Application of genetic shared gene to prediction of obesity and endometrial cancer disease risk

Abstract

The genetic shared gene is used for predicting obesity and endometrial cancer disease risks. The research shows that the reagent for detecting the expression quantity of MST1R or SULT1A2 is applied to preparation of the reagent for predicting the illness risk of obesity complicated with endometrial cancer. The reagent for detecting the expression quantity of the SULT1A2 is applied to preparation of a reagent for prognosis prediction of endometrial cancer. According to the application of the reagent for detecting the expression quantity of the polymorphic sites in the preparation of the reagent for predicting the illness risk of obesity combined with endometrial cancer, the polymorphic sites are one or more of rs1013402, rs11065987, rs11066188, rs11639988, rs12602912, rs3888190, rs4776976, rs7141420 and rs8055197. The invention lays a foundation for developing a detection reagent and a detection kit for predicting and treating obesity complicated with endometrial cancer.

Description

Technical Field

[0001] The present invention belongs to diagnostic markers, and specifically relates to a combined diagnostic marker for predicting the risk and prognosis of obesity and endometrial cancer. Background Art

[0002] Endometrial cancer (EC) is a group of epithelial malignancies originating from the endometrium. The known causes of EC are mainly related to the overexposure of the endometrium to estrogen. Other risk factors include obesity, aging, hypertension, diabetes, and Lynch syndrome. EC is highly associated with obesity, and multiple cohort studies have shown a statistically significant positive correlation between body mass index (BMI) and EC. Studies have shown that obese women (defined as BMI >30 and <35 kg / m²) have a 2.6-fold higher risk of developing EC than normal-weight women (BMI <25 kg / m²), while severe obesity (BMI >35 kg / m²) increases the risk to 4.7 times. In addition, a higher BMI is also significantly associated with EC-related mortality, and obesity doubles the mortality rate of EC patients. Obesity-induced dysregulation of endogenous steroid hormone secretion, insulin resistance, and accumulation of inflammatory mediators may be potential pathways that induce endometrial cancer.

[0003] Current research has focused on the phenotypic risk associations and molecular mechanisms between obesity and EC, but relatively little research has been conducted on the overlapping genetic architecture between these two conditions. A systematic analysis of the shared genetic basis is needed. A deeper understanding of the causal relationships and regulatory mechanisms behind the risk associations could improve weight management-based EC prevention strategies and facilitate the identification of potential molecular biomarkers. Summary of the invention

[0004] The purpose of the present invention is to more accurately identify people at high risk of obesity and endometrial cancer by clarifying genetic markers and risk sites associated with obesity, so as to develop personalized screening and intervention strategies.

[0005] Based on large-scale summary statistics from GWAS, the genetic association between obesity and EC was analyzed from multiple perspectives, including shared genetics, bidirectional causality, and transcriptome association. We found a significant positive genetic correlation between BMI and EC, suggesting that there may be a potential genetic shared structure between the two. Cross-trait analysis found 9 shared pleiotropic loci and annotated 7 mapped genes. Further using transcriptome-wide association study (TWAS) and Mendelian randomization analysis (SMR) based on summary data, 13 shared genes enriched in subcutaneous fat and 3 shared genes in the uterus were identified. Finally, the predictive efficacy of the shared risk gene SULT1A2 was confirmed by a real-world cohort, providing support for promoting early detection and effective management of EC patients.

[0006] The technical solution of the present invention is: The present invention discloses the use of a reagent for detecting the expression amount of MST1R or SULT1A2 in preparing a reagent for predicting the risk of obesity combined with endometrial cancer.

[0007] The present invention also discloses the use of a reagent for detecting the expression amount of MST1R or SULT1A2 in the preparation of a reagent for predicting the risk of obesity combined with endometrial cancer.

[0008] Preferably, the gene expression detection reagents are two antibodies against human MST1R and SULT1A2.

[0009] The higher the expression of genes MST1R and SULT1A2, the higher the likelihood of obesity and endometrial cancer.

[0010] The higher the expression level of the gene SULT1A2, the worse the prognosis of patients with endometrial cancer.

[0011] The present invention also discloses the use of a reagent for detecting the expression amount of polymorphic sites in the preparation of a reagent for predicting the risk of obesity combined with endometrial cancer, characterized in that the polymorphic sites are one or more of rs1013402, rs11065987, rs11066188, rs11639988, rs12602912, rs3888190, rs4776976, rs7141420 and rs8055197.

[0012] Preferably, the reagent for detecting the expression of polymorphic sites is a gene chip for detecting rs1013402, rs11065987, rs11066188, rs11639988, rs12602912, rs3888190, rs4776976, rs7141420 and rs8055197. The higher the expression of the target SNP, the higher the possibility of obesity and endometrial cancer.

[0013] The present invention will be further explained and described below: (1) The workflow of this study is as follows: Figure 1 As shown. We observed a positive genetic correlation between BMI and EC by using linkage disequilibrium score regression (LDSC). When the regression intercept was considered, the genetic correlation coefficient (rg) was 0.474 and the p-value was 2.2999e-23; when the regression intercept was not considered, the rg was 0.345 and the p-value was 2.3488e-76. This suggests that there may be a potential genetic shared structure between BMI and EC.

[0014] (2) In order to identify SNPs that play pleiotropic roles in the genetic sharing of BMI and EC, we used composite negative pleiotropic analysis (PLACO), multi-trait GWAS analysis (MTAG), and cross-phenotype association test (CPASSOC) to screen and obtained 221, 1990, and 45,506 SNPs, respectively. Combining the results of the three methods, we finally obtained nine genetic pleiotropic SNPs ( Figure 2 ): rs1013402, rs11065987, rs11066188, rs11639988, rs12602912, rs3888190, rs4776976, rs7141420, and rs8055197. They were significant (p<5e-8), independent, and novel. Based on VEP annotation, we obtained seven mapped genes. We also explored the linear nearest genes and 3D interacting genes associated with these pleiotropic SNPs based on the 3DSNP website.

[0015] (3) Next, in the MR analysis of the association between BMI and EC, one abnormal SNP (rs10121187) was removed based on MR-PRESSO, and finally 504 SNPs were included in the MR analysis. Five MR analysis methods (inverse variance weighted, MR Egger, simple model, weighted median and weighted model) all showed that an increase in BMI significantly increased the risk of EC (p<0.05, OR>1, Figure 3 A-3C). Steiger test further verified the causal relationship between BMI and EC. Pleiotropy test did not find horizontal pleiotropy in MR (p=0.771). Funnel plot showed low heterogeneity in MR ( Figure 3 D). Sensitivity analysis after eliminating each SNP showed that the overall effect tended to be stable after excluding each SNP, thus verifying the reliability of the results ( Figure 3 E). This study also performed a reverse MR analysis. Based on the two abnormal SNPs (rs7981863 and rs3184504) removed by MR-PRESSO, four SNPs were finally obtained for inclusion in the MR analysis. All five MR analyses showed that there was no significant causal association between EC and BMI (p>0.05, Figure 3 A).

[0016] (4) We used TWAS to explore genes enriched in specific tissues. In the BMI trait, we obtained 728 genes enriched in subcutaneous fat or uterus. In the EC trait, we obtained 98 enriched genes. Interestingly, these 98 enriched genes in the EC trait were also enriched in the BMI trait ( Figure 4A). GO pathway enrichment analysis of these 98 enriched genes showed that they were mainly enriched in various metabolic pathways, including amine metabolism, sulfide metabolism, and ethanol metabolism ( Figure 4 B). Further screening yielded 22 genes that were enriched in both subcutaneous fat and uterus, which were called common tissue enriched genes (CTEGs). We used SMR to explore the causal relationship between gene expression in tissues and the risk of BMI and EC, and obtained 16 shared risk / protective genes for BMI and EC, of ​​which 13 were from subcutaneous fat data and 3 were from uterine data ( Figure 4 C). The Upset graph shows that FAM85B in subcutaneous fat and uterine tissue is a common protective gene for BMI and EC. MST1R is a shared risk gene enriched in subcutaneous fat, and SULT1A2 is a shared risk gene enriched in the uterus ( Figure 4 D). All shared risk / protective genes enriched in subcutaneous fat and uterus obtained by the SMR analysis are shown in the following table: Table 1 SMR analysis exploring genes as common risk / protective exposures for BMI and EC from different tissues.

[0017]

[0018] (5) We collected uterine tissue samples from 65 EC patients (17 of whom were obese) and 25 normal individuals (6 of whom were obese) in the XY3-EC cohort and detected the protein concentration of SULT1A2 in the samples by enzyme-linked immunosorbent assay (ELISA). Figure 5 A shows the ELISA normalized regression curve between protein concentration and OD450nm. We found that the concentration of SULT1A2 in obese individuals was significantly higher than that in non-obese individuals ( Figure 5 B), while the SULT1A2 concentration in EC patients was significantly higher than that in normal individuals ( Figure 5 C). Comprehensive subgroup analysis of these two characteristics showed that obese EC patients had the highest SULT1A2 concentrations, while non-obese normal individuals had the lowest SULT1A2 concentrations ( Figure 5 D). As a common indicator for predicting the risk of EC and obesity, the ROC curve of SULT1A2 protein level in uterine tissue showed that its AUC value for predicting EC was 0.894 ( Figure 5 E, 95% CI: 0.814-0.952), and the AUC value for predicting obesity was 0.845 ( Figure 5F, 95% CI: 0.723-0.923), indicating that SULT1A2 has a high predictive efficacy for the onset of EC or obesity. KM survival analysis of EC showed that the overall survival (OS) and progression-free survival (PFS) of obese patients were significantly lower than those of non-obese patients ( Figure 5 G). Patients with high SULT1A2 expression had significantly reduced OS and PFS compared with those with low SULT1A2 expression ( Figure 5 H). The results of clinical subgroup analysis showed that the SULT1A2 protein concentration in the uterine tissue of patients with death, progression and type II EC was significantly increased. The SULT1A2 protein concentration in patients with stage III was significantly higher than that in patients with stage I ( Figure 5 I).

[0019] (6) We analyzed the association between SULT1A2 mRNA expression data and clinical characteristics in the TCGA-UCEC cohort. The expression level of SULT1A2 in EC tissues was significantly higher than that in adjacent adjacent tissues ( Figure 6 A). SULT1A2 is considered a significant risk factor for OS and PFS in EC patients ( Figure 6 B). The results of clinical subgroup analysis showed that the proportion of CN_HIGH in the high SULT1A2 expression group was significantly higher than that in the low SULT1A2 expression group, and correspondingly, the proportion of CN_LOW was significantly lower than that in the low expression group ( Figure 6 C). The expression level of SULT1A2 in CN_HIGH type patients was significantly higher than that in other subtypes ( Figure 6 D). Death and progression were associated with high SULT1A2 expression levels, and the SULT1A2 expression levels of stage IV patients were significantly higher than those of stage I patients. Compared with patients with endometrioid uterine adenocarcinoma (EEA), patients with serous uterine adenocarcinoma (SEA) had significantly higher SULT1A2 expression levels ( Figure 6 E).

[0020] Compared with the prior art, the advantages of the present invention are: The advantages of the present invention are mainly reflected in the in-depth exploration of the genetic association between obesity and endometrial cancer, the analysis of the pleiotropy of genes, and the validation of SULT1A2 as a potential biomarker. First, LDSC suggests that there is a positive genetic correlation between BMI and EC. Using two-way MR analysis to explore the causal relationship between the two, it was found that an increase in BMI significantly increased the risk of EC. This finding provides strong support for the important role of obesity in the occurrence of EC and emphasizes the importance of intervention in obesity in clinical practice. Secondly, with regard to the relationship between BMI and EC, 9 SNPs with genetic pleiotropy were systematically identified and screened through the three analysis methods of MTAG, PLACO and CPASSOC, providing important evidence for understanding the shared genetic structure of obesity and EC. This genetic-based research perspective provides a clearer biological basis for the comorbidity of obesity and EC, which is of great significance for designing effective intervention measures. In addition, we used TWAS and SMR analysis to identify shared risk / protective genes enriched in different tissues, and further explored the risk gene SULT1A2 enriched in uterine tissue. Through the validation of SULT1A2 in a real-world cohort, we confirmed that high expression of SULT1A2 is associated with obesity and poor prognosis of EC, and shows the highest expression level in obese EC patients. Most importantly, this study proposed SULT1A2 as a new biomarker that helps predict the risk of obesity and EC, and suggests that the EC subgroup with high expression of SULT1A2 has a worse prognosis. This discovery provides new ideas for the early diagnosis and prevention of EC, especially in obese female patients. By screening SULT1A2, more accurate management and treatment strategies can be provided for the clinic. Therefore, comorbid genes can be used as target sites in EC early screening reagents.

[0021] The detailed structure of the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is the workflow diagram of this study.

[0023] Figure 2 The figure is the Manhattan plot of the MTAG results. The genetic pleiotropic SNPs obtained through the joint screening of PLACO, MTAG and CPASSOC and their distribution on chromosomes are shown in the figure.

[0024] Figure 3Figure 2 is a bidirectional Mendelian randomization analysis of BMI and EC. (A) Forest plot showing the overall view of bidirectional MR. (B) Scatter plot showing the association between the effect of SNP on BMI (exposure) and the effect of SNP on EC (outcome) calculated by five MR regression methods. Each black dot represents a single SNP, and the line segment represents the 95% confidence interval. (C) Forest plot showing the effect of 504 SNPs included in the analysis on the MR results, as well as the combined effect obtained by MR Egger and inverse variance weighted regression. (D) Funnel plot showing the heterogeneity test of MR. Each black dot represents a single SNP, and SNPs evenly distributed on both sides of the line indicate less heterogeneity. (E) Forest plot visualizes the one-by-one elimination sensitivity analysis of MR.

[0025] Figure 4 Screening diagram of tissue-enriched shared risk / protective genes for BMI and EC based on TWAS and SMR. (A) Intersection of genes enriched in subcutaneous fat or uterus in BMI and EC traits based on TWAS. (B) GO pathway enrichment analysis using overlapping genes in (A). (C) Screening of genes in subcutaneous fat or uterus as shared risk / protective exposures for BMI and EC. (D) Upset diagram showing TWAS-based CTEGs and SMR-based shared risk / protective genes for BMI and EC in subcutaneous fat and uterus.

[0026] Figure 5 Figure 1 is a diagram of the shared risk validation and clinical significance exploration of SULT1A2 in uterine tissue based on the XY3-EC cohort. (A) Regression curve of SULT1A2 protein expression level in tissue and OD450nm obtained by fitting the ELISA standard data. (BD) Differences in SULT1A2 protein expression levels between obese and non-obese groups (B), between EC and normal groups (C), and between the combined subgroups of obesity and EC (D) based on ELISA. (E) ROC curve was used to validate the predictive efficacy of tissue SULT1A2 levels on the risk of EC. (F) ROC curve was used to validate the predictive efficacy of tissue SULT1A2 levels on the risk of obesity. (G, H) KM survival analysis showed the effects of obesity (G) and SULT1A2 protein expression level (H) on the overall survival (OS) and progression-free survival (PFS) of EC patients. (I) Differences in SULT1A2 protein expression levels in patients with different clinical characteristics.

[0027] Figure 6This is an exploration diagram of the clinical significance of SULT1A2 in EC tissue based on the TCGA-UCEC cohort. (A) Comparison of the difference in SULT1A2 expression levels between EC and normal uterine tissue based on the UALCAN database. (B) KM survival analysis of patients with high and low SULT1A2 expression to compare their OS and PFS. (C) Distribution of TCGA molecular subtypes in the high SULT1A2 group and the low SULT1A2 group. (D) Difference in SULT1A2 expression levels among four TCGA molecular subtypes. (E) Difference in SULT1A2 expression levels among patients with different clinical characteristics. All expression data in this figure are at the mRNA level. EEA, endometrioid uterine adenocarcinoma; MSE, mixed serous and endometrioid; SEA, serous uterine adenocarcinoma. DETAILED DESCRIPTION

[0028] 1. Acquisition of GWAS data The GWAS data for EC in this study came from a comprehensive cohort consisting of the Endometrial Cancer Association Consortium (ECAC), the Endometrial Cancer Epidemiology Consortium (E2C2), and the UK Biobank, including 12,906 EC and 108,979 healthy matched controls. The GWAS data for BMI in this study are meta-data jointly provided by the Giant Consortium and UK Biobank, which can be downloaded from the Giant Consortium project (https: / / portals.broadinstitute.org / collaboration / giant / index.php / GIANT_consortium_data_files). Trait-associated SNPs were obtained at all sites identified in the GWAS data. All samples of the GWAS data in this study were derived from European populations, and all original studies were approved by relevant ethics committees and informed consent was obtained from the participants.

[0029] 2. Linkage disequilibrium score regression (LDSC) LDSC is an important method for estimating global genetic correlations of multiple traits or diseases and was used to explore the common genetic basis between EC and BMI in this study. The pre-calculated LD scores of the 1000 Genomes European Project were used as a reference template. The “ldsc_rg” function of the “Fast2TWAS” R package was used to perform LDSC and calculate the rg value, which represents the genetic correlation between EC and BMI. The higher the rg value, the stronger the genetic association between the traits. p < 0.05 represents the significance of the genetic association. In order to exclude the influence of the basic risk of complex traits, rg and p values ​​were calculated separately under the condition of considering or not the intercept.

[0030] 3. Screening and annotation of shared risk loci between EC and BMI To explore which SNPs play a moderating role in the genetic association between EC and BMI, we used three methods to do so. PLACO is a new method for investigating pleiotropic loci between complex traits, based on Z scores and their variances, to discover genetic overlap between traits. Z score matrices and p-value matrices were constructed to estimate the variance of PLACO. Considering the potential associations between traits, the Z score correlation matrix was also estimated and incorporated into PLACO, and the p-values ​​of pleiotropic effects were finally obtained. In addition, we also used two cross-trait GWAS meta-analyses to screen for shared risk SNPs for EC and BMI: MTAG and CPASSOC. MTAG is applied when the SNP heritabilities of traits are the same to generate trait-specific association statistics. The “mtag_cross_trait” function in the “Fast2TWAS” R package was used to perform MTAG and test the equal variance-covariance assumption by calculating the assumed upper bound of the shared SNP effect size. CPASSOC is actually a weighted meta-analysis based on GWAS summary statistics, which is suitable for the presence of heterogeneous effects. The “CPASSOC_cross_trait” function in the “Fast2TWAS” R package was used to perform CPASSOC. For the three methods, p < 5e-8 was considered significant. Finally, significant, independent, and novel SNPs were screened out by combining the results of the three methods, which may be shared risk loci for EC and BMI.

[0031] In order to identify the genes corresponding to the screened SNPs, we used two websites for annotation: VEP (https: / / grch37.ensembl.org / info / docs / tools / vep / index.html) and 3DSNP (https: / / omic.tech / 3dsnpv2 / ). The 3DSNP website was used to screen the closest linear genes, and the annotation results of the two websites were combined to obtain the final corresponding genes.

[0032] 4. Causal analysis Bidirectional Mendelian randomization (MR) analysis was used to test the potential bidirectional causal association between BMI and EC. MR analysis should meet three basic assumptions: (1) SNPs are associated with exposure factors; (2) SNPs are independent of any confounders; and (3) SNPs can only affect outcomes through exposure factors. The “TwoSampleMR” R package and the “friendly2MR” R package were used to perform two-sample MR analysis. First, SNPs with strong trait association were screened with p < 5e-8 as the condition, and SNPs with linkage disequilibrium (r2 < 0.001) were removed by the “clump_data_local” function. F>10 was used as the threshold to remove weak instrumental variables. MR analysis included five methods: inverse variance weighting (IVW), MR Egger method, simple model, weighted median, and weighted model. Among them, IVW was considered the most important, but it required the absence of horizontal pleiotropy and heterogeneity. The results of IVW can be decisive, provided that the odds ratio (OR) values ​​obtained by the five methods are consistent in direction. p < 0.05 is considered to be a significant causal association between exposure and outcome. Mendelian randomization of pleiotropy residuals and outliers (MR-PRESSO) was used to detect pleiotropy outlier variants and remove outlier SNPs, and the “mr_pleiotropy_test” function was used to perform horizontal pleiotropy tests. The “mr_heterogeneity” function performed heterogeneity tests based on IVW and MR-Egger methods and visualized them with funnel plots. p ≥ 0.05 was considered to be absent of horizontal pleiotropy or heterogeneity. The leave-one-out method was used for sensitivity analysis.

[0033] 5. Transcriptome Wide Association Study (TWAS) To explore the association between tissue-specific gene enrichment and traits, TWAS analysis was performed by integrating GWAS and expression quantitative trait loci (eQTL) data. The expression and enrichment of genes were characterized based on the SNP heritability of eQTL. The transcriptome data of "GTExv8.ALL.Adipose_Subcutaneous" and "GTExv8.ALL.Uterus" were used as reference panels to train expression prediction models in the inference based on training function summaries (FUSION). BMI and EC were used as the research traits for TWAS analysis using the "TWAS_fusion_multiprocess" function of the "Fast2TWAS" R package. p<0.05 was considered to be tissue-specifically enriched and associated with the trait. Genes that were enriched in BMI or EC in both subcutaneous adipose tissue and uterine tissue were screened, and the intersection was taken to obtain CTEGs. The "clusterProfiler" R package was used to perform GO enrichment analysis on CTEGs.

[0034] 6. Mendelian randomization (SMR) based on pooled data To explore the causal association between tissue gene expression and traits, SMR analysis was performed under the framework of MR by integrating GWAS and eQTL data. Similarly, SMR analysis was performed in subcutaneous adipose tissue and uterine tissue based on GTExv8 transcriptome data. The “SMR_HEIDI_analysis” function of the “Fast2TWAS” R package performed SMR analysis and the heterogeneity dependency tool (HEIDI) test, which was used to identify heterogeneity and pleiotropy. The minimum number of SNPs required for SMR was 10. p SMR <0.05 with p HEIDI >0.05 was the screening condition for significant gene-trait association. SMR Corrected by FDR.

[0035] 7. Real World Queues This study included uterine tissue samples from 65 EC cases and 25 control cases collected from December 2017 to August 2024. All samples were obtained from the gynecological operating room of the Third Xiangya Hospital. The pathological diagnosis was confirmed independently by two pathologists. Part of the patient's non-lesional endometrial and myometrial tissue was removed during surgery and stored in liquid nitrogen within 30 minutes after ex vivo for subsequent research. This study was approved by the Ethics Committee of the Third Xiangya Hospital (No.: 2023-S168). All patients signed informed consent before surgery.

[0036] 6. Enzyme-linked immunosorbent assay (ELISA) Protein expression levels of all tissue samples were determined by ELISA. Tissues were washed with pre-cooled double-distilled water to remove residual blood, then weighed and cut into small pieces. The cut tissues were thoroughly homogenized with the corresponding volume of double-distilled water (usually a weight ratio of 1:9, for example, 1 gram of tissue sample to 9 ml of double-distilled water) in a glass homogenizer on ice. To further lyse tissue cells, the homogenate was then sonicated. Finally, the homogenate was centrifuged at 5000×g for 15 minutes, and the supernatant was collected for analysis. The double antibody one-step sandwich ELISA kit (Shanghai ELISA Biotechnology Co., Ltd.) was used to detect human sulfotransferase 1A2 (SULT1A2) in serum, plasma, tissue homogenate, and related liquid samples. Specific capture antibodies were pre-coated in microtiter wells, and then samples, standards, and HRP-labeled detection antibodies were added in sequence, followed by incubation and thorough washing. The color reaction was initiated by adding TMB substrate, and the color turned blue in the presence of peroxidase and yellow under acidic conditions. The depth of the color was positively correlated with the level of SULT1A2 in the sample. The absorbance (OD value) was measured at 450 nm using an ELISA reader to calculate the sample concentration. Based on the ELISA results, we verified the association of SULT1A2 expression in uterine tissue with obesity and EC and explored its clinical significance.

[0037] 9. Exploration of the clinical significance of SULT1A2 in EC tissue The TCGA-UCEC cohort data were downloaded from the GDC database using the “TCGAbiolinks” R package, including 545 cases of gene expression profile data stored in log2(TPM+1) format, 548 cases of clinical characteristics data, and 507 cases of molecular subtype data. The cohort patients were divided into four subtypes: copy number high (CN_HIGH), copy number low (CN_LOW), microsatellite instability high mutation (MSI-H), and POLE hypermutation (POLE). The difference in SULT1A2 expression between EC and normal uterine tissue was compared using the UALCAN database. KM survival analysis was performed based on the optimal cutoff value grouping to compare the differences in overall survival (OS) and progression-free survival (PFS) between patients in the high expression group and the low expression group. In addition, we compared the differences in SULT1A2 expression in patients with different clinical characteristics subgroups.

[0038] 10. Statistical analysis All bioinformatics analyses in this study were performed using R (v4.2.1), while the Perl programming language was used for batch processing and data cleaning. Unless otherwise stated, differential analysis was performed using the “limma” R package. The Wilcoxon test was used for comparisons between two groups, while the Kruskal-Wallis test was applied for comparisons involving three or more groups. When performing correlation analysis, the default method was Pearson correlation. The chi-square test was used to assess differences in rates and component ratios among groups. Kaplan-Meier survival analysis and Log-rank test were performed using the “survival” and “survminer” R packages to assess survival differences between groups. Two-tailed p values ​​less than 0.05 were considered statistically significant. For statistical analysis of ELISA tests, GraphPad Prism (version 9.0) was used, and independent Student-t tests were performed for differences between groups. All statistical tests were two-sided, and p values ​​less than 0.05 were considered significant.

Claims

1. Application of reagents for detecting the expression of MST1R or SULT1A2 in the preparation of risk prediction reagents for obesity combined with endometrial cancer.

2. Application of reagents for detecting SULT1A2 expression in the preparation of reagents for predicting the prognosis of endometrial cancer.

3. The use of a reagent for detecting and expressing polymorphic sites in the preparation of a reagent for predicting the risk of obesity combined with endometrial cancer, characterized in that: The polymorphic sites are one or more of rs1013402, rs11065987, rs11066188, rs11639988, rs12602912, rs3888190, rs4776976, rs7141420 and rs8055197.

4. The use according to claim 3, characterized in that: The reagent for detecting the expression of polymorphic sites is a gene chip that detects the above 9 sites: rs1013402, rs11065987, rs11066188, rs11639988, rs12602912, rs3888190, rs4776976, rs7141420 and rs8055197. The higher the target SNP content, the higher the risk of obesity combined with endometrial cancer.

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