Open Access, Peer-reviewed
pISSN 2005-0380   eISSN 2005-0399
    J Gynecol Oncol. 2023 Nov;34(6):e69. English.
    Published online Jun 05, 2023.
    https://doi.org/10.3802/jgo.2023.34.e69
    © 2023. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology, and Japan Society of Gynecologic Oncology
    Original Article

    Development and validation of a prognostic model based on metabolic risk score to predict overall survival of endometrial cancer in Chinese patients

    Xingchen Li,1,* Xiao Yang,1,* Yuan Cheng,1 Yangyang Dong,1 Jingyuan Wang,1 and Jianliu Wang1,2
      • 1Department of Obstetrics and Gynecology, Peking University People’s Hospital, Beijing, China.
      • 2Beijing Key Laboratory of Female Pelvic Floor Disorders Diseases, Beijing, China.
    • Correspondence to Jianliu Wang. Department of Obstetrics and Gynecology, Peking University People's Hospital, No. 11, Xizhimen South Street, Xicheng District, Beijing 100044, China. Email: wangjianliu@pkuph.edu.cn

      *Xingchen Li and Xiao Yang contributed equally to this work.
    Received January 10, 2023; Revised April 19, 2023; Accepted May 01, 2023.

    This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Abstract

    Objective

    Metabolic syndrome (MetS) is closely related to the increased risk and poor prognosis of endometrial cancer (EC). The purpose of this study was to analyze the relationship between metabolic risk score (MRS) and EC, and establish a predictive model to predict the prognosis of EC.

    Methods

    A retrospective study was designed of 834 patients admitted between January 2004 to December 2019. Univariate and multivariate Cox analysis were performed to screen independent prognostic factors for overall survival (OS). A predictive nomogram is built based on independent risk factors for OS. Consistency index (C-index), calibration plots and receiver operating characteristic curve were used to evaluate the predictive accuracy of the nomogram.

    Results

    The patients were randomly divided into training cohort (n=556) and validation cohort (n=278). The MRS of EC patients, ranging from −8 to 15, was calculated. Univariate and multivariate Cox analysis indicated that age, MRS, FIGO stage, and tumor grade were independent risk factors for OS (p<0.05). The Kaplan–Meier analysis demonstrated that EC patients with low score showed a better prognosis in OS. Then, a nomogram was established and validated based on the above four variables. The C-index of nomogram were 0.819 and 0.829 in the training and validation cohorts, respectively. Patients with high-risk score had a worse OS according to the nomogram.

    Conclusion

    We constructed and validated a prognostic model based on MRS and clinical prognostic factors to predict the OS of EC patients accurately, which may help clinicians personalize prognostic assessments and effective clinical decisions.

    Synopsis

    Metabolic syndrome risk score (MSRs) was an independent risk factor affecting the overall survival of endometrial cancer (EC) patients. A prognostic model based on MSRs was constructed and validated to predict the overall survival of EC patients accurately. The prognostic model was verified to have a better accuracy than traditional staging system.

    Graphical Abstract

    Keywords
    Endometrial Cancer; Metabolic Syndrome; Risk factor; Nomogram; Prognosis

    INTRODUCTION

    Endometrial cancer (EC) is one of the most common gynecological malignancies. Its incidence rate increases with the prevalence of metabolic diseases such as obesity and diabetes, and shows a younger trend [1]. According to global cancer statistics, there are estimated 413,800 new cases of EC and 97,400 deaths worldwide in 2020, and the incidence rate ranks fifth and mortality ranks 11th among women [2]. According to the pathological type, EC is divided into two types. Type I (endometrial endometrioid adenocarcinoma, EEA) is estrogen dependent, accounting for about 70%–80% of EC. Its pathogenesis is mostly related to excessive estrogen, obesity, insulin resistance and metabolic disorder. Type II (non-endometrioid carcinoma) is non estrogen dependent, including pathological type of serous endometrioid adenocarcinoma, clear cell carcinoma, et al. [3, 4]. The 5-year survival rate of EC is about 80%, mainly because most patients were diagnosed at an early stage and benefit from surgery or adjuvant treatment. Meanwhile, the prognosis of patients with advanced and recurrent EC is poor, and the 5-year survival rate of advanced EC is less than 20% [5]. Therefore, the development of early diagnosis and prognosis evaluation methods of EC is of great significance to improve the survival rate of patients with EC.

    The International Federation of Obstetrics and gynecology (FIGO) staging system is most commonly used to evaluate the prognosis of patients with EC. However, due to its low accuracy and ignoring other risk factors such as age, it has great limitations in predicting the individualized survival and prognosis of EC patients [6]. In recent years, with the deepening of biomedical research, many studies have established risk prognosis models of EC based on TCGA database and clinicopathological features, such as immune gene related prognosis model and DNA methylation related prognosis model, which provided new methods to evaluate the prognosis of EC at molecular biological level [7, 8]. Recent evidence also suggested that the evaluation of molecular classification provides an accurate method to assess the prognosis of EC patients [9]. The addition of radiomics features might be useful in identifying new signals for a better personalization of the treatment [10]. However, due to complexity of EC, there is still no accurate and effective individualized prognosis model in clinic to evaluate the survival and recurrence risk of EC patients.

    Metabolic syndrome (MetS) is characterized by a cluster of metabolic disorder, including abdominal obesity or overweight, hyperglycemia, hypertension and abnormal blood lipid metabolism [11]. In recent years, epidemiological and clinical studies have found that METS is closely related to the incidence of EC and may be an independent prognostic factor of EC [12]. Our latest retrospective analysis of 506 cases of EC also showed that MetS and its components were closely related to the poor prognosis of EC patients [13]. In addition, MetS and its components have also been reported to be closely associated with the increased risk and poor prognosis of breast cancer, colorectal cancer, and other tumors [14]. In consideration of the important role of MetS in cancer risk and prognosis, it is in urgent need to bring new factors related to MetS into the relevant cancer prognosis evaluation system. Recent studies have shown that the metabolic risk score (MRS) can be used to predict cancer-specific survival of esophageal cancer well [15]. However, there are currently no reports of MRS study on predicting prognosis of EC.

    In this study, we further expanded the sample size on the basis of our previous study clarifying the relationship between MetS and EC, and used a constructed evaluating MRS system based on five metabolic related markers. Furthermore, we evaluated the association between MRS and the prognosis of EC patients in training cohort and validation cohort, and established an individualized usable nomogram based on the MRS and clinicopathological features to predict overall survival (OS) of EC. It will help to predict the prognosis of EC patients more accurately.

    MATERIALS AND METHODS

    1. Patients and data collection

    This study included 834 patients who were surgically and pathologically confirmed to have EC in Peking University People’s Hospital from January 2004 to December 2019. For preoperative diagnosis, all patients underwent diagnostic curettage or hysteroscopy examination. The confirmation as primary EC was based on the histopathology of specimens after the surgery. An experienced pathologist interpreted the samples. The exclusion criteria were: 1) combination with other malignant tumors; 2) absence of medical records; 3) a history of any preoperative therapy. The patients were randomly divided into training cohort (including 556 patients) and validation cohort (including 278 patients) according to the ratio of 2:1. We use the method of random numbers in SPSS to arrange and rank random numbers for all patients. Finally, multiples of 3 and 3 are considered as validation group and the rest patients are the training group. The OS times of patients were obtained by telephone follow-up, and the postoperative follow-up time was up to December 2019. The study protocol was approved by the ethics committee of the Peking University People’s Hospital (2022PHB379-001). What’s more, our study conform to the EQUATOR network guidelines.

    2. Cohort definition and variable record

    The training cohort of 556 patients was used to screen variables and construct the nomogram, and the validation cohort of 278 patients was used as validation data set for internal validation. According to a previous literature review, the following variables were retrieved for each patient from the electronic medical records. The following variables are included.

    • 1) basic demographics, including age at diagnosis, menopausal status, and body mass index (BMI)

    • 2) comorbidities, including diabetes and hypertension

    • 3) vital signs, including systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP)

    • 4) laboratory tests, including serum fasting blood glucose (FBG), cholesterol, triglyceride (TG), high-density lipoprotein (HDL), and MRS

    • 5) pathological features including lymph-vascular space invasion (LVSI), ascites cytology, histology, grade, lymph node metastasis (LNM), myometrial invasion (MI), and cervical stromal invasion.

    MRS was calculated according to diagnostic criteria proposed by the Chinese Diabetes Society in 2004 [13], and PP, the difference between SBP and DBP. As these factors of metabolic origin tend to occur together, MRS was hence generated based on baseline BMI, PP, fasting glucose, TG and high-density lipoprotein cholesterol (HDL-C). In the present study, we employed the rationales of ‘points’ system and the validity of shrinkage method to generate MRS when all five metabolism-related factors were analyzed in quintiles, and the detailed process is illustrated in Table S1.

    3. Statistical analysis

    Categorical variables were expressed as frequency and percentage (%) and as mean ± standard deviation for continuous variables. χ2 or Fisher’s exact test were applied for categorical variables. Univariate Cox were used to screen factors related to the OS of EC patients. Then multivariate Cox regression was conducted to evaluate whether MRS was independent risk factor. Kaplan-Meier (K-M) method was used to calculate the survival rate between different MRS groups. Then, a nomogram was established to accurately evaluate the prognosis of EC based on MRS and several clinicopathological features. Calibration plots and receiver operating characteristic (ROC) curve analysis were performed to assess the predictive accuracy of nomogram in predicting the prognosis of EC. Consistency index (C-index) and AUC values greater than 0.7 suggest a reasonable estimation. Statistical analyses were conducted using the SPSS version 26.0 software (IBM Inc., Armonk, NY, USA), the statistical software package R (http://www.R-project.org The R Foundation) and Empower-Stats (http://www.empowerstats.com, X&Y Solutions, Inc, Boston, MA, USA). We use the “Random Number Generators” function of SPSS software to randomly group patients. Unless otherwise indicated, all tests were two-sided and p<0.05 was considered as statistically significant.

    RESULTS

    1. Baseline characteristics of patients

    A total of 834 EC patients were included in this study, and the patients were randomly divided into training cohort with 556 patients and validation cohort with 278 patients according to a ratio of 2:1. The clinicopathological characteristics and basic information of patients were showed in Table S2.

    MRS was developed based on five metabolic related factors (BMI, PP, FBG, TG and HDL-C) according to the previous study [15]. The distribution of MRS of all the patients ranging from −8 to 15 as showed in Fig. S1. The mean MRS was 2.46 (2.46±4.46) in the training cohort and 2.58 (2.58±4.30) in the validation cohort. The mean age at diagnosis of EC was 56.49 years (56.49±9.33) in training cohort and 55.85 years (55.85±9.59) in validation cohort. There are 34.53% patients with MetS in training cohort and 27.34% in validation cohort. Postmenopausal patients were 65.47% and 63.67% in the training cohort and validation cohort, respectively. The mean OS time was 2106.24 days (2,106.24±1,350.77) in training cohort and 2,185.26 days (2,106.24±1,350.77) in validation cohort. The number of patients with hypertension was 273 (42.63%) and 108 (38.85%) in two groups. The proportions of EEA are 91.37% and 90.65% in training and validation cohorts, respectively. Proportions of patients with different clinicopathological features are even in both training and validation groups. All these baseline clinicopathological characteristics of patients showed no significant difference between the training cohort and validation cohort (p<0.05).

    2. Univariate and multivariate analysis of OS in training and validation cohorts

    We first performed univariate Cox analysis to screen variables affecting OS as shown in the Table 1. There were 12 variables, including age (hazard ratio [HR]=1.04, 95% confidence interval [CI]=1.02–1.07, p=0.002; HR=1.08, 95% CI=1.04–1.12, p<0.001 in training and validation sets, respectively), menopause status (HR=2.13, 95% CI=1.15–3.97, p=0.017; HR=4.13, 95% CI=1.24–13.83, p=0.021 in training and validation sets, respectively), HDL (HR=0.25, 95% CI=0.10–0.61, p=0.003; HR=0.11, 95% CI=0.22–0.62, p=0.012 in training and validation sets, respectively), MRS (HR=1.11, 95% CI=1.15–3.97, p<0.001; HR=1.14, 95% CI=1.05–1.25, p=0.002 in training and validation sets, respectively), ascites cytology (HR=5.75, 95% CI=3.22–10.26, p<0.001; HR=6.27, 95% CI=2.11–18.64, p=0.001 in training and validation sets, respectively), histological type (HR=6.31, 95% CI=3.67–10.87, p<0.001; HR=5.97, 95% CI=2.50–14.25, p<0.001 in training and validation sets, respectively), LNM (HR=4.53, 95% CI=2.97–7.97, p<0.001; HR=7.85, 95% CI=3.23–19.09, p<0.001 in training and validation sets, respectively), LVSI (HR=3.29, 95% CI=1.92–5.63, p<0.001; HR=5.55, 95% CI=2.35–13.09, p<0.001 in training and validation sets, respectively), MI (HR=2.47, 95% CI=1.46–4.61, p<0.001; HR=6.36, 95% CI=2.82–14.35, p<0.001 in training and validation sets, respectively), cervical invasion (HR=3.00, 95% CI=1.71–5.31, p<0.001; HR=3.43, 95% CI=1.35–8.71, p<0.001 in training and validation sets, respectively), FIGO stage (HR=40.27, 95% CI=19.76–82.09, p<0.001; HR=40.15, 95% CI=13.10–123.02, p<0.001 for FIGO stage IV in training and validation sets, respectively), and tumor grade (HR=10.32, 95% CI=4.56–23.34, p<0.001; HR=2.84, 95% CI=0.98–8.22; p=0.0543 in training and validation sets, respectively), were associated with OS both in the training and validation cohorts (p<0.05). Furthermore, multivariate Cox analysis were conducted to investigate the independent risk factors for OS in EC patients (Table 2). Finally, we found that there are four factors associating with prognosis of EC, including age (HR=1.06, 95% CI=1.02–1.10, p=0.006 for training cohort, and HR=1.07, 95% CI=1.02–1.13, p=0.008 for validation cohort), MRS (HR=1.11, 95% CI=1.05–1.08, p<0.001 for training cohort, and HR=1.14, 95% CI=1.03–1.27, p= 0.010 for validation cohort), FIGO stage (HR=26.78, 95% CI=6.93–103.45, p<0.001 for training cohort, and HR=21.09, 95% CI=8.56–51.97, p<0.001 for validation cohort), and tumor grade (HR=3.61, 95% CI=1.45–8.96, p=0.0057 for training cohort, and HR=6.09, 95% CI=2.35–15.76, p<0.001 for validation cohort). On the other hand, the rest of the clinicopathological characteristics had no effects on OS after adjusting other factors. These results indicated that the 12 signatures may have an obvious influence on OS for patients with EC, and age, MRS, FIGO stage, and tumor grade are four independent prognostic factors.

    Table 1
    Univariate Cox analyses of overall survival in endometrial cancer patients

    Table 2
    Multivariate Cox analysis of overall survival in endometrial cancer patients

    3. Prognostic effects of MRS on OS of patients with EC

    In order to further evaluate and control confounding factors between MRS and OS, we conducted a stratification analysis as showed in the Table 3. The result indicated that MRS showed association with OS in different age (<60 or ≥60), postmenopausal status, EEA type, LNM negative, cervical invasion negative and grade 3 subgroups both in the training cohort and validation cohort (p<0.05). These data suggested that the influence of MRS is diverse in different kinds of clinicopathological features on OS for patients with EC.

    Table 3
    Stratified analysis of metabolic risk score on overall survival in different clinicopathological features of endometrial cancer patients

    4. Effects of MRS on OS of EC patients based on multivariate Cox analyses

    To evaluate whether MRS was an independent prognostic factor of EC patients we performed multivariate cox analysis of OS to evaluate whether MRS was an independent prognostic factor. First, we divided the patients into five subgroups according to different MRS (<0 vs. 0–2 vs. 3–5 vs. 6–8 vs. >8 scores), and the K–M analysis showed that higher scores showed worse OS of EC patients in both training cohort (p=5e-04) and validation cohort (p=0.0018) (Fig. 1A and B).

    Fig. 1
    Kaplan-Meier (K-M) analysis of five subgroups according to different MRS for OS in the training cohort (A) and validation cohort (B).
    MRS, metabolic risk score.

    5. Construction and performance assessment of the nomogram

    Since the multivariate cox analysis also showed that age, MRS, FIGO stage, and tumor grade were independent ris factors associated with OS both in the training cohort and validation cohort, we constructed a prognostic nomogram model to predict the prognosis of EC patients based on the above four factors in the training cohort as shown in Fig. S2. Total points were obtained by adding the points of each variable in the prediction model. The 1, 3, 5-year OS rate can be obtained by drawing a vertical line from the total points to the bottom axis. Further, the predictive ability of the nomogram model was evaluated. The C-index values of nomogram were 0.819 and 0.829 in the training cohort and validation cohorts, respectively. Calibration plots was conducted to evaluated the degree of fitting of the nomogram at 1, 3, 5-year survival. The results indicated that the predicted probability survival showed good consistence with actual survival at 1, 3, 5-year survival both in the training cohort (Fig. S3A) and validation cohort (Fig. S3B).

    6. Validation of nomogram in predicting OS for EC patients

    According to the total scores of nomogram and the cut-off value, we divided the patients into three subgroups including low score (0–30), moderate score (32.5–65) and high score (>67.5) in the training cohort (Fig. 2A) and validation cohort (Fig. 2B). The K-M curves analysis showed that there was significant differences in survival rate among the three groups both in the training cohort and validation cohort (p<0.05). EC patients with low score showed a better prognosis in OS. While, EC patients with high scores had lower OS. By using this method, we can calculate the risk score for each patient with EC based on these four characteristics (age, tumor grade, stage, and MRS) in clinical applications. Finally, we can find the survival rate corresponding to the total score from the nomogram, and then predict 1-, 3-, and 5-year OS for EC patients. Afterwards, we can easily and quickly obtain the prognosis of EC patients. Meanwhile, it can provide personalized treatment and postoperative guidance for patients with EC. In addition, the AUC of the nomogram (AUC=0.857) was greater than a single index, including MRS (AUC=0.732), age (AUC=0.693), FIGP stage (AUC=0.790), and tumor grade (AUC=0.803) in the training cohort (Fig. 3A). There were consistent results in the validation cohort (Fig. 3B). We also performed AUC curves changed over time in the training cohort and validation cohort as exhibited in Fig. 3C and D, and the AUC is higher than 0.8 in both groups, which showed a great performance for the nomogram. All these results indicated that the nomogram model constructed can predict survival of EC patients accurately. Our results indicated that the nomogram had a great performance in predicting OS for patients with EC. What’s more, MRS might significantly improve the predictive accuracy for these patients.

    Fig. 2
    The K-M curves analysis of overall survival in different nomogram score stratifications including low score, moderate score, and high score group in the training cohort (A) and validation cohort (B).

    Fig. 3
    AUCs for predicting survival of EC patients. (A-B). AUCs of metabolic risk score, age, stage, grade and all factors for predicting survival of EC patients in the training cohort. (C-D). Time-dependent AUCs of nomogram in the training cohort and validation cohort. AUC values greater than 0.7 suggest a reasonable estimation.
    AUC, area under the receiver operating characteristic curve; EC, endometrial cancer.

    DISCUSSION

    The correct judgment of tumor prognosis is helpful to clinical decision-making of treatments. At present, clinicians usually judge the prognosis of EC patients according to surgical pathology or clinical stage [16]. However, FIGO staging system has limitations in accurately predicting the prognosis of EC patients. Especially, it cannot explain why patients with the same stage and grade have different clinical outcomes [17]. The reason may be that ignoring the influence of other prognostic factors. Thus, it is necessary to develop personalized prognostic models based on prognostic factors closely related to EC. In this retrospective study, we analyzed the effects of MRS on OS in EC patients and found that MRS was an independent risk factor influencing the OS of EC. Based on MRS and several clinicopathological features, we constructed and validated a nomogram to predict the prognosis of postoperative EC patients. Through C-index, calibration plots and ROC curve, the nomogram was verified to have good discriminative and calibration abilities in predicting the prognosis of patients with EC.

    The prevalence of MetS is increasing year by year, and the prevalence of the MetS was 35% in United States, and 33.9% in adult population of China [18, 19]. In recent years, the role of MetS in the occurrence and development of cancers has attracted extensive attention [20, 21]. Although the mechanism of the association between MetS and cancers is not exact, the changes of downstream signaling pathways activated by insulin resistance, high glucose or adipokines, such as PI3K-Akt, mTOR/P70s6K considered to be important mechanisms [11]. A recent matched case-control study showed that MetS was associated with a higher risk of 11 types of common cancers, including EC (odd ratio [OR]=2.14–2.46), colorectal cancer (OR=1.28), thyroid cancer (OR=1.71) etc. [22]. It is noteworthy that the OR value was the highest in EC among these cancers. In addition, many studies have confirmed that there was a close association between MetS and EC. A study based on SEER-Medicare database reported that MetS was associated with worse cancer-specific survival (HR=1.28) [23]. Our previous study also found that MetS was closely related to the poor prognosis of EC patients based on Chinese population [13]. Thus, MetS is not only a high-risk factor of EC, but also an important factor affecting the prognosis of EC, and it is necessary to include MetS in the prognostic evaluation system of EC.

    MetS is a cluster of multiple metabolic abnormalities including obesity, hypertension, hyperglycemia and dyslipidemia. Not only the MetS as a whole, the components of MetS also increased the risk of EC as reported by a case-control study that ORs of EC were 2.18 for type 2 diabetes, 1.77 for hypertension, 1.20 for dyslipidemia, and 3.83 for obesity women [24, 25]. In addition, our recent study indicated that EC patients with more MetS components got a worse prognosis [13]. Prognostic prediction models based on MetS have been established in other cancers, such as gastric cancer [26], prostate cancer [27] and colorectal cancer [28]. However, there is little research on prognostic model based on MetS in EC. Our research shows that MRS generally conforms to the “normal distribution” in patients with EC. However, the proportion of patients with higher scores did not gradually decrease, according to the law of normal distribution. This indicates that the overall MRS of patients with EC is higher. Metabolic related features plays an important role in the occurrence and development of EC. MRS is increasingly recognized among cancers, including endometrial and esophageal cancers [15, 29, 30]. Our research also confirms that the OS accuracy and predictive value of MRS for evaluating EC are higher.

    In this study, we used this “points” system to construct MRS based on five metabolic related factors associated with MetS. In order to ensure the reliability of the research results, we randomly divided the patients into training cohort and validation cohort. Further, the relationship between MRS and OS was analyzed in the training cohort and validated in the validation cohort. It is suggested that the effect of MRS on OS depended on age, menopausal status and pathological characteristics, etc. The effect of MRS on OS is not obvious in non-endometrioid EC (other types) in validation cohort. As we know, the pathogenesis of EEA is closely related to estrogen. MetS includes glucose metabolism and lipid metabolism, and the production of estrogen is an intermediate product of lipid metabolism [31]. According to some studies, abnormal lipid metabolism has an influence on the secretion of estrogen and the balance of estrogen and progesterone [32]. Therefore, the impact of MRS on the prognosis of EC is more significant in EEA. Further, the multivariate analysis indicated that MRS was an independent factor for OS after adjusting several basic prognostic factors. Consistent results were observed in both training cohort and validation cohort. The K-M analysis demonstrated the higher the MRS, the worse the prognosis of EC patients (p<0.05). In the present study, age, stage and grade were also found to be independent risk factors of OS with consistency in the training cohort and validation cohort. It is consistent with previous studies, such as a SEER based study containing 63,729 EC patients have reported that age, stage and grade were assessed as independent prognostic factors of cancer-specific survival [33]. Recently, modified fragility index (mFI), an index including obesity, comorbidities, and fragility on overall and severe complication, was used to evaluate the prognosis of patients [34]. One study revealed that mFI was an important predictor of complications among patients treated for EC and could be a useful tool for assisting clinicians in perioperative management [35]. Our study found that elderly patients had a higher risk score and a worse prognosis according to our nomogram. We may investigate the function of mFI in our further study.

    Nomogram, as a tool for evaluating the risk and prognosis of disease, raise the efficiency and accuracy for making clinical decisions [36]. At present, nomograms for different tumors have been widely established, and its prediction accuracy on tumor prognosis is better than the traditional TNM staging [37]. However, nomograms based on metabolic related factors were rare in EC [38]. Given that MRS was independent factor for OS of EC patients both in the training cohort and validation cohort, a prognostic nomogram for OS was constructed based on MRS and other independent factors, including age, stage, and grade. Based on the total scores of nomogram, 1-, 3-, 5-year survival rate of patients can be calculated accordingly. The C-index of nomogram for predicting EC were 0.819 and 0.829 in the training cohort and validation cohort. Generally, a C-index value more than 0.7 indicates a good accuracy [39]. More importantly, the AUCs of the nomogram were greater than age, grade, stage alone, showing a better accuracy than traditional staging system. Therefore, the nomogram prediction model based on MRS and clinical features has a good ability to predict the prognosis of EC patients.

    There are several limitations in our study. First, this retrospective study was based on data from the EC patients from 2004 to 2019 in a single center. Although the results in the training cohort and validation cohort showed good consistency, it will be more reliable if our data conclusions can be repeated in multiple centers. Second, we developed MRS based on preoperative baseline of metabolism related factors in this study. Detecting the dynamic changes of metabolic related factors may be more helpful to accurately judge the prognosis of EC patients. Finally, other information that may influence MetS such as diet, drug therapy or physical activity were not adjusted in the present study.

    In conclusion, based on the previous study that MetS was closely related to the prognosis of EC, this study further constructed the MRS by using a new metabolic related scoring system. MRS was confirmed to be an independent prognostic factor affecting the OS of EC patients. Further, we constructed and validated a nomogram prediction model based on MRS and clinical prognostic factors. The nomogram was verified to have good discriminative and calibrated abilities in predicting the prognosis of patients with EC, and also showing a better accuracy than traditional FIGO staging system. Taken together, our prognostic model will help clinicians predict the prognosis of patients with EC precisely and make effective clinical decisions.

    SUPPLEMENTARY MATERIALS

    Table S1

    Determination of scores associated with each of the quintile ranges of metabolic risk factors in derivation group

    Click here to view.(37K, xls)

    Table S2

    Clinicopathological characteristics and basic information of endometrial cancer patients

    Click here to view.(43K, xls)

    Fig. S1

    Distribution of patients in different groups of MRS.

    Click here to view.(417K, ppt)

    Fig. S2

    Nomogram was established to predict 1-, 3- and 5-year survival of EC patients.

    Click here to view.(518K, ppt)

    Fig. S3

    Calibration curve of the nomogram. (A) Calibration curves of 1-, 3-, and 5-year overall survival for EC patients in the training cohort. (B) Calibration curves of 1-, 3-, and 5-year for EC patients in the validation cohort.

    Click here to view.(1003K, ppt)

    Notes

    Funding:This study is supported by the Research and Development Fund of Peking University People’s Hospital (grant No. RS2021-05, RDY2021-13, and RDJP2022-09), National Natural Science Foundation of China (rant No. 82103419 and 82203568).

    Conflicts of Interest:No potential conflict of interest relevant to this article was reported.

    Data Availability Statement:The data underlying this article are available in the article and in its online supplementary material.

    Author Contributions:

    • Conceptualization: L.X., C.Y.

    • Data curation: L.X., D.Y.

    • Formal analysis: L.X.

    • Funding acquisition: L.X., Y.X.

    • Methodology: L.X.

    • Project administration: W.J.1

    • Resources: W.J.2, Y.X.

    • Software: L.X., Y.X.

    • Validation: L.X., C.Y.

    • Writing - original draft: Y.X., L.X.

    • Writing - review & editing: L.X., Y.X., W.J.1

    W.J.,1 Jianliu Wang; W.J.,2 Jingyuan Wang.

    ACKNOWLEDGEMENTS

    We especially appreciate Jiayang Jin, PhD of Peking University People’s Hospital for statistics, study deign and editing the manuscript.

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    MeSH Terms
    Calibration
    Cohort Studies
    Endometrial Neoplasms*
    Female
    Humans
    Metabolic Syndrome
    Nomograms
    Prognosis
    ROC Curve
    Respect
    Retrospective Studies
    Risk Factors*
    Metrics
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