculated from the date of diagnosis to the last contact or death. All statistical analyses were performed using Python (JupyterLab environment), with appropriate statistical packages. Complete case analysis was performed, and statistical significance was defined as p < 0.05. Results Table 1 summarizes baseline patient and tumor characteristics stratified by p16 status. Most patients were ≥ 40 years, with obesity being the most common BMI category. The majority presented at advanced FIGO stages (III–IV) and with grade 2 or 3 histology. No significant associations were observed between p16 status and age, BMI, tumor stage, or grade, though widowed patients were more likely to be p16 negative (p = 0.007). Table 1 Baseline Patient and Tumor Characteristics. Characteristic Total (N = 81*) P16 Positive (N = 77) P16 Negative (N = 4) p-value Age (years) <30 2 (2.5%) 2 (2.6%) 0 (0%) 1.000 30–39 13 (16.0%) 13(16.9%) 0 (0%) 1.000 40–49 21 (25.9%) 20 (26.0%) 1 (25.0%) 1.000 50–59 18 (22.2%) 18 (23.4%) 0 (0%) 0.570 ≥60 27 (33.4%) 24 (31.2%) 3 (75.0%) 0.105 BMI Categories Underweight (< 18.5) 5 (6.2%) 5 (6.5%) 0 (0%) 1.000 Normal (18.5–24.9) 15 (18.5%) 13 (16.9%) 2 (50%) 0.154 Overweight (25-29.9) 22 (27.2%) 22 (28.6%) 0 (0%) 0.570 Obese (≥ 30) 38 (46.9%) 36 (46.8%) 2 (50%) 1.000 Missing 1(1.2%) 1(1.3%) 0 (0%) - FIGO Stage Stage I 4 (4.9%) 4 (5.2%) 0 (0%) 1.000 Stage II 17 (21.0%) 17 (22.1%) 0 (0%) 0.366 Stage III 39 (48.1%) 37 (48.1%) 2 (50%) 1.000 Stage IV 21 (25.9%) 19 (24.7%) 2 (50%) 0.275 Histologic Grade Grade 1 6 (7.4%) 6 (7.8%) 0 (0%) 1.000 Grade 2 49 (60.5%) 48 (62.3%) 1 (25.0%) 0.296 Grade 3 22 (27.1%) 21 (27.3%) 1 (25.0%) 1.000 Not reported 4 (4.9%) 2 (2.6%) 2 (50.0%) 0.11 Marital Status Married 49 (60.5%) 48 (62.3%) 1 (25%) 0.299 Single 15 (18.5%) 15 (19.5%) 0 (0%) 1.000 Divorced/Separated 6 (7.4%) 6 (7.8%) 0 (0%) 1.000 Widowed 11 (3.6%) 8 (10.4%) 3 (75%) 0.007 Data presented as n (%). Statistical comparisons performed using Chi-square test. *Out of 85 patients, 4 with Not reported P16 status and 1 labeled as Variable were excluded, leaving 81 patients (77 P16-positive and 4 P16-negative) for analysis. The comparative analysis between squamous cell carcinoma and adenocarcinoma revealed several notable differences in patient characteristics and clinical presentation (Table 2 ). However, menopausal status emerged as a significant distinguishing factor, with squamous cell carcinoma patients more likely to be postmenopausal compared to adenocarcinoma patients. Age demonstrated a marginally significant trend, with squamous cell carcinoma patients being older on average compared to adenocarcinoma patients. This age difference aligns with the menopausal status findings and suggests potential age-related variations in histological subtype development. Table 2 Comparison of Patient Characteristics and Clinical Features by Histological Subtype in Cervical Cancer (2020–2024) Variable Squamous Cell Carcinoma (n = 69) Adenocarcinoma (n = 16) OR 95% CI (Low–High) p-value P16 status Positive: 62 (89.9%) Negative: 3 (4.3%) Not reported: 3 (4.3%) Variable: 1 (1.4%) Positive: 15 (93.8%) Negative: 1 (6.2%) 2.17 0.16–29.5 0.45 Degree of differentiation Well: 6 (8.7%) Moderate: 41 (59.4%) Poor: 20 (29.0%) Not reported: 2 (2.9%) Well: 0 (0.0%) Moderate: 12 (75.0%) Poor: 2 (12.5%) Not reported: 2 (12.5%) 0.87 0.35–2.15 0.76 Clinical stage Early (I–II): 16 (23.2%) Advanced (III–IV): 53 (76.8%) Early (I–II): 5 (31.2%) Advanced (III–IV): 11 (68.8%) 1.15 0.54–2.45 0.71 Outcome Alive: 50 (72.5%) Deceased: 11 (15.9%) Unknown: 8 (11.6%) Alive: 10 (62.5%) Deceased: 3 (18.8%) Unknown: 3 (18.8%) 1.70 0.39–7.41 0.48 Marital status Married: 43 (62.3%) Single: 13 (18.8%) Divorced: 5 (7.2%) Widowed: 18 (11.6%) Married: 11 (68.8%) Single: 3 (18.8%) Divorced: 1 (6.2%) Widowed: 0 (0.0%) Unknown: 1 (6.2%) 0.87 0.77–0.98 0.021 Menopausal status Positive: 36 (52.2%) Negative: 33 (47.8%) Positive: 2 (12.5%) Negative: 14 (87.9%) 0.20 0.04–1.07 0.05 Age (yrs) Mean = 52.2 Mean = 48.2 0.95 0.90–1.00 0.06 BMI (kg/m²) Mean = 29.5 Mean = 28.9 1.02 0.94–1.11 0.65 Note: Cases with “Not reported” or “Unknown” categories were included in descriptive statistics but excluded from regression models. The multivariate logistic regression analysis identified distinct patterns of association with high clinical stage cervical cancer presentation (Fig. 1 ). P16 positivity emerged as the strongest predictor of advanced disease (OR = 2.45, 95% CI: 1.01–5.95, p = 0.047), conferring nearly 2.5-fold increased odds of high clinical stage presentation. Marital status demonstrated a significant protective effect (OR = 0.87, 95% CI: 0.77–0.98, p = 0.021), with married patients having 13% lower odds of advanced stage disease compared to unmarried patients. Notably, age and menopausal status exhibited significant negative associations with high clinical stage, suggesting older patients and postmenopausal women paradoxically present with earlier stage disease (p < 0.05). BMI and cancer type showed non-significant positive trends toward advanced stage presentation. These findings underscore P16's role as a molecular predictor of disease severity while revealing unexpected protective effects of age and marital status in this cohort. The LASSO regression analysis was performed with 10-fold cross validation to identify predictors of high clinical stage cervical cancer while performing automatic feature selection to minimize overfitting (Fig. 2 ). The optimal penalty parameters were selected based on the minimum cross-validation error, corresponding to an optimal C (1/λ) of 7.96. Among the retained variables, age demonstrated the strongest positive association with advanced stage disease (coefficient = + 0.00044), indicating that older patients have increased odds of presenting with high clinical stage. Menopausal status showed a weaker but positive association (coefficient = + 0.00015), with postmenopausal women having slightly elevated odds of advanced disease. Squamous cell carcinoma histology exhibited a minimal positive association with high stage presentation (coefficient = + 0.00009). Conversely, marital status revealed protective effects, with married patients showing reduced odds of advanced stage disease (coefficient = -0.0002). Interestingly, BMI demonstrated the strongest negative association (coefficient = -0.0003), suggesting that higher BMI may be protective against advanced stage presentation or associated with earlier disease detection. p16 positivity was retained by the LASSO algorithm despite having a coefficient near zero, indicating its relevance for model performance even with minimal individual contribution. To assess model stability, we performed the bootstrap resampling with 100 runs. This demonstrated that clinical stage (98%), degree of differentiation (96%), BMI (84%), marital status (77%), and age (67%) were consistently selected across bootstrap models, supporting their robustness as predictors. In contrast, most Pap smear subcategories demonstrated low selection frequencies (< 30%), suggesting limited reproducibility. These LASSO findings contrast with the traditional logistic regression results, particularly regarding age and menopausal status, highlighting the different analytical approaches' sensitivity to variable interactions and the regularization effect of LASSO in identifying the most parsimonious set of predictors for clinical stage determination. The boxplot analyses revealed several significant associations between key demographic, clinical, and pathological variables in the cervical cancer cohort (Fig. 3 ). Postmenopausal women demonstrated a marginally significant trend toward lower BMI compared to premenopausal patients (p = 0.051), suggesting potential metabolic differences associated with menopausal status. Most notably, deceased patients presented with significantly more advanced clinical stages than living patients (p = 0.003), confirming the prognostic importance of staging at diagnosis. Age distribution varied by histological subtype, with squamous cell carcinoma patients trending toward older age compared to adenocarcinoma patients (p = 0.08), though this difference did not reach statistical significance. As expected, postmenopausal women were significantly older than premenopausal patients (p < 0.001), validating the biological relationship between age and menopausal status. The temporal analysis trend revealed distinct patterns in key clinical variables over the five-year study period from 2020 to 2024 (Fig. 4 ). p16 positivity demonstrated a significant increasing trend over time (p = 0.008), rising from approximately 80% in 2020 to nearly complete positivity, then stabilizing at high levels around 94–96% through 2024. This trend suggests either improved diagnostic testing protocols, changes in patient population characteristics, or evolving epidemiological patterns of HPV-associated cervical cancers. In contrast, the proportion of advanced-stage cases showed considerable year-to-year variation without a significant overall trend (p = 0.796), fluctuating from 70% in 2020 to peaks of approximately 88% in 2021, followed by substantial variation including a notable drop to around 74% in 2023 before returning to 75% in 2024. The lack of a consistent temporal pattern in advanced-stage presentation suggests that factors influencing disease stage at diagnosis remain relatively stable over time, despite the increasing prevalence of p16 positivity. The Kaplan-Meier survival analysis demonstrated a significant difference in survival outcomes between patients with high and low clinical stage cervical cancer over the five-year study period (log-rank p = 0.03) (Fig. 5 ). Patients with low clinical stage disease maintained excellent survival probability, remaining at 1.0 (100% survival) throughout the entire follow-up period from 2020 to 2024. In stark contrast, patients with high clinical stage disease showed progressive decline in survival probability over time, beginning at approximately 0.95 (95%) in 2020 and decreasing to 0.91 (91%) in 2021, followed by a more pronounced decline to 0.77 (77%) in both 2022 and 2023, and reaching 0.73 (73%) by 2024. The median follow-up time was 3.0 years for both groups. This represents a 22 percentage point absolute survival reduction (95% to 73%) or a 23% relative survival reduction over the five-year period for patients with advanced stage disease. Censoring events are indicated in the curves, and the numbers at risk at each year are displayed below the x-axis. The widening gap between survival curves over time underscores the critical prognostic importance of clinical staging at diagnosis and validates clinical staging as a robust predictor of long-term outcomes. However, survival differences were not adjusted for treatment effects, which may partially account for outcome variation. Discussion This retrospective analysis of cervical cancer patients (N = 85) provides insights into the comparative clinicopathological features of squamous cell carcinoma and adenocarcinoma. Our findings demonstrate that while both histological subtypes share similar molecular characteristics and clinical presentations, distinct demographic patterns and prognostic factors differentiate these major cervical cancer subtypes, challenging some established paradigms while confirming others. The observed age and menopausal status differences between SCC and ADC patients represent our most reliable findings, consistent with well-documented epidemiological shifts in multiple international studies. Our finding that SCC patients were marginally older (52.16 vs 48.2 years, p = 0.057) and more likely to be postmenopausal (p = 0.050) aligns with the well-documented temporal trends reported in a previous study where international trends showing increasing adenocarcinoma incidence in younger age groups across multiple countries were observed [ 28 ]. This finding suggests a cohort effect related to changing sexual behaviors and HPV exposure patterns, consistent with the rising incidence of adenocarcinoma relative to squamous cell carcinoma in the United States, with adenocarcinoma predominantly affecting younger women [ 29 ]. However, the marginal statistical significance of the age difference requires validation in larger cohorts before clinical application. However, our findings require interpretation within the context of well-documented epidemiological shifts in cervical cancer patterns during the screening era. Population-based studies have consistently demonstrated disproportionate increases in adenocarcinoma incidence among younger women, with Bray et al. (2005) showing rapid increases in women born after 1935, particularly affecting the 30–39 age group [ 4 ]. This temporal pattern directly explains the younger age distribution observed in our ADC cohort and aligns with recent comprehensive US cancer registry analysis demonstrating that 17 of 34 cancer types show increasing incidence rates in successively younger birth cohorts, supporting generational differences in cancer risk related to early-life exposures [ 30 ]. The age differential between histological subtypes likely reflects both cohort-specific HPV exposure patterns and distinct tumor biology, with adenocarcinoma potentially exhibiting compressed latency from initial infection to invasive disease compared to squamous cell carcinoma. The menopausal status differential provides compelling insight into the distinct pathophysiology of cervical cancer histotypes. Postmenopausal women with SCC likely represent a population with chronic HPV infections that underwent gradual progression through decades of hormonal transitions, while premenopausal women with ADC may reflect more recent infections characterized by accelerated carcinogenic pathways. This hypothesis is supported by documented evidence that adenocarcinoma demonstrates reduced susceptibility to conventional screening strategies, with cytology screening proving less effective against adenocarcinoma than squamous carcinoma, evidenced by increasing ADC proportions from 13.2% to 22.1% between 1989–2009 despite substantial SCC reductions in screened populations [ 31 ]. The compressed natural history suggested for adenocarcinoma, combined with its predominance in younger, premenopausal women, underscores fundamental differences in tumor biology that may necessitate adapted screening and prevention approaches. The similar p16 positivity rates between SCC and ADC (89.8% vs 88.2%) confirm the overwhelming dominance of HPV in contemporary cervical cancer, consistent with the landmark study by Walboomers et al., which established HPV as a necessary cause in 99.7% of cervical cancers [ 32 ]. The high prevalence of HPV DNA in different histological subtypes of cervical adenocarcinoma is well documented by Pirog et al., who found HPV in 94% of adenocarcinomas, with HPV types 16 and 18 being most common [ 32 ]. Similarly, Andersson et al. confirmed the critical role of human papillomavirus in cervical adenocarcinoma carcinogenesis, demonstrating comparable HPV prevalence rates across histological subtypes [ 33 ]. Our observation that p16 positivity associates with advanced clinical stage (OR = 2.45, p = 0.047) differs from established literature demonstrating associations between p16 overexpression and absence of lymph node metastasis, improved overall survival, and enhanced disease-free survival [ 34 , 35 ], with favorable outcomes reported in specific cervical cancer populations [ 36 ]. This discordance likely reflects methodological factors including single-center selection bias, temporal changes in diagnostic practices evidenced by increasing p16 positivity from 80% to > 94%, and limited statistical power given high p16 prevalence (> 88%) in our cohort. Established studies by Castle et al. and Ronco et al. support the protective implications of HPV/p16 testing [ 37 , 38 ], suggesting our contrary finding requires validation in larger, multi-center cohorts with standardized protocols before clinical interpretation. This unexpected finding highlights important methodological considerations that warrant careful evaluation when interpreting our data. First, our single-center retrospective design may have introduced selection bias, where p16 testing was preferentially ordered for more clinically concerning cases, creating an artificial association between p16 positivity and advanced stage. Second, temporal changes in p16 testing practices over our study period (evidenced by the increase from 80% to > 94% positivity) suggest evolving diagnostic criteria that may have confounded the stage–p16 relationship. Because > 88% of our cohort was p16-positive, opportunities to detect robust differences between p16-positive and p16-negative groups were inherently limited. The relatively small p16-negative subset likely reflects the rarity of true HPV-negative cervical cancers in contemporary populations and may also encompass tumors with technical staining variability, low-level HPV infections, or distinct histological variants. The observed correlation between HPV positivity and more advanced disease presentation appears biologically implausible, suggesting that the finding is more likely attributable to residual confounding or study design factors rather than a true causal relationship. The work by Castle et al. demonstrated the superior performance of carcinogenic HPV testing for cervical cancer screening [ 37 ], while Ronco et al. confirmed that HPV-based screening is highly effective for preventing invasive cervical cancer [ 38 ]. These findings strongly support the protective and favorable prognostic implications of HPV/p16 positivity, indicating that our contrary observation should be interpreted with caution and explored further. Our observed p16–stage association is best understood in the context of methodological considerations common to single-center retrospective studies, including small comparison groups and evolving diagnostic practices. The protective effect of marital status on advanced-stage presentation (OR = 0.87, p = 0.021) represents a robust finding that extends beyond a simple demographic association, reflecting complex interactions between social determinants and cancer outcomes. This observation aligns with the broader social epidemiology literature. For example, Azerkan et al. in Swedish populations demonstrated significant variations in cervical screening participation by social factors, with married women showing higher compliance rates [ 39 ]. Similarly, Marlow et al. reported that sociodemographic predictors significantly influenced HPV testing and vaccination acceptability among British women [ 40 ]. This association highlights that marital status acts as a proxy for broader socioeconomic and behavioral factors rather than a direct causal mechanism. A recent study by Meng et al. (2024) confirmed that social relationships function as protective factors across multiple health outcomes [ 41 ]. Social support networks available to married individuals may facilitate earlier healthcare seeking, increased screening participation, and better adherence to follow-up care. This interpretation is further supported by evidence from Kroenke et al., who demonstrated that social networks and support significantly influenced survival after breast cancer diagnosis, underscoring the broader relevance of social support mechanisms across cancer types [ 42 ]. The unexpected negative association between age and advanced stage disease offers an intriguing divergence from established. This observation persisted across both traditional logistic regression and LASSO analyses, suggesting robustness despite its counterintuitive nature. Several explanations merit consideration: first, older women may have increased healthcare contact for comorbid conditions, leading to incidental earlier detection; second, competing mortality risks may result in preferential identification of earlier-stage cancers in older patients; third, cervical cancers developing in older women may have different biological characteristics, potentially reflecting different HPV types or co-carcinogenic factors. This finding is a contrast to those in published literature showing worse outcomes with advancing age. For example, Ou et al. (2025) demonstrated that persistent socioeconomic disparities typically worsen with age, thereby contributing to poorer outcomes [ 43 ]. The discrepancy between the p16 data in our study and those of previous studies may reflect selection bias in our single-center cohort or unique characteristics of our patient population. Alternatively, it may suggest that, in the contemporary screening era, age-related detection patterns have fundamentally shifted compared with earlier epidemiological observations, such as those reported in the classic Swedish study by Adami et al. (1994) [ 44 ]. The temporal increase in p16 positivity from 80% to > 94% over our study period represents a remarkable finding that reflects the evolution of molecular diagnostics in cervical cancer. This trend likely encompasses multiple factors: standardization of immunohistochemical protocols, increased pathologist familiarity with p16 interpretation criteria, and potential changes in patient population characteristics. The stabilization at high levels suggests achievement of diagnostic maturity in p16 testing. This trend raises important questions about historical under-detection versus contemporary over-interpretation of p16 positivity. Bergeron et al. demonstrated that conjunctive p16 testing improves diagnostic accuracy for high-grade cervical lesions, though interpretation standards remain variable [ 26 ]. Our observed increase may therefore reflect the implementation of more stringent and standardized diagnostic criteria rather than true epidemiological changes. The stability of advanced-stage presentation rates despite increasing p16 positivity suggests that molecular diagnostic improvements have not translated into earlier clinical detection. This finding is concerning and may indicate persistent barriers to early detection, inadequate screening coverage, or inherent limitations of current screening methodologies in detecting adenocarcinoma. Our survival analysis confirms the prognostic supremacy of clinical staging, with 100% survival in early-stage patients compared with 73% in late-stage patients over five years. This 27% absolute survival difference aligns closely with international registry data and supports the critical importance of early detection in cervical cancer outcomes through effective screening programs [ 22 , 23 ]. However, the absence of survival differences between SCC and ADC in our cohort differs from earlier studies suggesting worse prognosis for adenocarcinoma. For example, Liu et al. (2022) identified adenocarcinoma as an independent risk factor for disease recurrence [ 23 ], while Chen et al. (2022) also reported differences in clinical behavior between histological subtypes [ 22 ]. Our contrary finding may reflect improvements in treatment protocols, changes in adenocarcinoma subtypes detected in the contemporary era, or the overriding prognostic significance of stage, which supersedes histological subtype effects. This observation carries important clinical implications, suggesting that treatment decisions should prioritize staging and other clinicopathological factors over histological subtype alone. The historical view of adenocarcinoma as having a worse prognosis warrants reevaluation in light of contemporary treatment approaches and advances in molecular understanding. Recent global studies on HPV etiology [ 45 ] and worldwide mortality trend analyses [ 46 ] further support the need to reconsider histological subtype as a dominant prognostic factor. Given the limitation due to study type and the contradictory nature of some findings in this study, the clinical implications of this study should be interpreted with caution. The apparent predictive value of p16 positivity for advanced disease should not be applied in clinical practice without independent validation in larger, multicenter cohorts with standardized p16 testing protocols and adequately powered subgroup analyses. The demographic differences between SCC and ADC (age and menopausal status) represent our most reliable findings, as they align with established epidemiological patterns and demonstrate biological plausibility. These observations support age-stratified approaches to cervical cancer prevention and reinforce the importance of maintaining screening recommendations across age groups, particularly for adenocarcinoma detection in younger women. The concentration of adenocarcinoma in younger, premenopausal women further underscores the value of HPV vaccination programs targeting adolescent populations, as demonstrated in a comprehensive global meta-analysis [ 47 ]. The primary contribution of our study lies in its methodological innovation in utilizing data from single center cohort, highlighting the essential role of rigorous design in advancing cancer research. To build upon these findings, future investigations would benefit from prospective studies with standardized protocols, larger cohorts that allow robust subgroup analyses, detailed treatment documentation, comprehensive screening history assessments, and multi-center validation to strengthen the clinical applicability of emerging prognostic associations. The temporal trends we observed in p16 positivity warrant immediate attention in clinical practice, as they suggest concerning inconsistencies in diagnostic standards that could directly affect patient care. Institutions should implement standardized p16 staining protocols, inter-observer reliability training, and quality assurance programs to ensure consistent and clinically meaningful results. Most importantly, our findings regarding p16 and age–stage relationships should serve as cautionary examples of how methodological limitations can produce spurious associations that contradict established biological knowledge. Researchers must prioritize methodological rigor over novel but potentially misleading findings, and clinicians should remain skeptical of single-center studies reporting results that deviate from well-established literature without compelling methodological justification. Strengths and limitations This study's strengths include comprehensive molecular characterization using standardized p16 immunohistochemistry protocols, rigorous statistical methodology incorporating multivariate logistic regression and LASSO analysis, temporal trend analysis over five years, and consistent data collection from a tertiary care center with international accreditation standards. However, this single-center retrospective analysis of 85 patients has significant limitations including limited generalizability, insufficient statistical power for subgroup analyses and effect size detection, heavily skewed p16 distribution (> 88% positive) precluding robust comparative analyses, and potential selection bias from tertiary referral patterns. Our contradictory finding that p16 positivity predicts advanced disease directly opposes established literature and likely reflects methodological artifacts or evolving diagnostic practices evidenced by increasing p16 positivity from 80% to > 94% over the study period. Critical missing data include treatment details, screening histories, HPV vaccination status, HPV genotyping, important prognostic factors (lymphovascular invasion, parametrial involvement), and long-term survival outcomes, while several findings contradict established epidemiological patterns and require multi-center validation with larger sample sizes and comprehensive covariate adjustment before clinical application. Conclusion This retrospective analysis demonstrates that while squamous cell carcinoma and adenocarcinoma share similar molecular characteristics with comparable p16 positivity rates, they exhibit distinct demographic patterns, with squamous cell carcinoma preferentially affecting older, postmenopausal women and adenocarcinoma predominantly occurring in younger patients. Clinical staging emerged as the paramount prognostic determinant, demonstrating excellent survival in early-stage disease versus progressive decline in advanced stages, while the protective effect of marital status underscores the importance of social determinants in cancer outcomes. Our contradictory finding that p16 positivity predicts advanced disease opposes established literature and likely reflects methodological limitations rather than biological relationships, highlighting the need for standardized diagnostic protocols. These findings reinforce the critical importance of early detection through effective screening programs, support continued HPV vaccination emphasis for younger populations at adenocarcinoma risk, and confirm that clinical staging remains the most reliable prognostic factor transcending histological differences, though larger multicenter studies with standardized protocols are essential to validate these observations and resolve methodological inconsistencies before clinical implementation. Abbreviations SCC Squamous Cell Carcinoma ADC/AC Adenocarcinoma HPV Human Papillomavirus FIGO International Federation of Gynecology and Obstetrics BMI Body Mass Index OR Odds Ratio CI Confidence Interval LASSO Least Absolute Shrinkage and Selection Operator KFSHRC King Faisal Specialist Hospital & Research Centre Declarations Ethics approval and consent to participate This study was approved by the King Faisal Specialist Hospital and Research Centre (KFSHRC) Institutional Review Board (IRB)/Research Ethics Committee (REC) (IRB # 2251677, approved November 4, 2025). Given the retrospective nature of this study using de-identified patient data from medical records, the KFSHRC IRB/REC granted a waiver for informed consent requirements in accordance with institutional guidelines for retrospective chart reviews using secondary data. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research received no external funding. Author Contribution EA conceived and designed the study, acquired and analyzed data, performed statistical analyses, drafted the original manuscript, and contributed to critical revision. MJA contributed to study design and methodology, assisted with data collection and analysis, and participated in manuscript revision. AYA contributed to study design and statistical methodology, assisted with data interpretation, participated in manuscript revision, and served as corresponding author. AT contributed to pathological data collection and p16 immunohistochemistry interpretation, assisted with data analysis, and participated in manuscript revision. SA assisted with data collection and clinical data verification, contributed to data analysis, and participated in manuscript revision. AS contributed to data collection and clinical documentation, assisted with data analysis, and participated in manuscript revision. ZYA assisted with data collection and patient record review, contributed to data verification, and participated in manuscript revision. AAS provided clinical expertise and patient data access, contributed to data interpretation, and participated in manuscript revision. AAA assisted with data collection and analysis, contributed to manuscript preparation, and participated in critical revision. AAA contributed to statistical analysis and data interpretation, assisted with methodology, and participated in manuscript revision. FA contributed to molecular data interpretation and HPV-related analysis, assisted with data analysis, and participated in manuscript revision. AAA supervised the overall research project, contributed to study conception and design, provided critical oversight of data interpretation, and critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I. Jemal: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2021;71(3):209–49. Arbyn M, Weiderpass E, Bruni L, de Sanjosé S, Saraiya M, Ferlay. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Global Health. 2020;8(2):e191. Organization WH. Cervical cancer [Internet]. 2023. Bray F, Carstensen B, Møller H, Zappa M, Žakelj MP, Lawrence G, Hakama. Incidence trends of adenocarcinoma of the cervix in 13 European countries. Cancer Epidemiol Biomarkers Prev. 2005;14(9):2191–9. Stolnicu S, Barsan I, Hoang L, Patel P, Terinte C, Pesci A, Aviel-Ronen S, Kiyokawa T, Alvarado-Cabrero I, Pike MC. International endocervical adenocarcinoma criteria and classification (IECC): a new pathological classification for invasive adenocarcinomas of the endocervix. Am J Surg Pathol. 2018;42(2):214–26. de Sanjose S, Quint WG, Alemany L, Geraets DT, Klaustermeier JE, Lloveras B, Tous S, Felix A, Bravo LE, Shin HR. Human papillomavirus genotype attribution in invasive cervical cancer: a retrospective cross-sectional worldwide study. Lancet Oncol. 2010;11(11):1048–56. Hausen z. Papillomaviruses in the causation of human cancers — a brief historical account. Virology. 2009;384(2):260–5. Galic V, Herzog TJ, Lewin SN, Neugut AI, Burke WM, Lu Y-S, Hershman DL, Wright JD. Prognostic significance of adenocarcinoma histology in women with cervical cancer. Gynecol Oncol. 2012;125(2):287–91. Zhang Y, Shu P, Wang X, Ouyang G, Zhou J, Zhao Y, Li Z, Wang Y, Shen Y. Comparison of Survival Between Different Histological Subtypes in Cervical Cancer Patients: A Retrospective and Propensity Score-matched Analysis. J Cancer. 2024;15(19):6326–35. Lei J, Andrae B, Ploner A, Lagheden C, Eklund C, Nordqvist Kleppe S, Wang J, Fang F, Dillner J, Elfström KM, et al. Cervical screening and risk of adenosquamous and rare histological types of invasive cervical carcinoma: population based nested case-control study. BMJ. 2019;365:l1207. Stolnicu S, Barsan I, Hoang L, Patel P, Terinte C, Pesci A, Aviel-Ronen S, Kiyokawa T, Alvarado-Cabrero I, Pike MC, et al. International Endocervical Adenocarcinoma Criteria and Classification (IECC): A New Pathogenetic Classification for Invasive Adenocarcinomas of the Endocervix. Am J Surg Pathol. 2018;42(2):214–26. Vaccarella S, Franceschi S, Engholm G, Lönnberg S, Khan S, Bray F. 50 years of screening in the Nordic countries: quantifying the effects on cervical cancer incidence. Br J Cancer. 2014;111(5):965–9. Chen F, Chen L, Zhang Y, Shi L, Xu He, Song T. Survival Comparison Between Squamous Cell Carcinoma and Adenocarcinoma for Radiotherapy-Treated Patients with Stage IIB-IVA Cervical Cancer. Front Oncol 2022, Volume 12–2022. Klaes R, Friedrich T, Spitkovsky D, Ridder R, Rudy W, Petry U, Dallenbach-Hellweg G, Schmidt. Overexpression of p16INK4A as a specific marker for dysplastic and neoplastic epithelial cells of the cervix uteri. Int J Cancer. 2001;92(2):276–84. Tsoumpou I, Arbyn M, Kyrgiou M, Wentzensen N, Koliopoulos G, Martin-Hirsch P. Malamou‐Mitsi: p16INK4a immunostaining in cytological and histological specimens from the uterine cervix: a systematic review and meta‐analysis. Cancer Treat Rev. 2009;35(3):210–20. Conesa-Zamora P, Doménech-Peris A, Orantes-Casado FJ, Ortiz-Reina S, Sahuquillo-Frías L, Acosta-Ortega J. García-Solano: Effect on cell cycle markers of HPV 16 E7 protein expression in cervical carcinoma-derived cell lines. BMC Cancer 2007:1–11. Mittal S, Mandal AK, Sharma M, Maheshwari A, Kumar S, Chauhan A, Kumar. P16INK4a immunoexpression in carcinoma cervix. J Cancer Res Ther. 2009;5(4):262–5. Bhatla N, Berek JS, Cuello Fredes M, Denny LA, Grenman S, Karunaratne K, Kehoe ST, Konishi I, Olawaiye AB, Prat J. Revised FIGO staging for carcinoma of the cervix uteri. Int J Gynaecol Obstet. 2019;145(1):129–35. Matsuo K, Machida H, Mandelbaum RS, Konishi. Validation of the 2018 FIGO cervical cancer staging system. Gynecol Oncol. 2019;152(1):87–93. Monk BJ, Tewari KS, Koh WJ. Multimodality therapy for locally advanced cervical carcinoma: state of the art and future directions. J Clin Oncol. 2007;25(20):2952–65. Rose PG, Bundy BN, Watkins EB, Thigpen JT, Deppe G, Maiman MA. Clarke-Pearson: Concurrent cisplatin‐based radiotherapy and chemotherapy for locally advanced cervical cancer. N Engl J Med. 1999;340(15):1144–53. Chen F, Chen L, Zhang Y, Shi L, Xu. Survival comparison between squamous cell carcinoma and adenocarcinoma for radiotherapy-treated patients with stage IIB-IVA cervical cancer. Front Oncol 2022:895122. Liu P, Ji M, Kong Y, Li G, Liu X, Wang C, Lang. Comparison of survival outcomes between squamous cell carcinoma and adenocarcinoma/adenosquamous carcinoma of the cervix after radical radiotherapy and chemotherapy. BMC Cancer. 2022;22(1):326. King Faisal Specialist Hospital. & Research Centre (KFSHRC) [ https://www.kfshrc.edu.sa/] Lesnikova I, Lidang M, Hamilton-Dutoit S, Koch J. p16 as a diagnostic marker of cervical neoplasia: a tissue microarray study of 796 archival specimens. Diagn Pathol. 2009;4:22. Bergeron C, Ordi J, Schmidt D, Trunk MJ, Keller. Conjunctive p16INK4a testing significantly increases accuracy in diagnosing high-grade cervical intraepithelial neoplasia. Am J Clin Pathol. 2010;133(3):395–406. Darragh TM, Colgan TJ, Cox JT, Heller DS, Henry MR, Luff RD, McCalmont T, Nayar R, Palefsky JM, Stoler MH. The Lower Anogenital Squamous Terminology Standardization Project for HPV-associated lesions: background and consensus recommendations from the College of American Pathologists and the American Society for Colposcopy and Cervical Pathology. Arch Pathol Lab Med. 2012;136(10):1266–97. Smith HO, Tiffany MF, Qualls CR, Key CR. The rising incidence of adenocarcinoma relative to squamous cell carcinoma of the uterine cervix in the United States—a 24-year population-based study. Gynecol Oncol. 2000;78(2):97–105. Bulk S, Visser O, Rozendaal L, Verheijen RH, Meijer CJ. Cervical cancer in the Netherlands: a population-based study on incidence, survival and the influence of screening. Eur J Cancer. 2003;39(17):2518–25. Bosch FX, Lorincz A, Muñoz N, Meijer CJ, Shah KV. The causal relation between human papillomavirus and cervical cancer. J Clin Pathol. 2002;55(4):244–65. Walboomers JM, Jacobs MV, Manos MM, Bosch FX, Kummer JA, Shah KV, Snijders PJ, Peto J. Meijer: Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol. 1999;189(1):12–9. Pirog EC, Kleter B, Olgac S, Bobkiewicz P, Lindeman J, Quint WG, Richart RM, Isacson C, Snijders PJ. Prevalence of human papillomavirus DNA in different histological subtypes of cervical adenocarcinoma. Am J Pathol. 2000;157(4):1055–62. Andersson S, Rylander E, Larsson B, Strand A, Silfversvärd. The role of human papillomavirus in cervical adenocarcinoma carcinogenesis. Eur J Cancer. 2001;37(2):246–50. Huang K, Li LA, Meng YG, Fu XY. p16 expression in patients with cervical cancer and its prognostic significance: meta-analysis of published literature. Eur J Obstet Gynecol Reproductive Biology 2014:64–9. Lin J, Albers AE, Qin J, Kaufmann AM. Prognostic Significance of Overexpressed p16INK4a in Patients with Cervical Cancer: A Meta-Analysis. PLoS ONE. 2014;9(9):e106384. da Mata S, Ferreira J, Nicolás I, Esteves S, Esteves G, Lérias S, Silva F, Saco A, Cochicho D, Cunha M. P16 and HPV genotype significance in HPV-associated cervical cancer—a large cohort of two tertiary referral centers. Int J Mol Sci. 2021;22(5):2294. Castle PE, Stoler MH, Wright TC Jr, Sharma A, Wright TL, Behrens CM. Performance of carcinogenic human papillomavirus (HPV) testing and HPV16 or HPV18 genotyping for cervical cancer screening of women aged 25 years and older: a subanalysis of the ATHENA study. Lancet Oncol. 2011;12(9):880–90. Ronco G, Dillner J, Elfström KM, Tunesi S, Snijders PJ, Arbyn M, Kitchener H, Segnan N, Gilham C, Giorgi-Rossi P. Efficacy of HPV-based screening for prevention of invasive cervical cancer: follow-up of four European randomised controlled trials. Lancet. 2014;383(9916):524–32. Azerkan F, Sparén P, Sandin S, Tillgren. Cervical screening participation and risk among Swedish-born and immigrant women in Sweden. Int J Cancer. 2012;130(4):937–47. Marlow LA, Waller. Sociodemographic predictors of HPV testing and vaccination acceptability: results from a population-representative sample of British women. J Med Screen. 2008;15(2):91–6. Meng M, Ma Z, Zhou H, Xie Y, Lan R, Zhu S. Miao: The impact of social relationships on the risk of stroke and post-stroke mortality: a systematic review and meta-analysis. BMC Public Health 2024:2403. Kroenke CH, Kubzansky LD, Schernhammer ES, Holmes. Social networks, social support, and survival after breast cancer diagnosis. J Clin Oncol. 2006;24(7):1105–11. Ou Y, Chokkakula S, Chong SM, Wang H, Huang MDSAIC, Xu L, Lyu X. J, Huang: Age and socioeconomic disparities in cervical cancer incidence and mortality: a SEER-based analysis. Front Public Health 2025:1591883. Adami HO, Pontén J, Sparén P, Bergström R, Gustafsson L, Friberg LG. Survival trend after invasive cervical cancer diagnosis in Sweden before and after cytologic screening, 1960–1988. Survival trend after invasive Cerv cancer diagnosis Swed before after cytologic Screen. 1994;73(1):140–7. Sasieni P, Castanon. Effectiveness of cervical screening with age: population based case-control study of prospectively recorded data. BMJ 2009. Franco EL, Cuzick J, Hildesheim. Chap. 20: Issues in planning cervical cancer screening in the era of HPV vaccination. Vaccine 2006, 24. Lei J, Ploner A, Elfström KM, Wang J, Roth A, Fang F, Sundström K, Dillner J, Sparén P. HPV Vaccination and the Risk of Invasive Cervical Cancer. N Engl J Med. 2020;383(14):1340–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8259753","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562203562,"identity":"c05e959d-701a-40bb-8fe9-8d02996f15dd","order_by":0,"name":"Emad Alqassim","email":"","orcid":"","institution":"King Faisal Specialist Hospital and Research Center","correspondingAuthor":false,"prefix":"","firstName":"Emad","middleName":"","lastName":"Alqassim","suffix":""},{"id":562203563,"identity":"d3a9621a-7174-49e9-8453-ab83bc793b66","order_by":1,"name":"Mashael J. Abu-Alola","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Mashael","middleName":"J.","lastName":"Abu-Alola","suffix":""},{"id":562203565,"identity":"fc1ea99d-744e-4e03-b9fb-b0c22966566b","order_by":2,"name":"Ahmad Y. Alqassim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYFAC5gYQycPA3pAAEzIgoIURqoXnAIlaGBgk4DoIaOFvP9h448efOhndmQ8efvxRcyexgb15mwTDHzucWiTOJDZb9rYd5jG7nZAsIXHsWWIDz7EyCca2ZNzWHEhsk+BtOADSkiBhwHY4sUEix0yCsYEZpw758w/bJP/8qeMxu3kg+UfCP6AW+TdmQIfV49RicCOxTZqHjZnH7AZDmsTBNpAtPEAtbIdxajG88bDZWhbklzMJaZaNfYeN23jSii0S247j1CJ3PvngzTd/6uzNjp9Jvvnj22HZfvbDG298+FON2/tAIAGheBLAFBuISMCrAa6F/QABdaNgFIyCUTBSAQCjhlnTvNHPOgAAAABJRU5ErkJggg==","orcid":"","institution":"Jazan University","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"Y.","lastName":"Alqassim","suffix":""},{"id":562203566,"identity":"5b4bf335-c28b-43c9-b3b8-153d83170e2c","order_by":3,"name":"Asma Tulbah","email":"","orcid":"","institution":"King Faisal Specialist Hospital and Research Center","correspondingAuthor":false,"prefix":"","firstName":"Asma","middleName":"","lastName":"Tulbah","suffix":""},{"id":562203567,"identity":"5b2ffa21-f175-47fa-ade4-8d1ff86071d3","order_by":4,"name":"Sarah Alawami","email":"","orcid":"","institution":"King Faisal Specialist Hospital and Research Center","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Alawami","suffix":""},{"id":562203568,"identity":"87d33f76-ffbd-45f6-9e5a-54fcaaf0f780","order_by":5,"name":"Abdulrahman Samman","email":"","orcid":"","institution":"King Faisal Specialist Hospital and Research Center","correspondingAuthor":false,"prefix":"","firstName":"Abdulrahman","middleName":"","lastName":"Samman","suffix":""},{"id":562203569,"identity":"02c60bbd-ccee-4ac7-b312-76e2981723fa","order_by":6,"name":"Zainab Y. Azzouni","email":"","orcid":"","institution":"King Faisal Specialist Hospital and Research Center","correspondingAuthor":false,"prefix":"","firstName":"Zainab","middleName":"Y.","lastName":"Azzouni","suffix":""},{"id":562203570,"identity":"8690ac4d-9b7c-41a5-861d-a39275c9db7d","order_by":7,"name":"Amnah A. Shubayli","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Amnah","middleName":"A.","lastName":"Shubayli","suffix":""},{"id":562203571,"identity":"09306356-630b-406a-9664-15f89197d3dc","order_by":8,"name":"Arwa A. Al-Qahtani","email":"","orcid":"","institution":"Imam Mohammad ibn Saud Islamic University","correspondingAuthor":false,"prefix":"","firstName":"Arwa","middleName":"A.","lastName":"Al-Qahtani","suffix":""},{"id":562203572,"identity":"dca08ad0-499b-4df9-9b11-67ec085f514f","order_by":9,"name":"Abdulrahman A. Alahmari","email":"","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Abdulrahman","middleName":"A.","lastName":"Alahmari","suffix":""},{"id":562203573,"identity":"cefff975-0bbe-401f-88df-ef695b5fa5f4","order_by":10,"name":"Fatimah Alhamlan","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Fatimah","middleName":"","lastName":"Alhamlan","suffix":""},{"id":562203574,"identity":"e7324550-e42e-4df2-9314-04b395d6afd5","order_by":11,"name":"Ahmed A. Al-Qahtani","email":"","orcid":"","institution":"King Faisal Specialist Hospital \u0026 Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"A.","lastName":"Al-Qahtani","suffix":""}],"badges":[],"createdAt":"2025-12-02 11:08:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8259753/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8259753/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99316009,"identity":"6a9f7761-b178-4307-973e-83e6835a135a","added_by":"auto","created_at":"2025-12-31 16:27:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":417644,"visible":true,"origin":"","legend":"","description":"","filename":"Figures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/b77a0bbdac8d80daff71393d.docx"},{"id":99316638,"identity":"14217a0c-52e6-46c2-83d6-9229aaea8c4a","added_by":"auto","created_at":"2025-12-31 16:28:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84770,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/33307d8963e40f9e513913b0.docx"},{"id":99187557,"identity":"abad1960-18bd-4f91-9ef5-d82a2b742256","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30948,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/d31f15a2b49ce71aaa6e260a.docx"},{"id":99187563,"identity":"71613a5a-1ece-4939-a3eb-5d15b24dffbd","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14245,"visible":true,"origin":"","legend":"","description":"","filename":"9ac69a94f50d464591c68bf75b777390.json","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/6c38c303b32ecb26bbb0a8bd.json"},{"id":99187567,"identity":"3404cbc4-0053-45fc-929a-9e01f6a048b9","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14513,"visible":true,"origin":"","legend":"","description":"","filename":"AuthorContributionsStatement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/03e9d0cf6ba1c7787fd9e58d.docx"},{"id":99187564,"identity":"981ef610-436e-43ee-bd43-03674e18beb8","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13499,"visible":true,"origin":"","legend":"","description":"","filename":"declarationStatement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/49299b2340a1fb11d6624d5b.docx"},{"id":99187571,"identity":"0e5e4466-982a-48c9-8d2d-4608b26c8b7f","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165149,"visible":true,"origin":"","legend":"","description":"","filename":"9ac69a94f50d464591c68bf75b7773901enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/cb8b236c673569e0d74588cb.xml"},{"id":99187561,"identity":"a8b263d8-916e-491c-9c06-69845dcad0c6","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56042,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/86deb1ef3bed81e44207ac16.png"},{"id":99187580,"identity":"a111cc63-c5c4-4a6a-81f3-9659e616b95e","added_by":"auto","created_at":"2025-12-30 00:11:00","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67987,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/a22ee04e95890b4e290ab1e9.png"},{"id":99316010,"identity":"4b0e978c-d350-41a1-97d7-4a613652442f","added_by":"auto","created_at":"2025-12-31 16:27:33","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111095,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/052c55158b141d41ad69a729.png"},{"id":99316732,"identity":"1ca10927-6c79-4ff3-a5f2-394b2f7f0656","added_by":"auto","created_at":"2025-12-31 16:29:05","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":345145,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/8d8b27a2637570079a8d2cb5.jpeg"},{"id":99316579,"identity":"dc24a361-8c5e-48e7-9d85-bfd5b12720c3","added_by":"auto","created_at":"2025-12-31 16:28:36","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56129,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/880def88761d10d81a512334.png"},{"id":99187569,"identity":"71b277ae-20f9-4a22-995d-f5e1bf47cd65","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14631,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/d4f6d7fb5b2aaa34b3a8f3db.png"},{"id":99187573,"identity":"73b7dd91-0ce6-486b-8881-74e7cb7ee5e8","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15477,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/5b763e83f08db163ac326174.png"},{"id":99187576,"identity":"eb00babd-e5e1-4e60-8be4-5561f9886fb0","added_by":"auto","created_at":"2025-12-30 00:11:00","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26773,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/697b2fcc4834b96705cf4a9c.png"},{"id":99187574,"identity":"691ad767-06bc-4ca4-abda-c10d9855896a","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90927,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/fcc955ebb322f33a1948013b.png"},{"id":99187578,"identity":"5042c675-96fb-4a5c-92d6-74b300920418","added_by":"auto","created_at":"2025-12-30 00:11:00","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10528,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/d0e3fed8bb7ae4e86c6cd372.png"},{"id":99187579,"identity":"5401dca7-edcd-4008-bd1f-36d468dd0ff8","added_by":"auto","created_at":"2025-12-30 00:11:00","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":162850,"visible":true,"origin":"","legend":"","description":"","filename":"9ac69a94f50d464591c68bf75b7773901structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/499643021c2e97422c20801e.xml"},{"id":99187575,"identity":"ce8687ca-802e-49a0-9c16-a30d56c0bef7","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":175412,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/7fe057ef55248c9c9addea15.html"},{"id":99315874,"identity":"03945952-3b39-4d22-8317-1824d0d7f4bc","added_by":"auto","created_at":"2025-12-31 16:27:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26090,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate Logistic Regression Analysis of Factors Associated with High Clinical Stage in Cervical Cancer Patients. Forest plot displaying odds ratios (OR) with 95% confidence intervals (CI) for predictors of advanced clinical stage (FIGO III-IV) versus early stage (FIGO I-II) disease. P16 positivity emerged as the strongest predictor of advanced disease (OR = 2.45, 95% CI: 1.01-5.95, p = 0.047), while marital status demonstrated protective effects (OR = 0.87, 95% CI: 0.77-0.98, p = 0.021). Age and menopausal status showed paradoxical negative associations with advanced stage presentation. The vertical dashed line represents OR = 1.0 (no effect). Points to the right indicate increased odds of advanced stage disease, while points to the left indicate decreased odds. Sample size: n = [85]; statistical significance set at p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/4852a1df6f0841b0b6790e8c.png"},{"id":99187556,"identity":"39204812-2ab7-4fc4-bb33-5a46b839bedd","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23962,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO Regression Coefficients for Predictors of High Clinical Stage in Cervical Cancer Patients. Forest plot displaying standardized regression coefficients from Least Absolute Shrinkage and Selection Operator (LASSO) analysis for clinical stage prediction. The LASSO algorithm performed automatic feature selection while minimizing overfitting through L1 regularization with 10-fold cross-validation to select the optimal penalty parameter (C=7.96). Age demonstrated the strongest positive association with advanced stage (coefficient = +0.00044), followed by menopausal status (coefficient = +0.00015) and squamous cell carcinoma histology (coefficient = +0.00009). Protective factors included BMI (coefficient = -0.0003) and marital status (coefficient = -0.0002). P16 positivity was retained despite minimal coefficient magnitude, indicating model relevance. Model stability was further assessed by bootstrap resampling (100 runs), confirming consistent selection of the key predictors. Positive coefficients (right of zero line) indicate increased odds of advanced stage; negative coefficients indicate decreased odds.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/48240a5647e47d634ef0dea8.jpg"},{"id":99316931,"identity":"4d890301-71e7-4187-841a-8d809deeb27e","added_by":"auto","created_at":"2025-12-31 16:29:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101161,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between Demographic, Clinical, and Pathological Variables in Cervical Cancer Patients. Multi-panel boxplot analysis examining relationships between key variables in the study cohort. Panel A: BMI distribution by menopausal status showing marginally significant trend toward lower BMI in postmenopausal women (p = 0.051). Panel B: Clinical stage distribution by vital status demonstrating significantly higher stages in deceased patients versus survivors (p = 0.003). Panel C: Age distribution by histological subtype with squamous cell carcinoma patients trending older than adenocarcinoma patients (p = 0.08). Panel D: Age distribution by menopausal status confirming expected biological relationship (p \u0026lt; 0.001). Box plots display median (central line), interquartile range (box boundaries), and 1.5×IQR whiskers with outliers shown as individual points. Statistical comparisons performed using appropriate tests (t-test for continuous variables, Mann-Whitney U test for non-parametric distributions). Sample sizes and exact p-values provided for each comparison.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/fa52c991d8575e3d7212528b.jpg"},{"id":99187565,"identity":"fec1340b-dd4a-46c5-aef8-c375197c69d0","added_by":"auto","created_at":"2025-12-30 00:10:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209545,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal Trends in p16 Positivity and Advanced-Stage Disease Presentation (2020-2024). Dual-axis line graph illustrating five-year trends in key clinical parameters. The solid blue line with circles represents p16 positivity rates (left y-axis), showing significant increasing trend from ~80% in 2020 to stabilized levels of 94-96% by 2024 (linear regression p = 0.008). The dashed red line with triangles depicts advanced-stage disease presentation (FIGO III-IV) as percentage of total cases (right y-axis), demonstrating considerable year-to-year variation without significant temporal trend (p = 0.796). Notable peaks occurred in 2021 (~88%) with subsequent fluctuations including a decline to ~74% in 2023. Error bars represent 95% confidence intervals. Trend analysis performed using linear regression with statistical significance set at p \u0026lt; 0.05. The divergent patterns suggest improving molecular diagnostic capabilities independent of clinical presentation severity over the study period.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/520a4d5cd17eed61edd1b33a.jpg"},{"id":99317044,"identity":"f81642bb-6cd5-4210-9ba5-75e08cbab0ed","added_by":"auto","created_at":"2025-12-31 16:29:37","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":35227,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Survival Curves by Clinical Stage in Cervical Cancer Patients (2020-2024). Time-to-event analysis comparing overall survival between low clinical stage (FIGO I-II, blue solid line) and high clinical stage (FIGO III-IV, red dashed line) cervical cancer patients over 60-month follow-up period. Low-stage patients maintained 100% survival probability throughout the entire observation period, while high-stage patients demonstrated progressive survival decline from 95% at baseline to 73% by 60 months (22% absolute survival reduction). The widening gap between curves demonstrates increasing mortality risk over time for advanced-stage disease. Log-rank test confirmed statistically significant difference between groups (p = 0.03). Number at risk tables are provided below the x-axis, censored observations are indicated by vertical tick marks, and shaded areas represent 95% confidence intervals. Median follow-up time was 3.0 years for both groups. This analysis validates clinical staging as a robust predictor of long-term outcomes and emphasizes the prognostic importance of early detection.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/953d8e094060b21f5d0fde42.jpg"},{"id":99789631,"identity":"f4baa6ca-dcbb-400d-98cb-d7ac7966eab1","added_by":"auto","created_at":"2026-01-08 12:50:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1409654,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8259753/v1/02649bcd-ad9f-440c-a8e5-2a1bb699e2dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Clinicopathological Features, HPV Status, and Prognostic Determinants of Squamous Cell Carcinoma and Adenocarcinoma of the Cervix: A Retrospective Cohort Analysis (2020–2024)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer represents the fourth most common malignancy among women worldwide, with approximately 660,000 new cases and deaths reported in 2020 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advances in HPV vaccination and screening programs implemented over the past decade, significant disparities in outcomes persist globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The two predominant histological subtypes, squamous cell carcinoma (SCC) and adenocarcinoma (ADC), account for approximately 83% and 12% of cervical cancers, respectively [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe biological and clinical differences between SCC and ADC have been the subject of considerable research interest. While both subtypes are predominantly associated with high-risk HPV infection, with significant regional variation in genotype distribution beyond the traditional HPV 16 and 18 focus, emerging evidence suggests distinct epidemiological patterns, molecular characteristics, and clinical behaviors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Adenocarcinoma has been associated with younger patient age at diagnosis, different anatomical distribution within the cervix, and potentially distinct treatment responses compared to squamous cell carcinoma. However, prognostic differences between histological subtypes remain controversial, with some studies demonstrating significantly worse survival for adenocarcinoma in both early-stage (HR\u0026thinsp;=\u0026thinsp;1.39) and advanced-stage disease (HR\u0026thinsp;=\u0026thinsp;1.21) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], while other contemporary analyses report no significant histotype-specific survival differences. Contemporary treatment standardization following international guidelines minimizes therapeutic confounding variables, enhancing the validity of histological subtype comparisons [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Epidemiological studies have documented significant shifts in cervical cancer patterns that are particularly relevant for comparative clinicopathological analysis. Recent population-based evidence demonstrates that adenocarcinoma now comprises a substantial proportion of cervical cancers in developed countries, with a pronounced predilection for younger women [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This epidemiological shift reflects differential screening effectiveness, as cytological screening demonstrates superior detection of squamous cell carcinoma precursors compared to adenocarcinoma precursors, resulting in disproportionate reductions in squamous cell carcinoma incidence while adenocarcinoma rates remain stable or increase [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These changing demographic and screening patterns raise important questions about whether traditional comparative analyses between squamous cell carcinoma and adenocarcinoma remain valid in contemporary patient populations, particularly regarding disease presentation, staging, and prognostic outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe role of HPV in cervical carcinogenesis is well-established, with p16 immunohistochemistry serving as a reliable surrogate marker for HPV-mediated transformation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. P16 overexpression, resulting from functional inactivation of the retinoblastoma pathway by HPV E7 oncoprotein, has emerged as both a diagnostic tool and favorable prognostic marker in cervical cancer, with P16-positive tumors demonstrating better treatment response and improved survival outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the comparative prevalence and prognostic significance of p16 positivity across different histological subtypes requires further elucidation.\u003c/p\u003e \u003cp\u003eClinical staging remains the cornerstone of cervical cancer prognosis and treatment planning, with the International Federation of Gynecology and Obstetrics (FIGO) staging system serving as the primary framework for risk stratification [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nevertheless, the identification of additional demographic, molecular, and clinical factors that influence disease presentation and outcomes could enhance prognostic accuracy and inform personalized treatment approaches [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough previous studies have examined survival differences between SCC and ADC [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the comparative prevalence and prognostic significance of molecular markers, particularly P16 positivity, across these histological subtypes in contemporary populations requires further investigation. Furthermore, the prognostic significance of demographic factors, molecular markers, and their interactions with clinical staging requires comprehensive multivariate analysis to identify independent predictors of disease severity and survival outcomes.\u003c/p\u003e \u003cp\u003eThe primary aim of this study was to evaluate demographic, clinical, and pathological differences between squamous cell carcinoma and adenocarcinoma of the cervix in a contemporary patient cohort. Secondary objectives included exploratory analysis of factors associated with advanced stage presentation and preliminary assessment of survival outcomes, acknowledging that definitive prognostic conclusions would require larger sample sizes and prospective validation. These comparative insights have direct clinical applications: histotype-specific risk factors can enhance patient counseling with more precise prognostic information, demographic and molecular determinants can strengthen risk stratification models for optimized screening and treatment intensity, and identification of predictive markers can guide individualized treatment decisions regarding surgical approaches, adjuvant therapy selection, and surveillance strategies in contemporary personalized cervical cancer care. Through comprehensive statistical analysis, we sought to identify key prognostic determinants and provide insights into the comparative clinical behavior of these two major cervical cancer subtypes to inform patient counseling, contribute to risk stratification models, and guide individualized treatment planning.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective observational cohort study analyzing cervical cancer patients diagnosed and treated at King Faisal Specialist Hospital \u0026amp; Research Centre (KFSHRC), Riyadh, Saudi Arabia, between January 2020 and December 2024. KFSHRC is a tertiary care academic medical center, serving as a major regional referral center for Saudi Arabia and the Middle East [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The institution maintains international accreditation standards including Magnet Recognition Program\u0026reg; and HIMSS Stage 7 certification, ensuring standardized oncology care protocols and comprehensive data collection systems.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study included all patients with histologically confirmed cervical cancer diagnosed during the study period. Inclusion criteria comprised: (1) histologically confirmed primary cervical cancer; (2) complete clinical staging information; (3) available demographic and clinical data; and (4) confirmed histological subtype (squamous cell carcinoma or adenocarcinoma). Exclusion criteria included: (1) other histological subtypes (neuroendocrine tumors, sarcomas); (2) recurrent disease; (3) incomplete staging information; and (4) patients lost to follow-up within 30 days of diagnosis.\u003c/p\u003e \u003cp\u003eOf 90 patients initially screened with cervical cancer diagnosis, 85 met inclusion criteria, yielding a final study population of 85 patients (69 squamous cell carcinoma, 16 adenocarcinoma). All eligible cases meeting inclusion criteria during the study period (2020\u0026ndash;2024) were included to maximize statistical power and ensure comprehensive representation of the patient population.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003ePatient data were collected from medical records and included the following variables: demographic characteristics (age at diagnosis, body mass index [BMI], marital status, menopausal status), clinical parameters (FIGO stage, degree of differentiation), molecular markers (p16 immunohistochemistry status), histological subtype, and outcome measures (vital status at last follow-up). Clinical staging was performed according to the 2018 FIGO staging system, with stages categorized as low (I-II) versus high (III-IV) for analytical purposes.\u003c/p\u003e \u003cp\u003e Data quality was ensured through dual verification by two independent reviewers, with discrepancies resolved by consensus. Missing data patterns were assessed, and cases with \u0026gt;\u0026thinsp;20% missing critical variables were excluded from analysis. Complete case analysis was performed for multivariable modeling, with sensitivity analyses conducted to assess the impact of missing data on primary outcomes.\u003c/p\u003e\n\u003ch3\u003eP16 Immunohistochemistry\u003c/h3\u003e\n\u003cp\u003eP16 immunohistochemistry was performed on formalin-fixed, paraffin-embedded tumor tissue using standard protocols [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Primary antibody (E6H4 clone, Ventana, Medical Systems Inc. Tucson, AZ, USA) was used at 1.0\u0026micro;g/ml on VENTANA BenchMark ULTRA, Ventana. Quality control measures included positive and negative controls with each batch. P16 positivity was defined as continuous strong nuclear and cytoplasmic staining of the basal cell layer with extension upward involving at least one-third of the epithelial thickness (\"diffuse staining pattern\"), consistent with established criteria for HPV-associated cervical cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Focal or patchy nuclear staining patterns were considered negative.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and approved by the King Faisal Specialist Hospital and Research Centre (KFSHRC) Institutional Review Board (IRB)/Research Ethics Committee (REC) (IRB # 2251677, approved November 4, 2025, valid for 12 months). Given the retrospective nature of this study using de-identified patient data from medical records, the KFSHRC IRB/REC granted a waiver for informed consent requirements in accordance with institutional guidelines for retrospective chart reviews using secondary data. Patient confidentiality and data privacy were maintained throughout the study period.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were calculated for all variables, with continuous variables presented as means with standard deviations and categorical variables as frequencies and percentages. Comparative analysis between histological subtypes employed Chi-square tests for categorical variables and t-tests for continuous variables. Multivariate logistic regression analysis was performed to identify predictors of high clinical stage presentation, with results presented as odds ratios (OR) with 95% confidence intervals. Variables with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in univariate analysis were included in the multivariate model. LASSO (Least Absolute Shrinkage and Selection Operator) regression was implemented for feature selection and regularization to identify the most parsimonious set of predictors for clinical stage determination while minimizing overfitting. The optimal lambda parameter was selected through cross-validation. Temporal trend analysis was conducted using linear regression to assess changes in p16 positivity rates and advanced-stage disease presentation over the study period. Survival analysis was performed using the Kaplan-Meier method, with survival curves compared using the log-rank test. Follow-up time was calculated from the date of diagnosis to the last contact or death. All statistical analyses were performed using Python (JupyterLab environment), with appropriate statistical packages. Complete case analysis was performed, and statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes baseline patient and tumor characteristics stratified by p16 status. Most patients were \u0026ge;\u0026thinsp;40 years, with obesity being the most common BMI category. The majority presented at advanced FIGO stages (III\u0026ndash;IV) and with grade 2 or 3 histology. No significant associations were observed between p16 status and age, BMI, tumor stage, or grade, though widowed patients were more likely to be p16 negative (p\u0026thinsp;=\u0026thinsp;0.007).\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 Patient and Tumor Characteristics.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \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\u003eTotal (N\u0026thinsp;=\u0026thinsp;81*)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP16 Positive (N\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP16 Negative (N\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u0026lt;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\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\u003e13 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \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\u003e21 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\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\u003e18 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.570\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\u003e27 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI Categories\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\u0026nbsp;\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\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5\u0026ndash;24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (25-29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (46.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFIGO Stage\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\u0026nbsp;\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\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic Grade\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\u0026nbsp;\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\u003eGrade 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\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\u0026nbsp;\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData presented as n (%). Statistical comparisons performed using Chi-square test. *Out of 85 patients, 4 with \u003cem\u003eNot reported\u003c/em\u003e P16 status and 1 labeled as \u003cem\u003eVariable\u003c/em\u003e were excluded, leaving 81 patients (77 P16-positive and 4 P16-negative) for analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe comparative analysis between squamous cell carcinoma and adenocarcinoma revealed several notable differences in patient characteristics and clinical presentation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, menopausal status emerged as a significant distinguishing factor, with squamous cell carcinoma patients more likely to be postmenopausal compared to adenocarcinoma patients. Age demonstrated a marginally significant trend, with squamous cell carcinoma patients being older on average compared to adenocarcinoma patients. This age difference aligns with the menopausal status findings and suggests potential age-related variations in histological subtype development.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Patient Characteristics and Clinical Features by Histological Subtype in Cervical Cancer (2020\u0026ndash;2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquamous Cell Carcinoma (n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdenocarcinoma (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI (Low\u0026ndash;High)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP16 status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive: 62 (89.9%)\u003c/p\u003e \u003cp\u003eNegative: 3 (4.3%)\u003c/p\u003e \u003cp\u003eNot reported: 3 (4.3%)\u003c/p\u003e \u003cp\u003eVariable: 1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive: 15 (93.8%)\u003c/p\u003e \u003cp\u003eNegative: 1 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u0026ndash;29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDegree of differentiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWell: 6 (8.7%)\u003c/p\u003e \u003cp\u003eModerate: 41 (59.4%)\u003c/p\u003e \u003cp\u003ePoor: 20 (29.0%)\u003c/p\u003e \u003cp\u003eNot reported: 2 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWell: 0 (0.0%)\u003c/p\u003e \u003cp\u003eModerate: 12 (75.0%)\u003c/p\u003e \u003cp\u003ePoor: 2 (12.5%)\u003c/p\u003e \u003cp\u003eNot reported: 2 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u0026ndash;2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly (I\u0026ndash;II): 16 (23.2%)\u003c/p\u003e \u003cp\u003eAdvanced (III\u0026ndash;IV): 53 (76.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly (I\u0026ndash;II): 5 (31.2%)\u003c/p\u003e \u003cp\u003eAdvanced (III\u0026ndash;IV): 11 (68.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u0026ndash;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlive: 50 (72.5%)\u003c/p\u003e \u003cp\u003eDeceased: 11 (15.9%)\u003c/p\u003e \u003cp\u003eUnknown: 8 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive: 10 (62.5%)\u003c/p\u003e \u003cp\u003eDeceased: 3 (18.8%)\u003c/p\u003e \u003cp\u003eUnknown: 3 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39\u0026ndash;7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried: 43 (62.3%)\u003c/p\u003e \u003cp\u003eSingle: 13 (18.8%)\u003c/p\u003e \u003cp\u003eDivorced: 5 (7.2%)\u003c/p\u003e \u003cp\u003eWidowed: 18 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried: 11 (68.8%)\u003c/p\u003e \u003cp\u003eSingle: 3 (18.8%)\u003c/p\u003e \u003cp\u003eDivorced: 1 (6.2%)\u003c/p\u003e \u003cp\u003eWidowed: 0 (0.0%)\u003c/p\u003e \u003cp\u003eUnknown: 1 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMenopausal status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive: 36 (52.2%)\u003c/p\u003e \u003cp\u003eNegative: 33 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive: 2 (12.5%)\u003c/p\u003e \u003cp\u003eNegative: 14 (87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (yrs)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;=\u0026thinsp;52.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;=\u0026thinsp;48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;=\u0026thinsp;29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;=\u0026thinsp;28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Cases with \u0026ldquo;Not reported\u0026rdquo; or \u0026ldquo;Unknown\u0026rdquo; categories were included in descriptive statistics but excluded from regression models.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe multivariate logistic regression analysis identified distinct patterns of association with high clinical stage cervical cancer presentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). P16 positivity emerged as the strongest predictor of advanced disease (OR\u0026thinsp;=\u0026thinsp;2.45, 95% CI: 1.01\u0026ndash;5.95, p\u0026thinsp;=\u0026thinsp;0.047), conferring nearly 2.5-fold increased odds of high clinical stage presentation. Marital status demonstrated a significant protective effect (OR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.77\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.021), with married patients having 13% lower odds of advanced stage disease compared to unmarried patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNotably, age and menopausal status exhibited significant negative associations with high clinical stage, suggesting older patients and postmenopausal women paradoxically present with earlier stage disease (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). BMI and cancer type showed non-significant positive trends toward advanced stage presentation. These findings underscore P16's role as a molecular predictor of disease severity while revealing unexpected protective effects of age and marital status in this cohort.\u003c/p\u003e \u003cp\u003eThe LASSO regression analysis was performed with 10-fold cross validation to identify predictors of high clinical stage cervical cancer while performing automatic feature selection to minimize overfitting (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The optimal penalty parameters were selected based on the minimum cross-validation error, corresponding to an optimal C (1/λ) of 7.96.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the retained variables, age demonstrated the strongest positive association with advanced stage disease (coefficient\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.00044), indicating that older patients have increased odds of presenting with high clinical stage. Menopausal status showed a weaker but positive association (coefficient\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.00015), with postmenopausal women having slightly elevated odds of advanced disease. Squamous cell carcinoma histology exhibited a minimal positive association with high stage presentation (coefficient\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.00009).\u003c/p\u003e \u003cp\u003eConversely, marital status revealed protective effects, with married patients showing reduced odds of advanced stage disease (coefficient = -0.0002). Interestingly, BMI demonstrated the strongest negative association (coefficient = -0.0003), suggesting that higher BMI may be protective against advanced stage presentation or associated with earlier disease detection. p16 positivity was retained by the LASSO algorithm despite having a coefficient near zero, indicating its relevance for model performance even with minimal individual contribution.\u003c/p\u003e \u003cp\u003eTo assess model stability, we performed the bootstrap resampling with 100 runs. This demonstrated that clinical stage (98%), degree of differentiation (96%), BMI (84%), marital status (77%), and age (67%) were consistently selected across bootstrap models, supporting their robustness as predictors. In contrast, most Pap smear subcategories demonstrated low selection frequencies (\u0026lt;\u0026thinsp;30%), suggesting limited reproducibility.\u003c/p\u003e \u003cp\u003eThese LASSO findings contrast with the traditional logistic regression results, particularly regarding age and menopausal status, highlighting the different analytical approaches' sensitivity to variable interactions and the regularization effect of LASSO in identifying the most parsimonious set of predictors for clinical stage determination.\u003c/p\u003e \u003cp\u003eThe boxplot analyses revealed several significant associations between key demographic, clinical, and pathological variables in the cervical cancer cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Postmenopausal women demonstrated a marginally significant trend toward lower BMI compared to premenopausal patients (p\u0026thinsp;=\u0026thinsp;0.051), suggesting potential metabolic differences associated with menopausal status. Most notably, deceased patients presented with significantly more advanced clinical stages than living patients (p\u0026thinsp;=\u0026thinsp;0.003), confirming the prognostic importance of staging at diagnosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAge distribution varied by histological subtype, with squamous cell carcinoma patients trending toward older age compared to adenocarcinoma patients (p\u0026thinsp;=\u0026thinsp;0.08), though this difference did not reach statistical significance. As expected, postmenopausal women were significantly older than premenopausal patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), validating the biological relationship between age and menopausal status.\u003c/p\u003e \u003cp\u003eThe temporal analysis trend revealed distinct patterns in key clinical variables over the five-year study period from 2020 to 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). p16 positivity demonstrated a significant increasing trend over time (p\u0026thinsp;=\u0026thinsp;0.008), rising from approximately 80% in 2020 to nearly complete positivity, then stabilizing at high levels around 94\u0026ndash;96% through 2024. This trend suggests either improved diagnostic testing protocols, changes in patient population characteristics, or evolving epidemiological patterns of HPV-associated cervical cancers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the proportion of advanced-stage cases showed considerable year-to-year variation without a significant overall trend (p\u0026thinsp;=\u0026thinsp;0.796), fluctuating from 70% in 2020 to peaks of approximately 88% in 2021, followed by substantial variation including a notable drop to around 74% in 2023 before returning to 75% in 2024. The lack of a consistent temporal pattern in advanced-stage presentation suggests that factors influencing disease stage at diagnosis remain relatively stable over time, despite the increasing prevalence of p16 positivity.\u003c/p\u003e \u003cp\u003eThe Kaplan-Meier survival analysis demonstrated a significant difference in survival outcomes between patients with high and low clinical stage cervical cancer over the five-year study period (log-rank p\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Patients with low clinical stage disease maintained excellent survival probability, remaining at 1.0 (100% survival) throughout the entire follow-up period from 2020 to 2024. In stark contrast, patients with high clinical stage disease showed progressive decline in survival probability over time, beginning at approximately 0.95 (95%) in 2020 and decreasing to 0.91 (91%) in 2021, followed by a more pronounced decline to 0.77 (77%) in both 2022 and 2023, and reaching 0.73 (73%) by 2024.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe median follow-up time was 3.0 years for both groups. This represents a 22 percentage point absolute survival reduction (95% to 73%) or a 23% relative survival reduction over the five-year period for patients with advanced stage disease. Censoring events are indicated in the curves, and the numbers at risk at each year are displayed below the x-axis. The widening gap between survival curves over time underscores the critical prognostic importance of clinical staging at diagnosis and validates clinical staging as a robust predictor of long-term outcomes. However, survival differences were not adjusted for treatment effects, which may partially account for outcome variation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective analysis of cervical cancer patients (N\u0026thinsp;=\u0026thinsp;85) provides insights into the comparative clinicopathological features of squamous cell carcinoma and adenocarcinoma. Our findings demonstrate that while both histological subtypes share similar molecular characteristics and clinical presentations, distinct demographic patterns and prognostic factors differentiate these major cervical cancer subtypes, challenging some established paradigms while confirming others.\u003c/p\u003e \u003cp\u003eThe observed age and menopausal status differences between SCC and ADC patients represent our most reliable findings, consistent with well-documented epidemiological shifts in multiple international studies. Our finding that SCC patients were marginally older (52.16 vs 48.2 years, p\u0026thinsp;=\u0026thinsp;0.057) and more likely to be postmenopausal (p\u0026thinsp;=\u0026thinsp;0.050) aligns with the well-documented temporal trends reported in a previous study where international trends showing increasing adenocarcinoma incidence in younger age groups across multiple countries were observed [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This finding suggests a cohort effect related to changing sexual behaviors and HPV exposure patterns, consistent with the rising incidence of adenocarcinoma relative to squamous cell carcinoma in the United States, with adenocarcinoma predominantly affecting younger women [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, the marginal statistical significance of the age difference requires validation in larger cohorts before clinical application.\u003c/p\u003e \u003cp\u003eHowever, our findings require interpretation within the context of well-documented epidemiological shifts in cervical cancer patterns during the screening era. Population-based studies have consistently demonstrated disproportionate increases in adenocarcinoma incidence among younger women, with Bray et al. (2005) showing rapid increases in women born after 1935, particularly affecting the 30\u0026ndash;39 age group [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This temporal pattern directly explains the younger age distribution observed in our ADC cohort and aligns with recent comprehensive US cancer registry analysis demonstrating that 17 of 34 cancer types show increasing incidence rates in successively younger birth cohorts, supporting generational differences in cancer risk related to early-life exposures [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The age differential between histological subtypes likely reflects both cohort-specific HPV exposure patterns and distinct tumor biology, with adenocarcinoma potentially exhibiting compressed latency from initial infection to invasive disease compared to squamous cell carcinoma.\u003c/p\u003e \u003cp\u003eThe menopausal status differential provides compelling insight into the distinct pathophysiology of cervical cancer histotypes. Postmenopausal women with SCC likely represent a population with chronic HPV infections that underwent gradual progression through decades of hormonal transitions, while premenopausal women with ADC may reflect more recent infections characterized by accelerated carcinogenic pathways. This hypothesis is supported by documented evidence that adenocarcinoma demonstrates reduced susceptibility to conventional screening strategies, with cytology screening proving less effective against adenocarcinoma than squamous carcinoma, evidenced by increasing ADC proportions from 13.2% to 22.1% between 1989\u0026ndash;2009 despite substantial SCC reductions in screened populations [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The compressed natural history suggested for adenocarcinoma, combined with its predominance in younger, premenopausal women, underscores fundamental differences in tumor biology that may necessitate adapted screening and prevention approaches.\u003c/p\u003e \u003cp\u003eThe similar p16 positivity rates between SCC and ADC (89.8% vs 88.2%) confirm the overwhelming dominance of HPV in contemporary cervical cancer, consistent with the landmark study by Walboomers et al., which established HPV as a necessary cause in 99.7% of cervical cancers [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The high prevalence of HPV DNA in different histological subtypes of cervical adenocarcinoma is well documented by Pirog et al., who found HPV in 94% of adenocarcinomas, with HPV types 16 and 18 being most common [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, Andersson et al. confirmed the critical role of human papillomavirus in cervical adenocarcinoma carcinogenesis, demonstrating comparable HPV prevalence rates across histological subtypes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur observation that p16 positivity associates with advanced clinical stage (OR\u0026thinsp;=\u0026thinsp;2.45, p\u0026thinsp;=\u0026thinsp;0.047) differs from established literature demonstrating associations between p16 overexpression and absence of lymph node metastasis, improved overall survival, and enhanced disease-free survival [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], with favorable outcomes reported in specific cervical cancer populations [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This discordance likely reflects methodological factors including single-center selection bias, temporal changes in diagnostic practices evidenced by increasing p16 positivity from 80% to \u0026gt;\u0026thinsp;94%, and limited statistical power given high p16 prevalence (\u0026gt;\u0026thinsp;88%) in our cohort. Established studies by Castle et al. and Ronco et al. support the protective implications of HPV/p16 testing [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], suggesting our contrary finding requires validation in larger, multi-center cohorts with standardized protocols before clinical interpretation.\u003c/p\u003e \u003cp\u003eThis unexpected finding highlights important methodological considerations that warrant careful evaluation when interpreting our data. First, our single-center retrospective design may have introduced selection bias, where p16 testing was preferentially ordered for more clinically concerning cases, creating an artificial association between p16 positivity and advanced stage. Second, temporal changes in p16 testing practices over our study period (evidenced by the increase from 80% to \u0026gt;\u0026thinsp;94% positivity) suggest evolving diagnostic criteria that may have confounded the stage\u0026ndash;p16 relationship.\u003c/p\u003e \u003cp\u003eBecause \u0026gt;\u0026thinsp;88% of our cohort was p16-positive, opportunities to detect robust differences between p16-positive and p16-negative groups were inherently limited. The relatively small p16-negative subset likely reflects the rarity of true HPV-negative cervical cancers in contemporary populations and may also encompass tumors with technical staining variability, low-level HPV infections, or distinct histological variants. The observed correlation between HPV positivity and more advanced disease presentation appears biologically implausible, suggesting that the finding is more likely attributable to residual confounding or study design factors rather than a true causal relationship.\u003c/p\u003e \u003cp\u003eThe work by Castle et al. demonstrated the superior performance of carcinogenic HPV testing for cervical cancer screening [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], while Ronco et al. confirmed that HPV-based screening is highly effective for preventing invasive cervical cancer [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These findings strongly support the protective and favorable prognostic implications of HPV/p16 positivity, indicating that our contrary observation should be interpreted with caution and explored further. Our observed p16\u0026ndash;stage association is best understood in the context of methodological considerations common to single-center retrospective studies, including small comparison groups and evolving diagnostic practices.\u003c/p\u003e \u003cp\u003eThe protective effect of marital status on advanced-stage presentation (OR\u0026thinsp;=\u0026thinsp;0.87, p\u0026thinsp;=\u0026thinsp;0.021) represents a robust finding that extends beyond a simple demographic association, reflecting complex interactions between social determinants and cancer outcomes. This observation aligns with the broader social epidemiology literature. For example, Azerkan et al. in Swedish populations demonstrated significant variations in cervical screening participation by social factors, with married women showing higher compliance rates [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Similarly, Marlow et al. reported that sociodemographic predictors significantly influenced HPV testing and vaccination acceptability among British women [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis association highlights that marital status acts as a proxy for broader socioeconomic and behavioral factors rather than a direct causal mechanism. A recent study by Meng et al. (2024) confirmed that social relationships function as protective factors across multiple health outcomes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Social support networks available to married individuals may facilitate earlier healthcare seeking, increased screening participation, and better adherence to follow-up care. This interpretation is further supported by evidence from Kroenke et al., who demonstrated that social networks and support significantly influenced survival after breast cancer diagnosis, underscoring the broader relevance of social support mechanisms across cancer types [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe unexpected negative association between age and advanced stage disease offers an intriguing divergence from established. This observation persisted across both traditional logistic regression and LASSO analyses, suggesting robustness despite its counterintuitive nature. Several explanations merit consideration: first, older women may have increased healthcare contact for comorbid conditions, leading to incidental earlier detection; second, competing mortality risks may result in preferential identification of earlier-stage cancers in older patients; third, cervical cancers developing in older women may have different biological characteristics, potentially reflecting different HPV types or co-carcinogenic factors.\u003c/p\u003e \u003cp\u003eThis finding is a contrast to those in published literature showing worse outcomes with advancing age. For example, Ou et al. (2025) demonstrated that persistent socioeconomic disparities typically worsen with age, thereby contributing to poorer outcomes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The discrepancy between the p16 data in our study and those of previous studies may reflect selection bias in our single-center cohort or unique characteristics of our patient population. Alternatively, it may suggest that, in the contemporary screening era, age-related detection patterns have fundamentally shifted compared with earlier epidemiological observations, such as those reported in the classic Swedish study by Adami et al. (1994) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe temporal increase in p16 positivity from 80% to \u0026gt;\u0026thinsp;94% over our study period represents a remarkable finding that reflects the evolution of molecular diagnostics in cervical cancer. This trend likely encompasses multiple factors: standardization of immunohistochemical protocols, increased pathologist familiarity with p16 interpretation criteria, and potential changes in patient population characteristics. The stabilization at high levels suggests achievement of diagnostic maturity in p16 testing.\u003c/p\u003e \u003cp\u003eThis trend raises important questions about historical under-detection versus contemporary over-interpretation of p16 positivity. Bergeron et al. demonstrated that conjunctive p16 testing improves diagnostic accuracy for high-grade cervical lesions, though interpretation standards remain variable [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our observed increase may therefore reflect the implementation of more stringent and standardized diagnostic criteria rather than true epidemiological changes.\u003c/p\u003e \u003cp\u003eThe stability of advanced-stage presentation rates despite increasing p16 positivity suggests that molecular diagnostic improvements have not translated into earlier clinical detection. This finding is concerning and may indicate persistent barriers to early detection, inadequate screening coverage, or inherent limitations of current screening methodologies in detecting adenocarcinoma.\u003c/p\u003e \u003cp\u003eOur survival analysis confirms the prognostic supremacy of clinical staging, with 100% survival in early-stage patients compared with 73% in late-stage patients over five years. This 27% absolute survival difference aligns closely with international registry data and supports the critical importance of early detection in cervical cancer outcomes through effective screening programs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the absence of survival differences between SCC and ADC in our cohort differs from earlier studies suggesting worse prognosis for adenocarcinoma. For example, Liu et al. (2022) identified adenocarcinoma as an independent risk factor for disease recurrence [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], while Chen et al. (2022) also reported differences in clinical behavior between histological subtypes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our contrary finding may reflect improvements in treatment protocols, changes in adenocarcinoma subtypes detected in the contemporary era, or the overriding prognostic significance of stage, which supersedes histological subtype effects.\u003c/p\u003e \u003cp\u003eThis observation carries important clinical implications, suggesting that treatment decisions should prioritize staging and other clinicopathological factors over histological subtype alone. The historical view of adenocarcinoma as having a worse prognosis warrants reevaluation in light of contemporary treatment approaches and advances in molecular understanding. Recent global studies on HPV etiology [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and worldwide mortality trend analyses [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] further support the need to reconsider histological subtype as a dominant prognostic factor.\u003c/p\u003e \u003cp\u003eGiven the limitation due to study type and the contradictory nature of some findings in this study, the clinical implications of this study should be interpreted with caution. The apparent predictive value of p16 positivity for advanced disease should not be applied in clinical practice without independent validation in larger, multicenter cohorts with standardized p16 testing protocols and adequately powered subgroup analyses.\u003c/p\u003e \u003cp\u003eThe demographic differences between SCC and ADC (age and menopausal status) represent our most reliable findings, as they align with established epidemiological patterns and demonstrate biological plausibility. These observations support age-stratified approaches to cervical cancer prevention and reinforce the importance of maintaining screening recommendations across age groups, particularly for adenocarcinoma detection in younger women. The concentration of adenocarcinoma in younger, premenopausal women further underscores the value of HPV vaccination programs targeting adolescent populations, as demonstrated in a comprehensive global meta-analysis [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe primary contribution of our study lies in its methodological innovation in utilizing data from single center cohort, highlighting the essential role of rigorous design in advancing cancer research. To build upon these findings, future investigations would benefit from prospective studies with standardized protocols, larger cohorts that allow robust subgroup analyses, detailed treatment documentation, comprehensive screening history assessments, and multi-center validation to strengthen the clinical applicability of emerging prognostic associations. The temporal trends we observed in p16 positivity warrant immediate attention in clinical practice, as they suggest concerning inconsistencies in diagnostic standards that could directly affect patient care. Institutions should implement standardized p16 staining protocols, inter-observer reliability training, and quality assurance programs to ensure consistent and clinically meaningful results.\u003c/p\u003e \u003cp\u003eMost importantly, our findings regarding p16 and age\u0026ndash;stage relationships should serve as cautionary examples of how methodological limitations can produce spurious associations that contradict established biological knowledge. Researchers must prioritize methodological rigor over novel but potentially misleading findings, and clinicians should remain skeptical of single-center studies reporting results that deviate from well-established literature without compelling methodological justification.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003e This study's strengths include comprehensive molecular characterization using standardized p16 immunohistochemistry protocols, rigorous statistical methodology incorporating multivariate logistic regression and LASSO analysis, temporal trend analysis over five years, and consistent data collection from a tertiary care center with international accreditation standards. However, this single-center retrospective analysis of 85 patients has significant limitations including limited generalizability, insufficient statistical power for subgroup analyses and effect size detection, heavily skewed p16 distribution (\u0026gt;\u0026thinsp;88% positive) precluding robust comparative analyses, and potential selection bias from tertiary referral patterns. Our contradictory finding that p16 positivity predicts advanced disease directly opposes established literature and likely reflects methodological artifacts or evolving diagnostic practices evidenced by increasing p16 positivity from 80% to \u0026gt;\u0026thinsp;94% over the study period. Critical missing data include treatment details, screening histories, HPV vaccination status, HPV genotyping, important prognostic factors (lymphovascular invasion, parametrial involvement), and long-term survival outcomes, while several findings contradict established epidemiological patterns and require multi-center validation with larger sample sizes and comprehensive covariate adjustment before clinical application.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis retrospective analysis demonstrates that while squamous cell carcinoma and adenocarcinoma share similar molecular characteristics with comparable p16 positivity rates, they exhibit distinct demographic patterns, with squamous cell carcinoma preferentially affecting older, postmenopausal women and adenocarcinoma predominantly occurring in younger patients. Clinical staging emerged as the paramount prognostic determinant, demonstrating excellent survival in early-stage disease versus progressive decline in advanced stages, while the protective effect of marital status underscores the importance of social determinants in cancer outcomes. Our contradictory finding that p16 positivity predicts advanced disease opposes established literature and likely reflects methodological limitations rather than biological relationships, highlighting the need for standardized diagnostic protocols. These findings reinforce the critical importance of early detection through effective screening programs, support continued HPV vaccination emphasis for younger populations at adenocarcinoma risk, and confirm that clinical staging remains the most reliable prognostic factor transcending histological differences, though larger multicenter studies with standardized protocols are essential to validate these observations and resolve methodological inconsistencies before clinical implementation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSquamous Cell Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADC/AC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Papillomavirus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Federation of Gynecology and Obstetrics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKFSHRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKing Faisal Specialist Hospital \u0026amp; Research Centre\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the King Faisal Specialist Hospital and Research Centre (KFSHRC) Institutional Review Board (IRB)/Research Ethics Committee (REC) (IRB # 2251677, approved November 4, 2025). Given the retrospective nature of this study using de-identified patient data from medical records, the KFSHRC IRB/REC granted a waiver for informed consent requirements in accordance with institutional guidelines for retrospective chart reviews using secondary data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEA conceived and designed the study, acquired and analyzed data, performed statistical analyses, drafted the original manuscript, and contributed to critical revision. MJA contributed to study design and methodology, assisted with data collection and analysis, and participated in manuscript revision. AYA contributed to study design and statistical methodology, assisted with data interpretation, participated in manuscript revision, and served as corresponding author. AT contributed to pathological data collection and p16 immunohistochemistry interpretation, assisted with data analysis, and participated in manuscript revision. SA assisted with data collection and clinical data verification, contributed to data analysis, and participated in manuscript revision. AS contributed to data collection and clinical documentation, assisted with data analysis, and participated in manuscript revision. ZYA assisted with data collection and patient record review, contributed to data verification, and participated in manuscript revision. AAS provided clinical expertise and patient data access, contributed to data interpretation, and participated in manuscript revision. AAA assisted with data collection and analysis, contributed to manuscript preparation, and participated in critical revision. AAA contributed to statistical analysis and data interpretation, assisted with methodology, and participated in manuscript revision. FA contributed to molecular data interpretation and HPV-related analysis, assisted with data analysis, and participated in manuscript revision. AAA supervised the overall research project, contributed to study conception and design, provided critical oversight of data interpretation, and critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I. Jemal: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArbyn M, Weiderpass E, Bruni L, de Sanjos\u0026eacute; S, Saraiya M, Ferlay. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Global Health. 2020;8(2):e191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. Cervical cancer [Internet]. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray F, Carstensen B, M\u0026oslash;ller H, Zappa M, Žakelj MP, Lawrence G, Hakama. Incidence trends of adenocarcinoma of the cervix in 13 European countries. Cancer Epidemiol Biomarkers Prev. 2005;14(9):2191\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStolnicu S, Barsan I, Hoang L, Patel P, Terinte C, Pesci A, Aviel-Ronen S, Kiyokawa T, Alvarado-Cabrero I, Pike MC. International endocervical adenocarcinoma criteria and classification (IECC): a new pathological classification for invasive adenocarcinomas of the endocervix. Am J Surg Pathol. 2018;42(2):214\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Sanjose S, Quint WG, Alemany L, Geraets DT, Klaustermeier JE, Lloveras B, Tous S, Felix A, Bravo LE, Shin HR. Human papillomavirus genotype attribution in invasive cervical cancer: a retrospective cross-sectional worldwide study. Lancet Oncol. 2010;11(11):1048\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHausen z. Papillomaviruses in the causation of human cancers \u0026mdash; a brief historical account. Virology. 2009;384(2):260\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalic V, Herzog TJ, Lewin SN, Neugut AI, Burke WM, Lu Y-S, Hershman DL, Wright JD. Prognostic significance of adenocarcinoma histology in women with cervical cancer. Gynecol Oncol. 2012;125(2):287\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Shu P, Wang X, Ouyang G, Zhou J, Zhao Y, Li Z, Wang Y, Shen Y. Comparison of Survival Between Different Histological Subtypes in Cervical Cancer Patients: A Retrospective and Propensity Score-matched Analysis. J Cancer. 2024;15(19):6326\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei J, Andrae B, Ploner A, Lagheden C, Eklund C, Nordqvist Kleppe S, Wang J, Fang F, Dillner J, Elfstr\u0026ouml;m KM, et al. Cervical screening and risk of adenosquamous and rare histological types of invasive cervical carcinoma: population based nested case-control study. BMJ. 2019;365:l1207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStolnicu S, Barsan I, Hoang L, Patel P, Terinte C, Pesci A, Aviel-Ronen S, Kiyokawa T, Alvarado-Cabrero I, Pike MC, et al. International Endocervical Adenocarcinoma Criteria and Classification (IECC): A New Pathogenetic Classification for Invasive Adenocarcinomas of the Endocervix. Am J Surg Pathol. 2018;42(2):214\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaccarella S, Franceschi S, Engholm G, L\u0026ouml;nnberg S, Khan S, Bray F. 50 years of screening in the Nordic countries: quantifying the effects on cervical cancer incidence. Br J Cancer. 2014;111(5):965\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen F, Chen L, Zhang Y, Shi L, Xu He, Song T. Survival Comparison Between Squamous Cell Carcinoma and Adenocarcinoma for Radiotherapy-Treated Patients with Stage IIB-IVA Cervical Cancer. Front Oncol 2022, Volume 12\u0026ndash;2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlaes R, Friedrich T, Spitkovsky D, Ridder R, Rudy W, Petry U, Dallenbach-Hellweg G, Schmidt. Overexpression of p16INK4A as a specific marker for dysplastic and neoplastic epithelial cells of the cervix uteri. Int J Cancer. 2001;92(2):276\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsoumpou I, Arbyn M, Kyrgiou M, Wentzensen N, Koliopoulos G, Martin-Hirsch P. Malamou‐Mitsi: p16INK4a immunostaining in cytological and histological specimens from the uterine cervix: a systematic review and meta‐analysis. Cancer Treat Rev. 2009;35(3):210\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConesa-Zamora P, Dom\u0026eacute;nech-Peris A, Orantes-Casado FJ, Ortiz-Reina S, Sahuquillo-Fr\u0026iacute;as L, Acosta-Ortega J. Garc\u0026iacute;a-Solano: Effect on cell cycle markers of HPV 16 E7 protein expression in cervical carcinoma-derived cell lines. BMC Cancer 2007:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMittal S, Mandal AK, Sharma M, Maheshwari A, Kumar S, Chauhan A, Kumar. P16INK4a immunoexpression in carcinoma cervix. J Cancer Res Ther. 2009;5(4):262\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatla N, Berek JS, Cuello Fredes M, Denny LA, Grenman S, Karunaratne K, Kehoe ST, Konishi I, Olawaiye AB, Prat J. Revised FIGO staging for carcinoma of the cervix uteri. Int J Gynaecol Obstet. 2019;145(1):129\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuo K, Machida H, Mandelbaum RS, Konishi. Validation of the 2018 FIGO cervical cancer staging system. Gynecol Oncol. 2019;152(1):87\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonk BJ, Tewari KS, Koh WJ. Multimodality therapy for locally advanced cervical carcinoma: state of the art and future directions. J Clin Oncol. 2007;25(20):2952\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRose PG, Bundy BN, Watkins EB, Thigpen JT, Deppe G, Maiman MA. Clarke-Pearson: Concurrent cisplatin‐based radiotherapy and chemotherapy for locally advanced cervical cancer. N Engl J Med. 1999;340(15):1144\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen F, Chen L, Zhang Y, Shi L, Xu. Survival comparison between squamous cell carcinoma and adenocarcinoma for radiotherapy-treated patients with stage IIB-IVA cervical cancer. Front Oncol 2022:895122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu P, Ji M, Kong Y, Li G, Liu X, Wang C, Lang. Comparison of survival outcomes between squamous cell carcinoma and adenocarcinoma/adenosquamous carcinoma of the cervix after radical radiotherapy and chemotherapy. BMC Cancer. 2022;22(1):326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKing Faisal Specialist Hospital. \u0026amp; Research Centre (KFSHRC) [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kfshrc.edu.sa/]\u003c/span\u003e\u003cspan address=\"https://www.kfshrc.edu.sa/]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLesnikova I, Lidang M, Hamilton-Dutoit S, Koch J. p16 as a diagnostic marker of cervical neoplasia: a tissue microarray study of 796 archival specimens. Diagn Pathol. 2009;4:22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergeron C, Ordi J, Schmidt D, Trunk MJ, Keller. Conjunctive p16INK4a testing significantly increases accuracy in diagnosing high-grade cervical intraepithelial neoplasia. Am J Clin Pathol. 2010;133(3):395\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarragh TM, Colgan TJ, Cox JT, Heller DS, Henry MR, Luff RD, McCalmont T, Nayar R, Palefsky JM, Stoler MH. The Lower Anogenital Squamous Terminology Standardization Project for HPV-associated lesions: background and consensus recommendations from the College of American Pathologists and the American Society for Colposcopy and Cervical Pathology. Arch Pathol Lab Med. 2012;136(10):1266\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith HO, Tiffany MF, Qualls CR, Key CR. The rising incidence of adenocarcinoma relative to squamous cell carcinoma of the uterine cervix in the United States\u0026mdash;a 24-year population-based study. Gynecol Oncol. 2000;78(2):97\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulk S, Visser O, Rozendaal L, Verheijen RH, Meijer CJ. Cervical cancer in the Netherlands: a population-based study on incidence, survival and the influence of screening. Eur J Cancer. 2003;39(17):2518\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosch FX, Lorincz A, Mu\u0026ntilde;oz N, Meijer CJ, Shah KV. The causal relation between human papillomavirus and cervical cancer. J Clin Pathol. 2002;55(4):244\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalboomers JM, Jacobs MV, Manos MM, Bosch FX, Kummer JA, Shah KV, Snijders PJ, Peto J. Meijer: Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol. 1999;189(1):12\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePirog EC, Kleter B, Olgac S, Bobkiewicz P, Lindeman J, Quint WG, Richart RM, Isacson C, Snijders PJ. Prevalence of human papillomavirus DNA in different histological subtypes of cervical adenocarcinoma. Am J Pathol. 2000;157(4):1055\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersson S, Rylander E, Larsson B, Strand A, Silfversv\u0026auml;rd. The role of human papillomavirus in cervical adenocarcinoma carcinogenesis. Eur J Cancer. 2001;37(2):246\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang K, Li LA, Meng YG, Fu XY. p16 expression in patients with cervical cancer and its prognostic significance: meta-analysis of published literature. Eur J Obstet Gynecol Reproductive Biology 2014:64\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin J, Albers AE, Qin J, Kaufmann AM. Prognostic Significance of Overexpressed p16INK4a in Patients with Cervical Cancer: A Meta-Analysis. PLoS ONE. 2014;9(9):e106384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eda Mata S, Ferreira J, Nicol\u0026aacute;s I, Esteves S, Esteves G, L\u0026eacute;rias S, Silva F, Saco A, Cochicho D, Cunha M. P16 and HPV genotype significance in HPV-associated cervical cancer\u0026mdash;a large cohort of two tertiary referral centers. Int J Mol Sci. 2021;22(5):2294.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastle PE, Stoler MH, Wright TC Jr, Sharma A, Wright TL, Behrens CM. Performance of carcinogenic human papillomavirus (HPV) testing and HPV16 or HPV18 genotyping for cervical cancer screening of women aged 25 years and older: a subanalysis of the ATHENA study. Lancet Oncol. 2011;12(9):880\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRonco G, Dillner J, Elfstr\u0026ouml;m KM, Tunesi S, Snijders PJ, Arbyn M, Kitchener H, Segnan N, Gilham C, Giorgi-Rossi P. Efficacy of HPV-based screening for prevention of invasive cervical cancer: follow-up of four European randomised controlled trials. Lancet. 2014;383(9916):524\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzerkan F, Spar\u0026eacute;n P, Sandin S, Tillgren. Cervical screening participation and risk among Swedish-born and immigrant women in Sweden. Int J Cancer. 2012;130(4):937\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarlow LA, Waller. Sociodemographic predictors of HPV testing and vaccination acceptability: results from a population-representative sample of British women. J Med Screen. 2008;15(2):91\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng M, Ma Z, Zhou H, Xie Y, Lan R, Zhu S. Miao: The impact of social relationships on the risk of stroke and post-stroke mortality: a systematic review and meta-analysis. BMC Public Health 2024:2403.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke CH, Kubzansky LD, Schernhammer ES, Holmes. Social networks, social support, and survival after breast cancer diagnosis. J Clin Oncol. 2006;24(7):1105\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOu Y, Chokkakula S, Chong SM, Wang H, Huang MDSAIC, Xu L, Lyu X. J, Huang: Age and socioeconomic disparities in cervical cancer incidence and mortality: a SEER-based analysis. Front Public Health 2025:1591883.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdami HO, Pont\u0026eacute;n J, Spar\u0026eacute;n P, Bergstr\u0026ouml;m R, Gustafsson L, Friberg LG. Survival trend after invasive cervical cancer diagnosis in Sweden before and after cytologic screening, 1960\u0026ndash;1988. Survival trend after invasive Cerv cancer diagnosis Swed before after cytologic Screen. 1994;73(1):140\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasieni P, Castanon. Effectiveness of cervical screening with age: population based case-control study of prospectively recorded data. BMJ 2009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranco EL, Cuzick J, Hildesheim. Chap. 20: Issues in planning cervical cancer screening in the era of HPV vaccination. Vaccine 2006, 24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei J, Ploner A, Elfstr\u0026ouml;m KM, Wang J, Roth A, Fang F, Sundstr\u0026ouml;m K, Dillner J, Spar\u0026eacute;n P. HPV Vaccination and the Risk of Invasive Cervical Cancer. N Engl J Med. 2020;383(14):1340\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cervical cancer, squamous cell carcinoma, adenocarcinoma, p16, survival analysis, retrospective study, HPV, clinical staging","lastPublishedDoi":"10.21203/rs.3.rs-8259753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8259753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCervical cancer remains a significant global health burden, with squamous cell carcinoma (SCC) and adenocarcinoma representing the two predominant histological subtypes. While human papillomavirus (HPV) infection underlies most cervical cancers, comparative clinicopathological features and prognostic determinants between SCC and ADC remain incompletely characterized.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort analysis of cervical cancer patients treated between 2020\u0026ndash;2024. Patient demographics, clinical characteristics, p16 immunohistochemistry status, histological subtype, differentiation grade, clinical staging, and survival outcomes were analyzed. Comparative statistics, multivariate logistic regression, LASSO regression, temporal trend analysis, and Kaplan-Meier survival analysis were performed to identify prognostic determinants and histotype-specific differences.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe analysis included 85 patients: 69 with squamous cell carcinoma and 16 with adenocarcinoma. Both subtypes demonstrated similarly high p16 positivity rates (89.8% vs 88.2%, p\u0026thinsp;=\u0026thinsp;0.45), confirming HPV's predominant role regardless of histological type. Menopausal status emerged as a significant distinguishing factor (p\u0026thinsp;=\u0026thinsp;0.0495), with SCC patients more likely to be postmenopausal. SCC patients were older on average (52.16 vs 48.2 years, p\u0026thinsp;=\u0026thinsp;0.0565). Multivariate analysis identified p16 positivity as the strongest predictor of advanced disease (OR\u0026thinsp;=\u0026thinsp;2.45, p\u0026thinsp;=\u0026thinsp;0.047), while marital status demonstrated protective effects (OR\u0026thinsp;=\u0026thinsp;0.87, p\u0026thinsp;=\u0026thinsp;0.021). Kaplan-Meier analysis revealed significant survival differences by clinical stage (log-rank p\u0026thinsp;=\u0026thinsp;0.03), with high-stage patients showing progressive decline from 95% to 73% survival over five years, while low-stage patients maintained 100% survival.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWhile SCC and ADC share similar molecular characteristics and clinical presentations, SCC preferentially affects older, postmenopausal women. p16 positivity serves as a key molecular predictor of disease severity, and clinical staging remains the most critical prognostic determinant. These findings underscore the importance of early detection strategies and reinforce the prognostic value of molecular markers in cervical cancer management.\u003c/p\u003e","manuscriptTitle":"Comparative Clinicopathological Features, HPV Status, and Prognostic Determinants of Squamous Cell Carcinoma and Adenocarcinoma of the Cervix: A Retrospective Cohort Analysis (2020–2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 00:10:54","doi":"10.21203/rs.3.rs-8259753/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df9a0e86-225e-4a5d-9881-6356f0c2a25d","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-02T08:25:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 00:10:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8259753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8259753","identity":"rs-8259753","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}