Prognostic impact of metabolic syndrome in patients with primary endometrial cancer: A retrospective bicentric study

Prognostic impact of metabolic syndrome in patients with primary endometrial cancer: A retrospective bicentric study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic impact of metabolic syndrome in patients with primary endometrial cancer: A retrospective bicentric study Ina Shehaj, Slavomir Krajnak, Morva Tahmasbi Rad, Bahar Gasimli, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3809471/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Apr, 2024 Read the published version in Journal of Cancer Research and Clinical Oncology → Version 1 posted 7 You are reading this latest preprint version Abstract Purpose Endometrial cancer (EC) is the most common gynaecological cancer. Its incidence has been rising over the years with ageing and increased obesity of the high-income countries’ populations. Metabolic syndrome (MetS) has been suggested to be associated with EC. The aim of this study was to assess whether MetS has a significant impact on oncological outcome in patients with EC. Methods This retrospective study included patients treated for EC between January 2010 and December 2020 in two referral oncological centers. Obesity, arterial hypertension (AH) and diabetes mellitus (DM) were criteria for the definition of MetS. The impact of MetS on progression free survival (PFS) and overall survival (OS) was assessed with log-rank test and Cox regression analyses. Results Among the 415 patients with a median age of 64, 38 (9.2%) fulfilled the criteria for MetS. The median follow-up time was 43 months. Patients suffering from MetS did not show any significant differences regarding PFS (36.0 vs. 40.0 months, HR: 1.49, 95% CI 0.79-2.80 P=0.210) and OS (38.0 vs. 43.0 months, HR: 1.66, 95% CI 0.97-2.87, P=0.063) compared to patients without MetS. Patients with obesity alone had a significantly shorter median PFS compared to patients without obesity (34.5 vs. 44.0 months, P=0.029). AH and DM separately had no significant impact on PFS or OS (p>0.05). Conclusion In our analysis, MetS in patients with EC was not associated with impaired oncological outcome. However, our findings show that obesity itself is an important comorbidity associated with significantly reduced PFS. Endometrial cancer Metabolic syndrome Survival Obesity Figures Figure 1 Figure 2 Figure 3 Introduction With approximately 382,000 new cases annually worldwide, endometrial cancer (EC) represents the most prevalent gynaecological malignancy in industrialized countries ( 1 ). More than 80% of patients with EC present with the International Federation of Gynecology and Obstetrics (FIGO) early stages (I-II) and have a 5-year overall survival (OS) rate of 95% ( 2 ). However, the incidence rates have increased since the late 1990s in most developed countries ( 1 ). Metabolic diseases, including obesity (body mass index (BMI) > 30 kg/m2), and diabetes mellitus (DM), belong to the most important contributing factors for the rising rates ( 2 ). Although unopposed estrogen exposure is considered a major driver of endometrial carcinogenesis, other additional factors such as chronic inflammation, insulin resistance, and hyperinsulinemia are allied to substantial risk factors for EC ( 3 , 4 ). Previous epidemiological studies revealed that obesity, DM, and metabolic syndrome (MetS) are each linked to an increase in EC incidence, but their separate and combined influence on survival remains unclear ( 2 , 3 , 5 , 6 ). Conversely, Tang et al. described in a meta-analysis the perceived protective effects of metformin intake leading to a decrease in incidence rates and an improvement in OS in EC Patients. ( 7 ). Metformin disrupts cancer cell metabolism via direct inhibition of mitochondrial respiration, the mammalian target of rapamycin (mTOR), and the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) pathways in EC cells ( 8 – 10 ). In a case-control study conducted by Rosato et al., cancer risk was significantly increased for subjects with MetS (HR: 8.40, 95% CI 3.95–17.87) ( 11 ). The authors showed that the most strongly associated factors with EC included a BMI > 30 kg/m2 and meeting at least two criteria of AH, DM, and/or hyperlipidemia. Despite the strong relation between metabolic disease and EC, sufficient data about the impact of MetS on survival rates in patients with EC are lacking ( 12 – 14 ). This study aims to investigate the prognostic role of MetS in primary EC in order to provide evidence for cancer prevention and adjuvant treatment strategies. Materials and Methods For this retrospective analysis, we identified patients with primary EC from both gynaecological centers who underwent primary surgical therapy between January 2010 and December 2020. Clinicopathological data, treatment details, and follow-up information were obtained from the oncological registry, archives, and medical reports as of April 2022. The cohort was divided into two groups: patients with MetS and those without. Patients in the MetS group met the following criteria: obesity (defined as a body mass index (BMI) ≥ 30.0 kg/m2), DM, and AH requiring drug therapy. These patients were identified after screening clinical data for MetS. Both groups were compared regarding clinicopathological characteristics and survival data. Insufficient clinical information, particularly unclear MetS status, the diagnosis of synchronous malignant tumors, and severe internal diseases such as renal failure and/or liver cirrhosis, were defined as exclusion criteria. The classification of disease stage was based on the FIGO system, which was revised in 2008 ( 15 , 16 ). Due to the increasing use of minimally invasive surgery in EC, most patients were treated via laparoscopy, with particularly frequent usage in the later years of the study. All surgical procedures were performed by or under the assistance of specialized gynecologic oncologists from both centers following established international guidelines. Preoperative BMI and patient comorbidities were recorded upon admission to the clinic. Conventional histology and immunohistochemical methods were performed and the results were confirmed by two specialized gynecological pathologists at our centers. Regular implementation of next-generation sequencing (NGS) and determination of protein p53 and mismatch repair protein (MMRP) deficiency were not standard during the study period. After surgical treatment, all patients were discussed at a multidisciplinary tumor board (MTB), and adjuvant approaches were recorded in the MTB protocols. Following completion of primary therapy, patients underwent follow-up every three months for the first three years or when symptoms appeared. Routine follow-up care included clinical and sonographic examinations, and if recurrence was suspected, additional imaging examinations such as CT scans, MRI scans, or rarely PET-CT scans were conducted. The survival intervals progression free survival (PFS) and overall survival (OS) were defined as the time from the date of surgical treatment to the date of histological confirmation of cancer recurrence and death or the date of the last follow-up, respectively. Written informed consent was not required due to the retrospective nature of this study. This analysis was conducted within the framework of the UCT-19-2021 project and received approval from the Institutional Review Boards of the UCT and the Ethical Committee at the University Hospital Frankfurt. Statistical Analysis Statistical analyses were conducted using IBM SPSS Version 27.0 statistical software package (SPSS Inc., Chicago, IL, USA). Nonparametric survival functions, including Kaplan–Meier curves and the log-rank test, were employed to determine outcome probabilities. A Chi-square test was used to compare categorical clinicopathological parameters between patients with and without MetS. All statistical tests were two-sided, and a p-value < 0.05 was deemed significant. Univariate Cox regression was conducted to identify independent prognostic factors for PFS and OS in all patients. This analysis was also performed separately for patients with and without Metabolic Syndrome (MetS). Variables that demonstrated statistical significance were subsequently verified through multivariable analysis, incorporating the variables summarized in Tables 2 and 3 . Results A total of 415 patients with primary EC participated in this retrospective analysis (Figure 1 Flowchart of patients’ selection for analysis). The majority of the study's participants (80.7%) were postmenopausal, with a median age of 64 years (range: 28-91) for the entire cohort. The patients were categorized into two groups based on the presence of metabolic syndrome: the MetS group and the non-MetS group. The MetS group were significantly older compared to the non-MetS group (70.5 vs. 63.0 years; p=0.001). The characteristics of the patients are shown in (Table 1 Comparison of baseline clinicopathological data of patients with EC by MetS) . The most prevalent comorbidities included arterial hypertension (44.1%), diabetes mellitus (16.9%), and obesity (41.2%), with a median BMI of 28.0 kg/m2. As a result, 9.2% of the cases met the criteria for MetS. The majority of patients (83.1%) had a favorable ECOG performance score (ECOG 0/1). The endometrioid histological subtype (92.3%) was the most common, followed by serous (5.1%), mixed serous-endometrioid (1.4%), and clear cell (1.2%) EC. High-grade differentiation (G2/G3) of the tumor was observed in 201 cases (48.5%), with a significant proportion (70.8%) of them being in the early stages of the disease (FIGO IA and B). Pelvic and para-aortic lymph node metastasis, as well as distant metastasis, were identified in 36 cases (8.7%), 7 cases (1.7%), and 33 cases (8%), respectively. Adjuvant chemotherapy was more frequently administered to the non-MetS group than the MetS group (19.6% vs. 7.9%, p=0.093), despite similar advanced tumor stage distributions in both groups. However, statistical significance was not achieved. One possible explanation could be that patients in the MetS group, who often had comorbidities and were older, were less inclined to receive chemotherapy. All patients underwent hysterectomy and bilateral salpingo-oophorectomy (BSO), with 37.6% undergoing laparotomy and 54.9% receiving minimally invasive surgery. A total of one hundred and thirty-two patients (31.8%) underwent pelvic lymph node dissection, out of which eighty-seven (21.0%) had pelvic and para-aortic lymphadenectomy. No clinically significant differences in the outcome were observed in patients with or without pelvic (p=0.715) and paraaortic lymphadenectomy (p=0.683) between the MetS and non-MetS groups. Among those who underwent lymphadenectomy in the MetS group, five (13.2%) had pathologic pelvic lymph nodes, and one (2.6%) had pathologic paraaortic lymph nodes. Despite the specific challenges, laparoscopy was performed in most patients with obesity. The majority (52.6%) of patients in the MetS group received minimally invasive surgery, while 39.5% underwent laparotomy. Interestingly, there were no significant differences in the choice of surgical approach between both groups (p=0.271). In the comparative analysis between the MetS and non-MetS groups, significant differences were not observed in clinicopathological factors, including ECOG score (p=0.421), FIGO stage (p=0.617), histological subtype (p=0.370), histological grading (p=0.149), lymph node involvement (p=0.330), surgical approaches (p=0.271), and adjuvant treatment. Figure 1 Flowchart of patients’ selection for analysis Table 1 Comparison of baseline clinicopathological data of patients with EC by MetS Variables All patients total n= 415, (%) With MetS N (%) total n= 38, (9.2%) Without MetS N (%) total n=377, (90.8%) P value Demographic Characteristics Age , median (range) years 64.0 (28.0-91.0) 70,5 (40.0-91.0) 63.0 (28.0-91.0) 0.001 BMI, mean (range) kg/m2 28.0 (18.0-72.0) 33.0 (30.0-60.0) 27,52 (18.0-72.0) 0.001 ECOG score 0 225 (54.2) 17 (44.7) 208 (55.2) 0.421 1 120 (28.9) 14 (36.8) 106 (28.1) 2 37 (8.9) 6 (15.8) 31 (8.2) 3 12 (2.9) 1 (2.6) 11 (2.9) 4 3 (0.7) 0 3 (0.8) Unknown 18 0 18 (4.8) Diabetes mellitus 70 (16.9) 38 32 (8.5) <0.001 Arterial hypertension 183 (44.1) 38 145 (38.5) <0.001 Clinical-pathological tumor parameters Tumor Stage (FIGO) IA 191 (46) 15 (39.5) 176 (46.7) 0.617 IB 103 (24,8) 12 (31.6) 91 (24.1) II 36 (8.7) 4 (10.5) 32 (8.5) IIIA 10 2.4) 0 (0) 10 (2.7) IIIB 14 (3.4) 3 (7.9) 11 (2.9) IIIC 20 (4.8) 2 (5.3) 18 (4.8) IVA 4 (1.0) 0 (0) 4 (1.1) IVB 29 (7.0) 2 (5.3) 27 (7.2) Missing 8 (1.9) 0 (0) 8 (2.1) Histological Subtype EEC 383 (92.3) 37 (97.4) 346 (91.8) 0.370 Non-EEC 32 (7.7) 1 (2.6) 31 (8.2) Histological grading G1 205 (49.4) 16 (42.1) 189 (50.1) 0.149 G2 116 (28.0) 16 (42.1) 100 (26.5) G3 85 (20.5) 6 (15.8) 79 (21) Missing 9 (2.2) 0 (0) 9 (2.4) Lymph nodes N0 315 (75.9) 30 (78.9) 285 (75.6) 0.330 N1 36 (8.7) 5 (13.2) 31 (8.2) N2 7 (1.7) 1 (2.6) 6 (1.6) Missing 57 (13.7) 2 (5.3) 55 (14.6) Surgical approach Minimally invasive 228 (54.9) 20 (52.6) 208 (55.2) 0.271 Vaginal 12 (2.9) 3 (7.9) 9 (2.4) Laparotomy 156 (37.6) 15 (39.5) 141 (37.4) No surgical therapy 15 (3.6) 0 (0) 15 (4.0) Missing 4 (1.0) 0 (0) 4 (1.1) Lymphadenectomy Pelvic lymphadenectomy 132 (31.8) 11 (28.9) 121 (32.1) 0.715 Paraaortic lymphadenectomy 87 (21.0) 7 (18.4) 80 (19.1) 0.683 Pelvic and paraaortic lymphadenectomy 87 (21.0) 7 (18.4) 80 (19.1) 0.683 Adjuvant treatment None 296 (71.3) 26 (68.4) 270(71.6) Radiotherapy 119 (28.7) 14 (31.6) 107 (28.4) 0.919 EBRT 9 (2.2) 1 (2.6) 8 (2.1) VBT 94 (22.7) 10 (26.3) 84 (22.3) ERBT + VBT 16 (3.9) 1 (2.6) 15 (4.0) Chemotherapy 77 (18.6) 3 (7.9) 74 (19.6) 0.093 Mortality Recurrence 89 (21.4) 11 (28.9) 78 (20.7) 0.490 Mortality 111 26.7) 15 (39.5) 96 (25.5) 0.542 Association between metabolic syndrome and survival The median follow-up time was 43 months, ranging from 1 to 120 months. We compared the survival data between patients with MetS and those without MetS. The primary goal of this study was to determine the effect of MetS and its individual components on the PFS of patients with EC. The comparative analyses of the groups revealed no statistically significant differences in terms of overall survival (HR: 1.66, 95% CI 0.965-2.869, p=0.063) and recurrence-free survival (HR: 1.49, 95% CI 0.792-2.801, p=0.210), as shown in (Table 2). The PFS for patients with MetS versus patients without MetS was 36.0 months versus 40.0 months, respectively (p=0.210). Similarly, OS rates were worse for patients with MetS compared to patients without MetS: 38.0 months versus 43.0 months, respectively (p=0.063) (Figure 2). Figure 2 A. Kaplan-Meier analyses of OS regarding the presence of metabolic syndrome Patients with MetS versus without MetS, median OS: 38.0 versus 43.0 months, log rank: p= 0.063 B. Kaplan-Meier analyses of PFS regarding the presence of metabolic syndrome Patients with MetS versus without MetS, median PFS: 36.0 versus 40.0 months, log rank: p=0.210 Table 2 Univariate and multivariate analysis of metabolic syndrome and its components associated with prognosis (OS, overall survival; PFS, progression-free survival) in patients with endometrial cancer Variables Univariate OS HR (95% CI) P value Multivariate OS HR (95% CI) P value Univariate PFS HR (95% CI) P Value Multivariate PFS HR (95% CI) P value Obesity 1.21 (0.83 – 1.78) 0.323 1.21 (0.83 - 1.78) 0.029 1.02 (1.002 - 1.047) 0.029 Arterial hypertension 1.13 (0.78 – 1.64) 0.515 1.03 (0.68 - 1.57) 0.896 Diabetes mellitus 1.45 (0.93 – 2.27) 0.099 1.11 (0.64 - 1.90) 0.714 Metabolic syndrome 1.66 (0.97 - 2.87) 0.063 1.50 (0.79 - 2.80) 0.210 However, there was a significant correlation between obesity and PFS. No significant correlation was observed regarding obesity and OS (HR: 1.213, 95% CI 0.826-1.781, p=0.323) (Table 2 ). For patients with obesity alone PFS was significantly reduced compared to the cohort without obesity (34.5 vs. 44.0 months, HR: 1.606; 95% CI 1.043-2.472, p=0.029) (Figure 3 ). Figure 3 Kaplan-Meier analyses of OS and PFS regarding the presence of obesity A. Patients with obesity versus patients without obesity, median OS: 38.0 vs. 46.0 months, p=0.323 B. Patients with obesity versus patients without obesity, median PFS: 34.5 vs. 44.0 months log rank: p= 0.029 Our observations did not reveal statistically significant differences in terms of shorter OS (p>0.05) or poorer PFS (p>0.05) in patients with isolated arterial hypertension (AH) or diabetes mellitus (DM) (Table 2). Tables 3 and 4 present the results of Cox regression univariate and multivariate analysis for the risk of recurrence in all patients and in patients with MetS, respectively. In the univariate analysis for all patients, the significant variables affecting PFS were age, ECOG Score, FIGO stage, tumor grade, histological subtype, type of surgery and chemotherapy (p<0.05) (Table 3). In the univariate analysis for the MetS Group, age, histological grade of differentiation, and FIGO-Stage were associated with poorer PFS (all p<0.05), whereas FIGO-Stage alone was associated with worse OS (p=0.05) (Table 4). In the multivariate Cox regression analysis, we found that the following factors retained their prognostic significance for PFS in patients with MetS: age (HR 1.13; 95% CI 1.04 - 1.22, p=0.002) and FIGO-Stage (HR 4.67; 95% CI 1.91 - 11.39, p<0.001). Table 3 Univariate and multivariate analysis of factors associated with prognosis (OS, overall survival; PFS, progression-free survival) in patients with endometrial cancer Variables Univariate OS HR (95% CI) P value Multivariate OS HR (95% CI) P value Univariate PFS HR (95% CI) P value Multivariate PFS HR (95% CI) P value Patient age (years) 1.05 (1.03 - 1.06) < 0.001 1.04 (1.02 - 1.06) < 0.001 1.03 (1.01 - 1.05) < 0.001 1.02 (1.002 - 1.047) 0.029 Mean BMI (kg/m2) 0.99 (0.97 - 1.01) 0.331 1.0 (0.97 - 1.02) 0.693 ECOG Score 1.83 (1.50 - 2.22) < 0.001 1.5 (1.04 - 1.84) 0.024 1.64 (1.31 - 2.05) < 0.001 1.53 (1.155 - 2.021) 0.003 Histological subtype 1.584 (1.19 - 2.10) 0.007 0.64 (0.41 - 1.01) 0.054 1.69 (1.28 - 2.25) 0.002 0.62 (0.39 - 0.99) 0.049 Histological grade of differentiation 2.50 (1.97 - 3.20) < 0.001 1.66 (1.21 - 2.27) 0.002 2.93 (2.23 - 3.85) < 0.001 1.89 (1.34 - 2.67) < 0.001 FIGO Stage 1.97 (1.68 - 2.32) < 0.001 1.49 (1.22 - 1.84) < 0.001 2.02 (1.70 - 2.41) < 0.001 1.45 (1.17 - 1.81) < 0.001 Lymphadenectomy 1.18 (1.01 - 1.38) 0.041 1.13 (0.94 - 1.37) 0.197 0.99 (0.81 - 1.21) 0.901 Pelvic lymphadenectomy 1.36 (1.02 - 1.80) 0.043 1.17 (0.95 - 1.46) 0.142 1.24 (0.93 - 1.66) 0.142 Paraaortic lymphadenectomy 1.12 (0.85 – 1.48) 0.402 1.01 (0.78 - 1.32) 0.921 LVSI 2.26 (1.58 - 3.21) < 0.001 0.84 (0.44 - 1.61) 0.621 2.63 (1.88 - 3.66) < 0.001 0.75 (0.37 - 1.52) 0.425 Surgery (laparoscopic/laparotomy) 1.81 (1.38 – 2.37) < 0.001 0.50 (0.32 - 0.79) 0.003 1.52 (1.12 - 2.07) 0.011 0.67 (0.42 - 1.07) 0.09 Chemotherapy 3.56 (2.37 - 5.34) < 0.001 1.88 (1.12 - 3.16) 0.017 5.81 (3.77 - 8.98) < 0.001 3.20 (1.26 – 4.09) < 0.001 Radiotherapy 1.07 (0.69 - 1.67) 0.762 1.50 (0.95 - 2.35) 0.082 Table 4 Univariate and multivariate analysis of factors associated with prognosis (OS, overall survival; PFS, progression-free survival) in EC-Patients with MetS Variables Univariate OS HR (95% CI) P value Multivariate OS HR (95% CI) P value Univariate PFS HR (95% CI) P value Multivariate PFS HR (95% CI) P value Patient age (years) 1.02 (0.97 - 1.07) 0.459 1.08 (1.00 - 1.16) 0.041 1.13 (1.04 - 1.22) 0.002 Mean BMI (kg/m2) 1.00 (0.95 - 1.06) 0.942 0.95 (0.88 - 1.03) 0.212 ECOG Score 1.12 (0.66 - 2.18) 0.811 0.81 (0.36 - 1.81) 0.613 Histological subtype 1.71 (0.834 - 3.50) 0.223 0.36 (0.00 – 1.47) 0.491 Histological grade of differentiation 1.43 (0.91 - 2.24) 0.148 1.63 (1.05 - 2.54) 0.030 1.18 (0.74 - 1.89) 0.500 FIGO Stage 1.82 (1.03 - 3.22) 0.049 2.33 (1.32 - 4.11) 0.003 4.67 (1.91 - 11.39) < 0.001 Lymphadenectomyomy 1.34 (0.82 - 2.38) 0.272 0.98 (0.42 - 2.23) 0.951 Pelvic lymphadenectomy 1.74 (0.56 - 5.41) 0.347 2.19 (0.59 - 8.17) 0.258 Paraaortic lymphadenectomy 2.43 (0.73 - 8.11) 0.153 1.99 (0.50 - 7.98) 0.351 LVSI 0.71 (0.34 - 4.90) 0.711 1.56 (0.39 - 6.26) 0.542 Surgery (laparoscopic vs. laparotomy) 1.21 (0.58 - 2.55) 0.611 0.73 (0.29 - 1.88) 0.510 Chemotherapy 2.96 (0.60 - 14.7) 0.182 0.24 (0.05 - 1.18) 0.077 Radiotherapy 0.92 (0.23 - 3.07) 0.911 1.38 (0.34 - 5.51) 0.651 Discussion In this retrospective study, we observed no oncological impact of MetS in patients with primary EC. However, obesity alone represents an important comorbidity associated with a worse PFS in the present series. The reason might be associated with the number and characteristics of the population. The prognostic significance of MetS and its components on EC has been previously explored by few studies. In particular, epidemiological and preclinical studies have shown that the pathogenesis of endometrioid EC is closely related to estrogen ( 10 , 24 , 25 ). These pathophysiological changes may explain the role of MetS in endometrial carcinogenesis ( 13 ). The metabolic tumor microenvironments formed in MetS are closely involved in the development of EC via several potential mechanisms ( 19 ). Since the production of estrogen is an intermediate product of lipid metabolism, abnormal lipid metabolism has an influence on the secretion of estrogen and the balance of estrogen and progesterone ( 20 – 22 ). Especially in patients with obesity, a hyper-estrogenic state caused by the presence of the aromatase enzyme in adipose tissue is identified, which catalyzes the conversion of androgens to estrogen in postmenopausal women ( 23 ). In addition, MetS is associated with chronic insulin resistance, which can lead to the overproduction of reactive oxygen species and contribute to DNA damage ( 8 ). Hyperestrogenism and hyperglycemia, associated with obesity and metabolic syndrome, play pivotal roles in cancer pathogenesis. They stimulate cell proliferation and angiogenesis through distinct mechanisms and induce hyperplasia in endometrial tissue. Other published meta-analyses and cohort studies support a relationship between MetS components such as diabetes, obesity, and hypertension and an increased risk of EC ( 12 , 14 , 26 ). Our present analysis seeks to investigate the prognostic significance of individual and combined components of MetS in patients with EC. According to our knowledge, there have been limited reports on the prognostic effect of MetS in patients with EC ( 20 , 27 , 28 ). Controversial data have been published regarding survival data in EC related to the presence of MetS ( 11 ). Additionally, there is a lack of unified criteria for MetS ( 29 ). Kokts-Porietis et al. conducted a prospective cohort study with 540 patients with EC, of which 325 had MetS at diagnosis. They reported that MetS and an elevated waist circumference (≥ 88 cm) were associated with worse OS for EC (HR: 1.98, 95% CI 1.07–3.67, HR: 2.12, 95% CI 1.18–3.80, respectively) ( 27 ). Similiarly, Yang et al. retrospectively analyzed outcomes of 506 patients with EC diagnosed between 2010 and 2016, among whom 153 (31%) were diagnosed with MetS ( 28 ). Their results indicated that MetS was closely related to OS (HR: 2.14, 95% CI 1.07–4.28, P = 0.032) and PFS (HR: 1.80, 95% CI 1.0–3.3, P = 0.045) in EC patients. The OS decreased in patients with ≥ 3 components compared to those with 1–2 or 0 components (P = 0.045), while no apparent difference was observed for PFS rates (P = 0.069). After adjusting for other variables, including age, histological type, tumor grade, and stage, MetS was not associated with the prognosis of EC. Similar to the report by Yang et al., the results of our retrospective study showed that MetS in patients with EC was not associated with a worse oncological outcome. However, a trend toward significance was noticed regarding the OS (p = 0.063) Additionally, we could confirm that obesity alone remains an important comorbidity in patients with EC associated with a worse PFS. In the multivariable analysis, age and FIGO Stage were associated with worse PFS in patients with MetS. Since 2013, we have witnessed a significant shift in the diagnosis and treatment of EC. Following the The Cancer Genome Atlas (TCGA) research network's announcement of the four new molecular subtypes of EC, numerous societies (e.g. World Health Organization, the International Society of Gynecological Pathologists, European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology) have embraced these classifications and aligned their guidelines with the evolving understanding of disease development ( 17 ). An updated evaluation of risk stratification, along with more consistent adjuvant therapy concepts, is anticipated in the forthcoming ESGO/ESTRO/ESP guidelines ( 17 ). This promises to deliver risk-adapted therapies to patients, reducing the likelihood of under- and over-treatment, and thereby enhancing prognostic outcomes, while also alleviating the financial burden on healthcare systems. The potential limitations of this study include the retrospective design, which encompasses some missing data, a small number of enrolled patients with heterogeneity of patient and tumor charachteristics, as well as the lack of information about the molecular profile. However, the inclusion of well-documented cases and the performance of surgeries and pathological reviews by the same experienced team at our clinics may enhance the importance of our results. In conclusion, Metabolic Syndrome (MetS) could not be established as a prognostic factor for primary EC; however, obesity significantly reduced survival rates. These findings support the notion that modifying lifestyle factors and reducing obesity-related risk factors through dietary changes and regular exercise as preventive measures may not only decrease the risk of cancer but also reduce the mortality rate among patients with EC. Further research is needed to correlate the prognostic significance of molecular profiles with MetS and obesity. This could help clinicians to better predict the risk of recurrence and death in patients with EC and such accompanying comorbidities. Declarations Grants and funding The authors declare that this study was not funded by external sponsors. Conflict of interest statement Listed below are the personal conflicts of interest of the (co-)authors. The funders listed were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. S. Krajnak received speaker honoraria from Roche Pharma AG and Novartis Pharma GmbH Germany, research funding from Novartis Pharma GmbH Germany and travel reimbursement from PharmaMar and Novartis Pharma GmbH Germany. A. Hasenburg received honoraria from AstraZeneca, Celgen, GSK, LEO Pharma, MedConcept GmbH, Med update GmbH, Medicultus, Pfizer, Promedicis GmbH, Softconsult, Roche Pharma AG, Streamedup!GmbH, Tesaro Bio Germany GmbH. She is a member of the advisory board of AstraZeneca, GSK, LEO Pharma, PharmaMar, Promedicis GmbH, Roche Pharma AG, Tesaro Bio Germany GmbH, MSD Sharp&Dohme GmbH. M. Schmidt reports personal fees from AstraZeneca, BioNTech, Daiichi Sankyo, Eisai, Lilly, MSD, Novartis, Pantarhei Bioscience, Pfizer, Roche, and SeaGen outside the submitted work. Institutional research funding from AstraZeneca, BioNTech, Eisai, Genentech, German Breast Group, Novartis, Palleos, Pantarhei Bioscience, Pierre Fabre, and SeaGen. In addition, Marcus Schmidt has a patent for EP 2390370 B1 issued and a patent for EP 2951317 B1 issued. All other authors declare that they have no conflicts of interest. Author contributions Conceptualization, I.S. and G.K.; Statistical analysis, I.S., T.K. and S.K.; Methodology, I.S. and G.K.; Resources, I.S., G.K., V.M. and S.B. ,Writing—original draft, I.S. and G.K.; Writing—review and editing, S.K., B.G., A.H., T.K., M.T.R., M.S., V.M., S.B. All authors have read and agreed to the published version of the manuscript. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. Raglan O, Kalliala I, Markozannes G, Cividini S, Gunter MJ, Nautiyal J, et al. Risk factors for endometrial cancer: An umbrella review of the literature. Int J Cancer. 2019;145(7):1719–30. Perez-Martin AR, Castro-Eguiluz D, Cetina-Perez L, Velasco-Torres Y, Bahena-Gonzalez A, Montes-Servin E, et al. Impact of metabolic syndrome on the risk of endometrial cancer and the role of lifestyle in prevention. Bosn J Basic Med Sci. 2022;22(4):499–510. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33. Romanos-Nanclares A, Tabung FK, Sinnott JA, Trabert B, De Vivo I, Playdon MC, et al. Inflammatory and insulinemic dietary patterns and risk of endometrial cancer among US women. J Natl Cancer Inst. 2023;115(3):311–21. Morice P, Leary A, Creutzberg C, Abu-Rustum N, Darai E. Endometrial cancer. Lancet. 2016;387(10023):1094–108. Tang YL, Zhu LY, Li Y, Yu J, Wang J, Zeng XX, et al. Metformin Use Is Associated with Reduced Incidence and Improved Survival of Endometrial Cancer: A Meta-Analysis. Biomed Res Int. 2017;2017:5905384. Arcidiacono B, Iiritano S, Nocera A, Possidente K, Nevolo MT, Ventura V, et al. Insulin resistance and cancer risk: an overview of the pathogenetic mechanisms. Exp Diabetes Res. 2012;2012:789174. Yin XH, Jia HY, Xue XR, Yang SZ, Wang ZQ. Clinical analysis of endometrial cancer patients with obesity, diabetes, and hypertension. Int J Clin Exp Med. 2014;7(3):736–43. Zhao Y, Sun H, Feng M, Zhao J, Zhao X, Wan Q, et al. Metformin is associated with reduced cell proliferation in human endometrial cancer by inbibiting PI3K/AKT/mTOR signaling. Gynecol Endocrinol. 2018;34(5):428–32. Rosato V, Zucchetto A, Bosetti C, Dal Maso L, Montella M, Pelucchi C, et al. Metabolic syndrome and endometrial cancer risk. Ann Oncol. 2011;22(4):884–9. Luo J, Beresford S, Chen C, Chlebowski R, Garcia L, Kuller L, et al. Association between diabetes, diabetes treatment and risk of developing endometrial cancer. Br J Cancer. 2014;111(7):1432–9. Bjorge T, Stocks T, Lukanova A, Tretli S, Selmer R, Manjer J, et al. Metabolic syndrome and endometrial carcinoma. Am J Epidemiol. 2010;171(8):892–902. Bing RS, Tsui WL, Ding DC. The Association between Diabetes Mellitus, High Monocyte/Lymphocyte Ratio, and Survival in Endometrial Cancer: A Retrospective Cohort Study. Diagnostics (Basel). 2022;13(1). Pecorelli S. Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int J Gynaecol Obstet. 2009;105(2):103–4. Soslow RA, Tornos C, Park KJ, Malpica A, Matias-Guiu X, Oliva E, et al. Endometrial Carcinoma Diagnosis: Use of FIGO Grading and Genomic Subcategories in Clinical Practice: Recommendations of the International Society of Gynecological Pathologists. Int J Gynecol Pathol. 2019;38 Suppl 1(Iss 1 Suppl 1):S64-S74. Concin N, Matias-Guiu X, Vergote I, Cibula D, Mirza MR, Marnitz S, et al. ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Int J Gynecol Cancer. 2021;31(1):12–39. Berek JS, Matias-Guiu X, Creutzberg C, Fotopoulou C, Gaffney D, Kehoe S, et al. FIGO staging of endometrial cancer: 2023. J Gynecol Oncol. 2023;34(5):e85. Kyo S, Nakayama K. Endometrial Cancer as a Metabolic Disease with Dysregulated PI3K Signaling: Shedding Light on Novel Therapeutic Strategies. Int J Mol Sci. 2020;21(17). Li X, Yang X, Cheng Y, Dong Y, Wang J, Wang J. Development and validation of a prognostic model based on metabolic risk score to predict overall survival of endometrial cancer in Chinese patients. J Gynecol Oncol. 2023. Palmisano BT, Zhu L, Stafford JM. Role of Estrogens in the Regulation of Liver Lipid Metabolism. Adv Exp Med Biol. 2017;1043:227–56. Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis. 2017;11(8):215–25. Byers T, Sedjo RL. Body fatness as a cause of cancer: epidemiologic clues to biologic mechanisms. Endocr Relat Cancer. 2015;22(3):R125-34. Yin F, Shao X, Zhao L, Li X, Zhou J, Cheng Y, et al. Predicting prognosis of endometrioid endometrial adenocarcinoma on the basis of gene expression and clinical features using Random Forest. Oncol Lett. 2019;18(2):1597–606. Mitsuhashi A, Kiyokawa T, Sato Y, Shozu M. Effects of metformin on endometrial cancer cell growth in vivo: a preoperative prospective trial. Cancer. 2014;120(19):2986–95. Modesitt SC, Geffel DL, Via J, A LW. Morbidly obese women with and without endometrial cancer: are there differences in measured physical fitness, body composition, or hormones? Gynecol Oncol. 2012;124(3):431–6. Kokts-Porietis RL, McNeil J, Nelson G, Courneya KS, Cook LS, Friedenreich CM. Prospective cohort study of metabolic syndrome and endometrial cancer survival. Gynecol Oncol. 2020;158(3):727–33. Yang X, Li X, Dong Y, Fan Y, Cheng Y, Zhai L, et al. Effects of Metabolic Syndrome and Its Components on the Prognosis of Endometrial Cancer. Front Endocrinol (Lausanne). 2021;12:780769. Balkau B, Charles MA. Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med. 1999;16(5):442–3. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Apr, 2024 Read the published version in Journal of Cancer Research and Clinical Oncology → Version 1 posted Editorial decision: Revision requested 19 Feb, 2024 Reviews received at journal 11 Feb, 2024 Reviewers agreed at journal 21 Jan, 2024 Reviewers invited by journal 08 Jan, 2024 Submission checks completed at journal 27 Dec, 2023 Editor assigned by journal 27 Dec, 2023 First submitted to journal 26 Dec, 2023 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3809471","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263580056,"identity":"cf201596-a031-4f35-8ba0-04d87f376095","order_by":0,"name":"Ina 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Mainz","correspondingAuthor":false,"prefix":"","firstName":"Slavomir","middleName":"","lastName":"Krajnak","suffix":""},{"id":263580058,"identity":"3c5f4107-b9be-42ce-93b1-61fd4efc6a10","order_by":2,"name":"Morva Tahmasbi Rad","email":"","orcid":"","institution":"Johann Wolfgang Goethe University","correspondingAuthor":false,"prefix":"","firstName":"Morva","middleName":"Tahmasbi","lastName":"Rad","suffix":""},{"id":263580059,"identity":"e4a10e63-53a0-45de-a25b-ab240e2c66ce","order_by":3,"name":"Bahar Gasimli","email":"","orcid":"","institution":"Johann Wolfgang Goethe University","correspondingAuthor":false,"prefix":"","firstName":"Bahar","middleName":"","lastName":"Gasimli","suffix":""},{"id":263580060,"identity":"19646bad-0208-4b77-939c-677a5197ebcc","order_by":4,"name":"Annette Hasenburg","email":"","orcid":"","institution":"University Medical Center of the Johannes Gutenberg University Mainz","correspondingAuthor":false,"prefix":"","firstName":"Annette","middleName":"","lastName":"Hasenburg","suffix":""},{"id":263580061,"identity":"bf55f302-28bb-45c2-b794-4ab5d4b11193","order_by":5,"name":"Thomas Karn","email":"","orcid":"","institution":"Johann Wolfgang Goethe University","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Karn","suffix":""},{"id":263580062,"identity":"996e77f4-5a3b-4e16-8f6c-114edec09ead","order_by":6,"name":"Marcus Schmidt","email":"","orcid":"","institution":"University Medical Center of the Johannes Gutenberg University Mainz","correspondingAuthor":false,"prefix":"","firstName":"Marcus","middleName":"","lastName":"Schmidt","suffix":""},{"id":263580063,"identity":"4713f466-713e-4ae0-8b3c-1b3386976342","order_by":7,"name":"Volker Müller","email":"","orcid":"","institution":"Jung-Stilling Hospital","correspondingAuthor":false,"prefix":"","firstName":"Volker","middleName":"","lastName":"Müller","suffix":""},{"id":263580064,"identity":"6d4478df-066c-400c-a8b0-b121552a1402","order_by":8,"name":"Sven Becker","email":"","orcid":"","institution":"Johann Wolfgang Goethe University","correspondingAuthor":false,"prefix":"","firstName":"Sven","middleName":"","lastName":"Becker","suffix":""},{"id":263580065,"identity":"75d24c2c-f874-487a-9ccc-569a196fc4b6","order_by":9,"name":"Khayal Gasimli","email":"","orcid":"","institution":"Johann Wolfgang Goethe University","correspondingAuthor":false,"prefix":"","firstName":"Khayal","middleName":"","lastName":"Gasimli","suffix":""}],"badges":[],"createdAt":"2023-12-26 20:14:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3809471/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3809471/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00432-024-05699-1","type":"published","date":"2024-04-03T15:02:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49082656,"identity":"4313636d-bc03-4948-8208-fab07ce68c4d","added_by":"auto","created_at":"2024-01-02 20:14:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of patients’ selection for analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3809471/v1/58edde223fba92adc38d8946.jpg"},{"id":49082655,"identity":"291e3a6a-e462-410b-91bc-271be44c10f1","added_by":"auto","created_at":"2024-01-02 20:14:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":185142,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Kaplan-Meier analyses of OS regarding the presence of metabolic syndrome Patients with MetS versus without MetS, median OS: 38.0 versus 43.0 months, log rank: p= 0.063 B. Kaplan-Meier analyses of PFS regarding the presence of metabolic syndrome Patients with MetS versus without MetS, median PFS: 36.0 versus 40.0 months, log rank: p=0.210\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3809471/v1/43224f41380da3fff10d15d9.png"},{"id":49082654,"identity":"70a90f42-8763-4c5f-800b-f695117d8562","added_by":"auto","created_at":"2024-01-02 20:14:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117574,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier analyses of OS and PFS regarding the presence of obesity \u003cbr\u003e\nA. Patients with obesity versus patients without obesity, median OS: 38.0 vs. 46.0 months, p=0.323 B. Patients with obesity versus patients without obesity, median PFS: 34.5 vs. 44.0 months log rank: p= 0.029\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3809471/v1/e2a0c3dd092ace75c7027fa6.jpg"},{"id":54304233,"identity":"1376b882-60e1-41a4-af9a-28c45e572105","added_by":"auto","created_at":"2024-04-08 15:15:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":970435,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3809471/v1/e34716aa-bd9c-4165-9e87-dacbafcece1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic impact of metabolic syndrome in patients with primary endometrial cancer: A retrospective bicentric study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith approximately 382,000 new cases annually worldwide, endometrial cancer (EC) represents the most prevalent gynaecological malignancy in industrialized countries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). More than 80% of patients with EC present with the International Federation of Gynecology and Obstetrics (FIGO) early stages (I-II) and have a 5-year overall survival (OS) rate of 95% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, the incidence rates have increased since the late 1990s in most developed countries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Metabolic diseases, including obesity (body mass index (BMI)\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m2), and diabetes mellitus (DM), belong to the most important contributing factors for the rising rates (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although unopposed estrogen exposure is considered a major driver of endometrial carcinogenesis, other additional factors such as chronic inflammation, insulin resistance, and hyperinsulinemia are allied to substantial risk factors for EC (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Previous epidemiological studies revealed that obesity, DM, and metabolic syndrome (MetS) are each linked to an increase in EC incidence, but their separate and combined influence on survival remains unclear (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Conversely, Tang et al. described in a meta-analysis the perceived protective effects of metformin intake leading to a decrease in incidence rates and an improvement in OS in EC Patients. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Metformin disrupts cancer cell metabolism via direct inhibition of mitochondrial respiration, the mammalian target of rapamycin (mTOR), and the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) pathways in EC cells (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In a case-control study conducted by Rosato et al., cancer risk was significantly increased for subjects with MetS (HR: 8.40, 95% CI 3.95\u0026ndash;17.87) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The authors showed that the most strongly associated factors with EC included a BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m2 and meeting at least two criteria of AH, DM, and/or hyperlipidemia. Despite the strong relation between metabolic disease and EC, sufficient data about the impact of MetS on survival rates in patients with EC are lacking (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This study aims to investigate the prognostic role of MetS in primary EC in order to provide evidence for cancer prevention and adjuvant treatment strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eFor this retrospective analysis, we identified patients with primary EC from both gynaecological centers who underwent primary surgical therapy between January 2010 and December 2020. Clinicopathological data, treatment details, and follow-up information were obtained from the oncological registry, archives, and medical reports as of April 2022. The cohort was divided into two groups: patients with MetS and those without. Patients in the MetS group met the following criteria: obesity (defined as a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30.0 kg/m2), DM, and AH requiring drug therapy. These patients were identified after screening clinical data for MetS. Both groups were compared regarding clinicopathological characteristics and survival data. Insufficient clinical information, particularly unclear MetS status, the diagnosis of synchronous malignant tumors, and severe internal diseases such as renal failure and/or liver cirrhosis, were defined as exclusion criteria. The classification of disease stage was based on the FIGO system, which was revised in 2008 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Due to the increasing use of minimally invasive surgery in EC, most patients were treated via laparoscopy, with particularly frequent usage in the later years of the study. All surgical procedures were performed by or under the assistance of specialized gynecologic oncologists from both centers following established international guidelines. Preoperative BMI and patient comorbidities were recorded upon admission to the clinic. Conventional histology and immunohistochemical methods were performed and the results were confirmed by two specialized gynecological pathologists at our centers. Regular implementation of next-generation sequencing (NGS) and determination of protein p53 and mismatch repair protein (MMRP) deficiency were not standard during the study period.\u003c/p\u003e \u003cp\u003eAfter surgical treatment, all patients were discussed at a multidisciplinary tumor board (MTB), and adjuvant approaches were recorded in the MTB protocols. Following completion of primary therapy, patients underwent follow-up every three months for the first three years or when symptoms appeared. Routine follow-up care included clinical and sonographic examinations, and if recurrence was suspected, additional imaging examinations such as CT scans, MRI scans, or rarely PET-CT scans were conducted. The survival intervals progression free survival (PFS) and overall survival (OS) were defined as the time from the date of surgical treatment to the date of histological confirmation of cancer recurrence and death or the date of the last follow-up, respectively. Written informed consent was not required due to the retrospective nature of this study. This analysis was conducted within the framework of the UCT-19-2021 project and received approval from the Institutional Review Boards of the UCT and the Ethical Committee at the University Hospital Frankfurt.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using IBM SPSS Version 27.0 statistical software package (SPSS Inc., Chicago, IL, USA). Nonparametric survival functions, including Kaplan\u0026ndash;Meier curves and the log-rank test, were employed to determine outcome probabilities. A Chi-square test was used to compare categorical clinicopathological parameters between patients with and without MetS. All statistical tests were two-sided, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed significant. Univariate Cox regression was conducted to identify independent prognostic factors for PFS and OS in all patients. This analysis was also performed separately for patients with and without Metabolic Syndrome (MetS). Variables that demonstrated statistical significance were subsequently verified through multivariable analysis, incorporating the variables summarized in Tables \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 415 patients with primary EC participated in this retrospective analysis (Figure 1 Flowchart of patients\u0026rsquo; selection for analysis).\u003c/p\u003e\n\u003cp\u003eThe majority of the study\u0026apos;s participants (80.7%) were postmenopausal, with a median age of 64 years (range: 28-91) for the entire cohort. The patients were categorized into two groups based on the presence of metabolic syndrome: the MetS group and the non-MetS group. The MetS group were significantly older compared to the non-MetS group (70.5 vs. 63.0 years; p=0.001). The characteristics of the patients are shown in (Table 1 Comparison of baseline clinicopathological data of patients with EC by MetS) .\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The most prevalent comorbidities included arterial hypertension (44.1%), diabetes mellitus (16.9%), and obesity (41.2%), with a median BMI of 28.0 kg/m2. As a result, 9.2% of the cases met the criteria for MetS. The majority of patients (83.1%) had a favorable ECOG performance score (ECOG 0/1). The endometrioid histological subtype (92.3%) was the most common, followed by serous (5.1%), mixed serous-endometrioid (1.4%), and clear cell (1.2%) EC. High-grade differentiation (G2/G3) of the tumor was observed in 201 cases (48.5%), with a significant proportion (70.8%) of them being in the early stages of the disease (FIGO IA and B). Pelvic and para-aortic lymph node metastasis, as well as distant metastasis, were identified in 36 cases (8.7%), 7 cases (1.7%), and 33 cases (8%), respectively. Adjuvant chemotherapy was more frequently administered to the non-MetS group than the MetS group (19.6% vs. 7.9%, p=0.093), despite similar advanced tumor stage distributions in both groups. However, statistical significance was not achieved. One possible explanation could be that patients in the MetS group, who often had comorbidities and were older, were less inclined to receive chemotherapy. All patients underwent hysterectomy and bilateral salpingo-oophorectomy (BSO), with 37.6% undergoing laparotomy and 54.9% receiving minimally invasive surgery.\u003c/p\u003e\n\u003cp\u003eA total of one hundred and thirty-two patients (31.8%) underwent pelvic lymph node dissection, out of which eighty-seven (21.0%) had pelvic and para-aortic lymphadenectomy. No clinically significant differences in the outcome were observed in patients with or without pelvic (p=0.715) and paraaortic lymphadenectomy (p=0.683) between the MetS and non-MetS groups. Among those who underwent lymphadenectomy in the MetS group, five (13.2%) had pathologic pelvic lymph nodes, and one (2.6%) had pathologic paraaortic lymph nodes.\u003c/p\u003e\n\u003cp\u003eDespite the specific challenges, laparoscopy was performed in most patients with obesity. The majority (52.6%) of patients in the MetS group received minimally invasive surgery, while 39.5% underwent laparotomy. Interestingly, there were no significant differences in the choice of surgical approach between both groups (p=0.271). In the comparative analysis between the MetS and non-MetS groups, significant differences were not observed in clinicopathological factors, including ECOG score (p=0.421), FIGO stage (p=0.617), histological subtype (p=0.370), histological grading (p=0.149), lymph node involvement (p=0.330), surgical approaches (p=0.271), and adjuvant treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Flowchart of patients\u0026rsquo; selection for analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eComparison of baseline clinicopathological data of patients with EC by MetS\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003etotal n= 415, (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith MetS N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003etotal n= 38, (9.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout MetS N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003etotal n=377, (90.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic Characteristics \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eAge , median (range) years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e64.0 (28.0-91.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e70,5 (40.0-91.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e63.0 (28.0-91.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eBMI, mean (range) kg/m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e28.0 (18.0-72.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e33.0 (30.0-60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e27,52 (18.0-72.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eECOG score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e225 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e17 (44.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e208 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\" rowspan=\"6\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e120 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e14 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e106 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e37 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e6 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e31 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e12 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e1 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e11 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e3 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e18 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e70 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e32 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eArterial hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e183 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e145 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical-pathological tumor parameters \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor Stage (FIGO)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e191 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e15 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e176 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\" rowspan=\"9\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e103 (24,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e12 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e91 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e36 \u0026nbsp;(8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e4 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e32 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eIIIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e10 \u0026nbsp; 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e10 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eIIIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e14 \u0026nbsp;(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e3 \u0026nbsp;(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e11 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eIIIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e20 \u0026nbsp;(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e2 \u0026nbsp;(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e18 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eIVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e4 \u0026nbsp; (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e0 \u0026nbsp;(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e4 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eIVB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e29 \u0026nbsp;(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e2 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e27 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e8 \u0026nbsp; (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e8 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistological Subtype \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eEEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e383 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e37 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e346 (91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\" rowspan=\"2\"\u003e0.370\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eNon-EEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e32 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e1 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e31 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistological grading\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eG1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e205 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e16 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e189 (50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;0.149\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eG2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e116 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e16 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e100 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eG3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e85 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e6 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e79 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e9 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e9 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph nodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; N0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e315 (75.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e30 (78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e285 (75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;0.330\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; N1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e36 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e5 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e31 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; N2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e7 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e1 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e6 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e57 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e2 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e55 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical approach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eMinimally invasive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e228 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e20 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e208 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eVaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e12 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e3 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e9 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eLaparotomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e156 (37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e15 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e141 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eNo surgical therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e15 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e0 \u0026nbsp;(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e15 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e4 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e0 \u0026nbsp;(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e4 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphadenectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003ePelvic lymphadenectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e132 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e11 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e121 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eParaaortic lymphadenectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e87 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e7 \u0026nbsp;(18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e80 \u0026nbsp;(19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003ePelvic and paraaortic lymphadenectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e87 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e7 \u0026nbsp;(18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e80 \u0026nbsp;(19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjuvant treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e296 (71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e26 (68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e270(71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eRadiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e119 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e14 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e107 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\" rowspan=\"4\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eEBRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e9 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e1 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e8 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eVBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e94 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e10 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e84 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.742243436754176%\" valign=\"top\"\u003e\n \u003cp\u003eERBT + VBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.957040572792362%\" valign=\"top\"\u003e\n \u003cp\u003e16 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.002386634844868%\" valign=\"top\"\u003e\n \u003cp\u003e1 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.29832935560859%\" valign=\"top\"\u003e\n \u003cp\u003e15 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e77 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e3 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e74 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e89 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e11 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e78 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.521035598705502%\" valign=\"top\"\u003e\n \u003cp\u003eMortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.88673139158576%\" valign=\"top\"\u003e\n \u003cp\u003e111 26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e15 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.15210355987055%\" valign=\"top\"\u003e\n \u003cp\u003e96 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.200647249190936%\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between metabolic syndrome and survival\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median follow-up time was 43 months, ranging from 1 to 120 months. We compared the survival data between patients with MetS and those without MetS. The primary goal of this study was to determine the effect of MetS and its individual components on the PFS of patients with EC. The comparative analyses of the groups revealed no statistically significant differences in terms of overall survival (HR: 1.66, 95% CI 0.965-2.869, p=0.063) and recurrence-free survival (HR: 1.49, 95% CI 0.792-2.801, p=0.210), as shown in (Table 2).\u003c/p\u003e\n\u003cp\u003eThe PFS for patients with MetS versus patients without MetS was 36.0 months versus 40.0 months, respectively (p=0.210). Similarly, OS rates were worse for patients with MetS compared to patients without MetS: 38.0 months versus 43.0 months, respectively (p=0.063)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;A. Kaplan-Meier analyses of OS regarding the presence of metabolic syndrome\u0026nbsp;\u003cbr\u003e\u0026nbsp;Patients with MetS versus without MetS, median OS: 38.0 versus 43.0 months, log rank: p= 0.063\u003cbr\u003e\u0026nbsp;B.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eKaplan-Meier analyses of PFS regarding the presence of metabolic syndrome\u0026nbsp;\u003cbr\u003e\u0026nbsp;Patients with MetS versus without MetS, median PFS: 36.0 versus 40.0 months, log rank: p=0.210\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Univariate and multivariate analysis of metabolic syndrome and its components associated with prognosis (OS, overall survival; PFS, progression-free survival) in patients with endometrial cancer\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.992025518341308%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.556618819776714%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate OS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.556618819776714%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate OS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate PFS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.992025518341308%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate PFS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.293460925039872%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.992025518341308%\" valign=\"top\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003e1.21 (0.83 \u0026ndash; 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.21 (0.83 - 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.992025518341308%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (1.002 - 1.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.293460925039872%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.992025518341308%\" valign=\"top\"\u003e\n \u003cp\u003eArterial hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003e1.13 (0.78 \u0026ndash; 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.03 (0.68 - 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.992025518341308%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.293460925039872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.992025518341308%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003e1.45 (0.93 \u0026ndash; 2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.11 (0.64 - 1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.992025518341308%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.293460925039872%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.992025518341308%\" valign=\"top\"\u003e\n \u003cp\u003eMetabolic syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003e1.66 (0.97 - 2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.063\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.50 (0.79 - 2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.496012759170654%\" valign=\"top\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.992025518341308%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.293460925039872%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\n\u003cp\u003eHowever, there was a significant correlation between obesity and PFS. No significant correlation was observed regarding obesity and OS (HR: 1.213, 95% CI 0.826-1.781, p=0.323) (Table 2 ).\u003c/p\u003e\n\u003cp\u003eFor patients with obesity alone PFS was significantly reduced compared to the cohort without obesity (34.5 vs. 44.0 months, HR: 1.606; 95% CI 1.043-2.472, p=0.029) (Figure 3 ). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eKaplan-Meier analyses of OS and PFS regarding the presence of obesity\u0026nbsp;\u003cbr\u003e\u0026nbsp;A. Patients with obesity versus patients without obesity, median OS: 38.0 vs. 46.0 months, p=0.323 B. Patients with obesity versus patients without obesity, median PFS: 34.5 vs. 44.0 months log rank: p= 0.029\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur observations did not reveal statistically significant differences in terms of shorter OS (p\u0026gt;0.05) or poorer PFS (p\u0026gt;0.05) in patients with isolated arterial hypertension (AH) or diabetes mellitus (DM) (Table 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTables 3 and 4 present the results of Cox regression univariate and multivariate analysis for the risk of recurrence in all patients and in patients with MetS, respectively.\u003c/p\u003e\n\u003cp\u003eIn the univariate analysis for all patients, the significant variables affecting PFS were age, ECOG Score, FIGO stage, tumor grade, histological subtype, type of surgery and chemotherapy (p\u0026lt;0.05) (Table 3). In the univariate analysis for the MetS Group, age, histological grade of differentiation, and FIGO-Stage were associated with poorer PFS (all p\u0026lt;0.05), whereas FIGO-Stage alone was associated with worse OS (p=0.05) (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the multivariate Cox regression analysis, we found that the following factors retained their prognostic significance for PFS in patients with MetS: age (HR 1.13; 95% CI 1.04 - 1.22, p=0.002) and FIGO-Stage (HR 4.67; 95% CI 1.91 - 11.39, p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Univariate and multivariate analysis of factors associated with prognosis (OS, overall survival; PFS, progression-free survival) in patients with endometrial cancer\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate OS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate OS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate PFS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate PFS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003ePatient age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e1.05 (1.03 - 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.04 (1.02 - 1.06)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.03 (1.01 - 1.05)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (1.002 - 1.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003eMean BMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.99 (0.97 - 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.0 (0.97 - 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003eECOG Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e1.83 (1.50 - 2.22)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.5 (1.04 - 1.84)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;0.024\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e1.64 (1.31 - 2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e1.53 (1.155 - 2.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003eHistological subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e1.584 (1.19 - 2.10)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e0.64 (0.41 - 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e1.69 (1.28 - \u0026nbsp;2.25)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e0.62 (0.39 - 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003eHistological grade of differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e2.50 (1.97 - 3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e1.66 (1.21 - 2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e2.93 (2.23 - 3.85)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e1.89 (1.34 - 2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e1.97 (1.68 - 2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e1.49 (1.22 - 1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e2.02 (1.70 - 2.41)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e1.45 (1.17 - 1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphadenectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.18 (1.01 - 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e1.13 (0.94 - 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.81 - 1.21)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003ePelvic lymphadenectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e1.36 (1.02 - 1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e1.17 (0.95 - 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.24 (0.93 - 1.66)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003eParaaortic lymphadenectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e1.12 (0.85 \u0026ndash; 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.78 - 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003eLVSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;2.26 (1.58 - 3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e0.84 (0.44 - 1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e2.63 (1.88 - 3.66)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e0.75 (0.37 - 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003eSurgery (laparoscopic/laparotomy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.81 (1.38 \u0026ndash; 2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.50 (0.32 - 0.79)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.52 (1.12 - 2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e0.67 (0.42 - 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e3.56 (2.37 - 5.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.88 (1.12 - 3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e5.81 (3.77 - 8.98)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e3.20 (1.26 \u0026ndash; 4.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.580060422960724%\" valign=\"top\"\u003e\n \u003cp\u003eRadiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.350453172205437%\" valign=\"top\"\u003e\n \u003cp\u003e1.07 (0.69 - 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e1.50 (0.95 - 2.35)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.099697885196375%\" valign=\"top\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.293051359516616%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.7975830815709966%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Univariate and multivariate analysis of factors associated with prognosis (OS, overall survival; PFS, progression-free survival) in EC-Patients with MetS\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"656\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate OS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate OS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate PFS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate PFS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003ePatient age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (0.97 - 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e1.08 (1.00 - 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e1.13 (1.04 - 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eMean BMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (0.95 - 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 (0.88 - 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eECOG Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e1.12 (0.66 - 2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.81 (0.36 - 1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eHistological subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;1.71 (0.834 - 3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.36 (0.00 \u0026ndash; 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eHistological grade of differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e1.43 (0.91 - 2.24)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e1.63 (1.05 - 2.54)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e1.18 (0.74 - 1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e1.82 (1.03 - 3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e2.33 (1.32 - 4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e4.67 (1.91 - 11.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eLymphadenectomyomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e1.34 (0.82 - 2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e0.98 (0.42 - 2.23)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003ePelvic lymphadenectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e1.74 (0.56 - 5.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e2.19 (0.59 - 8.17)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eParaaortic lymphadenectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e2.43 (0.73 - 8.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e1.99 (0.50 - 7.98)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eLVSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e0.71 (0.34 - 4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e1.56 (0.39 - 6.26)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003cp\u003e(laparoscopic vs. laparotomy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e1.21 (0.58 - 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e0.73 (0.29 - 1.88)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e2.96 (0.60 - 14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e0.24 (0.05 - 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003eRadiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.282442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e0.92 (0.23 - 3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.175572519083969%\" valign=\"top\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.82442748091603%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.32824427480916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.977099236641221%\" valign=\"top\"\u003e\n \u003cp\u003e1.38 (0.34 - 5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.702290076335878%\" valign=\"top\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.419847328244275%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.145038167938932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective study, we observed no oncological impact of MetS in patients with primary EC. However, obesity alone represents an important comorbidity associated with a worse PFS in the present series. The reason might be associated with the number and characteristics of the population.\u003c/p\u003e \u003cp\u003eThe prognostic significance of MetS and its components on EC has been previously explored by few studies.\u003c/p\u003e \u003cp\u003eIn particular, epidemiological and preclinical studies have shown that the pathogenesis of endometrioid EC is closely related to estrogen (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These pathophysiological changes may explain the role of MetS in endometrial carcinogenesis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe metabolic tumor microenvironments formed in MetS are closely involved in the development of EC via several potential mechanisms (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Since the production of estrogen is an intermediate product of lipid metabolism, abnormal lipid metabolism has an influence on the secretion of estrogen and the balance of estrogen and progesterone (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Especially in patients with obesity, a hyper-estrogenic state caused by the presence of the aromatase enzyme in adipose tissue is identified, which catalyzes the conversion of androgens to estrogen in postmenopausal women (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In addition, MetS is associated with chronic insulin resistance, which can lead to the overproduction of reactive oxygen species and contribute to DNA damage (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Hyperestrogenism and hyperglycemia, associated with obesity and metabolic syndrome, play pivotal roles in cancer pathogenesis. They stimulate cell proliferation and angiogenesis through distinct mechanisms and induce hyperplasia in endometrial tissue.\u003c/p\u003e \u003cp\u003eOther published meta-analyses and cohort studies support a relationship between MetS components such as diabetes, obesity, and hypertension and an increased risk of EC (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur present analysis seeks to investigate the prognostic significance of individual and combined components of MetS in patients with EC. According to our knowledge, there have been limited reports on the prognostic effect of MetS in patients with EC (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Controversial data have been published regarding survival data in EC related to the presence of MetS (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Additionally, there is a lack of unified criteria for MetS (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Kokts-Porietis et al. conducted a prospective cohort study with 540 patients with EC, of which 325 had MetS at diagnosis. They reported that MetS and an elevated waist circumference (\u0026ge;\u0026thinsp;88 cm) were associated with worse OS for EC (HR: 1.98, 95% CI 1.07\u0026ndash;3.67, HR: 2.12, 95% CI 1.18\u0026ndash;3.80, respectively) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimiliarly, Yang et al. retrospectively analyzed outcomes of 506 patients with EC diagnosed between 2010 and 2016, among whom 153 (31%) were diagnosed with MetS (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Their results indicated that MetS was closely related to OS (HR: 2.14, 95% CI 1.07\u0026ndash;4.28, P\u0026thinsp;=\u0026thinsp;0.032) and PFS (HR: 1.80, 95% CI 1.0\u0026ndash;3.3, P\u0026thinsp;=\u0026thinsp;0.045) in EC patients. The OS decreased in patients with \u0026ge;\u0026thinsp;3 components compared to those with 1\u0026ndash;2 or 0 components (P\u0026thinsp;=\u0026thinsp;0.045), while no apparent difference was observed for PFS rates (P\u0026thinsp;=\u0026thinsp;0.069). After adjusting for other variables, including age, histological type, tumor grade, and stage, MetS was not associated with the prognosis of EC.\u003c/p\u003e \u003cp\u003eSimilar to the report by Yang et al., the results of our retrospective study showed that MetS in patients with EC was not associated with a worse oncological outcome. However, a trend toward significance was noticed regarding the OS (p\u0026thinsp;=\u0026thinsp;0.063) Additionally, we could confirm that obesity alone remains an important comorbidity in patients with EC associated with a worse PFS. In the multivariable analysis, age and FIGO Stage were associated with worse PFS in patients with MetS.\u003c/p\u003e \u003cp\u003eSince 2013, we have witnessed a significant shift in the diagnosis and treatment of EC. Following the The Cancer Genome Atlas (TCGA) research network's announcement of the four new molecular subtypes of EC, numerous societies (e.g. World Health Organization, the International Society of Gynecological Pathologists, European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology) have embraced these classifications and aligned their guidelines with the evolving understanding of disease development (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). An updated evaluation of risk stratification, along with more consistent adjuvant therapy concepts, is anticipated in the forthcoming ESGO/ESTRO/ESP guidelines (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This promises to deliver risk-adapted therapies to patients, reducing the likelihood of under- and over-treatment, and thereby enhancing prognostic outcomes, while also alleviating the financial burden on healthcare systems.\u003c/p\u003e \u003cp\u003eThe potential limitations of this study include the retrospective design, which encompasses some missing data, a small number of enrolled patients with heterogeneity of patient and tumor charachteristics, as well as the lack of information about the molecular profile. However, the inclusion of well-documented cases and the performance of surgeries and pathological reviews by the same experienced team at our clinics may enhance the importance of our results.\u003c/p\u003e \u003cp\u003eIn conclusion, Metabolic Syndrome (MetS) could not be established as a prognostic factor for primary EC; however, obesity significantly reduced survival rates. These findings support the notion that modifying lifestyle factors and reducing obesity-related risk factors through dietary changes and regular exercise as preventive measures may not only decrease the risk of cancer but also reduce the mortality rate among patients with EC. Further research is needed to correlate the prognostic significance of molecular profiles with MetS and obesity. This could help clinicians to better predict the risk of recurrence and death in patients with EC and such accompanying comorbidities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eGrants and funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this study was not funded by external sponsors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eListed below are the personal conflicts of interest of the (co-)authors. The funders listed were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. S. Krajnak received speaker honoraria from Roche Pharma AG and Novartis Pharma GmbH Germany, research funding from Novartis Pharma GmbH Germany and travel reimbursement from PharmaMar and Novartis Pharma GmbH Germany. A. Hasenburg received honoraria from AstraZeneca, Celgen, GSK, LEO Pharma, MedConcept GmbH, Med update GmbH, Medicultus, Pfizer, Promedicis GmbH, Softconsult, Roche Pharma AG, Streamedup!GmbH, Tesaro Bio Germany GmbH. She is a member of the advisory board of AstraZeneca, GSK, LEO Pharma, PharmaMar, Promedicis GmbH, Roche Pharma AG, Tesaro Bio Germany GmbH, MSD Sharp\u0026amp;Dohme GmbH. M. Schmidt reports personal fees from AstraZeneca, BioNTech, Daiichi Sankyo, Eisai, Lilly, MSD, Novartis, Pantarhei Bioscience, Pfizer, Roche, and SeaGen outside the submitted work. Institutional research funding from AstraZeneca, BioNTech, Eisai, Genentech, German Breast Group, Novartis, Palleos, Pantarhei Bioscience, Pierre Fabre, and SeaGen. In addition, Marcus Schmidt has a patent for EP 2390370 B1 issued and a patent for EP 2951317 B1 issued. All other authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, I.S. and G.K.; Statistical analysis, I.S., T.K. and S.K.; Methodology, I.S. and G.K.; Resources, I.S., G.K., V.M. and S.B. ,Writing\u0026mdash;original draft, I.S. and G.K.; Writing\u0026mdash;review and editing, S.K., B.G., A.H., T.K., M.T.R., M.S., V.M., S.B. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Br J Cancer. 2014;111(7):1432\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBjorge T, Stocks T, Lukanova A, Tretli S, Selmer R, Manjer J, et al. Metabolic syndrome and endometrial carcinoma. Am J Epidemiol. 2010;171(8):892\u0026ndash;902.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBing RS, Tsui WL, Ding DC. The Association between Diabetes Mellitus, High Monocyte/Lymphocyte Ratio, and Survival in Endometrial Cancer: A Retrospective Cohort Study. Diagnostics (Basel). 2022;13(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePecorelli S. Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int J Gynaecol Obstet. 2009;105(2):103\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoslow RA, Tornos C, Park KJ, Malpica A, Matias-Guiu X, Oliva E, et al. Endometrial Carcinoma Diagnosis: Use of FIGO Grading and Genomic Subcategories in Clinical Practice: Recommendations of the International Society of Gynecological Pathologists. Int J Gynecol Pathol. 2019;38 Suppl 1(Iss 1 Suppl 1):S64-S74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConcin N, Matias-Guiu X, Vergote I, Cibula D, Mirza MR, Marnitz S, et al. ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Int J Gynecol Cancer. 2021;31(1):12\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerek JS, Matias-Guiu X, Creutzberg C, Fotopoulou C, Gaffney D, Kehoe S, et al. FIGO staging of endometrial cancer: 2023. J Gynecol Oncol. 2023;34(5):e85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyo S, Nakayama K. Endometrial Cancer as a Metabolic Disease with Dysregulated PI3K Signaling: Shedding Light on Novel Therapeutic Strategies. Int J Mol Sci. 2020;21(17).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Yang X, Cheng Y, Dong Y, Wang J, Wang J. Development and validation of a prognostic model based on metabolic risk score to predict overall survival of endometrial cancer in Chinese patients. J Gynecol Oncol. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmisano BT, Zhu L, Stafford JM. Role of Estrogens in the Regulation of Liver Lipid Metabolism. Adv Exp Med Biol. 2017;1043:227\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis. 2017;11(8):215\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByers T, Sedjo RL. Body fatness as a cause of cancer: epidemiologic clues to biologic mechanisms. Endocr Relat Cancer. 2015;22(3):R125-34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin F, Shao X, Zhao L, Li X, Zhou J, Cheng Y, et al. Predicting prognosis of endometrioid endometrial adenocarcinoma on the basis of gene expression and clinical features using Random Forest. Oncol Lett. 2019;18(2):1597\u0026ndash;606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitsuhashi A, Kiyokawa T, Sato Y, Shozu M. Effects of metformin on endometrial cancer cell growth in vivo: a preoperative prospective trial. Cancer. 2014;120(19):2986\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModesitt SC, Geffel DL, Via J, A LW. Morbidly obese women with and without endometrial cancer: are there differences in measured physical fitness, body composition, or hormones? Gynecol Oncol. 2012;124(3):431\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokts-Porietis RL, McNeil J, Nelson G, Courneya KS, Cook LS, Friedenreich CM. Prospective cohort study of metabolic syndrome and endometrial cancer survival. Gynecol Oncol. 2020;158(3):727\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Li X, Dong Y, Fan Y, Cheng Y, Zhai L, et al. Effects of Metabolic Syndrome and Its Components on the Prognosis of Endometrial Cancer. Front Endocrinol (Lausanne). 2021;12:780769.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalkau B, Charles MA. Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med. 1999;16(5):442\u0026ndash;3.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Endometrial cancer, Metabolic syndrome, Survival, Obesity","lastPublishedDoi":"10.21203/rs.3.rs-3809471/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3809471/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003eEndometrial cancer (EC) is the most common gynaecological cancer. Its incidence has been rising over the years with ageing and increased obesity of the high-income countries’ populations. Metabolic syndrome (MetS) has been suggested to be associated with EC. The aim of this study was to assess whether MetS has a significant impact on oncological outcome in patients with EC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThis retrospective study included patients treated for EC between January 2010 and December 2020 in two referral oncological centers. Obesity, arterial hypertension (AH) and diabetes mellitus (DM) were criteria for the definition of MetS. The impact of MetS on progression free survival (PFS) and overall survival (OS) was assessed with log-rank test and Cox regression analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Among the 415 patients with a median age of 64, 38 (9.2%) fulfilled the criteria for MetS. The median follow-up time was 43 months.\u003c/p\u003e\n\u003cp\u003ePatients suffering from MetS did not show any significant differences regarding PFS (36.0 vs. 40.0 months, HR: 1.49, 95% CI 0.79-2.80 P=0.210) and OS (38.0 vs. 43.0 months, HR: 1.66, 95% CI 0.97-2.87, P=0.063) compared to patients without MetS. Patients with obesity alone had a significantly shorter median PFS compared to patients without obesity (34.5 vs. 44.0 months, P=0.029). AH and DM separately had no significant impact on PFS or OS (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eIn our analysis, MetS in patients with EC was not associated with impaired oncological outcome. However, our findings show that obesity itself is an important comorbidity associated with significantly reduced PFS.\u003c/p\u003e","manuscriptTitle":"Prognostic impact of metabolic syndrome in patients with primary endometrial cancer: A retrospective bicentric study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 20:14:00","doi":"10.21203/rs.3.rs-3809471/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-20T04:40:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-11T14:11:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c21ddf5f-d837-4c97-952a-41d0dc08415c","date":"2024-01-21T07:57:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-08T08:11:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-27T05:59:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-27T05:59:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cancer Research and Clinical Oncology","date":"2023-12-26T19:59:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1ea45e5b-6e3e-4053-89c2-ab1bf67809ef","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-08T15:11:10+00:00","versionOfRecord":{"articleIdentity":"rs-3809471","link":"https://doi.org/10.1007/s00432-024-05699-1","journal":{"identity":"journal-of-cancer-research-and-clinical-oncology","isVorOnly":false,"title":"Journal of Cancer Research and Clinical Oncology"},"publishedOn":"2024-04-03 15:02:03","publishedOnDateReadable":"April 3rd, 2024"},"versionCreatedAt":"2024-01-02 20:14:00","video":"","vorDoi":"10.1007/s00432-024-05699-1","vorDoiUrl":"https://doi.org/10.1007/s00432-024-05699-1","workflowStages":[]},"version":"v1","identity":"rs-3809471","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3809471","identity":"rs-3809471","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}