Genetic variation perspective reveals potential drug targets for subtypes of endometrial cancer

Genetic variation perspective reveals potential drug targets for subtypes of endometrial cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genetic variation perspective reveals potential drug targets for subtypes of endometrial cancer Jiamei Zhu, Youguo Chen, Ting Zhang, Juan Jiang, Nan Xia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4587130/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The study aims to identify potential drug targets for subtypes of endometrial cancer through a Mendelian randomization study and analyze their clinical value. Data from three quantitative trait loci and Genome-wide association studies (GWAS) Meta-analysis study explored potential drug targets in endometrial cancers (including endometrioid and non-endometrioid). Complementary analysis (including network analysis, therapeutic efficacy analysis, gene differential expression, and prognosis analysis) was investigated. Furthermore, immunohistochemical staining and clinical pathological features were explored to validate potential clinical significance. Five drug targets for endometrial carcinomas, seven drug targets for endometrioid histology, and seven drug targets for non-endometrioid histology were identified, with IGF2R (OR = 1.165; 95% CI 1.067–1.272; p = 1.046 × 10 − 2 ) and CST3 (OR = 0.523; 95% CI 0.339–0.804; p = 7.010×10 − 3 ) demonstrating core therapeutic potential supported by causal evidence at the transcriptional, translational, and tissue-specific levels. Our research explored potential therapeutic targets associated with endometrial cancer and provided new ideas for biomarker screening and drug development. Endometrial cancer protein drug target Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Endometrial cancer(EC) is the second gynecological malignancy after cervical cancer globally. It poses a severe threat to women's biological health, with morbidity and mortality rates increasing every year. The risk of endometrial cancer is mainly related to lifestyle factors (obesity, metabolic syndrome, age, and long-term exposure to oestrogen alone) and genetic factors (germline mutations) (1) . Endometrial cancer can be divided into two groups based on conventional pathohistology: endometrioid (estrogen-dependent, about 80%) and non-endometrioid (non-estrogen-dependent, about 20%). Non-endometrioid carcinosarcoma includes endometrial plasma carcinoma, endometrial clear cell carcinoma and carcinosarcoma. Due to the molecular heterogeneity of endometrial cancer, the TCGA has classified endometrial cancer into four major molecular subtypes, including POLE-mutated EC (5–15%), MSI/dMMR EC (25–30%), copy number high EC (serous-like group, 5–15%), copy number low EC (endometrioid-like group, 30–40%), of which 90% of copy number high EC had P53 mutations and had the worst prognosis. Molecular classification can help guide treatment and prognosis (2) . Although there has been considerable advancement in EC's precision and personalized therapy, there is still a lack of effective targets. It is essential to develop new effective drug targets for EC. The role of human plasma proteins in biological activities is significant, as they possess regions in their amino acid sequences that bind specifically to the targets of biologics and represent a substantial source of therapeutic targets (3) . In recent years, numerous studies have identified tens of thousands of protein quantitative trait loci (pQTL) through genome-wide association studies (GWAS) (4–8), which are associated with protein expression levels. These genetic variants affect protein levels in the human body by influencing the transcription and translation of proteins from specific genes. Mendelian randomization (MR) is an epidemiological method that employs genetic variation as an instrumental variable in observational data to assess the causal relationship between the exposure factor of interest and the outcome (disease) of interest. The development of MR and proteomics has facilitated the identification of diseases and potential therapeutic targets (e.g., colorectal cancer, diabetic nephropathy, Alzheimer's disease, etc.) (9, 10) . This study aimed to identify potential therapeutic targets for endometrial cancer in plasma proteins through a series of methods based on MR analysis, increasing the reliability of the results through additional studies. Furthermore, the two potential drug targets with the highest level of evidence were validated by immunohistochemistry of clinical samples to establish a scientific basis for selecting molecular markers for endometrial cancer. Results Screening candidate druggable proteins for EC Bayesian colocalization analysis identified 88 plasma proteins with endometrial cancer, 75 plasma proteins with endometrioid carcinoma, and 12 plasma proteins with non-endometrioid carcinoma that may share a causal variant (Supplementary Table 2–4). To reinforce colocalization results, five candidate drug targets for endometrial cancer, seven candidate drug targets for endometrioid carcinomas, and seven candidate targets for non-endometrioid carcinomas passed SMR analysis ( p 0.05) (Fig. 2 and Supplementary Table 5–7). The results demonstrated that the high expression of GSTO1 (OR = 1.073; 95%CI 1.032–1.117; p = 1.096×10 − 2 ), CBR3 (OR = 1.150; 95%CI 1.058–1.250; p = 2.097×10 − 2 ), and IGF2R (OR = 1.128; 95%CI 1.047–1.216; p = 2.481×10 − 2 ) and the low expression of MMP10 (OR = 0.831; 95%CI 0.730–0.905; p = 6.410×10 − 3 ) and HHIP (OR = 0.689; 95%CI 0.544–0.872; p = 2.511×10 − 2 ) were positively correlated with the risk of endometrial cancer. GSTO1 (OR = 1.100; 95% CI 1.050–1.152; p = 2.044×10 − 3 ), CBR3(OR = 1.175; 95% CI 1.065–1.297; p = 1.446×10 − 2 ), IGF2R (OR = 1.165; 95% CI 1.067–1.272; p = 1.046×10 − 2 ), and ACAP2 (OR = 3.997; 95% CI 1.681–9.506; p = 1.599×10 − 2 ) may exacerbate the progression of endometrioid carcinomas as risk proteins. Conversely, MMP10(OR = 0.762; 95% CI 0.669–0.868; p = 2.044×10 − 3 ), HHIP (OR = 0.625; 95% CI 0.469–0.831; p = 1.446×10 − 2 ), and TLR2 (OR = 0.211; 95% CI 0.073–0.609; p = 2.589×10 − 2 ) may help mitigate the risk of endometrioid carcinomas. Meanwhile, MAPK9 (OR = 3.070; 95% CI 1.695–5.561; p = 2.352×10 − 3 ) and FSTL5 (OR = 4.142; 95% CI 1.375–12.474; p = 1.811×10 − 2 ) were also significantly associated with an increased risk of non-endometrioid carcinomas, while DNAJB14 (OR = 0.069; 95% CI 0.014–0.346; p = 6.371×10 − 3 ), CST3 (OR = 0.523; 95% CI 0.339–0.804; p = 7.010×10 − 3 ), GMPR2 (OR = 0.547; 95% CI 0.371–0.808; p = 1.010×10 − 3 ), IFI16 (OR = 0.062; 95% CI 0.010–0.378; p = 7.010×10 − 3 ), and NEO1 (OR = 0.464; 95% CI 0.274–0.786; p = 7.873×10 − 3 ) may act as protective factors. In replication analysis of the UKBPPP and Gudjonsson A database, nine proteins (GSTO1, CRB3, IGF2R, MMP10, MAPK9, GMPR2, CST3, DNAJB14, NEO1) were successfully validated similarly to the primary analysis. Their directionality remained consistent (Fig. 3 ). Notably, IGF2R, a risk factor for endometrioid carcinoma, and CST3, a protective factor for non-endometrioid carcinoma, were supported by solid evidence in the primary analysis and replication analysis. Supplementary analysis We constructed protein-protein interactions of potential therapeutic targets using GeneMANIA, and only two interactions (Co-expression and genetic interactions) were identified (Supplementary Fig. 1 and Supplementary Table 8). In the druggability evaluation, these proteins (CRB3, GSTO1, HHIP, IGF2R, MMP10, TLR2, CST3, FSTL5, GMPR2, IFI16, MAPK9, NEO1) were predicted to be druggable targets (Supplementary Table 9). The DGIdb tool was employed to investigate the interactions of potential drug targets in endometrial cancer with currently known drugs. A total of 43 specific drugs were identified targeting five proteins (CRB3, IGF2R, TLR2, CST3, MAPK9), as detailed in Supplementary Table 10. Phenome-wide association studies of prior druggable proteins Given that most drugs act via the bloodstream, we employed PheWas on the two drug targets (IGF2R and CST3) based on the most substantial evidence to further investigate potential pleiotropic and toxic side effects (Fig. 4 and Supplementary Table 11). The results indicated that targeting IGF2R may influence the expression levels of proteins (LGMN, TPP1, NPC2), while CST3 primarily affected the expression of cystatin C. Additionally, no other side effects were linked to the druggable proteins. Differential and prognosis analysis of prior druggable proteins We observed that mRNA and protein expression levels of IGF2R were higher in endometrioid carcinoma than in normal tissues, with a statistically significant difference (Supplementary Fig. 2A1 and B1). The PFI of EC patients with high IGF2R expression was worse (Supplementary Fig. 2C1). In contrast, there was no significant difference in the mRNA and protein levels of CST3 in non-endometrioid histology (Supplementary Fig. 2A2 and B2). Notably, CST3 was protective against OS in endometrial cancer (Supplementary Fig. 2D2). Nevertheless, a trend was observed whereby the OS in patients with high IGF2R expression was worse than in patients with low IGF2R expression in endometrial cancer (Supplementary Fig. 2D1). Similarly, a trend was observed whereby the PFI in patients with high CST3 expression was higher than in patients with low CST3 expression (Supplementary Fig. 2C2), although the difference was not statistically significant. Immunohistochemical assay and clinicopathological features of prior druggable proteins We validated the differential protein expression of IGF2R and CST3 in patient tissue samples. Immunohistochemical results showed that IGF2R was expressed at higher levels in endometrioid carcinoma tissues than in normal tissues ( p < 0.0001), whereas CST3 was expressed at lower levels in non-endometrioid carcinoma tissues than in normal tissues ( p = 0.04) (Fig. 5 ). We separately performed IGF2R expression and CST 3 expression with clinicopathological features of endometrioid histology and non-endometrioid histology, including age, diabetes mellitus type 2, hypertension, FIGO stage, myometrial invasion, lymph node metastasis, and p53 mutation status. We found that IGF2R expression was significantly associated with the FIGO stage ( p = 0.018) and Myometrial invasion ( p = 0.002) (Table 1 ). Similarly, CST3 expression was significantly related to the FIGO stage ( p = 0.043) (Table 2 ). Table 1 Clinicopathological characteristics of endometrioid histological patients with IGF2R expression characteristics IGF2R p-value High expression Low expression n 31 20 Age, mean ± sd 60.323 ± 11.957 55.35 ± 8.0412 0.108 Diabetes mellitus type 2, n (%) 0.838 yes 5 (9.8%) 2 (3.9%) no 26 (51%) 18 (35.3%) Arterial hypertension, n (%) 0.469 yes 9 (17.6%) 4 (7.8%) no 22 (43.1%) 16 (31.4%) Tumor stage (FIGO), n (%) 0.018 I 21 (41.2%) 20 (39.2%) II 3 (5.9%) 0 (0%) III 7 (13.7%) 0 (0%) Histological grading, n (%) 0.145 G1 17 (33.3%) 15 (29.4%) G2-G3 14 (27.5%) 5 (9.8%) Myometrial invasion, n (%) 0.002 no 2 (3.9%) 4 (7.8%) shallow 16 (31.4%) 16 (31.4%) deep 13 (25.5%) 0 (0%) Lymph node metastasis, n (%) 0.254 yes 4 (7.8%) 0 (0%) no 27 (52.9%) 20 (39.2%) P53, n (%) 1 wild 30 (58.8%) 20 (39.2%) mutation 1 (2%) 0 (0%) Table 2 Clinicopathological characteristics of non-endometrioid histological patients with CST3 expression characteristics CST3 p-value High expression Low expression n 7 18 Age, mean ± sd 65.714 ± 5.4685 67.611 ± 6.6078 0.507 Diabetes mellitus type 2, n (%) 0.274 yes 0 (0%) 5 (20%) no 7 (28%) 13 (52%) Arterial hypertension, n (%) 0.672 yes 4 (16%) 8 (32%) no 3 (12%) 10 (40%) Tumor stage (FIGO), n (%) 0.043 I 6 (24%) 5 (20%) II 0 (0%) 6 (24%) III-IV 1 (4%) 7 (28%) Myometrial invasion, n (%) 0.231 no 1 (4%) 1 (4%) shallow 5 (20%) 8 (32%) deep 1 (4%) 9 (36%) Lymph node metastasis, n (%) 0.354 yes 1 (4%) 8 (32%) no 6 (24%) 10 (40%) p53, n (%) 0.179 mutation 1 (4%) 9 (36%) wild 6 (24%) 9 (36%) Discussion As endometrial cancer enters a new era of molecular characterization, the challenge of exploring new therapeutic targets and developing new drugs is enormous. In this study, we identified potential drug targets for different subtypes of endometrial cancer by colocalization analysis and SMR. We conducted a range of complementary analyses to explore the robustness and clinical significance of the results. The differential analysis of this study successfully verified that the trends in mRNA and protein levels of IGF2R were consistent with the causal direction of SMR. While the differential analysis of mRNA and protein levels for CST3 was not meaningful, the survival analysis indicated that CST3 may have a protective effect in non-endometrioid carcinoma. The findings of this study indicate potential drug targets for endometrial cancer, with initial support for these conclusions drawn from the existing literature. Several studies have demonstrated that GSTO1 is associated with various cancers, with elevated expression observed in colon, melanoma, and bladder cancers (11–13) . Furthermore, there is evidence that polymorphisms in GSTO1 may increase the risk of developing liver cancer and non-small cell lung cancer (NSCLC) (14–17) . Paul S et al. demonstrated that GSTO1 induces drug resistance in colon and breast cancer cells (18) . The number of studies and reports on the association of CBR3 and ACAP2 with cancer is relatively limited, whereas there are more studies on the association of LncRNA CBR3-AS1 and CircACAP2. The expression of CRB3-AS1 is elevated in cervical cancer, breast cancer, and osteosarcoma (19–21) . Hayashi-Okada M et al. identified 11 long non-coding RNAs (lncRNAs) in high-grade plasmacytoid carcinoma of the ovary and observed a trend toward malignant behavior of CBR3-AS1 (22) . It has been demonstrated that ACAP2 is downregulated in oesophageal cancer, leukemia, and lymphoma, suggesting it may act as a tumor suppressor (23, 24) . Nevertheless, CircACAP2 is markedly expressed in breast and colon cancers, where it exerts a pro-oncogenic effect, promoting cancer cell proliferation and metastasis (25, 26) . This discovery provides a direction for further study to validate the relationship between plasma proteins(GSTO1, CRB3, ACAP2) with endometrioid carcinoma and delve deeper into the mechanisms. Additional studies are needed in the future. IGF2R is a receptor in the IGF pathway that influences energy metabolism and regulation. Dysregulation of the IGF pathway has been linked to tumourigenesis, and the role of IGF2R in tumors is somewhat controversial. Previous studies have indicated that IGF2R may act as a tumor suppressor in certain cancers, including bladder and breast cancers (27–29) . Interestingly, some evidence supports an oncogenic role for IGF2R in specific cancers. A proteomic analysis revealed that IGF2R plays a role in developing laryngeal squamous cell carcinoma (30) . Takeda T et al. found that high IGF2R expression may be associated with a poor prognosis in cervical cancer (31) . The findings of this study indicate that IGF2R is a risk factor for endometrioid adenocarcinoma. Additionally, the study suggests that FIGO staging and depth of myometrial infiltration predict the prognosis of EC. These findings provide a framework for future research in this area. MMP10, belonging to the MMP family, is more highly expressed preoperatively than postoperatively in gastric cancer, suggesting that it may be a biomarker for the diagnosis of gastric cancer (32) . Zeng L et al. found that ovarian cancers with high MMP10 expression have a better prognosis (33) . The manuscript found that the MMP10 protein is negatively correlated with the development of endometrioid carcinoma, which appears to be at odds with previous studies. Nevertheless, another report indicates that MMP9, which belongs to the same MMP family, plays two distinct roles in tumors. Firstly, it has an oncogenic role in the tumor stroma, and secondly, it has an oncogenic role in the tumor epithelium (34) . This sets the stage for the role of MMP10 in cancer. It is well known that HHIP is a negative regulator of the hedgehog signaling pathway, and therefore, it plays an inhibitory role in tumourigenesis (35) . This is following our findings. The pathogenesis of non-endometrioid carcinoma differs from that of endometrioid carcinoma, with a correspondingly poorer prognosis. This study identified seven plasma proteins that were causally associated with non-endometrioid cancers. These included two risk factors (MAPK9, FSTL5) and five protective factors (DNAJB14, CST3, GMPR2, IFI16, NEO1). MAPK9, also known as JNK2, has been demonstrated to promote tumor formation by participating in the JNK signaling pathway (36, 37) , which is consistent with the results of this study. FSTL5 acts differently in different tumors; it inhibits tumor development in hepatocellular carcinoma (38) , whereas it is associated with a poor prognosis in medulloblastoma (39) . CST3, also known as cystatin C, is present in all body fluids (including serum, ascites, pleural fluid, and urine) and has diverse biological functions. However, it remains unclear whether its levels are altered in patients with malignancy (40) . CST3 is regulated by TGF-β, a factor in tumor development (41) . In addition, it has been demonstrated that CST3 plays a role in epigenetic regulation, as it contains extensive CpG islands that can suppress transcription through methylation (40) . By single-cell data analysis, Qianhua Wu et al. found that CST3 protein expression was down-regulated in non-endometrioid cancers (42) . These findings are consistent with those of the study mentioned above, which may be attributed to the reduced protein expression level observed following CST3 methylation. Further studies are required to confirm this hypothesis. The analysis of the above results leads to the conclusion that our results are plausible and provide new insights for our subsequent studies. The strengths of this study are as follows: firstly, the study employs a sufficiently large sample size, utilizes effective statistical methods with a low risk of bias, and employs multifaceted validation to ensure the robustness of the results; secondly, the study provides valuable insights into new therapeutic targets for endometrial cancer by assessing the druggability of the drug target and the interaction between the drug and the target. At the same time, some limitations should be acknowledged. The study population was limited to Europeans; further validation in different racial groups is necessary. The results of this study assessed the role of plasma proteins in endometrial cancer and validated the changes in the levels of the corresponding proteins in endometrial cancer tissues. Nevertheless, there may be differences between tissue proteins and plasma proteins. Consequently, further validation of changes in the levels of these proteins in plasma is needed. Methods The overall study design is presented in Fig. 1 . The specific data and methods are as follows. Proteomic Data Sources The primary proteomic data analyzed in this study were derived from the deCODE Genetics study dataset, identifying 4,907 plasma proteins from genome-wide association studies (GWASs) in 35,559 Icelanders (43) . To enhance the reliability of the findings, we used two independent protein GWAS sources, the UK Biobank Pharma Proteomics Project (UKBPPP) and the Gudjonsson A study, for replication. We selected 2923 plasma protein information from 54219 European participants in the UKBPPP study (44) . IVs were extracted from Gudjonsson A study, which included 5361 European participants and 2091 plasma proteins (45) (Supplementary Table 1). Outcome Data Sources The endometrial cancer data used in this study were derived from O'Mara TA's GWAS meta-analysis, based on 13 endometrial cancer studies (46) . The study included 12906 EC cases and 108979 controls of European ancestry. In stratified analysis by histological classification, the GWAS summary data were divided into 8758 endometrioid carcinoma cases and 46126 controls, 1230 non-endometrioid carcinoma cases, and 35447 controls(Supplementary Table 1). Colocalisation analysis Colocalization analysis aimed to confirm causal variants that may be shared by two signals(protein expression and endometrial cancer) in a given genomic region. This study used Bayesian colocalization analysis of the COLOC package (V.5.2.3) to ascertain the likelihood that two traits share causal variation. The posterior probability of five genetic loci sharing a common causal variation was employed to quantify this potential linkage. PPH0 was not associated with either protein expression or endometrial cancer; PPH1 was associated with protein expression only but not endometrial cancer risk; PPH2 was associated with endometrial cancer risk but not with protein expression; PPH3 was associated with both protein expression and endometrial cancer risk, but not at the same locus. PPH4 was associated with protein expression and endometrial cancer risk at the same locus (47) . The sum of the five posterior probabilities is equal to one. The plasma proteins selected for this study were PPH3 + PPH4 > 0.8 or PPH4 > 0.5, owing to being a relatively strong indicator of colocalization. MR analysis Mendelian randomization was a natural experiment that studied genetic variation and investigated the causal relationship between exposure and outcome. Mendelian randomization overcomes the limitations of traditional observational studies, is not susceptible to confounders and reverse causality bias, and has a reliability intermediate between observational studies and intervention trials (48) . Summary-data-based MR (SMR) is a method of MR that focuses on the causal relationship between gene or protein expression levels and disease. Analysis was conducted using the SMR software with the default parameters. In addition, IVs with MAFs (minor allele frequencies) below 0.01 were excluded to reduce the potential for bias due to rare mutations. The study screened endometrial cancer and its two subtypes (endometrioid carcinoma and non-endometrioid carcinoma) for drug targets within the deCODE genetics database through SMR analysis. The heterogeneity in dependent instruments (HEIDI) test was conducted on the SMR results. When the P > 0.05 indicated that the association of proteins with endometrial cancer was not driven by linkage disequilibrium, it was concluded that the association was not significant. The false discovery rate (FDR) was adjusted for in the context of multiple comparisons. The study validated the results through the UKBPPP and Gudjonsson A database. Supplementary analysis Protein interactions between EC plasma protein therapeutic targets were further explored using the GeneMANIA tool. Phenome-wide association studies (PheWAS) can be employed to assess the side effects of druggable genes and identify potential pleiotropy that SMR analyses fail to reveal. At a P-value of less than 1e − 8 , there is a significant correlation between gene expression and phenotype. Subsequently, the DGIdb database assessed the interrelationships between drugs and potential targets. This study aimed to identify potential drug targets in endometrial cancer at the translational level. To this end, we employed the UALCAN database to perform differential gene expression (mRNA and protein) analyses for the potential drug targets with the highest level of evidence. Additionally, Kaplan-Meier curves were employed to evaluate the predictive value of these drug targets in endometrial cancer. Hazard ratios (HR) were calculated using the Cox proportional hazard model. EC tissues A total of 51 cases of paraffin-embedded endometrioid histological tissues, 20 cases of paraffin-embedded normal endometrial tissues, and 25 cases of paraffin-embedded non-endometrioid histological tissues(including 18 endometrial serous carcinomas, five clear-cell carcinomas, and two carcinosarcoma) and 20 paraffin-embedded normal endometrial tissues were obtained from surgical patients who underwent surgery between October 2022 and May 2024 in the Department of Obstetrics and Gynaecology of Jingjiang People's Hospital. The clinical data were carried from medical records. All patients had not received radiotherapy, chemotherapy, or other biological treatments before surgery. The study was approved by the Ethics Committee of Jingjiang People's Hospital based on the declaration of Helsinki (2023-KY-019-01). All participants or their legal guardians were informed of the nature of the study and provided with an opportunity to consent to their participation. Immunohistochemistry and pathological features analysis Tissue sections of endometrioid carcinomas and their normal controls were incubated with an anti-IGF2R antibody (dilution 1:100, Cat No. 20253-1-AP, Proteintech, China) at 4°C overnight. Non-endometrioid carcinomas and their normal controls were incubated with an anti-CST3 (dilution 1:100, Cat No. 12245-1-AP, Proteintech, China) at 4°C overnight. Subsequently, the samples were incubated with a secondary antibody derived from the primary antibody(dilution 1:200, G1213, Servicebio, China). The sections were then visualized, stained with DAB, and restained with hematoxylin. Two senior pathologists will be required to assess the cases in question (using a double-blind method). Each case must be examined using a high-power microscope with at least five fields of view (each field of view must be greater than or equal to 200 cells). The assessment will be conducted following the two-tier scoring method. ( 1 ) According to the number of positive cell counts (the number of positive cells per 100 cells), the number of positive cells is assigned a negative score of 0 points, a score of 1 point for 50% of the total. ( 1 ) According to the number of positive cell counts (the number of positive cells per 100 cells), the number of positive cells is negative 0 points, < 10% of the total score of 1 point, 10% ~ 50% of the total score. ( 2 ) The following grades are applied according to the staining shade: 0 points for no color, 1 point for yellow, 2 points for brown-yellow, and 3 points for tan. The composite score is the product of the two scores, resulting in a four-grade scale: (-) 0–1; (+) 2; (++) 3–4; (+++) 6–9. A score of greater than five was classified as high expression, while a score of less than five was classified as low expression. Meanwhile, the study examined correlation analysis between IGF2R and CST3 expression and clinical pathological features of subtypes of endometrial cancer. Statistical Analysis The statistical analyses were conducted using R software (version 4.2.1) and GraphPad Prism 9 (GraphPad, Dotmatics, MA). The immunohistochemistry scores were subjected to statistical analysis using the Wilcoxon rank sum test, while the clinical data were analyzed using the Pearson’s Chi-square or Fisher’s Exact test. A p-value of less than 0.05 was considered the threshold for statistical significance. Conclusion The study has identified several plasma proteins associated with endometrial cancer, which could facilitate the early diagnosis of EC, the recognition of recurrence of EC, the assessment of risk and prognosis, and the development of related drugs. Further experimentation and clinical studies are required to assess the efficacy of these druggable targets in the future. Declarations Author contributions JZ and TZ conceptualized the study. JZ, TZ, NX, and YC developed the design. JZ, NX, and JJ performed data curation and analysis. TZ, and JJ supervised the MR analysis. JZ, TZ, and NX carried out data interpretation. All authors wrote and subsequently edited the original draft, which they approved as the final manuscript. Funding This work was supported by grants from Scientific Research Fund Program of Jingjiang People's Hospital(JRY-KY-2023-010). Competing interests All authors have no conflicts of interest to disclose. Acknowledgments We gratefully acknowledge the invaluable contributions of researchers at prestigious institutions, including the UK Biobank, the deCODE project, the Gudjonsson A study, and the eQTLGen consortium, who have significantly advanced quantitative trait loci research. Our sincere appreciation is also extended to the O'Mara TA's GWAS Meta-analysis study participants, who have actively participated and generously shared their genome-wide association studies data on endometrial cancer. Furthermore, we express our gratitude towards the diligent researchers and dedicated participants of other genome-wide association studies datasets used in this study. Their steadfast commitment has been crucial in propelling scientific advancements. We also thank BioRender.com for their assistance with illustrations. Data availability The original contributions presented in the study are included in the article/supplementary material. 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Yang X, Jia Q, Liu X, Wu W, Han Y, Zou Z, et al. MAPK9 is Correlated with a Poor Prognosis and Tumor Progression in Glioma. Frontiers in bioscience (Landmark edition). 2023;28(3):63. Zhang DY, Lei JS, Sun WL, Wang DD, Lu Z. Follistatin Like 5 (FSTL5) inhibits epithelial to mesenchymal transition in hepatocellular carcinoma. Chinese medical journal. 2020;133(15):1798-804. Remke M, Hielscher T, Korshunov A, Northcott PA, Bender S, Kool M, et al. FSTL5 is a marker of poor prognosis in non-WNT/non-SHH medulloblastoma. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2011;29(29):3852-61. Rivenbark AG, Coleman WB. Epigenetic regulation of cystatins in cancer. Frontiers in bioscience (Landmark edition). 2009;14(2):453-62. Keppler D. Towards novel anti-cancer strategies based on cystatin function. Cancer letters. 2006;235(2):159-76. Wu Q, Jiang G, Sun Y, Li B. Reanalysis of single-cell data reveals macrophage subsets associated with the immunotherapy response and prognosis of patients with endometrial cancer. Experimental cell research. 2023;430(2):113736. Ferkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, et al. Large-scale integration of the plasma proteome with genetics and disease. Nature genetics. 2021;53(12):1712-21. Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622(7982):329-38. Gudjonsson A, Gudmundsdottir V, Axelsson GT, Gudmundsson EF, Jonsson BG, Launer LJ, et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nature communications. 2022;13(1):480. O'Mara TA, Glubb DM, Amant F, Annibali D, Ashton K, Attia J, et al. Identification of nine new susceptibility loci for endometrial cancer. Nature communications. 2018;9(1):3166. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS genetics. 2014;10(5):e1004383. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ (Clinical research ed). 2018;362:k601. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.xlsx STROBEMRchecklist.doc supplementaryfigurelegends.doc supplementaryFigure1.jpg SupplementaryFigure2.jpg Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Sep, 2024 Reviews received at journal 02 Sep, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviews received at journal 27 Jul, 2024 Reviewers agreed at journal 21 Jul, 2024 Reviewers invited by journal 21 Jul, 2024 Editor assigned by journal 18 Jul, 2024 Editor invited by journal 21 Jun, 2024 Submission checks completed at journal 18 Jun, 2024 First submitted to journal 15 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4587130","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":320885923,"identity":"704c8f4a-5842-4693-95cd-65ca95078830","order_by":0,"name":"Jiamei Zhu","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Jiamei","middleName":"","lastName":"Zhu","suffix":""},{"id":320885924,"identity":"80b74db8-2c76-425b-86e2-1c5283c04e40","order_by":1,"name":"Youguo Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACCTiL+QADAxuIkUC0FrYEkrXwGBCnhX9287HHPBWH7TYc7/n8mafMjoGfPceA4ecOPJbcOZZuzHPmcPKGM2e3SfOcS2aQ7HljwNh7BrcWA4kcM2netsPJBjdytzHztjEzGNzIMWBmbMOnJf8bVEvO48+8bfUM9oS15LCBtNgBtTCAGCAR/FokbqSZSc45k54geeYYkHHuOI/EmWcFB3vxaOGfkfxM4k2FtT3f8ebHH96UVcvxtydvfPATjxYQYOJhYEhsgHJ4QMQB/BoYGBh/MDDYE1I0CkbBKBgFIxgAAD1ZTrKAmjUmAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Youguo","middleName":"","lastName":"Chen","suffix":""},{"id":320885925,"identity":"17189179-1e46-4aae-a576-3e929e92f6bc","order_by":2,"name":"Ting Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Zhang","suffix":""},{"id":320885926,"identity":"9ffde7ca-de0b-4b48-a6f9-ddde2c726be0","order_by":3,"name":"Juan Jiang","email":"","orcid":"","institution":"Jingjiang People's Hospital Affiliated to Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Jiang","suffix":""},{"id":320885927,"identity":"f0ec7de1-a0b0-4be1-bca0-3fa94269f085","order_by":4,"name":"Nan Xia","email":"","orcid":"","institution":"Jingjiang People's Hospital Affiliated to Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Xia","suffix":""}],"badges":[],"createdAt":"2024-06-15 15:26:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4587130/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4587130/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-78689-5","type":"published","date":"2024-11-15T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59605057,"identity":"01c705a6-f0de-492b-8d97-26e5df6354ff","added_by":"auto","created_at":"2024-07-03 18:28:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1009272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study design in our MR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTSMR: two-sample Mendelian randomization; SMR: summary data-based Mendelian randomization analysis; HEIDI: heterogeneity in the dependent instrument; pQTL: protein quantitative trait loci.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/8eb51d1942a5a899630408d3.jpg"},{"id":59605059,"identity":"9178a9f9-3d41-42d0-8a00-3a7f818dfe21","added_by":"auto","created_at":"2024-07-03 18:28:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":966794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe discovery phase uses SMR to search for therapeutic targets for Endometrial cancer (endometrioid and non-endometrioid carcinoma).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiscovery dataset: deCODE genetics database. OR: odds ratio; CI: confidence interval; HEIDI: heterogeneity in the dependent instrument.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/b1717dce87aeeaf0538cd8cd.jpg"},{"id":59606494,"identity":"1b2d0a5a-5ff7-495d-a8ec-a80d9479f16f","added_by":"auto","created_at":"2024-07-03 18:52:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1029663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRobustness evaluation of multi-data source replication analysis and SMR replication analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReplication datasets: UKBPPP and Gudjonsson A datasets. The highlighted background indicates therapeutic targets supported by the primary and replication analysis.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/cfd16acc5e4e7b5a751ac414.jpg"},{"id":59605134,"identity":"16240457-c415-4564-b8de-877bc3fad291","added_by":"auto","created_at":"2024-07-03 18:36:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":602235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePheWas results of drug targets IGF2R and CST3.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Manhattan plot for IGF2R. (B) Manhattan plot for CST3. In the figure, different colors represent different phenotypes, with each point or triangle representing a specific phenotype. Upward-facing triangles indicate that an increase in gene expression level is associated with an increased risk of the corresponding phenotype. When a point lies above the dashed line corresponding to the suggested threshold, the PheWAS result exceeds the recommended significance level.\u003c/p\u003e\n\u003cp\u003ePheWAS: phenome-wide association study.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/e9ca2a93de05986b63e48add.jpg"},{"id":59605491,"identity":"21231db0-76f2-4cac-afc7-9db5e964508c","added_by":"auto","created_at":"2024-07-03 18:44:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2452846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of IGF2R and CST3 in subtypes of endometrial cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B, C, D, I) The immunohistochemical staining of IGF2R protein in endometrioid histological sample and normal endometrial sample. (E, F, G, H, J) The immunohistochemical staining of CST3 protein in non-endometrioid histological sample and normal endometrial sample. (100x and 400x magnification, respectively. ns, P≥0.05; *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/153ada275182fe8efa1e8e29.jpg"},{"id":69275011,"identity":"8c25de10-3f66-4992-a98d-36aa087e5080","added_by":"auto","created_at":"2024-11-18 16:44:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6864645,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/0531f4ae-36e6-40d3-ab3b-3bf7600d317c.pdf"},{"id":59605066,"identity":"dd553862-c1f9-4870-9f9e-6456ec4ffa4d","added_by":"auto","created_at":"2024-07-03 18:28:54","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1595102,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/aed8233631060bd4883e1821.xlsx"},{"id":59605063,"identity":"4d4cd285-9ab4-4eaf-8a40-2596b0d0aec0","added_by":"auto","created_at":"2024-07-03 18:28:54","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":118784,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEMRchecklist.doc","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/dbbcb9b082393e167da7a6bb.doc"},{"id":59605490,"identity":"e9d8f008-87f9-484f-8bc0-b3458f7934bd","added_by":"auto","created_at":"2024-07-03 18:44:54","extension":"doc","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12800,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigurelegends.doc","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/e89163583ba317ad84a179c1.doc"},{"id":59605067,"identity":"a5fc143e-817d-467e-a98d-5c30c0083f8a","added_by":"auto","created_at":"2024-07-03 18:28:55","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":588918,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/84f4cb728516d6fa08835109.jpg"},{"id":59605065,"identity":"9c2dab0d-15d9-4250-9f9e-890ae822af97","added_by":"auto","created_at":"2024-07-03 18:28:54","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":681743,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4587130/v1/c1d57978ecbab569c563a4a9.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic variation perspective reveals potential drug targets for subtypes of endometrial cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer(EC) is the second gynecological malignancy after cervical cancer globally. It poses a severe threat to women's biological health, with morbidity and mortality rates increasing every year. The risk of endometrial cancer is mainly related to lifestyle factors (obesity, metabolic syndrome, age, and long-term exposure to oestrogen alone) and genetic factors (germline mutations)\u003csup\u003e(1)\u003c/sup\u003e. Endometrial cancer can be divided into two groups based on conventional pathohistology: endometrioid (estrogen-dependent, about 80%) and non-endometrioid (non-estrogen-dependent, about 20%). Non-endometrioid carcinosarcoma includes endometrial plasma carcinoma, endometrial clear cell carcinoma and carcinosarcoma. Due to the molecular heterogeneity of endometrial cancer, the TCGA has classified endometrial cancer into four major molecular subtypes, including POLE-mutated EC (5\u0026ndash;15%), MSI/dMMR EC (25\u0026ndash;30%), copy number high EC (serous-like group, 5\u0026ndash;15%), copy number low EC (endometrioid-like group, 30\u0026ndash;40%), of which 90% of copy number high EC had P53 mutations and had the worst prognosis. Molecular classification can help guide treatment and prognosis\u003csup\u003e(2)\u003c/sup\u003e. Although there has been considerable advancement in EC's precision and personalized therapy, there is still a lack of effective targets. It is essential to develop new effective drug targets for EC.\u003c/p\u003e \u003cp\u003eThe role of human plasma proteins in biological activities is significant, as they possess regions in their amino acid sequences that bind specifically to the targets of biologics and represent a substantial source of therapeutic targets\u003csup\u003e(3)\u003c/sup\u003e. In recent years, numerous studies have identified tens of thousands of protein quantitative trait loci (pQTL) through genome-wide association studies (GWAS)\u003csup\u003e(4\u0026ndash;8),\u003c/sup\u003e which are associated with protein expression levels. These genetic variants affect protein levels in the human body by influencing the transcription and translation of proteins from specific genes. Mendelian randomization (MR) is an epidemiological method that employs genetic variation as an instrumental variable in observational data to assess the causal relationship between the exposure factor of interest and the outcome (disease) of interest. The development of MR and proteomics has facilitated the identification of diseases and potential therapeutic targets (e.g., colorectal cancer, diabetic nephropathy, Alzheimer's disease, etc.)\u003csup\u003e(9, 10)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aimed to identify potential therapeutic targets for endometrial cancer in plasma proteins through a series of methods based on MR analysis, increasing the reliability of the results through additional studies. Furthermore, the two potential drug targets with the highest level of evidence were validated by immunohistochemistry of clinical samples to establish a scientific basis for selecting molecular markers for endometrial cancer.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eScreening candidate druggable proteins for EC\u003c/h2\u003e \u003cp\u003eBayesian colocalization analysis identified 88 plasma proteins with endometrial cancer, 75 plasma proteins with endometrioid carcinoma, and 12 plasma proteins with non-endometrioid carcinoma that may share a causal variant (Supplementary Table\u0026nbsp;2\u0026ndash;4). To reinforce colocalization results, five candidate drug targets for endometrial cancer, seven candidate drug targets for endometrioid carcinomas, and seven candidate targets for non-endometrioid carcinomas passed SMR analysis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and HEIDI test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;5\u0026ndash;7). The results demonstrated that the high expression of GSTO1 (OR\u0026thinsp;=\u0026thinsp;1.073; 95%CI 1.032\u0026ndash;1.117; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.096\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), CBR3 (OR\u0026thinsp;=\u0026thinsp;1.150; 95%CI 1.058\u0026ndash;1.250; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.097\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), and IGF2R (OR\u0026thinsp;=\u0026thinsp;1.128; 95%CI 1.047\u0026ndash;1.216; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.481\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and the low expression of MMP10 (OR\u0026thinsp;=\u0026thinsp;0.831; 95%CI 0.730\u0026ndash;0.905; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.410\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and HHIP (OR\u0026thinsp;=\u0026thinsp;0.689; 95%CI 0.544\u0026ndash;0.872; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.511\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) were positively correlated with the risk of endometrial cancer. GSTO1 (OR\u0026thinsp;=\u0026thinsp;1.100; 95% CI 1.050\u0026ndash;1.152; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.044\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), CBR3(OR\u0026thinsp;=\u0026thinsp;1.175; 95% CI 1.065\u0026ndash;1.297; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.446\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), IGF2R (OR\u0026thinsp;=\u0026thinsp;1.165; 95% CI 1.067\u0026ndash;1.272; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.046\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), and ACAP2 (OR\u0026thinsp;=\u0026thinsp;3.997; 95% CI 1.681\u0026ndash;9.506; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.599\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) may exacerbate the progression of endometrioid carcinomas as risk proteins. Conversely, MMP10(OR\u0026thinsp;=\u0026thinsp;0.762; 95% CI 0.669\u0026ndash;0.868; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.044\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), HHIP (OR\u0026thinsp;=\u0026thinsp;0.625; 95% CI 0.469\u0026ndash;0.831; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.446\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), and TLR2 (OR\u0026thinsp;=\u0026thinsp;0.211; 95% CI 0.073\u0026ndash;0.609; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.589\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) may help mitigate the risk of endometrioid carcinomas. Meanwhile, MAPK9 (OR\u0026thinsp;=\u0026thinsp;3.070; 95% CI 1.695\u0026ndash;5.561; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.352\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and FSTL5 (OR\u0026thinsp;=\u0026thinsp;4.142; 95% CI 1.375\u0026ndash;12.474; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.811\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) were also significantly associated with an increased risk of non-endometrioid carcinomas, while DNAJB14 (OR\u0026thinsp;=\u0026thinsp;0.069; 95% CI 0.014\u0026ndash;0.346; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.371\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), CST3 (OR\u0026thinsp;=\u0026thinsp;0.523; 95% CI 0.339\u0026ndash;0.804; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.010\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), GMPR2 (OR\u0026thinsp;=\u0026thinsp;0.547; 95% CI 0.371\u0026ndash;0.808; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.010\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), IFI16 (OR\u0026thinsp;=\u0026thinsp;0.062; 95% CI 0.010\u0026ndash;0.378; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.010\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), and NEO1 (OR\u0026thinsp;=\u0026thinsp;0.464; 95% CI 0.274\u0026ndash;0.786; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.873\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) may act as protective factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn replication analysis of the UKBPPP and Gudjonsson A database, nine proteins (GSTO1, CRB3, IGF2R, MMP10, MAPK9, GMPR2, CST3, DNAJB14, NEO1) were successfully validated similarly to the primary analysis. Their directionality remained consistent (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, IGF2R, a risk factor for endometrioid carcinoma, and CST3, a protective factor for non-endometrioid carcinoma, were supported by solid evidence in the primary analysis and replication analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSupplementary analysis\u003c/h2\u003e \u003cp\u003eWe constructed protein-protein interactions of potential therapeutic targets using GeneMANIA, and only two interactions (Co-expression and genetic interactions) were identified (Supplementary Fig.\u0026nbsp;1 and Supplementary Table\u0026nbsp;8). In the druggability evaluation, these proteins (CRB3, GSTO1, HHIP, IGF2R, MMP10, TLR2, CST3, FSTL5, GMPR2, IFI16, MAPK9, NEO1) were predicted to be druggable targets (Supplementary Table\u0026nbsp;9). The DGIdb tool was employed to investigate the interactions of potential drug targets in endometrial cancer with currently known drugs. A total of 43 specific drugs were identified targeting five proteins (CRB3, IGF2R, TLR2, CST3, MAPK9), as detailed in Supplementary Table\u0026nbsp;10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePhenome-wide association studies of prior druggable proteins\u003c/h2\u003e \u003cp\u003eGiven that most drugs act via the bloodstream, we employed PheWas on the two drug targets (IGF2R and CST3) based on the most substantial evidence to further investigate potential pleiotropic and toxic side effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Table\u0026nbsp;11). The results indicated that targeting IGF2R may influence the expression levels of proteins (LGMN, TPP1, NPC2), while CST3 primarily affected the expression of cystatin C. Additionally, no other side effects were linked to the druggable proteins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDifferential and prognosis analysis of prior druggable proteins\u003c/h2\u003e \u003cp\u003eWe observed that mRNA and protein expression levels of IGF2R were higher in endometrioid carcinoma than in normal tissues, with a statistically significant difference (Supplementary Fig.\u0026nbsp;2A1 and B1). The PFI of EC patients with high IGF2R expression was worse (Supplementary Fig.\u0026nbsp;2C1). In contrast, there was no significant difference in the mRNA and protein levels of CST3 in non-endometrioid histology (Supplementary Fig.\u0026nbsp;2A2 and B2). Notably, CST3 was protective against OS in endometrial cancer (Supplementary Fig.\u0026nbsp;2D2). Nevertheless, a trend was observed whereby the OS in patients with high IGF2R expression was worse than in patients with low IGF2R expression in endometrial cancer (Supplementary Fig.\u0026nbsp;2D1). Similarly, a trend was observed whereby the PFI in patients with high CST3 expression was higher than in patients with low CST3 expression (Supplementary Fig.\u0026nbsp;2C2), although the difference was not statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical assay and clinicopathological features of prior druggable proteins\u003c/h2\u003e \u003cp\u003eWe validated the differential protein expression of IGF2R and CST3 in patient tissue samples. Immunohistochemical results showed that IGF2R was expressed at higher levels in endometrioid carcinoma tissues than in normal tissues (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), whereas CST3 was expressed at lower levels in non-endometrioid carcinoma tissues than in normal tissues (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). We separately performed IGF2R expression and CST 3 expression with clinicopathological features of endometrioid histology and non-endometrioid histology, including age, diabetes mellitus type 2, hypertension, FIGO stage, myometrial invasion, lymph node metastasis, and p53 mutation status. We found that IGF2R expression was significantly associated with the FIGO stage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and Myometrial invasion (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, CST3 expression was significantly related to the FIGO stage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological\u0026nbsp;characteristics\u0026nbsp;of\u0026nbsp;\u0026nbsp;endometrioid\u0026nbsp;histological\u0026nbsp;patients\u0026nbsp;with\u0026nbsp;IGF2R\u0026nbsp;expression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIGF2R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u0026nbsp;expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u0026nbsp;expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge,\u0026nbsp;mean\u0026nbsp;\u0026plusmn;\u0026nbsp;sd\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.323\u0026nbsp;\u0026plusmn;\u0026nbsp;11.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.35\u0026nbsp;\u0026plusmn;\u0026nbsp;8.0412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u0026nbsp;mellitus\u0026nbsp;type\u0026nbsp;2,\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026nbsp;(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026nbsp;(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026nbsp;(51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026nbsp;(35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArterial\u0026nbsp;hypertension,\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u0026nbsp;(17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026nbsp;(7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u0026nbsp;(43.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u0026nbsp;(31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor\u0026nbsp;stage\u0026nbsp;(FIGO),\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026nbsp;(41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026nbsp;(39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026nbsp;(5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026nbsp;(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026nbsp;(13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026nbsp;(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological\u0026nbsp;grading,\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u0026nbsp;(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u0026nbsp;(29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2-G3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026nbsp;(27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026nbsp;(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMyometrial\u0026nbsp;invasion,\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026nbsp;(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026nbsp;(7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eshallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026nbsp;(31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u0026nbsp;(31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026nbsp;(25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026nbsp;(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymph\u0026nbsp;node\u0026nbsp;metastasis,\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026nbsp;(7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026nbsp;(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u0026nbsp;(52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026nbsp;(39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP53, n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026nbsp;(58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026nbsp;(39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026nbsp;(2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026nbsp;(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological\u0026nbsp;characteristics\u0026nbsp;of\u0026nbsp;\u0026nbsp;non-endometrioid\u0026nbsp;histological\u0026nbsp;patients\u0026nbsp;with\u0026nbsp;CST3\u0026nbsp;expression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCST3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u0026nbsp;expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u0026nbsp;expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge,\u0026nbsp;mean\u0026nbsp;\u0026plusmn;\u0026nbsp;sd\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.714\u0026nbsp;\u0026plusmn;\u0026nbsp;5.4685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.611\u0026nbsp;\u0026plusmn;\u0026nbsp;6.6078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u0026nbsp;mellitus\u0026nbsp;type\u0026nbsp;2,\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026nbsp;(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026nbsp;(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026nbsp;(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u0026nbsp;(52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArterial\u0026nbsp;hypertension,\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026nbsp;(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026nbsp;(32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026nbsp;(12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026nbsp;(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor\u0026nbsp;stage\u0026nbsp;(FIGO),\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026nbsp;(24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026nbsp;(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026nbsp;(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026nbsp;(24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026nbsp;(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u0026nbsp;(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMyometrial\u0026nbsp;invasion,\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026nbsp;(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026nbsp;(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eshallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026nbsp;(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026nbsp;(32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026nbsp;(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026nbsp;(36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymph\u0026nbsp;node\u0026nbsp;metastasis,\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026nbsp;(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026nbsp;(32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026nbsp;(24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026nbsp;(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep53,\u0026nbsp;n\u0026nbsp;(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026nbsp;(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026nbsp;(36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026nbsp;(24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026nbsp;(36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs endometrial cancer enters a new era of molecular characterization, the challenge of exploring new therapeutic targets and developing new drugs is enormous. In this study, we identified potential drug targets for different subtypes of endometrial cancer by colocalization analysis and SMR. We conducted a range of complementary analyses to explore the robustness and clinical significance of the results. The differential analysis of this study successfully verified that the trends in mRNA and protein levels of IGF2R were consistent with the causal direction of SMR. While the differential analysis of mRNA and protein levels for CST3 was not meaningful, the survival analysis indicated that CST3 may have a protective effect in non-endometrioid carcinoma.\u003c/p\u003e \u003cp\u003eThe findings of this study indicate potential drug targets for endometrial cancer, with initial support for these conclusions drawn from the existing literature. Several studies have demonstrated that GSTO1 is associated with various cancers, with elevated expression observed in colon, melanoma, and bladder cancers\u003csup\u003e(11\u0026ndash;13)\u003c/sup\u003e. Furthermore, there is evidence that polymorphisms in GSTO1 may increase the risk of developing liver cancer and non-small cell lung cancer (NSCLC)\u003csup\u003e(14\u0026ndash;17)\u003c/sup\u003e. Paul S et al. demonstrated that GSTO1 induces drug resistance in colon and breast cancer cells\u003csup\u003e(18)\u003c/sup\u003e. The number of studies and reports on the association of CBR3 and ACAP2 with cancer is relatively limited, whereas there are more studies on the association of LncRNA CBR3-AS1 and CircACAP2. The expression of CRB3-AS1 is elevated in cervical cancer, breast cancer, and osteosarcoma\u003csup\u003e(19\u0026ndash;21)\u003c/sup\u003e. Hayashi-Okada M et al. identified 11 long non-coding RNAs (lncRNAs) in high-grade plasmacytoid carcinoma of the ovary and observed a trend toward malignant behavior of CBR3-AS1\u003csup\u003e(22)\u003c/sup\u003e. It has been demonstrated that ACAP2 is downregulated in oesophageal cancer, leukemia, and lymphoma, suggesting it may act as a tumor suppressor\u003csup\u003e(23, 24)\u003c/sup\u003e. Nevertheless, CircACAP2 is markedly expressed in breast and colon cancers, where it exerts a pro-oncogenic effect, promoting cancer cell proliferation and metastasis\u003csup\u003e(25, 26)\u003c/sup\u003e. This discovery provides a direction for further study to validate the relationship between plasma proteins(GSTO1, CRB3, ACAP2) with endometrioid carcinoma and delve deeper into the mechanisms. Additional studies are needed in the future. IGF2R is a receptor in the IGF pathway that influences energy metabolism and regulation. Dysregulation of the IGF pathway has been linked to tumourigenesis, and the role of IGF2R in tumors is somewhat controversial. Previous studies have indicated that IGF2R may act as a tumor suppressor in certain cancers, including bladder and breast cancers\u003csup\u003e(27\u0026ndash;29)\u003c/sup\u003e. Interestingly, some evidence supports an oncogenic role for IGF2R in specific cancers. A proteomic analysis revealed that IGF2R plays a role in developing laryngeal squamous cell carcinoma\u003csup\u003e(30)\u003c/sup\u003e. Takeda T et al. found that high IGF2R expression may be associated with a poor prognosis in cervical cancer\u003csup\u003e(31)\u003c/sup\u003e. The findings of this study indicate that IGF2R is a risk factor for endometrioid adenocarcinoma. Additionally, the study suggests that FIGO staging and depth of myometrial infiltration predict the prognosis of EC. These findings provide a framework for future research in this area. MMP10, belonging to the MMP family, is more highly expressed preoperatively than postoperatively in gastric cancer, suggesting that it may be a biomarker for the diagnosis of gastric cancer\u003csup\u003e(32)\u003c/sup\u003e. Zeng L et al. found that ovarian cancers with high MMP10 expression have a better prognosis\u003csup\u003e(33)\u003c/sup\u003e. The manuscript found that the MMP10 protein is negatively correlated with the development of endometrioid carcinoma, which appears to be at odds with previous studies. Nevertheless, another report indicates that MMP9, which belongs to the same MMP family, plays two distinct roles in tumors. Firstly, it has an oncogenic role in the tumor stroma, and secondly, it has an oncogenic role in the tumor epithelium\u003csup\u003e(34)\u003c/sup\u003e. This sets the stage for the role of MMP10 in cancer. It is well known that HHIP is a negative regulator of the hedgehog signaling pathway, and therefore, it plays an inhibitory role in tumourigenesis\u003csup\u003e(35)\u003c/sup\u003e. This is following our findings.\u003c/p\u003e \u003cp\u003eThe pathogenesis of non-endometrioid carcinoma differs from that of endometrioid carcinoma, with a correspondingly poorer prognosis. This study identified seven plasma proteins that were causally associated with non-endometrioid cancers. These included two risk factors (MAPK9, FSTL5) and five protective factors (DNAJB14, CST3, GMPR2, IFI16, NEO1). MAPK9, also known as JNK2, has been demonstrated to promote tumor formation by participating in the JNK signaling pathway\u003csup\u003e(36, 37)\u003c/sup\u003e, which is consistent with the results of this study. FSTL5 acts differently in different tumors; it inhibits tumor development in hepatocellular carcinoma\u003csup\u003e(38)\u003c/sup\u003e, whereas it is associated with a poor prognosis in medulloblastoma\u003csup\u003e(39)\u003c/sup\u003e. CST3, also known as cystatin C, is present in all body fluids (including serum, ascites, pleural fluid, and urine) and has diverse biological functions. However, it remains unclear whether its levels are altered in patients with malignancy\u003csup\u003e(40)\u003c/sup\u003e. CST3 is regulated by TGF-β, a factor in tumor development\u003csup\u003e(41)\u003c/sup\u003e. In addition, it has been demonstrated that CST3 plays a role in epigenetic regulation, as it contains extensive CpG islands that can suppress transcription through methylation\u003csup\u003e(40)\u003c/sup\u003e. By single-cell data analysis, Qianhua Wu et al. found that CST3 protein expression was down-regulated in non-endometrioid cancers\u003csup\u003e(42)\u003c/sup\u003e. These findings are consistent with those of the study mentioned above, which may be attributed to the reduced protein expression level observed following CST3 methylation. Further studies are required to confirm this hypothesis. The analysis of the above results leads to the conclusion that our results are plausible and provide new insights for our subsequent studies.\u003c/p\u003e \u003cp\u003eThe strengths of this study are as follows: firstly, the study employs a sufficiently large sample size, utilizes effective statistical methods with a low risk of bias, and employs multifaceted validation to ensure the robustness of the results; secondly, the study provides valuable insights into new therapeutic targets for endometrial cancer by assessing the druggability of the drug target and the interaction between the drug and the target. At the same time, some limitations should be acknowledged. The study population was limited to Europeans; further validation in different racial groups is necessary. The results of this study assessed the role of plasma proteins in endometrial cancer and validated the changes in the levels of the corresponding proteins in endometrial cancer tissues. Nevertheless, there may be differences between tissue proteins and plasma proteins. Consequently, further validation of changes in the levels of these proteins in plasma is needed.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe overall study design is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The specific data and methods are as follows.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eProteomic Data Sources\u003c/h2\u003e \u003cp\u003eThe primary proteomic data analyzed in this study were derived from the deCODE Genetics study dataset, identifying 4,907 plasma proteins from genome-wide association studies (GWASs) in 35,559 Icelanders\u003csup\u003e(43)\u003c/sup\u003e. To enhance the reliability of the findings, we used two independent protein GWAS sources, the UK Biobank Pharma Proteomics Project (UKBPPP) and the Gudjonsson A study, for replication. We selected 2923 plasma protein information from 54219 European participants in the UKBPPP study\u003csup\u003e(44)\u003c/sup\u003e. IVs were extracted from Gudjonsson A study, which included 5361 European participants and 2091 plasma proteins\u003csup\u003e(45)\u003c/sup\u003e (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Data Sources\u003c/h2\u003e \u003cp\u003eThe endometrial cancer data used in this study were derived from O'Mara TA's GWAS meta-analysis, based on 13 endometrial cancer studies\u003csup\u003e(46)\u003c/sup\u003e. The study included 12906 EC cases and 108979 controls of European ancestry. In stratified analysis by histological classification, the GWAS summary data were divided into 8758 endometrioid carcinoma cases and 46126 controls, 1230 non-endometrioid carcinoma cases, and 35447 controls(Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eColocalisation analysis\u003c/h2\u003e \u003cp\u003eColocalization analysis aimed to confirm causal variants that may be shared by two signals(protein expression and endometrial cancer) in a given genomic region. This study used Bayesian colocalization analysis of the COLOC package (V.5.2.3) to ascertain the likelihood that two traits share causal variation. The posterior probability of five genetic loci sharing a common causal variation was employed to quantify this potential linkage. PPH0 was not associated with either protein expression or endometrial cancer; PPH1 was associated with protein expression only but not endometrial cancer risk; PPH2 was associated with endometrial cancer risk but not with protein expression; PPH3 was associated with both protein expression and endometrial cancer risk, but not at the same locus. PPH4 was associated with protein expression and endometrial cancer risk at the same locus\u003csup\u003e(47)\u003c/sup\u003e. The sum of the five posterior probabilities is equal to one. The plasma proteins selected for this study were PPH3\u0026thinsp;+\u0026thinsp;PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.8 or PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.5, owing to being a relatively strong indicator of colocalization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis\u003c/h2\u003e \u003cp\u003eMendelian randomization was a natural experiment that studied genetic variation and investigated the causal relationship between exposure and outcome. Mendelian randomization overcomes the limitations of traditional observational studies, is not susceptible to confounders and reverse causality bias, and has a reliability intermediate between observational studies and intervention trials\u003csup\u003e(48)\u003c/sup\u003e. Summary-data-based MR (SMR) is a method of MR that focuses on the causal relationship between gene or protein expression levels and disease. Analysis was conducted using the SMR software with the default parameters. In addition, IVs with MAFs (minor allele frequencies) below 0.01 were excluded to reduce the potential for bias due to rare mutations. The study screened endometrial cancer and its two subtypes (endometrioid carcinoma and non-endometrioid carcinoma) for drug targets within the deCODE genetics database through SMR analysis. The heterogeneity in dependent instruments (HEIDI) test was conducted on the SMR results. When the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicated that the association of proteins with endometrial cancer was not driven by linkage disequilibrium, it was concluded that the association was not significant. The false discovery rate (FDR) was adjusted for in the context of multiple comparisons. The study validated the results through the UKBPPP and Gudjonsson A database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSupplementary analysis\u003c/h2\u003e \u003cp\u003eProtein interactions between EC plasma protein therapeutic targets were further explored using the GeneMANIA tool. Phenome-wide association studies (PheWAS) can be employed to assess the side effects of druggable genes and identify potential pleiotropy that SMR analyses fail to reveal. At a P-value of less than 1e\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, there is a significant correlation between gene expression and phenotype. Subsequently, the DGIdb database assessed the interrelationships between drugs and potential targets.\u003c/p\u003e \u003cp\u003eThis study aimed to identify potential drug targets in endometrial cancer at the translational level. To this end, we employed the UALCAN database to perform differential gene expression (mRNA and protein) analyses for the potential drug targets with the highest level of evidence. Additionally, Kaplan-Meier curves were employed to evaluate the predictive value of these drug targets in endometrial cancer. Hazard ratios (HR) were calculated using the Cox proportional hazard model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEC tissues\u003c/h2\u003e \u003cp\u003eA total of 51 cases of paraffin-embedded endometrioid histological tissues, 20 cases of paraffin-embedded normal endometrial tissues, and 25 cases of paraffin-embedded non-endometrioid histological tissues(including 18 endometrial serous carcinomas, five clear-cell carcinomas, and two carcinosarcoma) and 20 paraffin-embedded normal endometrial tissues were obtained from surgical patients who underwent surgery between October 2022 and May 2024 in the Department of Obstetrics and Gynaecology of Jingjiang People's Hospital. The clinical data were carried from medical records. All patients had not received radiotherapy, chemotherapy, or other biological treatments before surgery. The study was approved by the Ethics Committee of Jingjiang People's Hospital based on the declaration of Helsinki (2023-KY-019-01). All participants or their legal guardians were informed of the nature of the study and provided with an opportunity to consent to their participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry and pathological features analysis\u003c/h2\u003e \u003cp\u003eTissue sections of endometrioid carcinomas and their normal controls were incubated with an anti-IGF2R antibody (dilution 1:100, Cat No. 20253-1-AP, Proteintech, China) at 4\u0026deg;C overnight. Non-endometrioid carcinomas and their normal controls were incubated with an anti-CST3 (dilution 1:100, Cat No. 12245-1-AP, Proteintech, China) at 4\u0026deg;C overnight. Subsequently, the samples were incubated with a secondary antibody derived from the primary antibody(dilution 1:200, G1213, Servicebio, China). The sections were then visualized, stained with DAB, and restained with hematoxylin. Two senior pathologists will be required to assess the cases in question (using a double-blind method). Each case must be examined using a high-power microscope with at least five fields of view (each field of view must be greater than or equal to 200 cells). The assessment will be conducted following the two-tier scoring method. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) According to the number of positive cell counts (the number of positive cells per 100 cells), the number of positive cells is assigned a negative score of 0 points, a score of 1 point for \u0026lt;\u0026thinsp;10% of the total, a score of 2 points for 10% ~ 50% of the total, and a score of 3 points for \u0026gt;\u0026thinsp;50% of the total. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) According to the number of positive cell counts (the number of positive cells per 100 cells), the number of positive cells is negative 0 points, \u0026lt;\u0026thinsp;10% of the total score of 1 point, 10% ~ 50% of the total score. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The following grades are applied according to the staining shade: 0 points for no color, 1 point for yellow, 2 points for brown-yellow, and 3 points for tan. The composite score is the product of the two scores, resulting in a four-grade scale: (-) 0\u0026ndash;1; (+) 2; (++) 3\u0026ndash;4; (+++) 6\u0026ndash;9. A score of greater than five was classified as high expression, while a score of less than five was classified as low expression. Meanwhile, the study examined correlation analysis between IGF2R and CST3 expression and clinical pathological features of subtypes of endometrial cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analyses were conducted using R software (version 4.2.1) and GraphPad Prism 9 (GraphPad, Dotmatics, MA). The immunohistochemistry scores were subjected to statistical analysis using the Wilcoxon rank sum test, while the clinical data were analyzed using the Pearson\u0026rsquo;s Chi-square or Fisher\u0026rsquo;s Exact test. A p-value of less than 0.05 was considered the threshold for statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study has identified several plasma proteins associated with endometrial cancer, which could facilitate the early diagnosis of EC, the recognition of recurrence of EC, the assessment of risk and prognosis, and the development of related drugs. Further experimentation and clinical studies are required to assess the efficacy of these druggable targets in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJZ and TZ conceptualized the study. JZ, TZ, NX, and YC developed the design. JZ, NX, and JJ performed data curation and analysis. TZ, and JJ supervised the MR analysis. JZ, TZ, and NX carried out data interpretation. All authors wrote and subsequently edited the original draft, which they approved as the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from Scientific Research Fund Program of Jingjiang People\u0026apos;s Hospital(JRY-KY-2023-010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the invaluable contributions of researchers at prestigious institutions, including the UK Biobank, the deCODE project, the\u0026nbsp;Gudjonsson A\u0026nbsp;study, and the eQTLGen consortium, who have significantly advanced quantitative trait loci research. Our sincere appreciation is also extended to the O\u0026apos;Mara TA\u0026apos;s GWAS Meta-analysis study participants, who have actively participated and generously shared their genome-wide association studies data on endometrial cancer. Furthermore, we express our gratitude towards the diligent researchers and dedicated participants of other genome-wide association studies datasets used in this study. Their steadfast commitment has been crucial in propelling scientific advancements. We also thank BioRender.com for their assistance with illustrations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMakker V, MacKay H, Ray-Coquard I, Levine DA, Westin SN, Aoki D, et al. 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BMJ (Clinical research ed). 2018;362:k601.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Endometrial cancer, protein, drug target, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4587130/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4587130/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study aims to identify potential drug targets for subtypes of endometrial cancer through a Mendelian randomization study and analyze their clinical value. Data from three quantitative trait loci and Genome-wide association studies (GWAS) Meta-analysis study explored potential drug targets in endometrial cancers (including endometrioid and non-endometrioid). Complementary analysis (including network analysis, therapeutic efficacy analysis, gene differential expression, and prognosis analysis) was investigated. Furthermore, immunohistochemical staining and clinical pathological features were explored to validate potential clinical significance. Five drug targets for endometrial carcinomas, seven drug targets for endometrioid histology, and seven drug targets for non-endometrioid histology were identified, with IGF2R (OR\u0026thinsp;=\u0026thinsp;1.165; 95% CI 1.067\u0026ndash;1.272; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.046 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and CST3 (OR\u0026thinsp;=\u0026thinsp;0.523; 95% CI 0.339\u0026ndash;0.804; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.010\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) demonstrating core therapeutic potential supported by causal evidence at the transcriptional, translational, and tissue-specific levels. Our research explored potential therapeutic targets associated with endometrial cancer and provided new ideas for biomarker screening and drug development.\u003c/p\u003e","manuscriptTitle":"Genetic variation perspective reveals potential drug targets for subtypes of endometrial cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-03 18:28:49","doi":"10.21203/rs.3.rs-4587130/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-04T02:34:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-03T01:08:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49995518979601708233182035019671933998","date":"2024-08-29T11:15:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93319847088359920487242981401457786935","date":"2024-08-26T23:01:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-27T23:10:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207864168068926843908792532490810163047","date":"2024-07-21T21:47:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-21T18:32:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-18T17:52:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-21T14:50:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-18T05:14:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-15T15:25:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a583669a-f0c2-47f5-95c5-fb6771300449","owner":[],"postedDate":"July 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T16:01:55+00:00","versionOfRecord":{"articleIdentity":"rs-4587130","link":"https://doi.org/10.1038/s41598-024-78689-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-15 15:57:28","publishedOnDateReadable":"November 15th, 2024"},"versionCreatedAt":"2024-07-03 18:28:49","video":"","vorDoi":"10.1038/s41598-024-78689-5","vorDoiUrl":"https://doi.org/10.1038/s41598-024-78689-5","workflowStages":[]},"version":"v1","identity":"rs-4587130","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4587130","identity":"rs-4587130","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}