Characteristics of serum lipid metabolism in postmenopausal obese women and its effect on the proliferation, invasion and migration of endometrial cancer cells

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Obesity is a high risk factor for many cancers, especially endometrial cancer. However, the effect of a single obesity factor on endometrial cancer has not been reported. Moreover, the characteristics of changes in serum lipid metabolites in obese patients are not clear. Methods. BMI, clinical data, lipids and pathological findings of 240 endometrial cancer patients were collected to analyse pathological differences between different BMI groups. Ishikawa and HEC-1A cells were treated with serum from obese and non-obese postmenopausal women, and the migration, invasion and proliferation of the two groups of cells were tested. LC-MS/MS technique was applied to detect the differences in lipid metabolism between obese and non-obese serum from postmenopausal women. Results. Endometrial cancer patients in the obese group had worse pathological staging. Also endometrial cancer patients with concomitant hyperlipidaemia had deeper myometrial infiltration. Endometrial cancer cells with obese serum effects had higher migration, invasion and proliferation. A total of 994 metabolites were identified in this study. 56 different metabolites were determined between the obese and non-obese groups. In the POS mode, DG35:3, DG 32:1lDG 14:0_18:1, DG 32:0, DG 34:1, TG 24:0lTG 8:0_8:0_8:0, TG O-32:1lTG O-16:1_8:0_8:0, TG 48:1lTG 14:0_16:0_18:1, TG 46:1lTG 14:0_16:0_16:1, TG 48:3lTG 14:0_16:1_18:2, CE 17:0, PI 32:1, DG 35:3lDG 17:1_18:2, TG 45:2lTG 11:0_16:0_18:2, TG 46:0lTG 14:0_16:0_16:0 were significantly downregulated in postmenopausal obese women, while the total level of PCs, DG 38:1, DG 48:6, PI-Cer 31:0;2O, SE 29:1/18:2, AHexCer 45:5;3OlAHexCer(O-15:1)30:4;3O, CAR 12:0, CAR 16:2, CAR 14:1, CAR 11:0, PE P-36:3|PE P-16:1_20:2, ASG 27:1;O, Hex, FA 10:0 were upregulated. In the NEG mode, LPE 16:0, LPE 22:5, LPE 20:3, PI 34:2|PI 16:0_18:2, PI 34:1|PI 16:0_18:1, PI 37:4|PI 17:0_20:4, PI 35:2|PI 17:0_18:2, PI 36:4|PI 16:0_20:4, PE-Cer 34:2;2O, PE 36:1;O|PE 18:1_18:0;2O, Cer 42:3;2O|Cer 18:2;2O/24:1, FA 24:6, FA 27:0, FA 20:4;O, FA 20:5 were downregulated in the obese group. SHexCer 40:1;2O, SHexCer, 42:3;2O, SHexCer 42:2;2O, SM 40:1;3O, PEO-44:8|PE O-22:2_22:6, PE O-46:8|PE, O-24:2_22:6, PE O-42:8|PE O-20:2_22:6 were upregulated in the obese group. Conclusions. The results of this study suggested that obesity might promote the progression of endometrial cancer. Changes in lipid metabolism suggested risk factors for endometrial cancer in postmenopausal obese women. postmenopausal obesity lipid metabolism profile endometrial cancer migration and invasion Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Endometrial cancer (EC) incidence and mortality rates continue to rise, with an estimated 97 000 cancer-specific deaths worldwide in 2020( 1 ). The incidence of EC has also markedly increased in China recently, in tandem with the escalating prevalence of obesity. Among all female malignant tumors, endometrial cancer has the strongest correlation with obesity, which is considered a very important risk factor for EC in postmenopausal women( 2 ). For every 5 kg/m 2 increase in body mass index (BMI), the risk of endometrial cancer increases by 60%( 3 ). Almost half of all endometrial cancers are attributed to overweight (BMI ≥ 25 kg/m 2 ) and obesity (BMI ≥ 30 kg/m 2 ) ( 4 ). Women with a BMI of at least 30 kg/m 2 before or at the time of endometrial cancer diagnosis had a 1.3- to 1.8-fold decrease in overall survival (OS) compared with those who had a BMI in the recommended range (18.5–24.9 kg/m 2 )( 5 – 7 ). Endometrial cancer survivors who had an increase in weight, BMI or waist circumference in the year before and during diagnosis experienced a nearly two-fold decrease in survival( 8 ). Bariatric surgery is associated with decreased risk of endometrial cancer( 9 ). While its aetiological importance is clear, the biology underpinning obesity-driven endometrial carcinogenesis is incompletely understood. According to the diagnostic criteria of the World Health Organization, overweight is defined as body mass index (BMI) of 25.0 to 29.9 kg/m 2 whereas obesity is defined as BMI of ≥ 30 kg/m 2 . The Chinese Working Group on Obesity recommends that overweight be defined as a BMI between 24 and < 28 kg/m 2 , and obesity be defined as a BMI ≥ 28 kg/m 2 . According to the International Agency for Research on Cancer (IARC), obesity is an independent risk factor for 13 types of cancer( 10 ), including endometrial, postmenopausal breast, renal cell, colorectal, gastrointestinal, and pancreatic cancer( 11 – 14 ). Colorectal cancer (relative risk, RR 1.3–1.4), postmenopausal breast cancer (RR 1.1–1.2), and endometrial cancer (RR 6.3–8.1) were more strongly associated with obesity than other cancer types( 10 ). Postmenopausal obesity is a growing concern because it might result in a spectrum of obesity-related diseases. The pathophysiological status of postmenopausal obesity is quite complex, and it is influenced by many pathogenic factors, in which estrogen deficiency plays a key role( 15 ). The complex interplay among these factors sometimes make the situation intractable, where estrogen deficiency can been known to cause fat accumulation and increase body weight (BW) ( 16 ), which are highly associated with MetS, type 2 diabetes (T2D), CVD, and stroke. Therefore, it is very helpful to clarify the metabolic characteristics of postmenopausal obesity, because it is useful to understand the pathophysiological mechanisms and influencing factors of postmenopausal obesity, which helps to select appropriate clinical strategies to prevent the development of endometrial cancer in postmenopausal women. Lipids play a crucial role in signal transduction, contribute to the structural integrity of cellular membranes, and regulate energy metabolism. Lipid metabolism affects many cellular processes that are critical for homeostasis including membrane synthesis and the use of lipids (i.e. triglycerides) as an energy store. Fatty acids (FAs) are essential lipids that constitute the major structural components of membrane lipids (i.e., glycerophospholipids and sphingolipids) while also serving as an important energy source through mitochondria-mediated beta-oxidation and tricarboxylic acid (TCA) cycle catabolism. Excessive levels of circulating lipids have been linked to metabolic diseases( 17 , 18 ) and cancer ( 19 ). Different lipid species have opposite effects on cancer proliferation and death, which is required careful mechanistic interrogation. Aberrant lipid metabolism is an important cause of endometrial cancer. Abnormal fat metabolism is one of the most significant metabolic changes in tumors. There is a need to develop deeper and more effective disease prevention measures and better treatment drugs. The purpose of this study was to investigate the influence of postmenopausal obesity on the occurrence and development of endometrial cancer and to investigate the lipid metabolism of postmenopausal obese women, which could provide information for subsequent research and provide a reference for clinical diagnosis and treatment. 2. METHODS AND MATERIALS 2.1 Participants Participants were recruited from the Xuzhou Central Hospital between January 2021 and December 2022. A total of 60 postmenopausal women were enrolled based on voluntary participation, including 30 with normal weight and 30 with obesity. Inclusion criteria were listed as follows:( 1 ) postmenopause, women aged 40 to 60 years with menstrual amenorrhea for ≧ 12 months ( 15 ), and ( 2 ) simple obesity (we used the Guidelines for Prevention and Control of Overweight and Obesity in Chinese Adults) ( 16 ). In short, waist circumference (WC) ≥ 80 cm or BMI ≥ 28 kg/m 2 were included. Exclusion criteria were: ( 1 ) type 1 diabetes, secondary hypertension; ( 2 ) serious heart, liver, kidney, or other complications; ( 3 ) psychiatric disorders; ( 4 ) secondary or druginduced obesity; and ( 5 ) pituitary tumors or Cushing syndrome. This study was designed and conducted in accordance with the Declaration of Helsinki of the World Medical Association (2000). It was approved and supervised by the Ethics Committee of Xuzhou Central Hospital (approval number :XZXY-LK-20230904-0150, study period: 2021–2022). Informed consent was obtained from each participant before the study began. The case data of 240 patients who underwent staging surgery for endometrial cancer in Xuzhou Central Hospital from January 2018 to December 2022 and were clearly diagnosed as endometrial cancer by postoperative pathology were included. The patient's age, height, preoperative weight, blood lipid level, pathological type, FIGO stage, tumor grade, lymph node metastasis and distant metastasis were included. This study was approved by the Ethics Committee of Xuzhou Central Hospital. Inclusion criteria: 1. First diagnosis in our hospital and complete full treatment such as surgery, radiotherapy and chemotherapy; 2. Postoperative pathology confirmed endometrial cancer; 3. Complete clinical data. Exclusion criteria: 1. History of other malignant tumors; 2. Incomplete clinical data. Demographic data and clinicopathological features of endometrial carcinoma were collected and analyzed. According to the standards of the Chinese Obesity Working Group and the Chinese Diabetes Society, the patients were classified into normal weight (BMI18.5 ~ 23.9 kg/m 2 ), overweight (24.0 ~ 27.9 kg/m 2 ) and obese ( ≧ 28 kg/m 2 ) according to BMI. The patients with endometrial cancer were divided into normal group, overweight group and obese group. The relationship between different BMI, lipid levels and clinicopathological features of endometrial carcinoma was retrospectively analyzed. 2.2 Clinical information and serum sample collection Clinical measures were measured during fasting, including height, weight, waist circumference, serum triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Body mass index Calculation method: Weight/height 2 (kg/m 2 ). Taking the horizontal edge of the midpoint of the lower margin of the costal arch as the perimeter, WC was measured with a tape measure. Venous blood from the elbow was collected on an empty stomach between 8:00 and 9:00 a.m. These (5 ml) were collected and centrifuged at 3500 RPM for 15 min, then stored at − 80℃ for subsequent experiments. 2.3 Cell culture Human endometrial cancer cell lines Ishikawa and HEC-1A cells were purchased from the cell bank of the Chinese Academy of Sciences. The above two kinds of cells were cultured in DMEM medium containing 10% fetal bovine serum at 37℃ and 5% CO 2 with saturated humidity. After cell adhesion, 0.25% trypsin was added for digestion and 1:3 passage. The experiment was carried out on the 3–5 generation of logarithmic growth cells with good growth. 2.4 Wound Healing Assay and Transwell invasion assay Ishikawa cells and HEC-1A cells were cultured overnight in six-well plates (10×10 5 cells / well). Upon reaching 100% confluence, a wound was created in the central area of single-layer cells using a 200 µL pipette tip. After the medium was discarded, the cells were washed twice with PBS, and added with DMEM containing 10% human serum in each well. The images of the migration area were captured under a light microscope at 0 h and 24 h, respectively, and the migration area was measured with Image J software. We use the following equation to calculate the wound closure : wound closure = {[(wound area at time 0) − (wound area at time 24) / (wound area at time 0)] × 100%}. 5×10 4 cells / well in culture mediums without serum were seeded into the apical of transwell chamber (pre-coated with Matrigel). And the basolateral chamber was filled with DMEM culture medium containing 10% human serum. After 48 h, the invaded cells were fixed, stained, photographed and counted. 2.5 Cell Counting Kit-8 Cell proliferation was assessed using Cell Counting Kit-8 (CCK8) assay. In brief, Ishikawa cells and HEC-1A cells were seeded at a density of 5 × 10 3 cells / well in a 96-well plates in 100 µL / well DMEM culture medium containing 10% human serum. At one, two and three days after culturing in the incubator, 10 µLCCK-8 reagent was added to each well, and the absorbance was measured at 450 nm in 2–3 h was measured using a microplate reader. The mean value of five wells was calculated, and each experiment was repeated three times. 2.6 Extraction of lipids The samples were removed from refrigerator at the − 20 ℃ and thawed in the 4 ℃ refrigerator for about 2–3 hours. The 100µL samples were taken and added with 200µL methyl tert-butyl ether and 40µL methanol. The samples were swirled at 1500–2500 rpm for 3–5 minutes, and then 180 µL supernatant was taken to centrifuged at 5000 rpm for 3–5 minutes. After centrifugation, 50 µL complex solution (acetonitrile/isopropyl alcohol = 1:9) was added for resolution. After high-speed centrifugation, supernatant was taken and transferred to sample vial for testing. 2.7 Instruments and Reagents Acquity High Performance Liquid Chromatograph (USA Waters, LTQ Orbitrap Velos High Resolution Mass Spectrometer (Thermo Fisher Technologies, USA), 5430R centrifuge (Eppendorf, Germany) were used. The chromatographic column was ACQUITY UPLC BEH C18 Column (2.1 mm×50 mm, 1.7 µm). In the mobile phase, phase A was water containing 0.1% formic acid and 2% acetonitrile and phase B was acetonitrile containing 0.1% formic acid and 2% Water. The column temperature was set at 45 ℃. The flow rate was set to 0.2 mL/ min. Mobile phase gradient system was performed as follows: 0 min, 5% B; 0 ~ 15 min, 5%~95% B; 15 ~ 15.2 min, 95%~5% B;15.2 ~ 20 min, 5% B. 2.8 Sample detection The UPLC/LTQ rbitrap Velos MS procedure was as follows: the sample size was 5 µL, the carrier gas was nitrogen, the flow rate was 0.2 mL/min and the column temperature was 45℃. MS setting: DDA mode, electrospray capillary voltage (positive ion was 2.5kv; negative ion was 1.5 kv), the capillary temperature is 320 ℃, the scanning range was 70 ~ 1 000 m/z, the cone hole voltage was 25 V, and the cone air flow rate was 50 L/h, the maximum injection time was 100 ms, the dynamic exclusion was 50 s, the MS resolution was 30 000, the MS2 resolution was 17 500. 2.9 Data processing and statistical analysis For large samples and multi-batch experiments, the ion signal was bound to be offset in the process of mass spectrum acquisition. If these effects were not removed, the quality of the data would be seriously affected. This project adopted data standardization of quality control samples. In simple terms, all samples to be collected were mixed together in equal amounts to form QC samples, and then when collecting data, every 10 samples, inserted a needle QC and a blank sample. Because QC samples were all the same, it was possible to use QC samples to simulate signal changes during data acquisition. After the data was obtained, QC was used as a training set for each peak, and then a prediction model was built to predict the signal change, so as to correct the signal in the sample. MS-DIAL 5.0 software was used to pre-process the original spectrum data to obtain the peak intensity matrix. The data were imported into SIMCA 14.1 software for multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis(OPLS-DA). The quality of OPLS-DA model was evaluated by using parameters such as R 2 X, R 2 Y,Q 2 in cross-validation, and the degree of model fitting was evaluated by 200 displacement tests. P 2 or 1 in univariate statistical analysis T-test were set as the screening criteria for differential metabolites. SPSS 26.0 software was used for statistical analysis. Data were presented as mean ± standard deviation. Independent sample t test was used for comparison between two groups, one-way analysis of variance was used for comparison between multiple groups, and LSD-t test was used for further pairwise comparison. Correlation analysis was performed by Pearson correlation analysis, and P < 0.05 was considered statistically significant. 3. Results 3.1 Clinical Characteristics In this study, 240 endometrial cancer patients with complete clinicopathological data could be included. The median age of these patients was 49.5 years. Among the patients with endometrial cancer, 35% (84 / 240) were normal weight, 25% (60 / 240) were overweight, and 40% (96 / 240) were obese. Among them, 38.33% ((92 / 240) had hyperlipidemia. Among these patients, 22.92% (55 / 240) had high blood pressure, 10%(24 / 240) had diabetes, 85% (204 / 240)had abnormal menstruation, and 4.58% (11 / 240) had infertility. According to FIGO stages, Stage IA accounted for 62.92% (151 / 240), stage IB14.59% ( 35 / 240), Stage II 8.33% (20 / 240), Stage III 13.33%(32 / 240), and Stage IV 0.83% (2 / 240). According to the pathological differentiation, highly differentiated accounted for 37.08% (89 / 240), moderately differentiated 43.34% (104 / 240), poorly differentiated 19.58% (47 / 240). The infiltrated muscle layer greater than 1/2 accounted for 32.08%. Lymph node metastasis occurred in 14.17% and vascular invasion 28.75% of these patients (Table 1 ). Table 1 Clinical data of endometrial cancer(n = 240) Patient clinical data N (%) Age Mean age 56.17 Median age 49.5 BMI ≥ 28 96( 40 ) 25 ~ 27.9 60( 25 ) < 25 84( 35 ) Hyperlipemia Yes 92(38.33) No 148(61.67) Hypertension Yes 55(22.92) No 185(77.08) Diabetes Yes 24( 10 ) No 216(90) Abnormal menstruation Yes 204(85) No 36( 15 ) Sterility Yes 11(4.58) No 229(95.42) FIGO Stage IA 151(62.92) IB 35(14.59) II 20(8.33) III 32(13.33) IV 2(0.83) Pathological differentiation Highly differentiated 89(37.08) Moderately differentiated 104(43.34) Poorly differentiated 47(19.58) Depth of infiltration No or ≦ 1/2 163(67.92) >1/2 77(32.08) Lymph node metastasis Yes 34(14.17) No 206(85.83) Vascular invasion Yes 69(28.75) No 171(71.25) Table 2 showed the relation between obese and pathological characteristics in the endometrial cancer. The pathological stage distribution of endometrial carcinoma was significantly different between non-obese group and obese group ( P 0.05 ) (Table 2 ). Table 3 analyzed the relationship between hyperlipidemia and pathological features of endometrial carcinoma. There were significant differences in myoinfiltration of endometrial carcinoma between hyperlipidemia group and non-hyperlipidemia group ( P 0.05 ) (Table 3 ). Table 2 Analysis of pathological features of obesity and endometrial carcinoma Pathological features n Normal Overweight Obesity P spearmancorrelation analysis Bilateral correlation coefficient test P-value FIGO Stage IA 151 52(34.44) 37(24.50) 62(41.06) 0.036 * -0.127 0.059 IB 35 8(22.86) 8(22.86) 19(54.29) II 20 9(45) 6( 30 ) 5( 25 ) III 32 13(40.63) 9(28.13) 10(31.25) IV 2 1(50) 0(0) 1(50) Pathological differentiation Highly 89 31(34.83) 21(23.60) 37(41.57) 0.947 0.008 0.907 Moderately 104 41(39.42) 26( 25 ) 37(35.58) Poorly 47 12(25.53) 13(27.66) 22(46.81) Lymph node metastasis Yes 34 11(32.35) 11(32.35) 12(35.30) 0.427 0.055 0.428 No 206 73(35.44) 49(23.79) 84(40.78) Vascular infiltration Yes 69 23(33.33) 21(30.43) 25(36.23) 0.437 0.052 0.439 No 171 61(35.67) 39(22.81) 71(41.52) Depth of infiltration No or ≦ 1/2 163 61(37.42) 39(23.93) 63(38.65) 0.359 -0.062 0.361 > 1/2 77 23(29.87) 21(27.27) 33(42.86) Table 3 The pathological relationship between hyperlipidemia and endometrial carcinoma Pathological feature n Non-hyperlipidemia(%) Hyperlipidemia(%) X² P FIGO Stage IA 151 94(62.25) 57(37.75) 0.89894 0.9247 IB 35 19(54.29) 16(45.71) II 20 12(60) 8( 40 ) III 32 20(62.5) 12(37.5) IV 2 1(50) 1(50) Differentiation Highly 89 52(58.43) 37(41.57) 0.01991 0.9901 Moderately 104 61(58.65) 43(41.35) Poorly 47 27(57.45) 20(42.55) Lymph node metastasis Yes 34 18() 16() 0.6784 0.7945 No 206 114() 92() Vascular infiltration Yes 69 44(63.77) 25(36.23) 2.4731 0.1158 No 171 90(52.63) 81(47.37) Depth of infiltration No or ≦ 1/2 163 99(60.74) 64(39.26) 4.9521 0.0261 * > 1/2 77 35(45.45) 42(54.55) 3.2 Obesity serum promotes the proliferation, invasion and migration of endometrial cancer cells The Transwell assay results indicated that significantly more migrated cells were observed in the obesity group compared with the vector group ( P = 0.0082). The results of wound healing assay in Ishikawa and HEC-1A cells revealed that after 24 hours, the wound closure in obesity group was significantly smaller and closer than that in non-obesity group, indicating that the migrated distance of tumor cells to the center of the wound increased significantly, indicating the obesity promoted the migration and invasion’ abilities in ishikawa and hec-1a cells. Cell invasion assay showed that human obese serum could enhance the invasion ability of ishikawa cells and HEC-1A cells. These results were shown in Fig. 1 A and B. CCK8 assay showed a significant reduction of Ishikawa and HEC-1A cells proliferation in the non-obesity group, compared with the obesity group (Fig. 1 C). 3.3 QC of the Present LC-MS Analysis After the different samples were injected into the mass spectrum, the data need to be normalized by summing all the identified peak intensities. This could eliminate the difference in the strength of the substance identified in the sample due to the difference in the amount of the sample. As could be seen from Fig. 2 , the high reproducibility and small differences in QC samples indicated that the data acquisition quality was very satisfactory. 3.4 Multivariate data analysis of serum lipid metabolites Figure 3 showed the results of multivariate data analysis of serum lipid metabolites. In the PCA model, PCA scatter plots of LC-MS/MS metabolic profiles of all samples were shown. In NEG and POS models, when the Chinese obesity diagnostic criteria (BMI ≧ 28.0kg/m2) were used for grouping, there was no significant separation between the normal group and the obese group (Fig. 3 A, 3 B). When we divided the obese group (BMI ≥ 30.0 kg/m 2 ) and the normal group (18.5 kg/m 2 ≦ BMI < 25.0 kg/m 2 ), the scatter plots between the normal and obese groups showed obvious separation in NEG mode and a separation in POS mode (Fig. 3 C, 3 D). 3.5 Identification of serum metabolites in obese and non-obese subjects A total of 994 metabolites were identified in this study (673 in POS mode and 321 in NEG mode). Under the conditions of T-test ( p 1 ), we determined 56 different metabolites between the PO and PN groups (33 in POS mode and 23 in NEG mode). Metabolites with a significant difference were visualized through volcano plots (Fig. 4 ). We used a complete linkage method to cluster these differential metabolites and form a heat map (Fig. 4 ), which shows that in the POS mode, DG35:3, DG 32:1lDG 14:0_18:1, DG 32:0, DG 34:1, TG 24:0lTG 8:0_8:0_8:0, TG O-32:1lTG O-16:1_8:0_8:0, TG 48:1lTG 14:0_16:0_18:1, TG 46:1lTG 14:0_16:0_16:1, TG 48:3lTG 14:0_16:1_18:2, CE 17:0, PI 32:1, DG 35:3lDG 17:1_18:2, TG 45:2lTG 11:0_16:0_18:2, TG 46:0lTG 14:0_16:0_16:0 were downregulated in the obese group. PC O-39:6, PC O-44:9, PC O-41:8, PC O-39:7, PC O-33:6, PC 40:11, DG 38:1, DG 48:6, PI-Cer 31:0;2O, SE 29:1/18:2, AHexCer 45:5;3OlAHexCer(O-15:1)30:4;3O, CAR 12:0, CAR 16:2, CAR 14:1, CAR 11:0, PE P-36:3|PE P-16:1_20:2, ASG 27:1;O, Hex, FA 10:0 were upregulated in the obese group. In the NEG mode, LPE 16:0, LPE 22:5, LPE 20:3, PI 34:2|PI 16:0_18:2, PI 34:1|PI 16:0_18:1, PI 37:4|PI 17:0_20:4, PI 35:2|PI 17:0_18:2, PI 36:4|PI 16:0_20:4, PE-Cer 34:2;2O, PE 36:1;O|PE 18:1_18:0;2O, Cer 42:3;2O|Cer 18:2;2O/24:1, FA 24:6, FA 27:0, FA 20:4;O, FA 20:5 were downregulated in the obese group. SHexCer 40:1;2O, SHexCer, 42:3;2O, SHexCer 42:2;2O, SM 40:1;3O, PEO-44:8|PE O-22:2_22:6, PE O-46:8|PE, O-24:2_22:6, PE O-42:8|PE O-20:2_22:6 were upregulated in the obese group. 4. Discussion In this study, we studied the effects of obesity serum on the proliferation and invasion of endometrial cancer cells, and explored the relationship between obesity and the pathological features of endometrial cancer. We used metabolomics to detect differences in lipid metabolites between obese and non-obese postmenopausal women. To the best of our knowledge, this study is the first to examine the effects of obese serum on endometrial cancer cells. The present study preliminarily confirmed that obesity could promote the progression of endometrial cancer. The prevalence of overweight and obesity has been expanded dramatically in almost all developing and developed countries, reaching pandemic levels of 60–70% of the adult population in industrialized countries, and being more frequent in females and in urban areas( 20 , 21 ). According to international standards, overweight and obesity are generally currently defined as a Body Mass Index (BMI) between 25-29.9 kg/m 2 and over 30 kg/m 2 respectively( 22 ). As a consequence of excessive or abnormal fat tissue accumulation which exceeds genetically and epigenetically determined adipose tissue stores, fat gets deposited and accumulates as ectopic fat tissue leading to increased risk for many disease entities. Ectopic fat deposition, which is described as the pathological expansion of white adipose tissue in areas where it should not be, such as intrahepatic, intra-abdominally, intramyocellular, etc, may cause metabolic, inflammatory, and immunologic alterations through a variety of pathways affecting Deoxyribonucleic Acid (DNA) repair, gene function, cell mutation rate as well as epigenetic changes that allow malignant transformation and progression( 23 ). Obesity, the most consistently cited risk factor, is strongly associated with EC, with a 60 percent increase in risk for every 5 units increase in BMI( 3 ). Excessive body fat is a major risk factor for endometrial cancer incidence, but its impact on recurrence and survival remains unclear. The results of a systematic review and meta-analysis showed that associations between higher BMI and all-cause mortality were observed for both Types I and II survivors, while recurrence associations were only significant among Type I cases. Obesity at the time of endometrial cancer diagnosis was associated with increased rates of cancer recurrence and all-cause mortality among endometrial cancer survivors, but not with endometrial cancer-specific mortality( 5 ). In our study, we analyzed the clinical and pathological features of 240 patients with endometrial cancer, of whom 40% were obese and 25% were overweight. And obesity was associated with the pathological stage of endometrial cancer. There was a significant correlation between hyperlipidemia and endometrial carcinoma muscle infiltration. A cohort study of 13061 patients in the United States showed that abnormal lipid metabolism is an independent risk factor for endometrial cancer. At the same time, the incidence rate of endometrial cancer in patients with elevated total cholesterol and high-density lipoprotein was 1.34 times and 1.65 times higher than that in patients with normal blood lipids, respectively( 24 ). Our research results were consistent with the above research results. Obesity can lead to metabolic abnormalities of adipose tissue, affecting the release of various hormones, adipokines, inflammatory cytokines, growth factors, enzymes, and free fatty acids ( 25 , 26 ). These multiple metabolic substrates have been implicated as risk factors for cancer incidence and mortality. In addition, the crosstalk between adipocyte and cancer cell leads to morphological and functional changes in adipose tissue, resulting in changes to endocrine and paracrine signaling( 27 , 28 ). The initiation and progression of several cancer types were contributed to these adipose tissue-specific secreted factors by driving metabolic reprograming of cells [20–22]. In turn, enhanced metabolic substrates released by altered adipose tissue physiology play a role in proliferation, invasion and metastasis of tumor cells. Our results showed that the proliferation and invasion of endometrial cancer cells were enhanced after treatment with obese serum. Abnormal fat metabolism is one of the most significant metabolic changes in tumors. Fat metabolism affects cells obtain energy, signaling molecules and biofilm components to gain the microstructure, and then influences the proliferation, survival, invasion, metastasis of tumors and the treatment of cancer( 29 ). Cancer cells in the tumor microenvironment change the availability of nutrients during tumor progression, but they mainly utilize lipid metabolism to support their rapid proliferation, survival, migration, invasion, and metastasis. So, the metabolome most closely reflects the human phenotype in health and disease as it is downstream of the genome, transcriptome and proteome( 30 ). In our research, a total of 994 metabolites were found (of those 673 in POS mode and 321 in NEG mode). There were 56 different lipid metabolites found to be significantly altered in postmenopausal women with obesity compared to normal weight, including 7 diacylglycerols (DGs), 7 triacylglycerols (TGs), 4 FAs, 1 cholesteryl ester (CE), 4 hexosylceramides (HexCers), 1 SM, 3 lysophosphatidylet hanolamines (LPEs), 6 phosphatidylcholines (PCs), 6 phosphatidylethanolamines (PEs), 6 phosphatidylinositols (PIs), 1 ceramide (Cer), and 1 complex of phosphatidyl inositol and ceramide (PI-Cer), 1ASG 4CAR. In this study, we found that PCs, PEs, SM, SE and SHexCers were significantly upregulated in postmenopausal obese women, while the total level of Cer, DGs, TGs, PIs, LPEs and FAs were decreased. Abnormal metabolism of phosphatidylcholine (PC) is an important sign of cancer cells. In female tumors such as ovarian cancer and endometrial cancer, PC changes are particularly pronounced, suggesting that PC-related metabolic enzymes may act through or be associated with estrogen and its receptors. The enzyme CK-α, which is responsible for the first step of de novo synthesis of PC, is upregulated in both ovarian cancer and endometrial cancer, resulting in an increase in PC( 31 , 32 ). Patel et al. found three key PC molecules, ePC 38:5, PC 40:3, and PC 42:4, as auxiliary means for early diagnosis of prostate cancer after comparing the lipidomics of serum from early-onset prostate cancer patients and healthy individuals( 33 ). In the serum of liver cancer patients, PC 32:0, PC 32:1, and o-PC-34:1 were upregulated( 34 ). In the present study, we found various types of phosphatidylcholine were increasing in postmenopausal obese women, which might be related to the increased risk of endometrial cancer in which may be related to the increased risk of endometrial cancer in obese women. Sphingolipids play important roles in cancer cell related functions such as cell proliferation, migration, inflammatory response, anticancer drug response, and prevention of cancer occurrence and development( 35 ). Sphingolipids are important components of membrane lipids and regulate the fluidity of lipid bilayer. Sphingolipids themselves have biological activity and signaling pathway regulation, among which SM, Cer, Sph, S1P and other bioactive lipids are the most studied. The regulation of cell function depends on the dynamic balance of these key lipids, and changes in their concentration and content affect cell fate. Cer is more closely related to apoptosis, senescence and growth capture, so it is considered as a tumor inhibitor, which mediates cancer inhibition function mainly by regulating the signaling pathways controlled by phosphatase, cathepsin, telomerase and various kinases. he evidence supporting Cer as a tumor suppressor included that the reduction of Cer in clinical samples was correlated with the malignancy degree and poor prognosis of astrocytoma. Compared with normal tissue, paired cancer tissue in ovarian cancer has a reduced total Cer content, which reduces tumor cell apoptosis and promotes tumor development( 36 ). Sphingomolipid SM is the most important sphingomolipid in the organism. Sphingomolipid synthetase SGMS/SMS and hydrolase SMPD form the SM cycle and regulate the content of SM and Cer. On the contrary, SM had more effects on cell proliferation and survival. Fatty acids, cholesterol, and phospholipids are closely related to the synthesis of cell biofilms. High expression of fatty acid synthase genes can be detected in endometrial cancer( 37 ), promoting endogenous fatty acid synthesis and providing raw materials and energy for the proliferation of endometrial cancer cells. Cholesterol is also a substrate for the synthesis of fat-soluble vitamins and steroid hormones( 38 ). As major components of glycolipids and phospholipids, fatty acids (FAs) can be esterified with a glycerol moiety to form triglycerides, which are nonpolar lipids synthesized and stored in lipid droplets during high nutrient availability and hydrolyzed to generate ATP by FA oxidation (FAO, also called β-oxidation) under energy stress conditions.sterols, including oxysterol and cholesterol, are critical regulators of sterol regulatory element (SRE)–binding protein (SREBP) activation for downstream gene expression, and thus their levels affect lipogenesis in cancer. Cholesterol is a component of lipid rafts for signaling and can also covalently modify Hedgehog and Smoothened proteins for Hedgehog signaling activation ( 39 , 40 )(Porter et al., 1996; Xiao et al., 2017). High concentrations of triglycerides produced by abnormal lipid metabolism can lead to a decrease in sex hormone binding proteins and an increase in free estrogen levels( 41 ). 5. Conclusions A metabolomics investigation was conducted to identify the differential metabolites in postmenopausal women with obesity. The results of this study suggested that obesity might promote the progression of endometrial cancer. Changes in lipid metabolism suggested risk factors for endometrial cancer in postmenopausal obese women. Although some mechanisms of these findings require further investigation for the direct evidence, we provided a pathophysiological scenario of postmenopausal women with obesity based on the metabolomic profiles explored by the UPLC/ LTQ Orbitrap Velos MS approach, which requires further verification. We believe the findings of this study are helpful for the clinicians to take measures in preventing the women with postmenopausal obesity from developing severe incurable obesity-related complications. Abbreviations EC, endometrial cancer; BMI, body mass index; OS, overall survival; FAs, fatty acids; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol. Declarations Ethics Statement The studies involving human participants were reviewed and approved by the ethics committee of the Xuzhou Central Hospital (approval number: XZXY-LK-20230904-0150). The patients/participants provided their written informed consent to participate in this study. Author Contributions JBZ and BZ contributed to the conception and design of the study. LK, JWL and MW provided access to tissue and prepared tissue samples. YYL, QW, and HX provided clinical information. YC, WRZ, and JY Z performed cell culture and Wound Healing Assay and Transwell invasion assay. YC and JBZ carried out cell proliferation experiments. JBZ and XYZ analyzed the data. JBZ, YC and YNZ prepared the figures and JBZ drafted the manuscript. All authors read and commented on the manuscript and approved the final version. Funding This study was supported by Xuzhou Science and Technology Projects (KC22167). This study was also supported by Jiangsu Province Traditional Chinese medicine science and technology development program(ZT202114). Acknowledgments The author would like to thank Professor Zhou Yuan from Xuzhou Medical University for providing a mass spectrometry platform and technical support. Availability of data and materials The dataset generated and/or analysed during the study are available from the corresponding author on reasonable request. 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 2021-05-01;71(3):209-49. Sung H, Siegel RL, Torre LA, Pearson-Stuttard J, Islami F, Fedewa SA, et al. Global patterns in excess body weight and the associated cancer burden. CA Cancer J Clin. 2019 2019-03-01;69(2):88-112. Crosbie EJ, Zwahlen M, Kitchener HC, Egger M, Renehan AG. Body mass index, hormone replacement therapy, and endometrial cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2010 2010-12-01;19(12):3119-30. 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CELL. 2019 2019-03-21;177(1):132-45. Beloribi-Djefaflia S, Vasseur S, Guillaumond F. Lipid metabolic reprogramming in cancer cells. ONCOGENESIS. 2016 2016-01-25;5(1):e189. Berger NA. Obesity and cancer pathogenesis. Ann N Y Acad Sci. 2014 2014-04-01;1311:57-76. Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond). 2008 2008-09-01;32(9):1431-7. Pischon T, Nimptsch K. Obesity and Risk of Cancer: An Introductory Overview. Recent Results Cancer Res. 2016 2016-01-20;208:1-15. Dalamaga M, Christodoulatos GS, Mantzoros CS. The role of extracellular and intracellular Nicotinamide phosphoribosyl-transferase in cancer: Diagnostic and therapeutic perspectives and challenges. METABOLISM. 2018 2018-05-01;82:72-87. Arthur RS, Kabat GC, Kim MY, Wild RA, Shadyab AH, Wactawski-Wende J, et al. Metabolic syndrome and risk of endometrial cancer in postmenopausal women: a prospective study. Cancer Causes Control. 2019 2019-04-01;30(4):355-63. Kahn CR, Wang G, Lee KY. Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome. J CLIN INVEST. 2019 2019-10-01;129(10):3990-4000. Stern JH, Rutkowski JM, Scherer PE. Adiponectin, Leptin, and Fatty Acids in the Maintenance of Metabolic Homeostasis through Adipose Tissue Crosstalk. CELL METAB. 2016 2016-05-10;23(5):770-84. Nieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR, et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. NAT MED. 2011 2011-10-30;17(11):1498-503. Hoy AJ, Balaban S, Saunders DN. Adipocyte-Tumor Cell Metabolic Crosstalk in Breast Cancer. TRENDS MOL MED. 2017 2017-05-01;23(5):381-92. Bian X, Liu R, Meng Y, Xing D, Xu D, Lu Z. Lipid metabolism and cancer. J EXP MED. 2021 2021-01-04;218(1). Kohler I, Hankemeier T, van der Graaf PH, Knibbe C, van Hasselt J. Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine. EUR J PHARM SCI. 2017 2017-11-15;109S:S15-21. Granata A, Nicoletti R, Tinaglia V, De Cecco L, Pisanu ME, Ricci A, et al. Choline kinase-alpha by regulating cell aggressiveness and drug sensitivity is a potential druggable target for ovarian cancer. Br J Cancer. 2014 2014-01-21;110(2):330-40. Trousil S, Lee P, Pinato DJ, Ellis JK, Dina R, Aboagye EO, et al. Alterations of Choline Phospholipid Metabolism in Endometrial Cancer Are Caused by Choline Kinase Alpha Overexpression and a Hyperactivated Deacylation Pathway. Cancer research: The official organ of the American Association for Cancer Research, Inc. 2014;74(23):6867-77. Patel N, Vogel R, Chandra-Kuntal K, Glasgow W, Kelavkar U. A novel three serum phospholipid panel differentiates normal individuals from those with prostate cancer. PLOS ONE. 2014 2014-01-20;9(3):e88841. Chen S, Yin P, Zhao X, Xing W, Hu C, Zhou L, et al. Serum lipid profiling of patients with chronic hepatitis B, cirrhosis, and hepatocellular carcinoma by ultra fast LC/IT-TOF MS. ELECTROPHORESIS. 2013 2013-10-01;34(19):2848-56. Canals D, Perry DM, Jenkins RW, Hannun YA. Drug targeting of sphingolipid metabolism: sphingomyelinases and ceramidases. Br J Pharmacol. 2011 2011-06-01;163(4):694-712. Saddoughi SA, Ogretmen B. Diverse functions of ceramide in cancer cell death and proliferation. ADV CANCER RES. 2013 2013-01-20;117:37-58. Rahman MT, Nakayama K, Ishikawa M, Rahman M, Katagiri H, Katagiri A, et al. Fatty acid synthase is a potential therapeutic target in estrogen receptor-/progesterone receptor-positive endometrioid endometrial cancer. Oncology. 2013 2013-01-20;84(3):166-73. Luo J, Yang H, Song BL. Mechanisms and regulation of cholesterol homeostasis. Nat Rev Mol Cell Biol. 2020 2020-04-01;21(4):225-45. Porter JA, Young KE, Beachy PA. Cholesterol modification of hedgehog signaling proteins in animal development. SCIENCE. 1996 1996-10-11;274(5285):255-9. Xiao X, Tang JJ, Peng C, Wang Y, Fu L, Qiu ZP, et al. Cholesterol Modification of Smoothened Is Required for Hedgehog Signaling. MOL CELL. 2017 2017-04-06;66(1):154-62. Lindemann K, Vatten LJ, Ellstrom-Engh M, Eskild A. Serum lipids and endometrial cancer risk: results from the HUNT-II study. INT J CANCER. 2009 2009-06-15;124(12):2938-41. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4479633","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307714411,"identity":"cd3ba967-8903-4d00-8456-012c3264b85f","order_by":0,"name":"Jingbo Zhang","email":"","orcid":"","institution":"Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingbo","middleName":"","lastName":"Zhang","suffix":""},{"id":307714412,"identity":"d3148f0c-edf5-40a9-a00b-97625b13a2ae","order_by":1,"name":"Ying Cao","email":"","orcid":"","institution":"Graduate School of 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10:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4479633/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4479633/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58172963,"identity":"d4401cf9-bd42-454b-bfeb-3427fca839b8","added_by":"auto","created_at":"2024-06-12 03:55:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1676313,"visible":true,"origin":"","legend":"\u003cp\u003eWound healing assay (A) and Transwell assay (B) and Proliferation detection (C) in the EC cell lines Ishikawa and HEC-1A. Quantitative analysis of scratch wound healing assay after a 24 h-treatment with obese or non-obese serum. Data are expressed as mean ± S.D. of three independent experiments. Ishikawa cells:*p \u0026lt; 0.05, obese vs non-obese serum. HEC-1A cells: **p \u0026lt; 0.01 obese vs non-obese serum.\u003c/p\u003e","description":"","filename":"Figure1proc.png","url":"https://assets-eu.researchsquare.com/files/rs-4479633/v1/f7947bbb9b036da55f28681a.png"},{"id":58172957,"identity":"3e8a4d41-c751-48e7-b6da-536b0680623c","added_by":"auto","created_at":"2024-06-12 03:55:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":959476,"visible":true,"origin":"","legend":"\u003cp\u003eQC of the Present LC-MS Analysis\u003c/p\u003e","description":"","filename":"Fig2QCofthePresentLCMSAnalysisproc.png","url":"https://assets-eu.researchsquare.com/files/rs-4479633/v1/d34a4248778cda9ac60860b4.png"},{"id":58172962,"identity":"1c788f9d-5f42-4fad-ba34-6b61e096f706","added_by":"auto","created_at":"2024-06-12 03:55:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":588950,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate analyses of serum lipid metabolites. (A,C) PCA (NEG); (B,D) PCA (POS)\u003c/p\u003e","description":"","filename":"Fig3proc.png","url":"https://assets-eu.researchsquare.com/files/rs-4479633/v1/baad1ba10600abb501286fa7.png"},{"id":58172964,"identity":"149c15d3-9ea5-4086-90e2-b1ba990008be","added_by":"auto","created_at":"2024-06-12 03:55:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2021551,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of serum metabolites in obese and non-obese subjects.\u003c/p\u003e","description":"","filename":"Fig.4proc.png","url":"https://assets-eu.researchsquare.com/files/rs-4479633/v1/ce17b556efb94fff1f377e6c.png"},{"id":96364022,"identity":"f8241a41-9522-45a0-aa7a-2a939b70926d","added_by":"auto","created_at":"2025-11-20 10:08:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6611348,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4479633/v1/e1acb187-f1ad-4e4a-b6b9-d64b451c89f5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characteristics of serum lipid metabolism in postmenopausal obese women and its effect on the proliferation, invasion and migration of endometrial cancer cells","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEndometrial cancer (EC) incidence and mortality rates continue to rise, with an estimated 97 000 cancer-specific deaths worldwide in 2020(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The incidence of EC has also markedly increased in China recently, in tandem with the escalating prevalence of obesity. Among all female malignant tumors, endometrial cancer has the strongest correlation with obesity, which is considered a very important risk factor for EC in postmenopausal women(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). For every 5 kg/m\u003csup\u003e2\u003c/sup\u003e increase in body mass index (BMI), the risk of endometrial cancer increases by 60%(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Almost half of all endometrial cancers are attributed to overweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e ) and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Women with a BMI of at least 30 kg/m\u003csup\u003e2\u003c/sup\u003e before or at the time of endometrial cancer diagnosis had a 1.3- to 1.8-fold decrease in overall survival (OS) compared with those who had a BMI in the recommended range (18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e)(\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Endometrial cancer survivors who had an increase in weight, BMI or waist circumference in the year before and during diagnosis experienced a nearly two-fold decrease in survival(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Bariatric surgery is associated with decreased risk of endometrial cancer(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). While its aetiological importance is clear, the biology underpinning obesity-driven endometrial carcinogenesis is incompletely understood.\u003c/p\u003e \u003cp\u003eAccording to the diagnostic criteria of the World Health Organization, overweight is defined as body mass index (BMI) of 25.0 to 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e whereas obesity is defined as BMI of \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e. The Chinese Working Group on Obesity recommends that overweight be defined as a BMI between 24 and \u0026lt;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e, and obesity be defined as a BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e. According to the International Agency for Research on Cancer (IARC), obesity is an independent risk factor for 13 types of cancer(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), including endometrial, postmenopausal breast, renal cell, colorectal, gastrointestinal, and pancreatic cancer(\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Colorectal cancer (relative risk, RR 1.3\u0026ndash;1.4), postmenopausal breast cancer (RR 1.1\u0026ndash;1.2), and endometrial cancer (RR 6.3\u0026ndash;8.1) were more strongly associated with obesity than other cancer types(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePostmenopausal obesity is a growing concern because it might result in a spectrum of obesity-related diseases. The pathophysiological status of postmenopausal obesity is quite complex, and it is influenced by many pathogenic factors, in which estrogen deficiency plays a key role(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The complex interplay among these factors sometimes make the situation intractable, where estrogen deficiency can been known to cause fat accumulation and increase body weight (BW) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), which are highly associated with MetS, type 2 diabetes (T2D), CVD, and stroke. Therefore, it is very helpful to clarify the metabolic characteristics of postmenopausal obesity, because it is useful to understand the pathophysiological mechanisms and influencing factors of postmenopausal obesity, which helps to select appropriate clinical strategies to prevent the development of endometrial cancer in postmenopausal women.\u003c/p\u003e \u003cp\u003eLipids play a crucial role in signal transduction, contribute to the structural integrity of cellular membranes, and regulate energy metabolism. Lipid metabolism affects many cellular processes that are critical for homeostasis including membrane synthesis and the use of lipids (i.e. triglycerides) as an energy store. Fatty acids (FAs) are essential lipids that constitute the major structural components of membrane lipids (i.e., glycerophospholipids and sphingolipids) while also serving as an important energy source through mitochondria-mediated beta-oxidation and tricarboxylic acid (TCA) cycle catabolism. Excessive levels of circulating lipids have been linked to metabolic diseases(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and cancer (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Different lipid species have opposite effects on cancer proliferation and death, which is required careful mechanistic interrogation.\u003c/p\u003e \u003cp\u003eAberrant lipid metabolism is an important cause of endometrial cancer. Abnormal fat metabolism is one of the most significant metabolic changes in tumors. There is a need to develop deeper and more effective disease prevention measures and better treatment drugs. The purpose of this study was to investigate the influence of postmenopausal obesity on the occurrence and development of endometrial cancer and to investigate the lipid metabolism of postmenopausal obese women, which could provide information for subsequent research and provide a reference for clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"2. METHODS AND MATERIALS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eParticipants were recruited from the Xuzhou Central Hospital between January 2021 and December 2022. A total of 60 postmenopausal women were enrolled based on voluntary participation, including 30 with normal weight and 30 with obesity. Inclusion criteria were listed as follows:(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) postmenopause, women aged 40 to 60 years with menstrual amenorrhea for ≧\u0026thinsp;12 months (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) simple obesity (we used the Guidelines for Prevention and Control of Overweight and Obesity in Chinese Adults) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In short, waist circumference (WC)\u0026thinsp;\u0026ge;\u0026thinsp;80 cm or BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e were included. Exclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) type 1 diabetes, secondary hypertension; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) serious heart, liver, kidney, or other complications; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) psychiatric disorders; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) secondary or druginduced obesity; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) pituitary tumors or Cushing syndrome. This study was designed and conducted in accordance with the Declaration of Helsinki of the World Medical Association (2000). It was approved and supervised by the Ethics Committee of Xuzhou Central Hospital (approval number :XZXY-LK-20230904-0150, study period: 2021\u0026ndash;2022). Informed consent was obtained from each participant before the study began.\u003c/p\u003e \u003cp\u003eThe case data of 240 patients who underwent staging surgery for endometrial cancer in Xuzhou Central Hospital from January 2018 to December 2022 and were clearly diagnosed as endometrial cancer by postoperative pathology were included. The patient's age, height, preoperative weight, blood lipid level, pathological type, FIGO stage, tumor grade, lymph node metastasis and distant metastasis were included.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of Xuzhou Central Hospital. Inclusion criteria: 1. First diagnosis in our hospital and complete full treatment such as surgery, radiotherapy and chemotherapy; 2. Postoperative pathology confirmed endometrial cancer; 3. Complete clinical data. Exclusion criteria: 1. History of other malignant tumors; 2. Incomplete clinical data. Demographic data and clinicopathological features of endometrial carcinoma were collected and analyzed. According to the standards of the Chinese Obesity Working Group and the Chinese Diabetes Society, the patients were classified into normal weight (BMI18.5\u0026thinsp;~\u0026thinsp;23.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (24.0\u0026thinsp;~\u0026thinsp;27.9 kg/m\u003csup\u003e2\u003c/sup\u003e) and obese (\u0026thinsp;≧\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e) according to BMI. The patients with endometrial cancer were divided into normal group, overweight group and obese group. The relationship between different BMI, lipid levels and clinicopathological features of endometrial carcinoma was retrospectively analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical information and serum sample collection\u003c/h2\u003e \u003cp\u003eClinical measures were measured during fasting, including height, weight, waist circumference, serum triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Body mass index Calculation method: Weight/height\u003csup\u003e2\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e). Taking the horizontal edge of the midpoint of the lower margin of the costal arch as the perimeter, WC was measured with a tape measure. Venous blood from the elbow was collected on an empty stomach between 8:00 and 9:00 a.m. These (5 ml) were collected and centrifuged at 3500 RPM for 15 min, then stored at \u0026minus;\u0026thinsp;80℃ for subsequent experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Cell culture\u003c/h2\u003e \u003cp\u003eHuman endometrial cancer cell lines Ishikawa and HEC-1A cells were purchased from the cell bank of the Chinese Academy of Sciences. The above two kinds of cells were cultured in DMEM medium containing 10% fetal bovine serum at 37℃ and 5% CO\u003csub\u003e2\u003c/sub\u003e with saturated humidity. After cell adhesion, 0.25% trypsin was added for digestion and 1:3 passage. The experiment was carried out on the 3\u0026ndash;5 generation of logarithmic growth cells with good growth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Wound Healing Assay and Transwell invasion assay\u003c/h2\u003e \u003cp\u003eIshikawa cells and HEC-1A cells were cultured overnight in six-well plates (10\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells / well). Upon reaching 100% confluence, a wound was created in the central area of single-layer cells using a 200 \u0026micro;L pipette tip. After the medium was discarded, the cells were washed twice with PBS, and added with DMEM containing 10% human serum in each well. The images of the migration area were captured under a light microscope at 0 h and 24 h, respectively, and the migration area was measured with Image J software. We use the following equation to calculate the wound closure : wound closure = {[(wound area at time 0) \u0026minus; (wound area at time 24) / (wound area at time 0)] \u0026times; 100%}. 5\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells / well in culture mediums without serum were seeded into the apical of transwell chamber (pre-coated with Matrigel). And the basolateral chamber was filled with DMEM culture medium containing 10% human serum. After 48 h, the invaded cells were fixed, stained, photographed and counted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Cell Counting Kit-8\u003c/h2\u003e \u003cp\u003eCell proliferation was assessed using Cell Counting Kit-8 (CCK8) assay. In brief, Ishikawa cells and HEC-1A cells were seeded at a density of 5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells / well in a 96-well plates in 100 \u0026micro;L / well DMEM culture medium containing 10% human serum. At one, two and three days after culturing in the incubator, 10 \u0026micro;LCCK-8 reagent was added to each well, and the absorbance was measured at 450 nm in 2\u0026ndash;3 h was measured using a microplate reader. The mean value of five wells was calculated, and each experiment was repeated three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Extraction of lipids\u003c/h2\u003e \u003cp\u003eThe samples were removed from refrigerator at the \u0026minus;\u0026thinsp;20 ℃ and thawed in the 4 ℃ refrigerator for about 2\u0026ndash;3 hours. The 100\u0026micro;L samples were taken and added with 200\u0026micro;L methyl tert-butyl ether and 40\u0026micro;L methanol. The samples were swirled at 1500\u0026ndash;2500 rpm for 3\u0026ndash;5 minutes, and then 180 \u0026micro;L supernatant was taken to centrifuged at 5000 rpm for 3\u0026ndash;5 minutes. After centrifugation, 50 \u0026micro;L complex solution (acetonitrile/isopropyl alcohol\u0026thinsp;=\u0026thinsp;1:9) was added for resolution. After high-speed centrifugation, supernatant was taken and transferred to sample vial for testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Instruments and Reagents\u003c/h2\u003e \u003cp\u003eAcquity High Performance Liquid Chromatograph (USA Waters, LTQ Orbitrap Velos High Resolution Mass Spectrometer (Thermo Fisher Technologies, USA), 5430R centrifuge (Eppendorf, Germany) were used. The chromatographic column was ACQUITY UPLC BEH C18 Column (2.1 mm\u0026times;50 mm, 1.7 \u0026micro;m). In the mobile phase, phase A was water containing 0.1% formic acid and 2% acetonitrile and phase B was acetonitrile containing 0.1% formic acid and 2% Water. The column temperature was set at 45 ℃. The flow rate was set to 0.2 mL/ min. Mobile phase gradient system was performed as follows: 0 min, 5% B; 0\u0026thinsp;~\u0026thinsp;15 min, 5%~95% B; 15\u0026thinsp;~\u0026thinsp;15.2 min, 95%~5% B;15.2\u0026thinsp;~\u0026thinsp;20 min, 5% B.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Sample detection\u003c/h2\u003e \u003cp\u003eThe UPLC/LTQ rbitrap Velos MS procedure was as follows: the sample size was 5 \u0026micro;L, the carrier gas was nitrogen, the flow rate was 0.2 mL/min and the column temperature was 45℃. MS setting: DDA mode, electrospray capillary voltage (positive ion was 2.5kv; negative ion was 1.5 kv), the capillary temperature is 320 ℃, the scanning range was 70\u0026thinsp;~\u0026thinsp;1 000 m/z, the cone hole voltage was 25 V, and the cone air flow rate was 50 L/h, the maximum injection time was 100 ms, the dynamic exclusion was 50 s, the MS resolution was 30 000, the MS2 resolution was 17 500.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Data processing and statistical analysis\u003c/h2\u003e \u003cp\u003eFor large samples and multi-batch experiments, the ion signal was bound to be offset in the process of mass spectrum acquisition. If these effects were not removed, the quality of the data would be seriously affected. This project adopted data standardization of quality control samples. In simple terms, all samples to be collected were mixed together in equal amounts to form QC samples, and then when collecting data, every 10 samples, inserted a needle QC and a blank sample. Because QC samples were all the same, it was possible to use QC samples to simulate signal changes during data acquisition. After the data was obtained, QC was used as a training set for each peak, and then a prediction model was built to predict the signal change, so as to correct the signal in the sample.\u003c/p\u003e \u003cp\u003eMS-DIAL 5.0 software was used to pre-process the original spectrum data to obtain the peak intensity matrix. The data were imported into SIMCA 14.1 software for multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis(OPLS-DA). The quality of OPLS-DA model was evaluated by using parameters such as R\u003csup\u003e2\u003c/sup\u003eX, R\u003csup\u003e2\u003c/sup\u003eY,Q\u003csup\u003e2\u003c/sup\u003e in cross-validation, and the degree of model fitting was evaluated by 200 displacement tests. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;2 or \u0026lt;\u0026thinsp;0.5, and OPLS-DA of variable influence on projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1 in univariate statistical analysis T-test were set as the screening criteria for differential metabolites. SPSS 26.0 software was used for statistical analysis. Data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Independent sample t test was used for comparison between two groups, one-way analysis of variance was used for comparison between multiple groups, and LSD-t test was used for further pairwise comparison. Correlation analysis was performed by Pearson correlation analysis, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.1 Clinical Characteristics\u003c/h2\u003e\n \u003cp\u003eIn this study, 240 endometrial cancer patients with complete clinicopathological data could be included. The median age of these patients was 49.5 years. Among the patients with endometrial cancer, 35% (84 / 240) were normal weight, 25% (60 / 240) were overweight, and 40% (96 / 240) were obese. Among them, 38.33% ((92 / 240) had hyperlipidemia. Among these patients, 22.92% (55 / 240) had high blood pressure, 10%(24 / 240) had diabetes, 85% (204 / 240)had abnormal menstruation, and 4.58% (11 / 240) had infertility. According to FIGO stages, Stage IA accounted for 62.92% (151 / 240), stage IB14.59% ( 35 / 240), Stage II 8.33% (20 / 240), Stage III 13.33%(32 / 240), and Stage IV 0.83% (2 / 240). According to the pathological differentiation, highly differentiated accounted for 37.08% (89 / 240), moderately differentiated 43.34% (104 / 240), poorly differentiated 19.58% (47 / 240). The infiltrated muscle layer greater than 1/2 accounted for 32.08%. Lymph node metastasis occurred in 14.17% and vascular invasion 28.75% of these patients (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eClinical data of endometrial cancer(n\u0026thinsp;=\u0026thinsp;240)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatient clinical data\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96(\u003cspan\u003e40\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026thinsp;~\u0026thinsp;27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(\u003cspan\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84(\u003cspan\u003e35\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92(38.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148(61.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(22.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185(77.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(\u003cspan\u003e10\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216(90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbnormal menstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(\u003cspan\u003e15\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSterility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(4.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229(95.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151(62.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(14.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathological differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(37.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104(43.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorly differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(19.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepth of infiltration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo or ≦\u0026thinsp;1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163(67.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77(32.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(14.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206(85.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(28.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171(71.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e showed the relation between obese and pathological characteristics in the endometrial cancer. The pathological stage distribution of endometrial carcinoma was significantly different between non-obese group and obese group ( \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ). In contrast, no significant difference was found between the two groups in terms of pathological differentiation, lymph node metastasis, vascular infiltration and depth of infiltration ( \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 ) (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e analyzed the relationship between hyperlipidemia and pathological features of endometrial carcinoma. There were significant differences in myoinfiltration of endometrial carcinoma between hyperlipidemia group and non-hyperlipidemia group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant difference was found between the two groups in terms of FIGO stage, pathological differentiation, lymph node metastasis and vascular infiltration ( \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 ) (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e ).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAnalysis of pathological features of obesity and endometrial carcinoma\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePathological features\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003espearmancorrelation analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBilateral correlation coefficient test P-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(34.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(24.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(41.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.036\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(22.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(22.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(54.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(\u003cspan\u003e30\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(\u003cspan\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(40.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(28.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(31.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathological differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(34.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(23.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(41.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(39.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(\u003cspan\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(35.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(25.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(27.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(46.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(32.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(32.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(35.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73(35.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(23.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84(40.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVascular infiltration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(30.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(36.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(35.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(22.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(41.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepth of infiltration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo or ≦\u0026thinsp;1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(37.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(23.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63(38.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(29.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(27.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(42.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe pathological relationship between hyperlipidemia and endometrial carcinoma\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePathological feature\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-hyperlipidemia(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eX\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(62.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(37.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.89894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.9247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(54.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(45.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(\u003cspan\u003e40\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifferentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(58.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(41.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.01991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.9901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(58.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(41.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(57.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(42.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18()\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16()\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.6784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.7945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114()\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92()\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVascular infiltration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(63.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(36.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2.4731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.1158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90(52.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81(47.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepth of infiltration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo or ≦\u0026thinsp;1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99(60.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(39.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e4.9521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.0261\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(45.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(54.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.2 Obesity serum promotes the proliferation, invasion and migration of endometrial cancer cells\u003c/h2\u003e\n \u003cp\u003eThe Transwell assay results indicated that significantly more migrated cells were observed in the obesity group compared with the vector group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0082). The results of wound healing assay in Ishikawa and HEC-1A cells revealed that after 24 hours, the wound closure in obesity group was significantly smaller and closer than that in non-obesity group, indicating that the migrated distance of tumor cells to the center of the wound increased significantly, indicating the obesity promoted the migration and invasion\u0026rsquo; abilities in ishikawa and hec-1a cells. Cell invasion assay showed that human obese serum could enhance the invasion ability of ishikawa cells and HEC-1A cells. These results were shown in Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003eA and B. CCK8 assay showed a significant reduction of Ishikawa and HEC-1A cells proliferation in the non-obesity group, compared with the obesity group (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.3 QC of the Present LC-MS Analysis\u003c/h2\u003e\n \u003cp\u003eAfter the different samples were injected into the mass spectrum, the data need to be normalized by summing all the identified peak intensities. This could eliminate the difference in the strength of the substance identified in the sample due to the difference in the amount of the sample. As could be seen from Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e, the high reproducibility and small differences in QC samples indicated that the data acquisition quality was very satisfactory.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.4 Multivariate data analysis of serum lipid metabolites\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan\u003e3\u003c/span\u003e showed the results of multivariate data analysis of serum lipid metabolites. In the PCA model, PCA scatter plots of LC-MS/MS metabolic profiles of all samples were shown. In NEG and POS models, when the Chinese obesity diagnostic criteria (BMI\u0026thinsp;≧\u0026thinsp;28.0kg/m2) were used for grouping, there was no significant separation between the normal group and the obese group (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eA, \u003cspan\u003e3\u003c/span\u003eB). When we divided the obese group (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e) and the normal group (18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;≦\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0 kg/m\u003csup\u003e2\u003c/sup\u003e), the scatter plots between the normal and obese groups showed obvious separation in NEG mode and a separation in POS mode (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eC, \u003cspan\u003e3\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.5 Identification of serum metabolites in obese and non-obese subjects\u003c/h2\u003e\n \u003cp\u003eA total of 994 metabolites were identified in this study (673 in POS mode and 321 in NEG mode). Under the conditions of T-test ( p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ) and OPLS-DA model ( VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 ), we determined 56 different metabolites between the PO and PN groups (33 in POS mode and 23 in NEG mode). Metabolites with a significant difference were visualized through volcano plots (Fig. \u003cspan\u003e4\u003c/span\u003e). We used a complete linkage method to cluster these differential metabolites and form a heat map (Fig. \u003cspan\u003e4\u003c/span\u003e), which shows that in the POS mode, DG35:3, DG 32:1lDG 14:0_18:1, DG 32:0, DG 34:1, TG 24:0lTG 8:0_8:0_8:0, TG O-32:1lTG O-16:1_8:0_8:0, TG 48:1lTG 14:0_16:0_18:1, TG 46:1lTG 14:0_16:0_16:1, TG 48:3lTG 14:0_16:1_18:2, CE 17:0, PI 32:1, DG 35:3lDG 17:1_18:2, TG 45:2lTG 11:0_16:0_18:2, TG 46:0lTG 14:0_16:0_16:0 were downregulated in the obese group. PC O-39:6, PC O-44:9, PC O-41:8, PC O-39:7, PC O-33:6, PC 40:11, DG 38:1, DG 48:6, PI-Cer 31:0;2O, SE 29:1/18:2, AHexCer 45:5;3OlAHexCer(O-15:1)30:4;3O, CAR 12:0, CAR 16:2, CAR 14:1, CAR 11:0, PE P-36:3|PE P-16:1_20:2, ASG 27:1;O, Hex, FA 10:0 were upregulated in the obese group. In the NEG mode, LPE 16:0, LPE 22:5, LPE 20:3, PI 34:2|PI 16:0_18:2, PI 34:1|PI 16:0_18:1, PI 37:4|PI 17:0_20:4, PI 35:2|PI 17:0_18:2, PI 36:4|PI 16:0_20:4, PE-Cer 34:2;2O, PE 36:1;O|PE 18:1_18:0;2O, Cer 42:3;2O|Cer 18:2;2O/24:1, FA 24:6, FA 27:0, FA 20:4;O, FA 20:5 were downregulated in the obese group. SHexCer 40:1;2O, SHexCer, 42:3;2O, SHexCer 42:2;2O, SM 40:1;3O, PEO-44:8|PE O-22:2_22:6, PE O-46:8|PE, O-24:2_22:6, PE O-42:8|PE O-20:2_22:6 were upregulated in the obese group.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we studied the effects of obesity serum on the proliferation and invasion of endometrial cancer cells, and explored the relationship between obesity and the pathological features of endometrial cancer. We used metabolomics to detect differences in lipid metabolites between obese and non-obese postmenopausal women. To the best of our knowledge, this study is the first to examine the effects of obese serum on endometrial cancer cells. The present study preliminarily confirmed that obesity could promote the progression of endometrial cancer.\u003c/p\u003e \u003cp\u003eThe prevalence of overweight and obesity has been expanded dramatically in almost all developing and developed countries, reaching pandemic levels of 60\u0026ndash;70% of the adult population in industrialized countries, and being more frequent in females and in urban areas(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). According to international standards, overweight and obesity are generally currently defined as a Body Mass Index (BMI) between 25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e and over 30 kg/m\u003csup\u003e2\u003c/sup\u003e respectively(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). As a consequence of excessive or abnormal fat tissue accumulation which exceeds genetically and epigenetically determined adipose tissue stores, fat gets deposited and accumulates as ectopic fat tissue leading to increased risk for many disease entities. Ectopic fat deposition, which is described as the pathological expansion of white adipose tissue in areas where it should not be, such as intrahepatic, intra-abdominally, intramyocellular, etc, may cause metabolic, inflammatory, and immunologic alterations through a variety of pathways affecting Deoxyribonucleic Acid (DNA) repair, gene function, cell mutation rate as well as epigenetic changes that allow malignant transformation and progression(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Obesity, the most consistently cited risk factor, is strongly associated with EC, with a 60 percent increase in risk for every 5 units increase in BMI(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExcessive body fat is a major risk factor for endometrial cancer incidence, but its impact on recurrence and survival remains unclear. The results of a systematic review and meta-analysis showed that associations between higher BMI and all-cause mortality were observed for both Types I and II survivors, while recurrence associations were only significant among Type I cases. Obesity at the time of endometrial cancer diagnosis was associated with increased rates of cancer recurrence and all-cause mortality among endometrial cancer survivors, but not with endometrial cancer-specific mortality(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In our study, we analyzed the clinical and pathological features of 240 patients with endometrial cancer, of whom 40% were obese and 25% were overweight. And obesity was associated with the pathological stage of endometrial cancer. There was a significant correlation between hyperlipidemia and endometrial carcinoma muscle infiltration. A cohort study of 13061 patients in the United States showed that abnormal lipid metabolism is an independent risk factor for endometrial cancer. At the same time, the incidence rate of endometrial cancer in patients with elevated total cholesterol and high-density lipoprotein was 1.34 times and 1.65 times higher than that in patients with normal blood lipids, respectively(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Our research results were consistent with the above research results.\u003c/p\u003e \u003cp\u003eObesity can lead to metabolic abnormalities of adipose tissue, affecting the release of various hormones, adipokines, inflammatory cytokines, growth factors, enzymes, and free fatty acids (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). These multiple metabolic substrates have been implicated as risk factors for cancer incidence and mortality. In addition, the crosstalk between adipocyte and cancer cell leads to morphological and functional changes in adipose tissue, resulting in changes to endocrine and paracrine signaling(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The initiation and progression of several cancer types were contributed to these adipose tissue-specific secreted factors by driving metabolic reprograming of cells [20\u0026ndash;22]. In turn, enhanced metabolic substrates released by altered adipose tissue physiology play a role in proliferation, invasion and metastasis of tumor cells. Our results showed that the proliferation and invasion of endometrial cancer cells were enhanced after treatment with obese serum.\u003c/p\u003e \u003cp\u003eAbnormal fat metabolism is one of the most significant metabolic changes in tumors. Fat metabolism affects cells obtain energy, signaling molecules and biofilm components to gain the microstructure, and then influences the proliferation, survival, invasion, metastasis of tumors and the treatment of cancer(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Cancer cells in the tumor microenvironment change the availability of nutrients during tumor progression, but they mainly utilize lipid metabolism to support their rapid proliferation, survival, migration, invasion, and metastasis. So, the metabolome most closely reflects the human phenotype in health and disease as it is downstream of the genome, transcriptome and proteome(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our research, a total of 994 metabolites were found (of those 673 in POS mode and 321 in NEG mode). There were 56 different lipid metabolites found to be significantly altered in postmenopausal women with obesity compared to normal weight, including 7 diacylglycerols (DGs), 7 triacylglycerols (TGs), 4 FAs, 1 cholesteryl ester (CE), 4 hexosylceramides (HexCers), 1 SM, 3 lysophosphatidylet hanolamines (LPEs), 6 phosphatidylcholines (PCs), 6 phosphatidylethanolamines (PEs), 6 phosphatidylinositols (PIs), 1 ceramide (Cer), and 1 complex of phosphatidyl inositol and ceramide (PI-Cer), 1ASG 4CAR. In this study, we found that PCs, PEs, SM, SE and SHexCers were significantly upregulated in postmenopausal obese women, while the total level of Cer, DGs, TGs, PIs, LPEs and FAs were decreased.\u003c/p\u003e \u003cp\u003eAbnormal metabolism of phosphatidylcholine (PC) is an important sign of cancer cells. In female tumors such as ovarian cancer and endometrial cancer, PC changes are particularly pronounced, suggesting that PC-related metabolic enzymes may act through or be associated with estrogen and its receptors. The enzyme CK-α, which is responsible for the first step of de novo synthesis of PC, is upregulated in both ovarian cancer and endometrial cancer, resulting in an increase in PC(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Patel et al. found three key PC molecules, ePC 38:5, PC 40:3, and PC 42:4, as auxiliary means for early diagnosis of prostate cancer after comparing the lipidomics of serum from early-onset prostate cancer patients and healthy individuals(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In the serum of liver cancer patients, PC 32:0, PC 32:1, and o-PC-34:1 were upregulated(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In the present study, we found various types of phosphatidylcholine were increasing in postmenopausal obese women, which might be related to the increased risk of endometrial cancer in which may be related to the increased risk of endometrial cancer in obese women.\u003c/p\u003e \u003cp\u003eSphingolipids play important roles in cancer cell related functions such as cell proliferation, migration, inflammatory response, anticancer drug response, and prevention of cancer occurrence and development(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Sphingolipids are important components of membrane lipids and regulate the fluidity of lipid bilayer. Sphingolipids themselves have biological activity and signaling pathway regulation, among which SM, Cer, Sph, S1P and other bioactive lipids are the most studied. The regulation of cell function depends on the dynamic balance of these key lipids, and changes in their concentration and content affect cell fate. Cer is more closely related to apoptosis, senescence and growth capture, so it is considered as a tumor inhibitor, which mediates cancer inhibition function mainly by regulating the signaling pathways controlled by phosphatase, cathepsin, telomerase and various kinases. he evidence supporting Cer as a tumor suppressor included that the reduction of Cer in clinical samples was correlated with the malignancy degree and poor prognosis of astrocytoma. Compared with normal tissue, paired cancer tissue in ovarian cancer has a reduced total Cer content, which reduces tumor cell apoptosis and promotes tumor development(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Sphingomolipid SM is the most important sphingomolipid in the organism. Sphingomolipid synthetase SGMS/SMS and hydrolase SMPD form the SM cycle and regulate the content of SM and Cer. On the contrary, SM had more effects on cell proliferation and survival.\u003c/p\u003e \u003cp\u003eFatty acids, cholesterol, and phospholipids are closely related to the synthesis of cell biofilms. High expression of fatty acid synthase genes can be detected in endometrial cancer(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), promoting endogenous fatty acid synthesis and providing raw materials and energy for the proliferation of endometrial cancer cells.\u003c/p\u003e \u003cp\u003eCholesterol is also a substrate for the synthesis of fat-soluble vitamins and steroid hormones(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). As major components of glycolipids and phospholipids, fatty acids (FAs) can be esterified with a glycerol moiety to form triglycerides, which are nonpolar lipids synthesized and stored in lipid droplets during high nutrient availability and hydrolyzed to generate ATP by FA oxidation (FAO, also called β-oxidation) under energy stress conditions.sterols, including oxysterol and cholesterol, are critical regulators of sterol regulatory element (SRE)\u0026ndash;binding protein (SREBP) activation for downstream gene expression, and thus their levels affect lipogenesis in cancer. Cholesterol is a component of lipid rafts for signaling and can also covalently modify Hedgehog and Smoothened proteins for Hedgehog signaling activation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)(Porter et al., 1996; Xiao et al., 2017). High concentrations of triglycerides produced by abnormal lipid metabolism can lead to a decrease in sex hormone binding proteins and an increase in free estrogen levels(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eA metabolomics investigation was conducted to identify the differential metabolites in postmenopausal women with obesity. The results of this study suggested that obesity might promote the progression of endometrial cancer. Changes in lipid metabolism suggested risk factors for endometrial cancer in postmenopausal obese women. Although some mechanisms of these findings require further investigation for the direct evidence, we provided a pathophysiological scenario of postmenopausal women with obesity based on the metabolomic profiles explored by the UPLC/ LTQ Orbitrap Velos MS approach, which requires further verification. We believe the findings of this study are helpful for the clinicians to take measures in preventing the women with postmenopausal obesity from developing severe incurable obesity-related complications.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eEC, endometrial cancer; BMI, body mass index; OS, overall survival; \u0026nbsp; FAs, fatty acids; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the ethics committee of the Xuzhou Central Hospital (approval number: XZXY-LK-20230904-0150). The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJBZ and BZ contributed to the conception and design of the study. LK, JWL and MW provided access to tissue and prepared tissue samples. YYL, QW, and HX provided clinical information. YC, WRZ, and JY Z performed cell culture and Wound Healing Assay and Transwell invasion assay. YC and JBZ carried out cell proliferation experiments. JBZ and XYZ analyzed the data. JBZ, YC and YNZ prepared the figures and JBZ drafted the manuscript. All authors read and commented on the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Xuzhou Science and Technology Projects (KC22167). This study was also supported by Jiangsu Province Traditional Chinese medicine science and technology development program(ZT202114).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author would like to thank Professor Zhou Yuan from Xuzhou Medical University for providing a mass spectrometry platform and technical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated and/or analysed during the study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\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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METABOLISM. 2018 2018-05-01;82:72-87.\u003c/li\u003e\n\u003cli\u003eArthur RS, Kabat GC, Kim MY, Wild RA, Shadyab AH, Wactawski-Wende J, et al. Metabolic syndrome and risk of endometrial cancer in postmenopausal women: a prospective study. Cancer Causes Control. 2019 2019-04-01;30(4):355-63.\u003c/li\u003e\n\u003cli\u003eKahn CR, Wang G, Lee KY. Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome. J CLIN INVEST. 2019 2019-10-01;129(10):3990-4000.\u003c/li\u003e\n\u003cli\u003eStern JH, Rutkowski JM, Scherer PE. Adiponectin, Leptin, and Fatty Acids in the Maintenance of Metabolic Homeostasis through Adipose Tissue Crosstalk. CELL METAB. 2016 2016-05-10;23(5):770-84.\u003c/li\u003e\n\u003cli\u003eNieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR, et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. 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Cholesterol Modification of Smoothened Is Required for Hedgehog Signaling. MOL CELL. 2017 2017-04-06;66(1):154-62.\u003c/li\u003e\n\u003cli\u003eLindemann K, Vatten LJ, Ellstrom-Engh M, Eskild A. Serum lipids and endometrial cancer risk: results from the HUNT-II study. INT J CANCER. 2009 2009-06-15;124(12):2938-41.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"postmenopausal obesity, lipid metabolism profile, endometrial cancer, migration and invasion","lastPublishedDoi":"10.21203/rs.3.rs-4479633/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4479633/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground.\u003c/b\u003e Obesity is a high risk factor for many cancers, especially endometrial cancer. However, the effect of a single obesity factor on endometrial cancer has not been reported. Moreover, the characteristics of changes in serum lipid metabolites in obese patients are not clear.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods.\u003c/b\u003e BMI, clinical data, lipids and pathological findings of 240 endometrial cancer patients were collected to analyse pathological differences between different BMI groups. Ishikawa and HEC-1A cells were treated with serum from obese and non-obese postmenopausal women, and the migration, invasion and proliferation of the two groups of cells were tested. LC-MS/MS technique was applied to detect the differences in lipid metabolism between obese and non-obese serum from postmenopausal women.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults.\u003c/b\u003e Endometrial cancer patients in the obese group had worse pathological staging. Also endometrial cancer patients with concomitant hyperlipidaemia had deeper myometrial infiltration. Endometrial cancer cells with obese serum effects had higher migration, invasion and proliferation. A total of 994 metabolites were identified in this study. 56 different metabolites were determined between the obese and non-obese groups. In the POS mode, DG35:3, DG 32:1lDG 14:0_18:1, DG 32:0, DG 34:1, TG 24:0lTG 8:0_8:0_8:0, TG O-32:1lTG O-16:1_8:0_8:0, TG 48:1lTG 14:0_16:0_18:1, TG 46:1lTG 14:0_16:0_16:1, TG 48:3lTG 14:0_16:1_18:2, CE 17:0, PI 32:1, DG 35:3lDG 17:1_18:2, TG 45:2lTG 11:0_16:0_18:2, TG 46:0lTG 14:0_16:0_16:0 were significantly downregulated in postmenopausal obese women, while the total level of PCs, DG 38:1, DG 48:6, PI-Cer 31:0;2O, SE 29:1/18:2, AHexCer 45:5;3OlAHexCer(O-15:1)30:4;3O, CAR 12:0, CAR 16:2, CAR 14:1, CAR 11:0, PE P-36:3|PE P-16:1_20:2, ASG 27:1;O, Hex, FA 10:0 were upregulated. In the NEG mode, LPE 16:0, LPE 22:5, LPE 20:3, PI 34:2|PI 16:0_18:2, PI 34:1|PI 16:0_18:1, PI 37:4|PI 17:0_20:4, PI 35:2|PI 17:0_18:2, PI 36:4|PI 16:0_20:4, PE-Cer 34:2;2O, PE 36:1;O|PE 18:1_18:0;2O, Cer 42:3;2O|Cer 18:2;2O/24:1, FA 24:6, FA 27:0, FA 20:4;O, FA 20:5 were downregulated in the obese group. SHexCer 40:1;2O, SHexCer, 42:3;2O, SHexCer 42:2;2O, SM 40:1;3O, PEO-44:8|PE O-22:2_22:6, PE O-46:8|PE, O-24:2_22:6, PE O-42:8|PE O-20:2_22:6 were upregulated in the obese group.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions.\u003c/b\u003e The results of this study suggested that obesity might promote the progression of endometrial cancer. Changes in lipid metabolism suggested risk factors for endometrial cancer in postmenopausal obese women.\u003c/p\u003e","manuscriptTitle":"Characteristics of serum lipid metabolism in postmenopausal obese women and its effect on the proliferation, invasion and migration of endometrial cancer cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 03:55:41","doi":"10.21203/rs.3.rs-4479633/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4637c35c-b84e-42e8-ba77-c8f35f63ec39","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T09:54:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 03:55:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4479633","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4479633","identity":"rs-4479633","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}