Clinicopathological Features and Prognostic-Diagnostic Value of PAX-8/Smad3 Co-expression in Gastric-type Endocervical Adenocarcinoma: A Retrospective Cohort Combined with Meta-Analysis

Clinicopathological Features and Prognostic-Diagnostic Value of PAX-8/Smad3 Co-expression in Gastric-type Endocervical Adenocarcinoma: A Retrospective Cohort Combined with Meta-Analysis | 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 Clinicopathological Features and Prognostic-Diagnostic Value of PAX-8/Smad3 Co-expression in Gastric-type Endocervical Adenocarcinoma: A Retrospective Cohort Combined with Meta-Analysis Yi Wang, Ziwen Xiao, Juntao Wang, Houmei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9151684/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objective: To characterize the clinicopathological, prognostic, and immunohistochemical features of gastric-type endocervical adenocarcinoma (G-EAC) and provide evidence for its precise diagnosis and therapy. Methods: A retrospective cohort analysis was conducted on 16 G-EAC and 60 HPV-associated endocervical adenocarcinoma (HPVA) patients treated between 2015 and 2023 to compare their clinical, prognostic, and immunohistochemical characteristics. Results: G-EAC had higher abnormal vaginal discharge rate, lower hr-HPV and TCT abnormality rates, more advanced FIGO stages, higher invasive/metastatic risk, and shorter OS and DFS than HPVA. Meta-analysis confirmed G-EAC patients were older, with larger tumors and higher risks of advanced disease, invasion, metastasis and recurrence. G-EAC had significantly higher PAX-8, Smad3, TP53, MUC6 positive rates but lower p16 rate than HPVA. PAX-8 was positively correlated with Smad3 in G-EAC (r = 0.77, P < 0.05), and both were associated with aggressive pathological features. PAX-8 (AUC=0.81) and Smad3 (AUC=0.60) combined detection for G-EAC achieved an AUC of 0.82, 96% specificity and 86% accuracy. Conclusion: G-EAC and HPVA differ significantly in clinical, immunohistochemical and prognostic features. PAX-8/Smad3 co-expression drives the malignant progression of G-EAC and exhibits high diagnostic efficacy, thus serving as potential biomarkers for the differential diagnosis and prognostic assessment of G-EAC their molecular interaction may reveal G-EAC pathogenesis. Trial registration: Not applicable. Gastric-type endocervical adenocarcinoma PAX-8 Smad3 Immunohistochemical phenotype Poor prognosis Differential diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 HIGHLIGHTS We compared 16 G-EAC and 60 HPVA patients with retrospective analysis and meta-analysis. G-EAC showed distinct clinical features and worse prognosis than HPVA patients. PAX-8/Smad3 co-expression acts as potential biomarkers for G-EAC diagnosis and prognosis. Combined detection of PAX-8 and Smad3 has high diagnostic efficacy for G-EAC. INTRODUCTION Cervical cancer represents the most common malignant neoplasm of the female reproductive system in China, with persistent infection by high-risk HPV identified as the primary etiological factor. Gastric-type endocervical adenocarcinoma (G-EAC), the most common subtype within non-HPV-associated adenocarcinoma (NHPVA), has exhibited a rising incidence, accounting for 10%-15% of cervical adenocarcinomas [ 1 ] . Compared to HPV-associated adenocarcinoma(HPVA), G-EAC constitutes a critical focus of clinical investigation with frequent early misdiagnosis, aggressive invasiveness, and poor prognosis. Emerging evidence suggests distinct molecular pathological characteristics may underlie its aggressive biological behavior and adverse clinical outcomes. Yet the clinical rarity of G-EAC has left investigations into its pathogenesis and molecular pathology in the nascent stage, and the absence of standardized clinical management guidelines and reliable prognostic stratification biomarkers renders imperative to elucidate its molecular expression profiles in association with clinicopathological parameters and immunohistochemical phenotypes and to examine their prognostic correlations to develop precise therapeutic strategies, stratify recurrence risk, and improved clinical outcomes. This study aims to provide novel insights into the pathogenesis of G-EAC, lay an evidence-based foundation for subsequent molecular mechanism research, identify potential targets for precision targeted therapy optimize diagnostic and therapeutic strategies for G-EAC, and ultimately improve patient prognosis. MATERIALS AND METHODS Clinical Study Study subjects: Clinicopathological data, follow-up records, and postoperative paraffin-embedded pathological specimens were collected from 16 G-EAC patients and 60 HPVA patients treated at the Affiliated Hospital of Guizhou Medical University and Guiyang Maternal and Child Health Hospital between Jan 2015 and Dec 2023. Inclusion criteria: (1) Pathological diagnosis of G-EAC/HPVA based on the 2018 IECC criteria [ 2 ] ; (2) Complete and standardized medical records; (3) Access to complete pathological data and follow-up records. Exclusion criteria: (1) Concurrent other malignant tumors; (2) Previous pelvic radiotherapy/chemotherapy; (3) Poor-quality pathological specimens unfit for IHC testing. The 60 HPVA cases included 53 usual type endocervical adenocarcinoma, 4 intestinal-type mucinous adenocarcinoma, 2 invasive stratified mucin-producing carcinoma and 1 villoglandular adenocarcinoma. Follow-up: Patients were followed up by telephone and outpatient visits from the surgery date to October 30, 2025 or death. Survival endpoints: OS was defined as the time from initial treatment to death from any cause or follow-up end; DFS as the time from initial treatment to disease recurrence or follow-up end. Both were calculated in months. Meta-Analysis Chinese (CNKI, Wanfang Data, VIP, CBM) and English (PubMed, Embase, Web of Science) databases were systematically searched for relevant studies published from January 2015 to October 2025. The Newcastle-Ottawa Scale (NOS) was used for the quality assessment of observational studies, and the ROBINS-I tool was used for bias evaluation (graded as low, moderate, serious, or critical). Statistical analysis was performed with RevMan 5.4: mean difference (MD) and standardized mean difference (SMD) were used for continuous variables, odds ratio (OR) with 95% confidence interval (CI) for dichotomous data, and sensitivity analysis was also conducted. Immunohistochemistry The SP method was applied to detect PAX-8/Smad3 protein expression in G-EAC and HPVA tumor tissues, with primary antibodies obtained from Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd. The experimental procedures included slide baking, dewaxing, ethanol gradient hydration, antigen retrieval, endogenous peroxidase inactivation with 3% H₂O₂, PBS rinsing, primary antibody incubation, hematoxylin counterstaining, hydrochloric acid-ethanol differentiation, dehydration and coverslip mounting. PAX-8 was mainly expressed in tumor cell nuclei, and Smad3 in both nuclei and cytoplasm. Two experienced pathologists independently scored the tissue sections semi-quantitatively under double-blind conditions: 2 high-power fields (×200) were randomly selected per section, and scores were averaged. Staining intensity (0–3 points) and positive cell proportion (0–4 points) were multiplied to obtain the total score, which was classified as negative (0–1), weakly positive (2–4), moderately positive (6–8) and strongly positive (9–12). The total score was treated as a continuous variable for subsequent diagnostic accuracy analysis and optimal diagnostic threshold determination. Statistical Analysis All statistical analyses were performed using GraphPad Prism 10.6.1 with a two-tailed test and α = 0.05 as the significance level. Categorical variables were expressed as n (%), and continuous variables as mean ± SD. Inter-group comparisons were conducted with the chi-square test/Fisher’s exact test for categorical variables, and independent samples t-test/Mann-Whitney U test for continuous variables. Survival analysis was performed with the Kaplan-Meier method and Log-rank test; variables with P < 0.05 in univariate analysis were included in multivariate Cox proportional hazards regression models to identify independent prognostic factors (entry α = 0.05, removal α = 0.10). The correlation between PAX-8 and Smad3 staining scores was analyzed by Spearman’s rank correlation coefficient. ROC curves were constructed to evaluate the diagnostic efficacy of PAX-8, Smad3 and their combined detection; the Delong test was used for AUC comparison, and Youden’s index for the optimal diagnostic cutoff value. RESULTS Clinicopathological and Prognostic Differences Between G-EAC and HPVA Comparison of Clinicopathological Indicators Compared to HPVA, G-EAC exhibited a higher incidence of abnormal vaginal discharge (37.5% vs. 5.0%), alongside lower positive rates for hr-HPV infection (0.0% vs. 93.3%) and abnormal TCT results (27.3% vs. 85.0%) (all P < 0.05). Furthermore, G-EAC presented with fewer FIGO stage I (37.5% vs. 86.7%) and more stages II-IV (62.5% vs. 13.3%), along with significantly higher rates of adverse pathological features (ovarian metastasis: 18.8% vs.0.0%; LNM: 25.0% vs.5.0%; parametrial invasion: 25.0% vs.0.0%; LVSI: 81.2% vs.10.0%; positive surgical margins: 18.8% vs.1.7%, outer 1/3 cervical wall invasion: 43.6% vs. 16.7%; tumor diameters exceeding 4 cm: 43.8% vs. 10.0%,all P < 0.05). with a larger mean tumor size(3.58 ± 1.20 cm vs. 2.57 ± 1.02 cm, P 0.05), See Supplementary Table S1 . Comparison of OS and DFS Of 76 patients, 74 completed follow-up (2 lost, 2.6% loss rate) with a median follow-up of 46.5 months (9-105 months). At follow-up, 50% (8/16) of G-EAC patients developed recurrence /metastasis vs. 8.3% (5/60) in HPVA. The 1-, 2-, 3-, and 5-year OS rates were 93.75%, 50.00%, 43.75%, 25% for G-EAC vs. 100.00%, 98.33%, 81.67%, 43.33% for HPVA. G-EAC patients had significantly shorter OS and DFS than HPVA(both P < 0.05) (Fig. 1 ). Univariate and Multivariate Analysis of Risk Factors for Recurrence Univariate analysis demonstrated advanced FIGO stage, ovarian metastasis, LNM, LVSI, outer 1/3 of the cervical stroma invasion, and tumor diameter > 4 cm as significant risk factors for OS and DFS in G-EAC (all P < 0.05). Positive surgical margins only affected DFS ( P < 0.05), see Fig. 2 for K-M curves. No independent prognostic factors were found in multivariate analysis, likely due to the small sample size (Table 1 ). Table 1 Univariate and multivariate analyses for OS and DFS in patients with G-EAC Clinicopathological factors N OS DFS Univariate Analysis Multivariate analysis Univariate Analysis Multivariate analysis χ 2 P -value H R (95%CI) P -value χ 2 P -value H R (95%CI) P -value Surgical approach Transabdominal 7 0.30 0.57 NA NA 0.28 0.59 NA NA Laparoscopic 9 2018 FIGO stage Early Stage 7 4.99 < 0.05* 0.099 (0.003–2.806) 0.175 5.08 < 0.05* NA 0.998 Locally advanced Stage 9 Ovarian Metastasis Positive 3 6.44 < 0.05* NA 0.997 8.11 < 0.05* NA 0.997 Negative 13 LNM Positive 4 6.15 < 0.05* 1.821 (0.164–20.197) 0.626 8.71 < 0.05** NA 0.997 Negative 12 Surgical margin status Positive 3 2.47 0.11 NA NA 4.84 < 0.05* NA 0.997 Negative 13 LVSI status Positive 9 13.51 < 0.05*** NA 0.996 15.13 < 0.05*** NA 0.996 Negative 7 Paracervical infiltration Positive 4 1.38 0.23 NA NA 0.97 0.32 NA NA Negarive 12 Deep invasion of the cervical wall Outer 1/3 7 13.63 < 0.05** 3.186 (0.597–17.003) 0.175 17.17 < 0.05*** NA 0.997 Middle 1/3 6 Inner 1/3 3 Tumor size ≤ 4 9 5.22 < 0.05* NA 0.997 6.19 4 7 Meta-Analysis for External Validation of G-EAC Invasiveness Literature Screening, Quality Assessment, and Bias Risk Analysis 1365 articles were initially retrieved, with 9 high-quality retrospective studies [ 3 – 11 ] (NOS score: 7–9) included. The literature screening flowchart is shown in Fig. 2 . Total sample size was 2268 cases (401 G-EAC, 1867 HPVA). See Table S2 for Basic information. ROBINS-I assessment indicated 2 studies with high bias and 7 with moderate bias, mainly from insufficient confounding control and missing follow-up data. (bias evaluation, Table S3). Quantitative Results of Core Indicators in Meta-Analysis The mean age of the G-EAC group was 4.24 years higher than that of the HPVA group. Compared with the HPVA group, the G-EAC group had 5.07-, 4.32-, 4.37-, 7.55-, 9.47-, and 4.80-fold higher rates of FIGO stage Ⅲ-Ⅳ, LVSI, LNM, ovarian metastasis, parametrial invasion, and recurrence, respectively. The mean tumor diameter was 7.74 mm larger in the G-EAC group (all P < 0.05).( brief summary, Table S4). See Figure S1 for forest and funnel plots by factor. Differences in Immunohistochemical Phenotypes Between G-EAC and HPVA G-EAC showed significantly higher positive rates of PAX-8 (68.8% vs. 30.0%), Smad3 (56.3% vs. 15.0%), TP53 (50.0% vs. 13.3%), and MUC6 (50.0% vs. 5.0%), but a lower p16 rate (12.5% vs. 93.3%, all P 0.05). See Table S5 for details. Correlation Between PAX8/Smad3 Expression and Clinicopathological Features of G-EAC G-EAC patients were stratified by median staining scores of PAX-8 (8.15) and Smad3 (7.5). The results showed that PAX-8 expression in G-EAC was positively correlated with LVSI, Deep invasion of the cervical wall, and tumor diameter (all P < 0.05). Smad3 expression and the co-expression of PAX-8 and Smad3 were also positively correlated with tumor diameter ( P 0.05) (Table 2 ). Table 2 Correlation between PAX-8 and Smad3 Expression and Clinical-Pathological Characteristics of G-EAC Group PAX-8 Smad3 Co-expression Positive rate(%) χ2 P Positive rate(%) χ2 P Positive rate(%) χ2 P 2018 FIGO stage Stage Ⅰ 2(12.5%) 2.00 0.57 1(6.2%) 4.67 0.20 1(6.2%) 3.20 0.36 Stage Ⅱ 3(18.8%) 4(25.0%) 3(18.8%) Stage Ⅲ 2(12.5%) 2(12.5%) 1(6.2%) Stage Ⅳ 1(6.2%) 1(6.2%) 1(6.2%) Ovarian Metastasis Positive 3(18.8%) 1.64 0.20 3(18.8%) 1.64 0.20 3(18.8%) 3.31 0.06 Negative 5(31.2%) 5(31.2%) 3(18.8%) Lymphatic node Metastasis(LNM) Positive 4(25.0%) 3.00 0.08 3(18.8%) 0.33 0.56 3(18.8%) 3.31 0.06 Negative 4(25.0%) 5(31.2%) 3(18.8%) LVSI status Positive 7(43.8%) 6.34 < 0.05* 6(37.5%) 0.29 0.58 5(31.2%) 2.81 0.09 Negative 1(6.2%) 2(12.5%) 1(6.2%) Paracervical infiltration Positive 3(18.8%) 0.33 0.56 4(25.0%) 3.00 0.08 3(18.8%) 1.42 0.23 Negative 5(31.2%) 4(25.0%) 3(18.8%) Cervical stromal invasion Outer 1/3 6(37.5%) 7.77 < 0.05* 5(31.2%) 4.28 0.11 4(25.0%) 3.00 0.22 Middle 1/3 2(12.5%) 3(18.8%) 2(12.5%) Inner 1/3 0(0.0%) 0(0.0%) 0(0.0%) Tumor size(cm) ≤ 4 2(12.5%) 6.35 < 0.05* 6(37.5%) 4.00 < 0.05* 1(6.2%) 6.11 4 6(37.5%) 2(12.5%) 5(31.2%) Correlation analysis between PAX-8/Smad3 expression and prognosis Survival analysis showed that G-EAC patients with high PAX-8 expression had significantly shorter OS and DFS than those with low expression (all P 0.05), as shown in Fig. 3 . Clinical Correlation of PAX8 and Smad3 Co-Expression HE staining demonstrated that G-EAC (Fig. 4 -a) displayed glandular atypia with features indicative of gastric differentiation, whereas HPVA (Fig. 4 -d) exhibited characteristic histopathological traits of HPV-related adenocarcinoma, such as apical mitoses. IHC analysis revealed diffuse overexpression of PAX-8 (Fig. 4 -b) and Smad3 (Fig. 4 -c) in G-EAC samples, but low expression in HPVA (Fig. 4 -e, Fig. 4 -f). Quantitative evaluation indicated that the mean PAX-8 staining scores were 8.18 ± 2.49 (G-EAC) vs. 5.63 ± 1.68 (HPVA), while the mean Smad3 staining scores were 7.03 ± 2.51(G-EAC) vs. 5.30 ± 1.45(HPVA), with both significantly higher in G-EAC ( P < 0.05), as illustrated by the raincloud plot (Fig. 4 -g). Spearman’s correlation analysis confirmed a positive correlation between PAX-8 and Smad3 expression in G-EAC (r = 0.77, P < 0.05; Fig. 4 -h), see Fig. 4 -j for contingency table. Evaluation of Diagnostic Efficacy of PAX8/Smad3 for G-EAC The diagnostic effectiveness of the proteins PAX-8 and Smad3 was assessed and compared (Fig. 4 , j-i). The optimal cut-off value for PAX-8 was 5.35. In diagnosing G-EAC, PAX-8 achieved an AUC of 0.81 (95% confidence interval [CI]: 0.69–0.92), with a sensitivity of 88% (95% CI: 64%-98%), specificity of 50% (95% CI: 37%-62%), PPV of 32% (95% CI: 20%-47%), and NPV of 94% (95% CI: 80%-98%), all statistically significant ( P < 0.05). For Smad3, the optimal cut-off was 6.55, yielding an AUC of 0.60 (95% CI: 0.48–0.72) for G-EAC diagnosis, with sensitivity at 62% (95% CI: 38%-82%), specificity at 58% (95% CI: 45%-70%), PPV of 25% (95% CI: 14%-41%), and NPV of 86% (95% CI: 73%-94%). the combination of PAX-8 and Smad3 achieved an AUC of 0.82 (95% CI: 0.71–0.93) for G-EAC diagnosis. This combined approach showed a sensitivity of 44% (95% CI: 23%-67%), specificity of 96% (95% CI: 89%-99%), PPV of 78% (95% CI: 45%-94%), and NPV of 87% (95% CI: 76%-93%). Pairwise AUC comparisons via Delong test with Bonferroni correction (adjusted α = 0.017) revealed no statistically significant differences between PAX-8, Smad3, and their combination (all adjusted P > 0.017). This finding is attributable to the standalone diagnostic performance of PAX-8 (AUC = 0.81), which provided sufficient discriminative ability for G-EAC, thereby limiting the incremental diagnostic value of Smad3 and the combined panel in terms of AUC. The combined detection primarily improved specificity (96%) rather than overall discriminative capacity, which is consistent with the study’s focus on reducing false-positive diagnoses. Additionally, the rarity of G-EAC leading to a small sample size may have reduced statistical power, precluding the detection of minor AUC differences. These results confirm that PAX-8 is a reliable standalone diagnostic marker for G-EAC, while the combined panel offers a more specific alternative for clinical application. DISCUSSION It is widely accepted internationally that G-EAC, the predominant subtype of NHPVA, exhibits significant heterogeneity from HPVA in terms of clinical behavior, pathogenesis, and survival outcomes. The aggressive biological behavior of G-EAC is likely driven by its unique molecular pathogenesis in conjunction [ 12 ] with the tumor immune microenvironment (TIME) [ 13 ] . The highly malignant biological characteristic of G-EAC poses ongoing challenges in clinical practice, including diagnostic difficulties and suboptimal therapeutic responses. Consequently, a comprehensive elucidation of the molecular mechanisms underlying G-EAC, the identification of reliable diagnostic and prognostic biomarkers, and the development of targeted interventions for its highly invasive and metastatic properties are critical priorities for clinical research and management. This study quantitatively validated the subtype distinctions between G-EAC and HPVA through a retrospective cohort analysis supplemented by a meta-analysis. Clinical data revealed G-EAC cohort exhibited a significantly higher prevalence of abnormal vaginal discharge, alongside markedly lower rates of hr-HPV infection and abnormal TCT results, compared to the HPVA group. Furthermore, G-EAC group demonstrated significantly increased incidences of advanced FIGO stages II–IV, ovarian metastasis, LNM, parametrial invasion, LVSI, positive surgical margins, deep cervical stromal invasion extending, and lager tumor diameter. Survival analyses indicated that both OS and DFS were significantly reduced in G-EAC group. Meta-analysis corroborated these findings, indicating the mean age of patients with G-EAC was 4.24 years older than that of the HPVA group. Additionally, the odds of stage III–IV disease, LVSI, LNM, ovarian metastasis, parametrial invasion, and recurrence were 5.07-, 4.32-, 4.37-, 7.55-, 9.47-, and 4.80-fold higher in the G-EAC cohort than in the HPVA cohort. Tumor size was also significantly larger in the G-EAC group, with an average increase of 7.74 mm. Immunohistochemical analysis revealed that the G-EAC group exhibited significantly higher positivity rates for PAX-8, Smad3, TP53, and MUC6 compared to the HPVA group, whereas p16 expression was markedly lower in G-EAC. Further analyses demonstrated that PAX-8 expression was positively correlated with LVSI, depth of cervical stromal invasion, tumor diameter, and inversely correlated with OS and DFS. Smad3 expression was positively associated with tumor diameter. Notably, PAX-8 and Smad3 were co-expressed in G-EAC, exhibiting a positive correlation. Quantitative assessment of the diagnostic performance of PAX-8 and Smad3 revealed that the AUC for PAX-8 in diagnosing G-EAC was 0.81, for Smad3 was 0.60, and for their combined detection was 0.82, with a specificity of 84%. These findings suggest that the combined application of PAX-8 and Smad3 immunostaining might serve as a valuable tool for the precise diagnosis and prognostic stratification of G-EAC. This study adopted a retrospective cohort design combined with meta-analysis to systematically explore the biological and clinicopathological disparities between G-EAC and HPVA. The core findings are consistent with current international literature and were further quantified by meta-analysis, offering robust evidence-based support for clinical practice. Clinically and pathologically, the incidence of abnormal vaginal discharge was significantly higher in the G-EAC group (37.5%) than in the HPVA group (5.0%). In contrast, the rates of hr-HPV positivity (0.0% vs. 93.3%) and abnormal TCT results (27.3% vs. 85.0%) were markedly lower in the G-EAC cohort. This discrepancy arises from the lack of hr-HPV involvement in G-EAC carcinogenesis, as well as the absence of key molecular events including p53 and retinoblastoma (Rb) pathway dysregulation mediated by E6/E7 oncogene integration. Consequently, conventional clinical screening strategies demonstrate extremely low sensitivity for G-EAC, contributing to a high rate of early-stage misdiagnosis. The risk of biopsy misdiagnosis secondary to well-differentiated cytological features further compounds this challenge, resulting in a disproportionately high percentage of patients presenting with advanced-stage disease at initial diagnosis.Concurrently, G-EAC displays a significantly more aggressive invasive phenotype. The proportion of cases with FIGO stages II–IV, ovarian metastasis rate (18.8% vs. 0.0%), LNM rate (25.0% vs. 5.0%), and incidence of LVSI (81.2% vs. 10.0%) were all significantly higher in the G-EAC group relative to the HPVA group. These observations are in accordance with the conclusions of Karamurzin’s [ 3 ] multicenter study and Park’s [ 4 ] retrospective cohort investigation. Meta-analysis further corroborated the existence of these intergroup differences. Although the incidence of G-EAC is substantially higher in Asian populations than in Western cohorts (approximately 10% in the United States [ 2 ] and up to 25% in Japan [ 14 ] ), its core hallmarks of high aggressiveness and unfavorable prognosis remain consistent across ethnic groups. Pooled analysis demonstrated that the mean age at diagnosis of G-EAC was 4.24 years higher than that of HPVA, the rates of FIGO stage III–IV disease and parametrial invasion were 5.07-fold and 9.47-fold higher, respectively; and the recurrence risk was 4.80-fold greater. The 5-year OS rate of G-EAC was merely 25%, compared with 43.33% in the HPVA group, a finding consistent with multiple international reports [ 8 , 10 , 11 ] .With respect to treatment and prognostic evaluation, G-EAC shows poor responsiveness to conventional radiotherapy and chemotherapy regimens for cervical cancer, and well-established prognostic stratification markers are currently lacking. Univariate analysis in the present study identified FIGO stage, ovarian metastasis, and LVSI as factors associated with prognosis in G-EAC patients. However, multivariate analysis did not confirm any independent prognostic factors, which was likely attributed to the limited sample size. While these identified clinicopathological differences provide evidence-based references for clinical triage, treatment selection, and prognostic assessment, the management of G-EAC still confronts numerous bottlenecks that demand urgent breakthroughs. Internationally, molecular-targeted personalized therapeutic strategies have not yet been established, leading to clinical decisions that are largely experience-dependent, with inadequate accuracy in predicting recurrence risk and ultimately compromising treatment efficacy and survival outcomes. Collectively, the development of highly sensitive and specific diagnostic tools, coupled with the identification of molecular biomarkers for reliable prognostic stratification, represents a critical unmet need to address the current challenges in the diagnosis and treatment of G-EAC. Existing gastric mucin markers, including HIK1083 and MUC6, show low diagnostic sensitivity for G-EAC, at 0.51 and 0.64 respectively [ 15 ] , and are insufficient for precise subtype discrimination. In recent years, Claudin-18 (CLDN18), a gastric differentiation-associated marker, has been widely investigated in G-EAC. Relevant studies [ 16 , 17 ] report its positivity rate in G-EAC to be 58.0%–78.0%, compared with only 18% in HPVA. However, improved differential diagnostic accuracy requires combined testing with CDH17, PAX-8 and HPV-ISH [ 18 ] . highlighting the inherent limitations of single-biomarker strategies. In this study, qualitative immunohistochemical analysis identified distinct expression profiles of five markers (PAX-8, Smad3, p16, TP53, MUC6) between G-EAC and HPVA. Notably, PAX-8 and Smad3 showed high positivity in G-EAC, at 68.8% and 56.3%, respectively. PAX-8 is a well-established, highly specific marker for Müllerian-derived tumors and is routinely used in clinical practice. Recent studies [ 19 , 20 ] demonstrate a consistently high PAX-8 positivity rate (68%–80%) in G-EAC, supporting its combined use with a panel of markers including MUC6, HIK1083, TFF2, CLDN18, p16, p53, ER, PR, CDX2 and CK20 for G-EAC diagnosis, indicating international recognition of its diagnostic value. Semi-quantitative analysis further validated elevated PAX-8 expression in G-EAC, which was significantly positively correlated with tumor diameter, LVSI and deep cervical stromal invasion, and inversely associated with recurrence and poor prognosis. Quantitatively, PAX-8 yielded an AUC of 0.81, with a sensitivity of 88% for G-EAC diagnosis. The close association between PAX-8 and aggressive clinicopathological features suggests its involvement in regulating the malignant behavior of G-EAC. Further investigation of its genetic alterations may provide novel insights into G-EAC origin and pathogenesis, explain its highly aggressive phenotype, and identify potential targets for precision therapy. Smad3, a key downstream effector of the TGF-β pathway, mediates diverse cellular processes including growth arrest, apoptosis, differentiation and epithelial-mesenchymal transition (EMT), and is critically implicated in cervical cancer metastasis. Epigenetically regulated, Smad3 exhibits a dual role in cervical cancer: suppressing proliferation in early stages but promoting invasion and metastasis in advanced disease [ 21 ] . In this study, Smad3 expression was positively correlated with tumor diameter in G-EAC, supporting its pro-invasive function, although no significant association with prognosis was detected, likely due to its context-dependent dual regulation. Smad3 alone showed an AUC of 0.60, sensitivity of 75% and specificity of 61% for G-EAC diagnosis. Notably, the clinical value of Smad3 extends beyond individual performance, and its synergy with PAX-8 reveals distinct translational and mechanistic potential. The PAX-8/Smad3 combination achieved an AUC of 0.82, enabling reliable differentiation between G-EAC and HPVA. Using a serial interpretation criterion, the combined panel reduced sensitivity to 44% (compared with 88% for PAX-8 alone) but markedly improved specificity and accuracy to 96% and 86%, respectively. Given the divergent clinical management of G-EAC and HPVA, minimizing false-positive results to avoid overtreatment in HPVA patients was a primary study objective, justifying this sensitivity-specificity trade-off as clinically appropriate. This high-specificity panel provides a robust tool for G-EAC diagnosis, with future optimization possible via parallel testing and multi-center validation. Correlation analysis confirmed significant co-expression of PAX-8 and Smad3 in G-EAC, and their concurrent high expression was associated with tumor diameter > 4 cm, suggesting a synergistic oncogenic effect. However, their regulatory crosstalk remains poorly defined. A previous study in thyroid cancer [ 22 ] demonstrated that activated TGF-β/Smad3 signaling physically disrupts the PAX-8 DNA-binding domain via protein–protein interaction, impairing its binding to the Na+/I− Symporter(NIS) enhancer and downregulating PAX-8 mRNA and protein expression, without delineating the precise transcriptional or translational mechanisms. Elucidating this interaction may be pivotal to understanding G-EAC invasiveness and metastasis, and targeted modulation of the Smad3/PAX-8 axis represents a promising therapeutic strategy. This study systematically characterized the unique clinicopathological and molecular features of G-EAC, validated the synergistic diagnostic and prognostic value of PAX-8 and Smad3, and demonstrated their coordinated upregulation and pro-tumorigenic function in G-EAC. These findings address critical gaps in current diagnostic and prognostic stratification tools, and offer new avenues for G-EAC evaluation and targeted therapy. However, the small sample size and inherent limitations of the retrospective study design may limit the generalizability of our findings. Future multi-center studies with larger cohorts are warranted to validate these findings and establish molecular subtype-based individualized treatment paradigms. Such advances are expected to drive the shift from empirical to molecular-targeted therapy for G-EAC, improving outcomes for this highly aggressive and refractory malignancy. Conclusion In conclusion, gastric-type endocervical adenocarcinoma (G-EAC) exhibits distinct clinicopathological features and significantly poorer prognosis compared with HPV-associated endocervical adenocarcinoma (HPVA), including higher rates of abnormal vaginal discharge, negative hr-HPV and normal TCT results, advanced FIGO stage, and increased risks of invasion, metastasis and recurrence, which were further validated by meta-analysis. Immunohistochemically, G-EAC shows significantly higher expression of PAX-8 and Smad3 than HPVA, and PAX-8 is positively correlated with Smad3 in G-EAC. High PAX-8 expression is associated with aggressive pathological features and unfavorable survival, while Smad3 expression is related to larger tumor size. The combined detection of PAX-8 and Smad3 presents high diagnostic specificity for G-EAC, indicating their potential as promising biomarkers for differential diagnosis and prognostic stratification. This study clarifies the unique biological characteristics of G-EAC and the clinical value of PAX-8/Smad3 co-expression, providing a reliable indicator for clinical diagnosis and prognostic evaluation. However, the single-center design and small sample size of G-EAC are the main limitations. Multi-center studies with larger cohorts are warranted to verify these findings and explore the molecular mechanism of PAX-8/Smad3 axis in G-EAC progression. Abbreviations AUC the Area under the curve DFS Disease-free survival EMT Epithelial-mesenchymal transition FIGO the Federation International of Gynecology and Obstetrics G-EAC Gastric-type endocervical adenocarcinoma HPV Human papillomavirus HPVA HPV-associated adenocarcinoma IECC the International Endocervical Adenocarcinoma Criteria and Classification IPD Individual patient data LNM Lymph node metastasis LVSI Lymphovascular space invasion NHPVA non-HPV-associated adenocarcinoma NOS The Newcastle-Ottawa Scale NPV Negative predictive value OS Overall survival PPV Positive predictive value Rb Retinoblastoma ROC Receiver operating characteristic curves TCT Thinprep Cytologic Test TIME Tumor immune microenvironment UEA the Usual endocervical adenocarcinoma Declarations Ethics approval and consent to participate: This study was conducted in accordance with the World Medical Association Declaration of Helsinki (75th WMA General Assembly, Helsinki, Finland, October 2024) and was approved by the Medical Ethics Committee of Guizhou Medical University (Approval No. 2023910), and a waiver of informed consent for study participation was granted given the retrospective nature of the study. Consent for publication: Not applicable. Availability of data and materials：The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: All authors declare that the research was conducted in the absence of any competing financial and/or non-financial interests in relation to the work described. Funding: This work was supported by grants from the Science and Technology Project of Guizhou (grant number Qiankehejichu-ZK(2024] Key project 041, Qiankehejichu-ZK[2022] General project 436) Author Houmei Wang. has received research support from the Science and Technology Project of Guizhou and Affiliated Hospital of Guizhou Medical University. Authors' contributions: Yi Wang: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization. Ziwen Xiao: Supervision； Juntao Wang: I nvestigation, Resources； Houmei Wang: Conceptualization, Methodology, Supervision, Writing - Review & Editing, Funding acquisition Acknowledgements : We thank the Affiliated Hospital of Guizhou Medical University for its support of the corresponding author, H. Wang, and the participating medical institutions for providing clinical samples and data. We also acknowledge the ethical approval from the Medical Ethics Committee of Guizhou Medical University (No. 2023910), and thank all clinical and pathological staff and patients enrolled in this study. Authors' information: Y.W, M.D, Department of Obstetrics and Gynecology, The People's Hospital of the Qiandongnan Miao and Dong Autonomous Prefecture, No. 31 Shaoshan South Road, Kaili City, Guizhou Province 556000, Guizhou, China. Major in gynaecology oncology. Tel: +8613007865211, E-mail Address: [email protected] ORCID：0009-0005-8443-5857 Z.X. PhD, Department of Gynecology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China, Address: No.28, Guiyi Street, Yunyan District, Guiyang, China. Major in gynaecology oncology. J.W Department of Gynecologic Oncology, Guiyang Maternal and Child Health Care Hospital·Guiyang Children's Hospital, No. 63 Ruijin South Road, Nanming District, Guiyang, 550004, Guizhou, China. Major in gynaecology oncology. Tel: +8613329609156, E-mail Address: [email protected] H.W, PhD, Department of Gynecology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China, Address: No.28, Guiyi Street, Yunyan District, Guiyang, China. Major in gynaecology oncology. Fax number: 085188519220, E-mail Address: [email protected] ORCID：0000-0003-1739-1338 Presentation: The content of this study has not been presented in whole or in part at any academic conference, symposium, or other relevant platform, and no related findings have been published in any journal, thesis, or other publication medium Preprint: This manuscript has not been deposited or posted as a preprint on any preprint server in any form. References Xu H, Zhang J. [Interpretation of updated pathological contents for cervical cancer in NCCN clinical practice guidelines, version 1, 2020]. Zhonghua Bing Li Xue Za Zhi. 2021;50(1):9–13. 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. Karamurzin YS, Kiyokawa T, Parkash V, Jotwani AR, Patel P, Pike MC, Soslow RA, Park KJ. Gastric-type Endocervical Adenocarcinoma: An Aggressive Tumor With Unusual Metastatic Patterns and Poor Prognosis. Am J Surg Pathol. 2015;39(11):1449–57. Park KJ, Kim MH, Kim JK, Cho KS. Gastric-Type Adenocarcinoma of the Uterine Cervix: Magnetic Resonance Imaging Features, Clinical Outcomes, and Prognostic Factors. Int J Gynecol Cancer. 2018;28(6):1203–10. Nishio S, Mikami Y, Tokunaga H, Yaegashi N, Satoh T, Saito M, Okamoto A, Kasamatsu T, Miyamoto T, Shiozawa T, et al. Analysis of gastric-type mucinous carcinoma of the uterine cervix - An aggressive tumor with a poor prognosis: A multi-institutional study. Gynecol Oncol. 2019;153(1):13–9. Xu H, Pan H, Wang Y, Zhang J. Expanded study on the risk of lymphovascular space invasion and lymph node metastasis of endocervical adenocarcinoma using Pattern Classification: a single-centre analysis of 213 cases. Pathology. 2019;51(6):570–8. Shi HY, Ye L, Lu WG, Lu BJ. Grading of endocervical adenocarcinoma: a novel prognostic system based on tumor budding and cell cluster size. Mod Pathol. 2022;35(4):524–32. Lee J, Chi SA, Choi S, Kim HS. Invasive Stratified Mucin-producing Carcinoma of the Uterine Cervix: Comparison of Its Clinicopathological Characteristics and Programmed Death-ligand 1 Expression Status With Those of Other Endocervical Adenocarcinomas. Anticancer Res. 2024;44(11):5007–22. Kamijo K, Miyamoto T, Oshima S, Asaka S, Shinagawa M, Sato Y, Ando H, Asaka R, Fujioka M, Uchiyama N, et al. Extensive Pathologic Invasion and Prognostic Implication of Gastric-Type Cervical Adenocarcinoma A Comparative Analysis With Human Papillomavirus-Associated Adenocarcinoma. Am J Surg Pathol. 2025;49(5):471–80. Xi Y, Zhou F, Liu Y, Zhou H, Lu X, Fang X, Yan L, Zhou J, Zhu T, Tang H. Clinical and pathological analyses of gastric-type cervical adenocarcinoma and its prognostic relevance. Therapeutic Adv Med Oncol 2025, 17. Zahan UF, Sohel HI, Nakayama K, Ishikawa M, Nagase M, Razia S, Kanno K, Yamashita H, Sonia SB, Kyo S. A Comparative Analysis of Usual- and Gastric-Type Cervical Adenocarcinoma in a Japanese Population Reveals Distinct Clinicopathological and Molecular Features with Prognostic and Therapeutic Insights. Int J Mol Sci. 2025;26(15):20. Garg S, Nagaria TS, Clarke B, Freedman O, Khan Z, Schwock J, Bernardini MQ, Oza AM, Han K, Smith AC, et al. Molecular characterization of gastric-type endocervical adenocarcinoma using next-generation sequencing. Mod Pathol. 2019;32(12):1823–33. Xu R, Liu H, Zhu T, Tang H, Wu M, Yan X, Li M, Yuan S, Yin T, Chen J et al. An immunosuppressive tertiary lymphoid structure is associated with adverse prognosis in gastric-type endocervical adenocarcinoma. J Natl Cancer Inst 2025. Turashvili G, Park KJ. Cervical Glandular Neoplasia: Classification and Staging. Surg Pathol Clin. 2019;12(2):281–313. Fulgione C, Raffone A, Travaglino A, Arciuolo D, Santoro A, Cianfrini F, Russo D, Varricchio S, Raimondo I, Inzani F et al. Diagnostic accuracy of HIK1083 and MUC6 as immunohistochemical markers of endocervical gastric-type adenocarcinoma: A systematic review and meta-analysis. Pathol Res Pract 2023, 241. Lin LH, Kaur H, Kolin DL, Nucci MR, Parra-Herran C. Claudin-18 and Mutation Surrogate Immunohistochemistry in Gastric-type Endocervical Lesions and their Differential Diagnoses. Am J Surg Pathol. 2025;49(3):206–16. Chen L, Liu B, Xu Y, Zhu Z, Zhao C. 955 Unveiling Molecular Signatures and Discovering Sensitive-Specific Diagnostic Biomarkers for Endocervical Gastric-type Adenocarcinoma through Integrated Transcriptome and DNA Methylation Profiling. In, vol. 105; 2025. Asaka S, Nakajima T, Ida K, Asaka R, Kobayashi C, Ito M, Miyamoto T, Uehara T, Ota H. Clinicopathological and prognostic significance of immunophenotypic characterization of endocervical adenocarcinoma using CLDN18, CDH17, and PAX8 in association with HPV status. Virchows Arch. 2022;480(2):269–80. Carleton C, Hoang L, Sah S, Kiyokawa T, Karamurzin YS, Talia KL, Park KJ, McCluggage WG. A Detailed Immunohistochemical Analysis of a Large Series of Cervical and Vaginal Gastric-type Adenocarcinomas. Am J Surg Pathol. 2016;40(5):636–44. Stolnicu S, Barsan I, Hoang L, Patel P, Chiriboga L, Terinte C, Pesci A, Aviel-Ronen S, Kiyokawa T, Alvarado-Cabrero I, et al. Diagnostic Algorithmic Proposal Based on Comprehensive Immunohistochemical Evaluation of 297 Invasive Endocervical Adenocarcinomas. Am J Surg Pathol. 2018;42(8):989–1000. Zhang L, Tian S, Zhao M, Yang T, Quan S, Song L, Yang X. SUV39H1-Mediated DNMT1 is Involved in the Epigenetic Regulation of Smad3 in Cervical Cancer. Anticancer Agents Med Chem. 2021;21(6):756–65. Costamagna E, García B, Santisteban P. The Functional Interaction between the Paired Domain Transcription Factor Pax8 and Smad3 Is Involved in Transforming Growth Factor-β Repression of the Sodium/Iodide Symporter Gene. J Biol Chem. 2004;279(5):3439–46. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx FigureS1.png Figure S1.Meta-Analysis Forest Plots and Funnel Plots Summary Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Apr, 2026 Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 23 Mar, 2026 Submission checks completed at journal 22 Mar, 2026 First submitted to journal 22 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-9151684","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627945699,"identity":"4067f9cf-9665-4af6-8117-25a4c1d68958","order_by":0,"name":"Yi Wang","email":"","orcid":"","institution":"The People's Hospital of the Qiandongnan Miao and Dong Autonomous Prefecture","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Wang","suffix":""},{"id":627945700,"identity":"63b3cb34-5f89-4675-8538-12a9a44f5d16","order_by":1,"name":"Ziwen Xiao","email":"","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziwen","middleName":"","lastName":"Xiao","suffix":""},{"id":627945701,"identity":"3ea914fe-3b93-4b84-96eb-625c19aaa188","order_by":2,"name":"Juntao Wang","email":"","orcid":"","institution":"Guiyang Maternal and Child Health Care Hospital·Guiyang Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Juntao","middleName":"","lastName":"Wang","suffix":""},{"id":627945702,"identity":"2fbccb15-7323-45d9-bd33-3f8e02df3b65","order_by":3,"name":"Houmei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYNCCChs5IMlGipYzacYwLRLE6WBsO5TYQLQW/tlnn0kwnDmQvuF487MHjDts6ghqkTiXbibBUHEnd8OZY+YGjGfSiHDYGTY2oC3PcjfcSDCTYGw7TFiLPEgLUGW6wf3n34CM/4S1GEC1JBjc4AHZcoCwFsMzbMwWwEA2nHkmp0wisS1ZsoGQFrkzbIw3gFEpz3f8+DaJj212/ARtAQIW6T9AUuEAkEggRj0QMH8AkfIEHTQKRsEoGAUjFgAAVoU6vRUi9UQAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Houmei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-17 18:09:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9151684/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9151684/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707356,"identity":"8a4009b0-036a-4ec3-af98-9251f2de3ac5","added_by":"auto","created_at":"2026-04-24 09:20:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":355033,"visible":true,"origin":"","legend":"\u003cp\u003eG-EAC and HPVA OS, DFS K-M Survival Curves and Univariate Analysis of G-EAC Prognosis.\u003c/p\u003e\n\u003cp\u003eOS(A) and DFS(B) curves comparing G-EAC vs. HPVA (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). and OS、DFS of G-EAC Stratified by Surgical approach(C,D),FIGO stage(E,F), Ovarian Metastasis(G,H), LNM(I,J), Surgical margin status(K,L),LVSI(M,N), Depth of cervical stromal invasion(O,P),and Tunmor size(Q,R)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9151684/v1/7a673e81ccfc91fa9983c64f.png"},{"id":107706038,"identity":"93732fa0-c178-470c-945c-ba011ac63c9c","added_by":"auto","created_at":"2026-04-24 09:17:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175644,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study selection\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9151684/v1/45d51d1834fefa45f373014b.png"},{"id":107619462,"identity":"06748a71-2367-442d-b858-ace9cf7b9a9c","added_by":"auto","created_at":"2026-04-23 09:28:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":224558,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Analysis of PAX-8 and Smad3 Protein Expression in G-EAC.\u003c/p\u003e\n\u003cp\u003eOS、DFS of G-EAC Stratified by PAX-8 high expression(PAX8-H) and low expression(PAX8-L) (A,D), Smad3 high expression(Smad3-H) and low expression(Smad3-H) (B,E) and PAX8/Smad3 combining Stratified by PAX8-H+Smad3-H, PAX8-L+Smad3-L, PAX8-H+Smad3-L, PAX8-L+Smad3-H.(C,F).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9151684/v1/231bad70d29377c7f0a69a52.png"},{"id":107706873,"identity":"2195eca9-d204-48a5-927b-be1aaec1b974","added_by":"auto","created_at":"2026-04-24 09:18:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1219713,"visible":true,"origin":"","legend":"\u003cp\u003eProtein expression of PAX-8 and Smad3 in G-EAC and HPVA tissues: Histological images, differential analysis, and correlation.\u003c/p\u003e\n\u003cp\u003e(A-F) Histological and IHC analysis of G-EAC: (A-C) and HPVA (D-F) tissues (×200). G-EAC displays robust PAX-8 nuclear staining (B) and Smad3 cytoplasmic/nuclear staining (C), while HPVA shows minimal expression (E-F). (G) Raincloud plots confirm significantly elevated PAX-8 and Smad3 expression in G-EAC (both \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05). (H-I) Correlation analysis reveals a strong positive association between PAX-8 and Smad3 in G-EAC with a synergistic pattern in high-expression subgroups (r = 0.77, P \u0026lt;0.05) (J-L) The combined PAX-8/Smad3 panel achieves an AUC of 0.82 (95% CI: 0.71–0.93), which is superior to either marker alone (PAX-8: AUC = 0.81; Smad3: AUC = 0.60). (J), with predictive probabilities (K) and performance metrics (L) demonstrating 86% accuracy and 96% specificity, supporting its clinical application in G-EAC diagnosis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9151684/v1/8214d5302bcaf79bb888d9f1.png"},{"id":107709218,"identity":"f7541d50-b451-48d9-ab06-6cb395e48d39","added_by":"auto","created_at":"2026-04-24 09:35:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2122976,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9151684/v1/29a557bd-b63a-462e-bb00-45e919fa0135.pdf"},{"id":107705881,"identity":"96fc78bc-e8d4-4620-8131-acaf5c8f8c92","added_by":"auto","created_at":"2026-04-24 09:15:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":502377,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9151684/v1/980372b201c21e998d93144e.docx"},{"id":107619459,"identity":"8b8914ba-7185-4cba-8296-76ea4282be31","added_by":"auto","created_at":"2026-04-23 09:28:54","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":443447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1.\u003c/strong\u003eMeta-Analysis Forest Plots and Funnel Plots Summary\u003c/p\u003e","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-9151684/v1/9fbbf1103520cc3b11dbc91e.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinicopathological Features and Prognostic-Diagnostic Value of PAX-8/Smad3 Co-expression in Gastric-type Endocervical Adenocarcinoma: A Retrospective Cohort Combined with Meta-Analysis","fulltext":[{"header":"HIGHLIGHTS","content":"\u003col\u003e\n\u003cli\u003eWe compared 16 G-EAC and 60 HPVA patients with retrospective analysis and meta-analysis.\u003c/li\u003e\n\u003cli\u003eG-EAC showed distinct clinical features and worse prognosis than HPVA patients.\u003c/li\u003e\n\u003cli\u003ePAX-8/Smad3 co-expression acts as potential biomarkers for G-EAC diagnosis and prognosis.\u003c/li\u003e\n\u003cli\u003eCombined detection of PAX-8 and Smad3 has high diagnostic efficacy for G-EAC.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eCervical cancer represents the most common malignant neoplasm of the female reproductive system in China, with persistent infection by high-risk HPV identified as the primary etiological factor. Gastric-type endocervical adenocarcinoma (G-EAC), the most common subtype within non-HPV-associated adenocarcinoma (NHPVA), has exhibited a rising incidence, accounting for 10%-15% of cervical adenocarcinomas\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Compared to HPV-associated adenocarcinoma(HPVA), G-EAC constitutes a critical focus of clinical investigation with frequent early misdiagnosis, aggressive invasiveness, and poor prognosis. Emerging evidence suggests distinct molecular pathological characteristics may underlie its aggressive biological behavior and adverse clinical outcomes. Yet the clinical rarity of G-EAC has left investigations into its pathogenesis and molecular pathology in the nascent stage, and the absence of standardized clinical management guidelines and reliable prognostic stratification biomarkers renders imperative to elucidate its molecular expression profiles in association with clinicopathological parameters and immunohistochemical phenotypes and to examine their prognostic correlations to develop precise therapeutic strategies, stratify recurrence risk, and improved clinical outcomes. This study aims to provide novel insights into the pathogenesis of G-EAC, lay an evidence-based foundation for subsequent molecular mechanism research, identify potential targets for precision targeted therapy optimize diagnostic and therapeutic strategies for G-EAC, and ultimately improve patient prognosis.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eClinical Study\u003c/p\u003e \u003cp\u003eStudy subjects: Clinicopathological data, follow-up records, and postoperative paraffin-embedded pathological specimens were collected from 16 G-EAC patients and 60 HPVA patients treated at the Affiliated Hospital of Guizhou Medical University and Guiyang Maternal and Child Health Hospital between Jan 2015 and Dec 2023. Inclusion criteria: (1) Pathological diagnosis of G-EAC/HPVA based on the 2018 IECC criteria\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e; (2) Complete and standardized medical records; (3) Access to complete pathological data and follow-up records. Exclusion criteria: (1) Concurrent other malignant tumors; (2) Previous pelvic radiotherapy/chemotherapy; (3) Poor-quality pathological specimens unfit for IHC testing.\u003c/p\u003e \u003cp\u003eThe 60 HPVA cases included 53 usual type endocervical adenocarcinoma, 4 intestinal-type mucinous adenocarcinoma, 2 invasive stratified mucin-producing carcinoma and 1 villoglandular adenocarcinoma.\u003c/p\u003e \u003cp\u003eFollow-up: Patients were followed up by telephone and outpatient visits from the surgery date to October 30, 2025 or death.\u003c/p\u003e \u003cp\u003eSurvival endpoints: OS was defined as the time from initial treatment to death from any cause or follow-up end; DFS as the time from initial treatment to disease recurrence or follow-up end. Both were calculated in months.\u003c/p\u003e \u003cp\u003eMeta-Analysis\u003c/p\u003e \u003cp\u003eChinese (CNKI, Wanfang Data, VIP, CBM) and English (PubMed, Embase, Web of Science) databases were systematically searched for relevant studies published from January 2015 to October 2025. The Newcastle-Ottawa Scale (NOS) was used for the quality assessment of observational studies, and the ROBINS-I tool was used for bias evaluation (graded as low, moderate, serious, or critical). Statistical analysis was performed with RevMan 5.4: mean difference (MD) and standardized mean difference (SMD) were used for continuous variables, odds ratio (OR) with 95% confidence interval (CI) for dichotomous data, and sensitivity analysis was also conducted.\u003c/p\u003e \u003cp\u003eImmunohistochemistry\u003c/p\u003e \u003cp\u003e The SP method was applied to detect PAX-8/Smad3 protein expression in G-EAC and HPVA tumor tissues, with primary antibodies obtained from Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd. The experimental procedures included slide baking, dewaxing, ethanol gradient hydration, antigen retrieval, endogenous peroxidase inactivation with 3% H₂O₂, PBS rinsing, primary antibody incubation, hematoxylin counterstaining, hydrochloric acid-ethanol differentiation, dehydration and coverslip mounting. PAX-8 was mainly expressed in tumor cell nuclei, and Smad3 in both nuclei and cytoplasm.\u003c/p\u003e \u003cp\u003eTwo experienced pathologists independently scored the tissue sections semi-quantitatively under double-blind conditions: 2 high-power fields (\u0026times;200) were randomly selected per section, and scores were averaged. Staining intensity (0\u0026ndash;3 points) and positive cell proportion (0\u0026ndash;4 points) were multiplied to obtain the total score, which was classified as negative (0\u0026ndash;1), weakly positive (2\u0026ndash;4), moderately positive (6\u0026ndash;8) and strongly positive (9\u0026ndash;12). The total score was treated as a continuous variable for subsequent diagnostic accuracy analysis and optimal diagnostic threshold determination.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using GraphPad Prism 10.6.1 with a two-tailed test and α\u0026thinsp;=\u0026thinsp;0.05 as the significance level. Categorical variables were expressed as n (%), and continuous variables as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Inter-group comparisons were conducted with the chi-square test/Fisher\u0026rsquo;s exact test for categorical variables, and independent samples t-test/Mann-Whitney U test for continuous variables. Survival analysis was performed with the Kaplan-Meier method and Log-rank test; variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were included in multivariate Cox proportional hazards regression models to identify independent prognostic factors (entry α\u0026thinsp;=\u0026thinsp;0.05, removal α\u0026thinsp;=\u0026thinsp;0.10). The correlation between PAX-8 and Smad3 staining scores was analyzed by Spearman\u0026rsquo;s rank correlation coefficient. ROC curves were constructed to evaluate the diagnostic efficacy of PAX-8, Smad3 and their combined detection; the Delong test was used for AUC comparison, and Youden\u0026rsquo;s index for the optimal diagnostic cutoff value.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eClinicopathological and Prognostic Differences Between G-EAC and HPVA\u003c/h2\u003e\n \u003cp\u003eComparison of Clinicopathological Indicators\u003c/p\u003e\n \u003cp\u003eCompared to HPVA, G-EAC exhibited a higher incidence of abnormal vaginal discharge (37.5% vs. 5.0%), alongside lower positive rates for hr-HPV infection (0.0% vs. 93.3%) and abnormal TCT results (27.3% vs. 85.0%) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, G-EAC presented with fewer FIGO stage I (37.5% vs. 86.7%) and more stages II-IV (62.5% vs. 13.3%), along with significantly higher rates of adverse pathological features (ovarian metastasis: 18.8% vs.0.0%; LNM: 25.0% vs.5.0%; parametrial invasion: 25.0% vs.0.0%; LVSI: 81.2% vs.10.0%; positive surgical margins: 18.8% vs.1.7%, outer 1/3 cervical wall invasion: 43.6% vs. 16.7%; tumor diameters exceeding 4 cm: 43.8% vs. 10.0%,all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). with a larger mean tumor size(3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20 cm vs. 2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02 cm, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No statistically differences in age and menopausal status were found between the two groups (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), See Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eComparison of OS and DFS\u003c/p\u003e\n \u003cp\u003eOf 76 patients, 74 completed follow-up (2 lost, 2.6% loss rate) with a median follow-up of 46.5 months (9-105 months). At follow-up, 50% (8/16) of G-EAC patients developed recurrence /metastasis vs. 8.3% (5/60) in HPVA. The 1-, 2-, 3-, and 5-year OS rates were 93.75%, 50.00%, 43.75%, 25% for G-EAC vs. 100.00%, 98.33%, 81.67%, 43.33% for HPVA. G-EAC patients had significantly shorter OS and DFS than HPVA(both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUnivariate and Multivariate Analysis of Risk Factors for Recurrence\u003c/p\u003e\n \u003cp\u003eUnivariate analysis demonstrated advanced FIGO stage, ovarian metastasis, LNM, LVSI, outer 1/3 of the cervical stroma invasion, and tumor diameter\u0026thinsp;\u0026gt;\u0026thinsp;4 cm as significant risk factors for OS and DFS in G-EAC (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Positive surgical margins only affected DFS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for K-M curves. No independent prognostic factors were found in multivariate analysis, likely due to the small sample size (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and multivariate analyses for OS and DFS in patients with G-EAC\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eClinicopathological factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eUnivariate Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eUnivariate Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi; \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH\u003cem\u003eR (95%CI)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026chi; \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eH\u003cem\u003eR (95%CI)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" align=\"left\"\u003e\n \u003cp\u003eSurgical approach\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransabdominal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaparoscopic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 FIGO stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEarly Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003cp\u003e(0.003\u0026ndash;2.806)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocally advanced Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" align=\"left\"\u003e\n \u003cp\u003eOvarian Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" align=\"left\"\u003e\n \u003cp\u003eLNM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.821\u003c/p\u003e\n \u003cp\u003e(0.164\u0026ndash;20.197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e8.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" align=\"left\"\u003e\n \u003cp\u003eSurgical margin status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVSI status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e15.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" align=\"left\"\u003e\n \u003cp\u003eParacervical infiltration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegarive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" align=\"left\"\u003e\n \u003cp\u003eDeep invasion of the cervical wall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOuter 1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e13.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e3.186\u003c/p\u003e\n \u003cp\u003e(0.597\u0026ndash;17.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e17.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle 1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInner 1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eMeta-Analysis for External Validation of G-EAC Invasiveness\u003c/h3\u003e\n\u003cp\u003eLiterature Screening, Quality Assessment, and Bias Risk Analysis\u003c/p\u003e\n\u003cp\u003e1365 articles were initially retrieved, with 9 high-quality retrospective studies\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e (NOS score: 7\u0026ndash;9) included. The literature screening flowchart is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Total sample size was 2268 cases (401 G-EAC, 1867 HPVA). See Table S2 for Basic information. ROBINS-I assessment indicated 2 studies with high bias and 7 with moderate bias, mainly from insufficient confounding control and missing follow-up data. (bias evaluation, Table S3).\u003c/p\u003e\n\u003cp\u003eQuantitative Results of Core Indicators in Meta-Analysis\u003c/p\u003e\n\u003cp\u003eThe mean age of the G-EAC group was 4.24 years higher than that of the HPVA group. Compared with the HPVA group, the G-EAC group had 5.07-, 4.32-, 4.37-, 7.55-, 9.47-, and 4.80-fold higher rates of FIGO stage Ⅲ-Ⅳ, LVSI, LNM, ovarian metastasis, parametrial invasion, and recurrence, respectively. The mean tumor diameter was 7.74 mm larger in the G-EAC group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).( brief summary, Table S4). See Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e for forest and funnel plots by factor.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDifferences in Immunohistochemical Phenotypes Between G-EAC and HPVA\u003c/h3\u003e\n\u003cp\u003eG-EAC showed significantly higher positive rates of PAX-8 (68.8% vs. 30.0%), Smad3 (56.3% vs. 15.0%), TP53 (50.0% vs. 13.3%), and MUC6 (50.0% vs. 5.0%), but a lower p16 rate (12.5% vs. 93.3%, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than HPVA. No significant differences in CDX-2, CK20, ER, and PR expression were found between the two groups (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). See Table S5 for details.\u003c/p\u003e\n\u003cp\u003eCorrelation Between PAX8/Smad3 Expression and Clinicopathological Features of G-EAC\u003c/p\u003e\n\u003cp\u003eG-EAC patients were stratified by median staining scores of PAX-8 (8.15) and Smad3 (7.5). The results showed that PAX-8 expression in G-EAC was positively correlated with LVSI, Deep invasion of the cervical wall, and tumor diameter (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Smad3 expression and the co-expression of PAX-8 and Smad3 were also positively correlated with tumor diameter (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant correlations were observed between their expression/co-expression and FIGO stage, ovarian metastasis, LNM, or parametrial invasion (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation between PAX-8 and Smad3 Expression and Clinical-Pathological Characteristics of G-EAC\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePAX-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eSmad3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eCo-expression\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive rate(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive rate(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive rate(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e2018 FIGO stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage Ⅰ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage Ⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage Ⅲ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage Ⅳ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eOvarian Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eLymphatic node Metastasis(LNM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eLVSI status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eParacervical infiltration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eCervical stromal invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOuter 1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle 1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInner 1/3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eTumor size(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eCorrelation analysis between PAX-8/Smad3 expression and prognosis\u003c/p\u003e\n\u003cp\u003eSurvival analysis showed that G-EAC patients with high PAX-8 expression had significantly shorter OS and DFS than those with low expression (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); Smad3 expression had no significant correlation with prognosis (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eClinical Correlation of PAX8 and Smad3 Co-Expression\u003c/p\u003e\n\u003cp\u003eHE staining demonstrated that G-EAC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e-a) displayed glandular atypia with features indicative of gastric differentiation, whereas HPVA (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e-d) exhibited characteristic histopathological traits of HPV-related adenocarcinoma, such as apical mitoses. IHC analysis revealed diffuse overexpression of PAX-8 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e-b) and Smad3 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e-c) in G-EAC samples, but low expression in HPVA (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e-e, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e-f). Quantitative evaluation indicated that the mean PAX-8 staining scores were 8.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49 (G-EAC) vs. 5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68 (HPVA), while the mean Smad3 staining scores were 7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51(G-EAC) vs. 5.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45(HPVA), with both significantly higher in G-EAC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as illustrated by the raincloud plot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e-g). Spearman\u0026rsquo;s correlation analysis confirmed a positive correlation between PAX-8 and Smad3 expression in G-EAC (r\u0026thinsp;=\u0026thinsp;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e-h), see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e-j for contingency table.\u003c/p\u003e\n\u003cp\u003eEvaluation of Diagnostic Efficacy of PAX8/Smad3 for G-EAC\u003c/p\u003e\n\u003cp\u003eThe diagnostic effectiveness of the proteins PAX-8 and Smad3 was assessed and compared (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, j-i). The optimal cut-off value for PAX-8 was 5.35. In diagnosing G-EAC, PAX-8 achieved an AUC of 0.81 (95% confidence interval [CI]: 0.69\u0026ndash;0.92), with a sensitivity of 88% (95% CI: 64%-98%), specificity of 50% (95% CI: 37%-62%), PPV of 32% (95% CI: 20%-47%), and NPV of 94% (95% CI: 80%-98%), all statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For Smad3, the optimal cut-off was 6.55, yielding an AUC of 0.60 (95% CI: 0.48\u0026ndash;0.72) for G-EAC diagnosis, with sensitivity at 62% (95% CI: 38%-82%), specificity at 58% (95% CI: 45%-70%), PPV of 25% (95% CI: 14%-41%), and NPV of 86% (95% CI: 73%-94%). the combination of PAX-8 and Smad3 achieved an AUC of 0.82 (95% CI: 0.71\u0026ndash;0.93) for G-EAC diagnosis. This combined approach showed a sensitivity of 44% (95% CI: 23%-67%), specificity of 96% (95% CI: 89%-99%), PPV of 78% (95% CI: 45%-94%), and NPV of 87% (95% CI: 76%-93%).\u003c/p\u003e\n\u003cp\u003ePairwise AUC comparisons via Delong test with Bonferroni correction (adjusted \u0026alpha;\u0026thinsp;=\u0026thinsp;0.017) revealed no statistically significant differences between PAX-8, Smad3, and their combination (all adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.017). This finding is attributable to the standalone diagnostic performance of PAX-8 (AUC\u0026thinsp;=\u0026thinsp;0.81), which provided sufficient discriminative ability for G-EAC, thereby limiting the incremental diagnostic value of Smad3 and the combined panel in terms of AUC. The combined detection primarily improved specificity (96%) rather than overall discriminative capacity, which is consistent with the study\u0026rsquo;s focus on reducing false-positive diagnoses. Additionally, the rarity of G-EAC leading to a small sample size may have reduced statistical power, precluding the detection of minor AUC differences. These results confirm that PAX-8 is a reliable standalone diagnostic marker for G-EAC, while the combined panel offers a more specific alternative for clinical application.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIt is widely accepted internationally that G-EAC, the predominant subtype of NHPVA, exhibits significant heterogeneity from HPVA in terms of clinical behavior, pathogenesis, and survival outcomes. The aggressive biological behavior of G-EAC is likely driven by its unique molecular pathogenesis in conjunction\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e with the tumor immune microenvironment (TIME) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The highly malignant biological characteristic of G-EAC poses ongoing challenges in clinical practice, including diagnostic difficulties and suboptimal therapeutic responses. Consequently, a comprehensive elucidation of the molecular mechanisms underlying G-EAC, the identification of reliable diagnostic and prognostic biomarkers, and the development of targeted interventions for its highly invasive and metastatic properties are critical priorities for clinical research and management. This study quantitatively validated the subtype distinctions between G-EAC and HPVA through a retrospective cohort analysis supplemented by a meta-analysis. Clinical data revealed G-EAC cohort exhibited a significantly higher prevalence of abnormal vaginal discharge, alongside markedly lower rates of hr-HPV infection and abnormal TCT results, compared to the HPVA group. Furthermore, G-EAC group demonstrated significantly increased incidences of advanced FIGO stages II\u0026ndash;IV, ovarian metastasis, LNM, parametrial invasion, LVSI, positive surgical margins, deep cervical stromal invasion extending, and lager tumor diameter. Survival analyses indicated that both OS and DFS were significantly reduced in G-EAC group. Meta-analysis corroborated these findings, indicating the mean age of patients with G-EAC was 4.24 years older than that of the HPVA group. Additionally, the odds of stage III\u0026ndash;IV disease, LVSI, LNM, ovarian metastasis, parametrial invasion, and recurrence were 5.07-, 4.32-, 4.37-, 7.55-, 9.47-, and 4.80-fold higher in the G-EAC cohort than in the HPVA cohort. Tumor size was also significantly larger in the G-EAC group, with an average increase of 7.74 mm. Immunohistochemical analysis revealed that the G-EAC group exhibited significantly higher positivity rates for PAX-8, Smad3, TP53, and MUC6 compared to the HPVA group, whereas p16 expression was markedly lower in G-EAC. Further analyses demonstrated that PAX-8 expression was positively correlated with LVSI, depth of cervical stromal invasion, tumor diameter, and inversely correlated with OS and DFS. Smad3 expression was positively associated with tumor diameter. Notably, PAX-8 and Smad3 were co-expressed in G-EAC, exhibiting a positive correlation. Quantitative assessment of the diagnostic performance of PAX-8 and Smad3 revealed that the AUC for PAX-8 in diagnosing G-EAC was 0.81, for Smad3 was 0.60, and for their combined detection was 0.82, with a specificity of 84%. These findings suggest that the combined application of PAX-8 and Smad3 immunostaining might serve as a valuable tool for the precise diagnosis and prognostic stratification of G-EAC.\u003c/p\u003e \u003cp\u003eThis study adopted a retrospective cohort design combined with meta-analysis to systematically explore the biological and clinicopathological disparities between G-EAC and HPVA. The core findings are consistent with current international literature and were further quantified by meta-analysis, offering robust evidence-based support for clinical practice. Clinically and pathologically, the incidence of abnormal vaginal discharge was significantly higher in the G-EAC group (37.5%) than in the HPVA group (5.0%). In contrast, the rates of hr-HPV positivity (0.0% vs. 93.3%) and abnormal TCT results (27.3% vs. 85.0%) were markedly lower in the G-EAC cohort. This discrepancy arises from the lack of hr-HPV involvement in G-EAC carcinogenesis, as well as the absence of key molecular events including p53 and retinoblastoma (Rb) pathway dysregulation mediated by E6/E7 oncogene integration. Consequently, conventional clinical screening strategies demonstrate extremely low sensitivity for G-EAC, contributing to a high rate of early-stage misdiagnosis. The risk of biopsy misdiagnosis secondary to well-differentiated cytological features further compounds this challenge, resulting in a disproportionately high percentage of patients presenting with advanced-stage disease at initial diagnosis.Concurrently, G-EAC displays a significantly more aggressive invasive phenotype. The proportion of cases with FIGO stages II\u0026ndash;IV, ovarian metastasis rate (18.8% vs. 0.0%), LNM rate (25.0% vs. 5.0%), and incidence of LVSI (81.2% vs. 10.0%) were all significantly higher in the G-EAC group relative to the HPVA group. These observations are in accordance with the conclusions of Karamurzin\u0026rsquo;s\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e multicenter study and Park\u0026rsquo;s\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e retrospective cohort investigation. Meta-analysis further corroborated the existence of these intergroup differences. Although the incidence of G-EAC is substantially higher in Asian populations than in Western cohorts (approximately 10% in the United States\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e and up to 25% in Japan\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e), its core hallmarks of high aggressiveness and unfavorable prognosis remain consistent across ethnic groups. Pooled analysis demonstrated that the mean age at diagnosis of G-EAC was 4.24 years higher than that of HPVA, the rates of FIGO stage III\u0026ndash;IV disease and parametrial invasion were 5.07-fold and 9.47-fold higher, respectively; and the recurrence risk was 4.80-fold greater. The 5-year OS rate of G-EAC was merely 25%, compared with 43.33% in the HPVA group, a finding consistent with multiple international reports\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.With respect to treatment and prognostic evaluation, G-EAC shows poor responsiveness to conventional radiotherapy and chemotherapy regimens for cervical cancer, and well-established prognostic stratification markers are currently lacking. Univariate analysis in the present study identified FIGO stage, ovarian metastasis, and LVSI as factors associated with prognosis in G-EAC patients. However, multivariate analysis did not confirm any independent prognostic factors, which was likely attributed to the limited sample size. While these identified clinicopathological differences provide evidence-based references for clinical triage, treatment selection, and prognostic assessment, the management of G-EAC still confronts numerous bottlenecks that demand urgent breakthroughs. Internationally, molecular-targeted personalized therapeutic strategies have not yet been established, leading to clinical decisions that are largely experience-dependent, with inadequate accuracy in predicting recurrence risk and ultimately compromising treatment efficacy and survival outcomes. Collectively, the development of highly sensitive and specific diagnostic tools, coupled with the identification of molecular biomarkers for reliable prognostic stratification, represents a critical unmet need to address the current challenges in the diagnosis and treatment of G-EAC.\u003c/p\u003e \u003cp\u003eExisting gastric mucin markers, including HIK1083 and MUC6, show low diagnostic sensitivity for G-EAC, at 0.51 and 0.64 respectively\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, and are insufficient for precise subtype discrimination. In recent years, Claudin-18 (CLDN18), a gastric differentiation-associated marker, has been widely investigated in G-EAC. Relevant studies\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003ereport its positivity rate in G-EAC to be 58.0%\u0026ndash;78.0%, compared with only 18% in HPVA. However, improved differential diagnostic accuracy requires combined testing with CDH17, PAX-8 and HPV-ISH\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. highlighting the inherent limitations of single-biomarker strategies. In this study, qualitative immunohistochemical analysis identified distinct expression profiles of five markers (PAX-8, Smad3, p16, TP53, MUC6) between G-EAC and HPVA. Notably, PAX-8 and Smad3 showed high positivity in G-EAC, at 68.8% and 56.3%, respectively. PAX-8 is a well-established, highly specific marker for M\u0026uuml;llerian-derived tumors and is routinely used in clinical practice. Recent studies\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e demonstrate a consistently high PAX-8 positivity rate (68%\u0026ndash;80%) in G-EAC, supporting its combined use with a panel of markers including MUC6, HIK1083, TFF2, CLDN18, p16, p53, ER, PR, CDX2 and CK20 for G-EAC diagnosis, indicating international recognition of its diagnostic value. Semi-quantitative analysis further validated elevated PAX-8 expression in G-EAC, which was significantly positively correlated with tumor diameter, LVSI and deep cervical stromal invasion, and inversely associated with recurrence and poor prognosis. Quantitatively, PAX-8 yielded an AUC of 0.81, with a sensitivity of 88% for G-EAC diagnosis. The close association between PAX-8 and aggressive clinicopathological features suggests its involvement in regulating the malignant behavior of G-EAC. Further investigation of its genetic alterations may provide novel insights into G-EAC origin and pathogenesis, explain its highly aggressive phenotype, and identify potential targets for precision therapy. Smad3, a key downstream effector of the TGF-β pathway, mediates diverse cellular processes including growth arrest, apoptosis, differentiation and epithelial-mesenchymal transition (EMT), and is critically implicated in cervical cancer metastasis. Epigenetically regulated, Smad3 exhibits a dual role in cervical cancer: suppressing proliferation in early stages but promoting invasion and metastasis in advanced disease\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In this study, Smad3 expression was positively correlated with tumor diameter in G-EAC, supporting its pro-invasive function, although no significant association with prognosis was detected, likely due to its context-dependent dual regulation. Smad3 alone showed an AUC of 0.60, sensitivity of 75% and specificity of 61% for G-EAC diagnosis. Notably, the clinical value of Smad3 extends beyond individual performance, and its synergy with PAX-8 reveals distinct translational and mechanistic potential. The PAX-8/Smad3 combination achieved an AUC of 0.82, enabling reliable differentiation between G-EAC and HPVA. Using a serial interpretation criterion, the combined panel reduced sensitivity to 44% (compared with 88% for PAX-8 alone) but markedly improved specificity and accuracy to 96% and 86%, respectively. Given the divergent clinical management of G-EAC and HPVA, minimizing false-positive results to avoid overtreatment in HPVA patients was a primary study objective, justifying this sensitivity-specificity trade-off as clinically appropriate. This high-specificity panel provides a robust tool for G-EAC diagnosis, with future optimization possible via parallel testing and multi-center validation. Correlation analysis confirmed significant co-expression of PAX-8 and Smad3 in G-EAC, and their concurrent high expression was associated with tumor diameter\u0026thinsp;\u0026gt;\u0026thinsp;4 cm, suggesting a synergistic oncogenic effect. However, their regulatory crosstalk remains poorly defined. A previous study in thyroid cancer\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e demonstrated that activated TGF-β/Smad3 signaling physically disrupts the PAX-8 DNA-binding domain via protein\u0026ndash;protein interaction, impairing its binding to the Na+/I\u0026minus; Symporter(NIS) enhancer and downregulating PAX-8 mRNA and protein expression, without delineating the precise transcriptional or translational mechanisms. Elucidating this interaction may be pivotal to understanding G-EAC invasiveness and metastasis, and targeted modulation of the Smad3/PAX-8 axis represents a promising therapeutic strategy.\u003c/p\u003e \u003cp\u003eThis study systematically characterized the unique clinicopathological and molecular features of G-EAC, validated the synergistic diagnostic and prognostic value of PAX-8 and Smad3, and demonstrated their coordinated upregulation and pro-tumorigenic function in G-EAC. These findings address critical gaps in current diagnostic and prognostic stratification tools, and offer new avenues for G-EAC evaluation and targeted therapy. However, the small sample size and inherent limitations of the retrospective study design may limit the generalizability of our findings. Future multi-center studies with larger cohorts are warranted to validate these findings and establish molecular subtype-based individualized treatment paradigms. Such advances are expected to drive the shift from empirical to molecular-targeted therapy for G-EAC, improving outcomes for this highly aggressive and refractory malignancy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, gastric-type endocervical adenocarcinoma (G-EAC) exhibits distinct clinicopathological features and significantly poorer prognosis compared with HPV-associated endocervical adenocarcinoma (HPVA), including higher rates of abnormal vaginal discharge, negative hr-HPV and normal TCT results, advanced FIGO stage, and increased risks of invasion, metastasis and recurrence, which were further validated by meta-analysis. Immunohistochemically, G-EAC shows significantly higher expression of PAX-8 and Smad3 than HPVA, and PAX-8 is positively correlated with Smad3 in G-EAC. High PAX-8 expression is associated with aggressive pathological features and unfavorable survival, while Smad3 expression is related to larger tumor size. The combined detection of PAX-8 and Smad3 presents high diagnostic specificity for G-EAC, indicating their potential as promising biomarkers for differential diagnosis and prognostic stratification. This study clarifies the unique biological characteristics of G-EAC and the clinical value of PAX-8/Smad3 co-expression, providing a reliable indicator for clinical diagnosis and prognostic evaluation. However, the single-center design and small sample size of G-EAC are the main limitations. Multi-center studies with larger cohorts are warranted to verify these findings and explore the molecular mechanism of PAX-8/Smad3 axis in G-EAC progression.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethe Area under the curve\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisease-free survival\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpithelial-mesenchymal transition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethe Federation International of Gynecology and Obstetrics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG-EAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGastric-type endocervical adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman papillomavirus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHPV-associated adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIECC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethe International Endocervical Adenocarcinoma Criteria and Classification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndividual patient data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLymphovascular space invasion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHPVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enon-HPV-associated adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Newcastle-Ottawa Scale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative predictive value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall survival\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive predictive value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetinoblastoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReceiver operating characteristic curves\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThinprep Cytologic Test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTumor immune microenvironment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethe Usual endocervical adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was conducted in accordance with the World Medical Association Declaration of Helsinki (75th WMA General Assembly, Helsinki, Finland, October 2024) and was approved by the Medical Ethics Committee of Guizhou Medical University (Approval No. 2023910), and a waiver of informed consent for study participation was granted given the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials：The datasets generated during and/or\u0026nbsp;analysed\u0026nbsp;during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eAll authors declare that the research was conducted in the absence of any competing financial and/or non-financial interests in relation to the work described.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by grants from the Science and Technology Project of Guizhou (grant number Qiankehejichu-ZK(2024] Key project 041, Qiankehejichu-ZK[2022] General project 436) Author Houmei Wang. has received research support from the Science and Technology Project of Guizhou and Affiliated Hospital of Guizhou Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e\u003cem\u003eYi Wang:\u0026nbsp;\u003c/em\u003eConceptualization, Methodology, Software, Validation, Formal analysis, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization. \u003cem\u003eZiwen Xiao:\u0026nbsp;\u003c/em\u003eSupervision；\u003cem\u003eJuntao Wang:\u0026nbsp;I\u003c/em\u003envestigation, Resources；\u0026nbsp;\u003cem\u003eHoumei Wang:\u0026nbsp;\u003c/em\u003eConceptualization, Methodology, Supervision, Writing - Review \u0026amp; Editing, Funding acquisition\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eWe thank the Affiliated Hospital of Guizhou Medical University for its support of the corresponding author, H. Wang, and the participating medical institutions for providing clinical samples and data. We also acknowledge the ethical approval from the Medical Ethics Committee of Guizhou Medical University (No. 2023910), and thank all clinical and pathological staff and patients enrolled in this study.\u003c/p\u003e\n\u003cp\u003eAuthors' information: Y.W, M.D,\u0026nbsp;Department of Obstetrics and Gynecology, The People's Hospital of the\u0026nbsp;Qiandongnan\u0026nbsp;Miao and Dong Autonomous Prefecture, No. 31\u0026nbsp;Shaoshan\u0026nbsp;South Road,\u0026nbsp;Kaili\u0026nbsp;City, Guizhou Province 556000, Guizhou, China. Major in gynaecology oncology.\u003c/p\u003e\n\u003cp\u003eTel: +8613007865211, E-mail Address:\u0026nbsp;[email protected]\u0026nbsp;ORCID：0009-0005-8443-5857\u003c/p\u003e\n\u003cp\u003eZ.X. PhD, Department of Gynecology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China, Address: No.28,\u0026nbsp;Guiyi\u0026nbsp;Street,\u0026nbsp;Yunyan\u0026nbsp;District, Guiyang, China. Major in gynaecology oncology.\u003c/p\u003e\n\u003cp\u003eJ.W Department of Gynecologic Oncology,\u0026nbsp;Guiyang Maternal and Child Health Care\u0026nbsp;Hospital·Guiyang\u0026nbsp;Children's Hospital,\u0026nbsp;No. 63\u0026nbsp;Ruijin\u0026nbsp;South Road,\u0026nbsp;Nanming\u0026nbsp;District, Guiyang,\u0026nbsp;550004, Guizhou, China. Major in gynaecology oncology.\u003c/p\u003e\n\u003cp\u003eTel: +8613329609156, E-mail Address:\u0026nbsp;[email protected]\u003c/p\u003e\n\u003cp\u003eH.W, PhD, Department of Gynecology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China, Address: No.28,\u0026nbsp;Guiyi\u0026nbsp;Street,\u0026nbsp;Yunyan\u0026nbsp;District, Guiyang, China. Major in gynaecology oncology.\u003c/p\u003e\n\u003cp\u003eFax number: 085188519220, E-mail Address:\u0026nbsp;[email protected]\u0026nbsp;ORCID：0000-0003-1739-1338\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePresentation:\u003c/strong\u003e The content of this study has not been presented in whole or in part at any academic conference, symposium, or other relevant platform, and no related findings have been published in any journal, thesis, or other publication medium\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreprint:\u0026nbsp;\u003c/strong\u003eThis manuscript has not been deposited or posted as a preprint on any preprint server in any form.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXu H, Zhang J. [Interpretation of updated pathological contents for cervical cancer in NCCN clinical practice guidelines, version 1, 2020]. Zhonghua Bing Li Xue Za Zhi. 2021;50(1):9\u0026ndash;13.\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\u003eKaramurzin YS, Kiyokawa T, Parkash V, Jotwani AR, Patel P, Pike MC, Soslow RA, Park KJ. Gastric-type Endocervical Adenocarcinoma: An Aggressive Tumor With Unusual Metastatic Patterns and Poor Prognosis. Am J Surg Pathol. 2015;39(11):1449\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark KJ, Kim MH, Kim JK, Cho KS. Gastric-Type Adenocarcinoma of the Uterine Cervix: Magnetic Resonance Imaging Features, Clinical Outcomes, and Prognostic Factors. Int J Gynecol Cancer. 2018;28(6):1203\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishio S, Mikami Y, Tokunaga H, Yaegashi N, Satoh T, Saito M, Okamoto A, Kasamatsu T, Miyamoto T, Shiozawa T, et al. Analysis of gastric-type mucinous carcinoma of the uterine cervix - An aggressive tumor with a poor prognosis: A multi-institutional study. Gynecol Oncol. 2019;153(1):13\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu H, Pan H, Wang Y, Zhang J. Expanded study on the risk of lymphovascular space invasion and lymph node metastasis of endocervical adenocarcinoma using Pattern Classification: a single-centre analysis of 213 cases. Pathology. 2019;51(6):570\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi HY, Ye L, Lu WG, Lu BJ. Grading of endocervical adenocarcinoma: a novel prognostic system based on tumor budding and cell cluster size. Mod Pathol. 2022;35(4):524\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J, Chi SA, Choi S, Kim HS. Invasive Stratified Mucin-producing Carcinoma of the Uterine Cervix: Comparison of Its Clinicopathological Characteristics and Programmed Death-ligand 1 Expression Status With Those of Other Endocervical Adenocarcinomas. Anticancer Res. 2024;44(11):5007\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamijo K, Miyamoto T, Oshima S, Asaka S, Shinagawa M, Sato Y, Ando H, Asaka R, Fujioka M, Uchiyama N, et al. Extensive Pathologic Invasion and Prognostic Implication of Gastric-Type Cervical Adenocarcinoma A Comparative Analysis With Human Papillomavirus-Associated Adenocarcinoma. Am J Surg Pathol. 2025;49(5):471\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXi Y, Zhou F, Liu Y, Zhou H, Lu X, Fang X, Yan L, Zhou J, Zhu T, Tang H. Clinical and pathological analyses of gastric-type cervical adenocarcinoma and its prognostic relevance. Therapeutic Adv Med Oncol 2025, 17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahan UF, Sohel HI, Nakayama K, Ishikawa M, Nagase M, Razia S, Kanno K, Yamashita H, Sonia SB, Kyo S. A Comparative Analysis of Usual- and Gastric-Type Cervical Adenocarcinoma in a Japanese Population Reveals Distinct Clinicopathological and Molecular Features with Prognostic and Therapeutic Insights. Int J Mol Sci. 2025;26(15):20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarg S, Nagaria TS, Clarke B, Freedman O, Khan Z, Schwock J, Bernardini MQ, Oza AM, Han K, Smith AC, et al. Molecular characterization of gastric-type endocervical adenocarcinoma using next-generation sequencing. Mod Pathol. 2019;32(12):1823\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu R, Liu H, Zhu T, Tang H, Wu M, Yan X, Li M, Yuan S, Yin T, Chen J et al. An immunosuppressive tertiary lymphoid structure is associated with adverse prognosis in gastric-type endocervical adenocarcinoma. J Natl Cancer Inst 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurashvili G, Park KJ. Cervical Glandular Neoplasia: Classification and Staging. Surg Pathol Clin. 2019;12(2):281\u0026ndash;313.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFulgione C, Raffone A, Travaglino A, Arciuolo D, Santoro A, Cianfrini F, Russo D, Varricchio S, Raimondo I, Inzani F et al. Diagnostic accuracy of HIK1083 and MUC6 as immunohistochemical markers of endocervical gastric-type adenocarcinoma: A systematic review and meta-analysis. Pathol Res Pract 2023, 241.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin LH, Kaur H, Kolin DL, Nucci MR, Parra-Herran C. Claudin-18 and Mutation Surrogate Immunohistochemistry in Gastric-type Endocervical Lesions and their Differential Diagnoses. Am J Surg Pathol. 2025;49(3):206\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Liu B, Xu Y, Zhu Z, Zhao C. 955 Unveiling Molecular Signatures and Discovering Sensitive-Specific Diagnostic Biomarkers for Endocervical Gastric-type Adenocarcinoma through Integrated Transcriptome and DNA Methylation Profiling. In, vol. 105; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsaka S, Nakajima T, Ida K, Asaka R, Kobayashi C, Ito M, Miyamoto T, Uehara T, Ota H. Clinicopathological and prognostic significance of immunophenotypic characterization of endocervical adenocarcinoma using CLDN18, CDH17, and PAX8 in association with HPV status. Virchows Arch. 2022;480(2):269\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarleton C, Hoang L, Sah S, Kiyokawa T, Karamurzin YS, Talia KL, Park KJ, McCluggage WG. A Detailed Immunohistochemical Analysis of a Large Series of Cervical and Vaginal Gastric-type Adenocarcinomas. Am J Surg Pathol. 2016;40(5):636\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStolnicu S, Barsan I, Hoang L, Patel P, Chiriboga L, Terinte C, Pesci A, Aviel-Ronen S, Kiyokawa T, Alvarado-Cabrero I, et al. Diagnostic Algorithmic Proposal Based on Comprehensive Immunohistochemical Evaluation of 297 Invasive Endocervical Adenocarcinomas. Am J Surg Pathol. 2018;42(8):989\u0026ndash;1000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Tian S, Zhao M, Yang T, Quan S, Song L, Yang X. SUV39H1-Mediated DNMT1 is Involved in the Epigenetic Regulation of Smad3 in Cervical Cancer. Anticancer Agents Med Chem. 2021;21(6):756\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCostamagna E, Garc\u0026iacute;a B, Santisteban P. The Functional Interaction between the Paired Domain Transcription Factor Pax8 and Smad3 Is Involved in Transforming Growth Factor-β Repression of the Sodium/Iodide Symporter Gene. J Biol Chem. 2004;279(5):3439\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gastric-type endocervical adenocarcinoma, PAX-8, Smad3, Immunohistochemical phenotype, Poor prognosis, Differential diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-9151684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9151684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo characterize the clinicopathological, prognostic, and immunohistochemical features of gastric-type endocervical adenocarcinoma (G-EAC) and provide evidence for its precise diagnosis and therapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A retrospective cohort analysis was conducted on 16 G-EAC and 60 HPV-associated endocervical adenocarcinoma (HPVA) patients treated between 2015 and 2023 to compare their clinical, prognostic, and immunohistochemical characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eG-EAC had higher abnormal vaginal discharge rate, lower hr-HPV and TCT abnormality rates, more advanced FIGO stages, higher invasive/metastatic risk, and shorter OS and DFS than HPVA. Meta-analysis confirmed G-EAC patients were older, with larger tumors and higher risks of advanced disease, invasion, metastasis and recurrence. G-EAC had significantly higher PAX-8, Smad3, TP53, MUC6 positive rates but lower p16 rate than HPVA. PAX-8 was positively correlated with Smad3 in G-EAC (r = 0.77, \u003cem\u003eP \u0026lt; \u003c/em\u003e0.05), and both were associated with aggressive pathological features. PAX-8 (AUC=0.81) and Smad3 (AUC=0.60) combined detection for G-EAC achieved an AUC of 0.82, 96% specificity and 86% accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eG-EAC and HPVA differ significantly in clinical, immunohistochemical and prognostic features. PAX-8/Smad3 co-expression drives the malignant progression of G-EAC and exhibits high diagnostic efficacy, thus serving as potential biomarkers for the differential diagnosis and prognostic assessment of G-EAC their molecular interaction may reveal G-EAC pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e Not applicable.\u003c/p\u003e","manuscriptTitle":"Clinicopathological Features and Prognostic-Diagnostic Value of PAX-8/Smad3 Co-expression in Gastric-type Endocervical Adenocarcinoma: A Retrospective Cohort Combined with Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:28:49","doi":"10.21203/rs.3.rs-9151684/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-25T02:53:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T01:40:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28494874069050577001412344148439357563","date":"2026-04-22T18:14:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T04:33:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194261515460938992337035694358931401901","date":"2026-04-15T21:53:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89599786204016352663515010042859281312","date":"2026-04-15T14:11:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T13:52:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T06:29:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T07:21:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-22T04:35:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-03-22T04:29:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"778bca3c-4b58-4d15-bd5f-d048e937b970","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:28:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:28:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9151684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9151684","identity":"rs-9151684","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}