食管癌生物标志物和潜在药物靶点的鉴定：孟德尔随机化研究-动脉网

Identification of biomarkers and potential drug targets for esophageal cancer: a Mendelian randomization study

Nature 等信源发布 2025-03-10 14:23

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Abstract

摘要

Esophageal cancer (EC) is a common and deadly malignancy of the digestive system. Currently, effective treatments for EC are limited and patient prognosis remains poor. In this study, we utilized Mendelian Randomization (MR) to identify potential drug targets for EC by analyzing proteins linked to the disease risk.

食管癌（EC）是消化系统常见且致命的恶性肿瘤。目前，食管癌的有效治疗方法有限，患者预后仍然较差。在本研究中，我们利用孟德尔随机化（MR）分析与疾病风险相关的蛋白质，以识别食管癌的潜在药物靶点。

A total of 734 plasma proteins and 4,479 druggable genes were obtained from recent studies, and two-sample MR analyses were conducted to investigate causal relationships between these proteins and EC. The cis-pQTL data of the proteins was analyzed after filtering. The inverse variance weighted (IVW) method was the primary analytical approach in MR analysis.

最近的研究获得了734种血浆蛋白和4,479个可成药基因，并进行了两样本MR分析以研究这些蛋白与EC之间的因果关系。对蛋白质的顺式pQTL数据进行了过滤和分析。逆方差加权（IVW）方法是MR分析中的主要分析方法。

Steiger filtering, heterogeneity and pleiotropy tests, Summary-data-based Mendelian Randomization (SMR) analysis, and Bayesian co-localization analysis were implemented to consolidate the results further. Moreover, drugs corresponding to the identified proteins were found in the DrugBank database. Five proteins HPSE, ST3GAL1, CEL, KLK13, and GNRH2 were identified as highly associated with EC.

实施了Steiger过滤、异质性和多效性检验、基于汇总数据的孟德尔随机化（SMR）分析以及贝叶斯共定位分析，以进一步巩固结果。此外，在DrugBank数据库中找到了与所鉴定蛋白质对应的药物。五种蛋白HPSE、ST3GAL1、CEL、KLK13和GNRH2被确定为与EC高度相关。

HPSE and GNRH2 showed protective effects with odds ratios (OR) of 0.80 (95% confidence interval [CI], 0.70–0.92) and 0.73 (95% CI 0.54–0.98), respectively. In contrast, increased expression of ST3GAL1(OR, 1.37; 95% CI 1.04–1.82), CEL (OR, 1.27; 95% CI 1.08–1.49), and KLK13 (OR, 1.22; 95% CI 1.04–1.42) were all associated with a higher risk of EC.

HPSE 和 GNRH2 显示出保护作用，其比值比（OR）分别为 0.80（95% 置信区间 [CI]，0.70–0.92）和 0.73（95% CI 0.54–0.98）。相反，ST3GAL1（OR，1.37；95% CI 1.04–1.82）、CEL（OR，1.27；95% CI 1.08–1.49）和 KLK13（OR，1.22；95% CI 1.04–1.42）的表达增加均与较高的 EC 风险相关。

In addition, the HPSE protein showed moderate colocalization with EC [coloc.abf-posterior probability of hypothesis 4 (PPH4) = 0.637]. Furthermore, the sensitivity analyses indicated no heterogeneity or pleiotropy. Therefore, these findings present promising drug targets for EC and deserve further clinical investigation..

此外，HPSE蛋白与EC显示出中度共定位[coloc.abf-假设4的后验概率（PPH4）= 0.637]。此外，敏感性分析表明不存在异质性或多效性。因此，这些发现为EC提供了有前景的药物靶点，值得进一步的临床研究。

Introduction

简介

Esophageal cancer (EC) ranks seventh in global incidence and is the sixth leading cause of cancer-related deaths worldwide

食管癌（EC）在全球发病率中排名第七，是全球癌症相关死亡的第六大原因。

. Consequently, EC causes over 500,000 deaths annually, accounting for 5.3% of global cancer deaths and posing a serious threat to human health

因此，食管癌每年导致超过50万人死亡，占全球癌症死亡的5.3%，对人类健康构成严重威胁。

. The two main histological subtypes of EC are esophageal squamous cell carcinoma (ESCC)and esophageal adenocarcinoma (EAC), with ESCC being the most common type worldwide. There are often no symptoms in the early stages of EC. However, progressive difficulty in swallowing, chest pain, and weight loss gradually develop over time.

食管癌的两种主要组织学亚型是食管鳞状细胞癌 (ESCC) 和食管腺癌 (EAC)，其中 ESCC 是全球最常见的类型。食管癌早期通常没有症状。然而，随着时间推移，吞咽困难、胸痛和体重减轻等症状会逐渐出现。

Furthermore, some patients may experience symptoms such as hoarseness and anemia.

此外，一些患者可能会出现声音嘶哑和贫血等症状。

. Unfortunately, both EAC and ESCC have low survival rates and are prone to early metastasis.

不幸的是，EAC 和 ESCC 的生存率均较低，并且容易早期转移。

The treatment strategies for EC can be conceptually divided into two categories: local treatment and systemic treatment. As such, each patient should be individually assessed to determine the appropriate treatment plan based on the type of cancer, the extent of local or regional involvement, and the patient’s overall functional status.

食管癌的治疗策略在概念上可分为两类：局部治疗和全身治疗。因此，应根据癌症的类型、局部或区域受累程度以及患者的整体功能状态，对每位患者进行单独评估，以确定适当的治疗方案。

. According to the TNM staging of AJCC Cancer Staging, endoscopic mucosal resection (EMR) is recommended for T1a stage tumors (confined to the mucosa) that are smaller than 2 cm

根据AJCC癌症分期的TNM分期，对于小于2厘米的T1a期肿瘤（局限于粘膜），建议进行内镜黏膜切除术（EMR）。

. For T1b stage tumors (invasion into the superficial submucosa), endoscopic submucosal dissection is the therapeutic option. When the tumor is larger than 2 cm or at the T1b stage (invasion into the deep submucosa), a multidisciplinary and multimodal treatment approach is optimal

对于T1b期肿瘤（侵犯至浅表黏膜下层），内镜黏膜下剥离术是治疗选择。当肿瘤大于2厘米或处于T1b期（侵犯至深部黏膜下层）时，多学科和多模式的治疗方法是最优的。

.For the T2 and T3 stages, patients typically undergo neoadjuvant chemotherapy followed by surgery. Standard chemotherapy regimens include cisplatin, 5-fluorouracil, carboplatin, and paclitaxel. Moreover, unresectable lesions are generally managed with palliative chemotherapy and radiotherapy. Immunotherapy and small molecule inhibitors are also often used in combination with chemotherapy, radiation therapy, and targeted therapy to enhance overall effectiveness.

对于T2和T3期患者，通常接受新辅助化疗后进行手术。标准化疗方案包括顺铂、5-氟尿嘧啶、卡铂和紫杉醇。此外，无法切除的病灶一般采用姑息性化疗和放疗。免疫疗法和小分子抑制剂也常与化疗、放疗和靶向治疗联合使用，以提高整体疗效。

. However, long-term use of chemotherapy drugs and targeted therapies may lead to drug resistance, often accompanied by significant side effects that may impact patients’ quality of life. In addition, some patients show poor response to current drug treatment regimens, resulting in limited therapeutic efficacy.

然而，长期使用化疗药物和靶向治疗可能导致药物耐受性，且常伴随显著的副作用，可能影响患者的生活质量。此外，部分患者对现有药物治疗方案反应不佳，导致治疗效果有限。

Therefore, there is an urgent need to develop more effective chemical compounds to treat this malignant disease..

因此，迫切需要开发更有效的化合物来治疗这一恶性疾病。

Accurately identifying candidate drugs for a specific disease is crucial for effective drug development targeting that disease. However, clinical trials for newly developed drugs are terminated due to small sample sizes, insufficient drug efficacy or safety data, and unsatisfactory results

准确识别针对特定疾病的候选药物对于有效开发治疗该疾病的新药至关重要。然而，由于样本量小、药物疗效或安全性数据不足以及结果不理想等原因，新开发药物的临床试验往往被迫终止。

. Human plasma proteins are vital in various biological processes and are primary drug targets. Studies have shown that if genetic associations support the link between a protein drug target and disease, its success rate in clinical development can double

人体血浆蛋白在各种生物过程中至关重要，并且是主要的药物靶点。研究表明，如果遗传关联支持蛋白质药物靶点与疾病之间的联系，其在临床开发中的成功率可以翻倍。

. In recent years, Mendelian randomization (MR) studies have been widely applied in drug target development

近年来，孟德尔随机化 (MR) 研究已广泛应用于药物靶点开发。

. Notably, this approach uses single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) as genetic instrumental variables to investigate the causal effects of exposures on outcomes. In contrast to observational studies, MR can effectively avoid confounding factors and reverse causation, providing more reliable evidence for causal inference.

特别是，这种方法利用全基因组关联研究（GWAS）中的单核苷酸多态性（SNPs）作为遗传工具变量，以探讨暴露对结果的因果效应。与观察性研究相比，孟德尔随机化（MR）能够有效避免混杂因素和反向因果关系，为因果推断提供更可靠的证据。

With the advancement of high-throughput genomics and proteomics technologies in plasma, MR-based studies have made it feasible to identify potential therapeutic drug targets for many diseases including Parkinson’s disease.

随着血浆高通量基因组学和蛋白质组学技术的进步，基于MR的研究使得识别包括帕金森病在内的多种疾病的潜在治疗药物靶点成为可能。

and Alzheimer’s disease

和阿尔茨海默病

.However, no studies have explored potential drug targets using MR analyses in EC. Furthermore, MR studies integrating GWAS data with protein quantitative trait loci (pQTL) data are rarely reported. Therefore, this study aimed to identify plasma proteins as potential therapeutic targets for EC.

然而，目前尚无研究利用孟德尔随机化分析来探索子宫内膜癌的潜在药物靶点。此外，很少有整合全基因组关联研究数据与蛋白质定量性状位点数据的孟德尔随机化研究报道。因此，本研究旨在鉴定血浆蛋白作为子宫内膜癌潜在治疗靶点。

This study is the first to identify potential drug targets for EC through MR analysis at the genetic level. First, we intersected the 734 plasma proteins identified by Zheng et al.

本研究首次通过基因水平的MR分析，鉴定了EC的潜在药物靶点。首先，我们交叉对比了Zheng等人鉴定出的734种血浆蛋白。

with the 4,479 druggable gene-encoded proteins from Finan et al.

来自Finan等人的4,479种可成药基因编码蛋白

, and obtained 511 plasma proteins encoded by druggable genes. A two-sample MR analysis was conducted using the GWAS summary statistics for EC from the IEU Open GWAS database to identify the causal relationships between the druggable gene-encoded proteins and EC. After obtaining and filtering the pQTL data for these proteins from the deCODE database, we identified cis-pQTL data for 19 proteins.

，获得了511种由可成药基因编码的血浆蛋白。使用IEU Open GWAS数据库中的EC的GWAS汇总统计数据进行了两样本MR分析，以确定可成药基因编码蛋白与EC之间的因果关系。在从deCODE数据库获取并过滤这些蛋白质的pQTL数据后，我们鉴定出19种蛋白质的顺式pQTL数据。

Subsequently, we performed the two-sample MR analysis to identify proteins with causal associations with EC. Heterogeneity and pleiotropy tests, Steiger filtering, SMR analysis, and Bayesian colocalization analysis validated our preliminary findings, ensuring the reliability and stability of the protein targets identified in EC.

随后，我们进行了两样本孟德尔随机化分析，以识别与EC具有因果关联的蛋白质。异质性和多效性检验、Steiger过滤、SMR分析以及贝叶斯共定位分析验证了我们的初步发现，确保了在EC中鉴定的蛋白质靶点的可靠性和稳定性。

Importantly, these targets may become novel drug targets in the future. Finally, we retrieved the drugs for the identified proteins from the DrugBank database. Thus, the selected drugs may be promising for future treatment of EC..

重要的是，这些靶点可能成为未来新颖的药物靶点。最后，我们从DrugBank数据库中检索了与鉴定出的蛋白质相关的药物。因此，所选药物可能对未来EC的治疗具有前景。

Materials and methods

材料与方法

Druggable gene selection

可成药基因选择

We obtained a total of 4479 druggable genes from Finan’s study

我们从Finan的研究中获得了总共4479个可成药基因

(Supplementary Data 1). The first group of 1427 genes included the efficacy targets of approved small molecule and biological therapeutic drugs and the clinical stage drug candidates. The second group of 682 genes encoded targets with known bioactive drug-like small molecule binding partners and ≥ 50% identity with approved drug targets (over ≥ 75% of the sequence).

（补充数据1）。第一组的1427个基因包括已批准的小分子和生物治疗药物的有效靶点，以及临床阶段的候选药物。第二组的682个基因编码具有已知生物活性、类似药物的小分子结合伙伴的靶点，并且与已批准药物靶点的序列一致性≥50%（在≥75%的序列上）。

The third group of 2370 genes encoded secreted or extracellular proteins, or proteins with slightly lower similarity to approved drug targets, as well as key druggable gene family members not yet included in the first and second groups..

第三组包含2370个基因，这些基因编码分泌蛋白或细胞外蛋白，或与已批准药物靶点相似度略低的蛋白，以及尚未包含在第一和第二组中的关键可成药基因家族成员。

pQTL dataset

pQTL 数据集

pQTL is utilized to study the correlation between genetic mutations and gene expression using protein expression as a quantifiable trait. Two sources of pQTL were used for the analysis. For the preliminary analysis, we used the cis-pQTL data from the study by Zheng et al.

pQTL被用于通过蛋白质表达作为可量化性状来研究基因突变与基因表达之间的相关性。分析中使用了两种来源的pQTL数据。在初步分析中，我们使用了郑等人研究中的顺式pQTL数据。

, including a total of 738 cis-SNPs (Single Nucleotide Polymorphism) of 734 proteins (Supplementary Data 2), to screen the druggable proteins. Then we obtained pQTL data of the screened proteins from 4674 proteins in the deCODE database from Ferkingstad’s study

，包括734种蛋白质的共738个顺式SNP（单核苷酸多态性）（补充数据2），以筛选可成药的蛋白质。随后，我们从Ferkingstad的研究中获取了deCODE数据库中4674种蛋白质的筛选蛋白质的pQTL数据。

. These data were used as the main pQTL to identify potential drug targets for EC. Furthermore, the instrumental variable cis-pQTL was selected using the following criteria: (1) P value < 5e-08; (2) F statistic > 10; (3) SNPs in the human major histocompatibility complex (MHC) region (chr6, 26-34 MB) were excluded; (4) SNPs within 1 Mb upstream and downstream of the gene; (5) removal of linkage disequilibrium r.

这些数据被用作主要的pQTL，以识别EC的潜在药物靶点。此外，选择工具变量顺式pQTL的标准如下：(1) P值<5e-08；(2) F统计量>10；(3) 排除人类主要组织相容性复合体（MHC）区域（chr6，26-34 MB）中的SNP；(4) 基因上游和下游1 Mb范围内的SNP；(5) 去除连锁不平衡r。

< 0.1; (6)

value > 5e-08 with outcome.

值 > 5e-08 与结果相关。

The selected datasets were all of European ethnic background.

所选数据集均具有欧洲种族背景。

Outcome dataset

结果数据集

We obtained the GWAS summary data of EC from the online IEU Open GWAS database (

我们从在线的IEU Open GWAS数据库中获取了EC的GWAS汇总数据 (

https://gwas.mrcieu.ac.uk/

)

using the R package TwoSampleMR. A total of 998 samples with EC in the experimental group and 475,308 control samples were included. The outcome data were from European ethnic backgrounds.

使用R包TwoSampleMR。实验组共纳入998例EC样本，对照组纳入475,308例样本。结局数据来源于欧洲种族背景。

Two-sample Mendelian randomization analysis

两样本孟德尔随机化分析

We conducted a two-sample Mendelian randomization analysis using the TwoSampleMR package, with the druggable protein studied by Zheng et al. as the exposure factor, and esophageal cancer as the outcome

我们使用TwoSampleMR包进行了两样本孟德尔随机化分析，以Zheng等人研究的可药用蛋白作为暴露因素，食管癌作为结局。

. To ensure the independence assumption’s validity, strict criteria were applied when selecting SNPs, including excluding SNPs associated with the MHC region and controlling for linkage disequilibrium, to minimize the impact of confounding bias. Moreover, we used the Wald ratio method to evaluate the results of Mendelian randomization for exposures containing only one SNP.

为确保独立性假设的有效性，在选择SNPs时应用了严格的标准，包括排除与MHC区域相关的SNPs并控制连锁不平衡，以尽量减少混杂偏倚的影响。此外，我们使用Wald比率方法评估仅包含一个SNP的暴露因素的孟德尔随机化结果。

The IVW method was also used to assess the results of Mendelian randomization for exposures containing two or more SNPs. Finally, we used the TwoSampleMR package for the heterogeneity test and pleiotropy test, followed by the Steiger directional test to determine the correctness of the direction of causality..

IVW 方法还用于评估包含两个或多个 SNP 的暴露因素的孟德尔随机化结果。最后，我们使用 TwoSampleMR 包进行异质性检验和多效性检验，随后进行 Steiger 方向检验以确定因果方向的正确性。

After selecting the proteins with significant causal relationships in EC, the pQTL data of the corresponding proteins were downloaded from the deCODE database as the exposure factors and esophageal cancer as the outcome. Using the same approach described above, a two-sample MR analysis was performed..

在筛选出与EC有显著因果关系的蛋白质后，从deCODE数据库下载相应蛋白质的pQTL数据作为暴露因素，并以食管癌作为结局。按照上述相同的方法，进行了两样本MR分析。

SMR analysis

SMR分析

SMR is a statistical method that utilizes GWAS and expression quantitative trait loci (eQTL) studies to investigate the pleiotropic associations between gene expression levels and complex traits of interest

SMR是一种统计方法，利用GWAS和表达数量性状基因座（eQTL）研究来探讨基因表达水平与感兴趣复杂性状之间的多效关联。

. Additionally, the heterogeneity in dependent instruments (HEIDI) test is used to assess the presence of possible horizontal pleiotropy in colocalization signals. The null hypothesis of the HEIDI test is that there is no horizontal pleiotropy in the colocalization signals. SMR and HEIDI methods can be used to explain whether protein expression rather than other pathways mediate the effect of SNPs on phenotypes.

此外，使用依赖工具的异质性（HEIDI）测试来评估共定位信号中可能存在的水平多效性。HEIDI测试的零假设是共定位信号中不存在水平多效性。SMR和HEIDI方法可以用来解释是否蛋白质表达而非其他途径介导了SNPs对表型的影响。

We downloaded the SMR Linux version (1.3.1) from the website (.

我们从网站上下载了SMR Linux版本（1.3.1）。（

https://yanglab.westlake.edu.cn/software/smr

) and conducted the SMR analysis using the default parameters.

) 并使用默认参数进行了SMR分析。

Co-localization analysis

共定位分析

We used the coloc package for colocalization analysis. The coloc package uses a Bayesian approach to assess the support for the following five exclusivity hypotheses: (1) SNPs were not associated with trait1 or trait2; (2) SNPs were associated with trait1; (3) SNPs were associated with trait2; (4) SNPs were associated with both trait1 and trait2 but were independent SNPs; (5) SNPs were associated with both trait1 and trait2 and they were shared.

我们使用了coloc包进行共定位分析。coloc包采用贝叶斯方法评估以下五个互斥假设的支持程度：(1) SNP与性状1和性状2均无关；(2) SNP与性状1相关；(3) SNP与性状2相关；(4) SNP与性状1和性状2均相关，但为独立的SNP；(5) SNP与性状1和性状2均相关，并且是共享的SNP。

The posterior probabilities of each test were H0, H1, H2, H3 and H4. To estimate the posterior probability of shared variation, for each selected protein, all SNPs within 250 kb upstream and downstream of its top SNP were retrieved for colocalization analysis. We considered a posterior probability of hypothesis 4 (PPH4)>0.8 as evidence of colocalization between GWAS and pQTL..

每个测试的后验概率为 H0、H1、H2、H3 和 H4。为了估计共享变异的后验概率，对于每个选定的蛋白质，检索其顶级 SNP 上下游 250 kb 范围内的所有 SNP 用于共定位分析。我们以假设 4 的后验概率 (PPH4)>0.8 作为 GWAS 和 pQTL 之间共定位的证据。

Drug target analysis

药物靶点分析

We searched in DrugBank

我们在DrugBank中进行了搜索

to identify drugs corresponding to the protein and their modes of action. Notably, the selected drugs can be used to treat EC.

识别与该蛋白对应的药物及其作用模式。值得注意的是，所选药物可用于治疗EC。

Statistical methods

统计方法

All data calculations and statistical analyses were performed using R software (

所有数据计算和统计分析均使用 R 软件进行（

https://www.r-project.org/?2827

). All p-values were considered statistically significant at

). 所有p值在统计学上被认为显著的水平为

<0.05 if not specified. In GWAS studies, SNPs are typically selected as instrumental variables (IVs) for MR analysis. However, due to the multiple comparisons across the genome, a more stringent p-value < 5e-08 was used to reduce false positives and enhance statistical significance. This p-value threshold is a recognized standard in GWAS analyses for identifying significant SNPs associated with complex traits or diseases across the entire genome.

如果未特别说明，p值通常小于0.05。在全基因组关联研究（GWAS）中，SNPs通常被选为工具变量（IVs）用于孟德尔随机化（MR）分析。然而，由于在整个基因组中进行多次比较，使用更为严格的p值<5e-08以减少假阳性并增强统计显著性。这一p值阈值是GWAS分析中公认的标准，用于识别与全基因组范围内复杂性状或疾病相关的重要SNPs。

，

。

Results

结果

Technology roadmap

技术路线图

The analysis flow of this study is shown in Fig.

本研究的分析流程如图所示。

。

Fig. 1

图1

Technology Roadmap. IEU: Integrative Epidemiology Unit; MR: Mendelian Randomization; SMR: Summary-data-based Mendelian Randomization; HEIDI: Heterogeneity in Dependent Instruments; pQTL: protein Quantitative Trait Locus.

技术路线图。IEU：综合流行病学单元；MR：孟德尔随机化；SMR：基于汇总数据的孟德尔随机化；HEIDI：依赖性工具中的异质性；pQTL：蛋白质数量性状基因座。

Full size image

全尺寸图像

MR analysis of druggable proteins

可成药蛋白的MR分析

Firstly, we intersected the 734 proteins studied by Zheng et al.

首先，我们对郑等人研究的734种蛋白质进行了交集分析。

with 4479 proteins encoded by druggable genes, resulting in 511 proteins encoded by druggable genes. Subsequently, we performed a two-sample Mendelian randomization analysis on the 511 proteins and EC by TwoSampleMR, with p value < 0.05 as the significant causal screening condition. From the results in Table .

编码了4479种由可成药基因编码的蛋白质，最终得到511种由可成药基因编码的蛋白质。随后，我们使用TwoSampleMR对这511种蛋白质和EC进行了两样本孟德尔随机化分析，以p值<0.05作为显著因果筛选条件。从表中的结果可以看出。

, a total of 24 proteins had a causal relationship with EC.

共有24种蛋白质与EC存在因果关系。

Since each of the 24 proteins contained only one SNP, sensitivity analysis could not be performed. Thus, we downloaded the pQTL files of these 24 proteins from the deCODE database for subsequent analysis. First, we screened the pQTL files according to the criteria of cis-pQTL and obtained the corresponding cis-pQTL of 19 proteins (Supplementary Data 3).

由于 24 种蛋白质中每种仅包含一个 SNP，因此无法进行敏感性分析。于是，我们从 deCODE 数据库下载了这 24 种蛋白质的 pQTL 文件以进行后续分析。首先，我们根据 cis-pQTL 的标准筛选了 pQTL 文件，并获得了 19 种蛋白质对应的 cis-pQTL（补充数据 3）。

Then, a two-sample Mendelian randomization analysis was performed on the 19 proteins and EC using TwoSampleMR. P- value < 0.05 was used as the significant causal screening criterion. Table .

随后，使用TwoSampleMR对这19种蛋白质和EC进行了两样本孟德尔随机化分析。P值<0.05被用作显著因果筛选标准。表。

; Fig.

图。

showed that a total of 13 proteins had causal relationships with EC. Among these, ST3GAL1, C9, HDGF, PLA2R1, HSP90B1, MMP10, KLK13, CEL, CTSF, ADH4, and VIT were positively correlated with the risk of EC. In contrast, GNRH2 and HPSE were negatively correlated with the risk. Finally, the scatter plots of the effect estimates of the Mendelian randomization IVW model for the 12 proteins and EC are shown in Fig. .

结果显示共有13种蛋白质与EC存在因果关系。其中，ST3GAL1、C9、HDGF、PLA2R1、HSP90B1、MMP10、KLK13、CEL、CTSF、ADH4和VIT与EC风险呈正相关，而GNRH2和HPSE则呈负相关。最后，图中展示了12种蛋白质与EC的孟德尔随机化IVW模型效应估计的散点图。

A-L. The analysis of HDGF contained only one SNP and cannot show the scatter plots.

A-L. 对HDGF的分析仅包含一个SNP，无法显示散点图。

Table 1 MR causal effect estimates of druggable proteins on EC (Zheng et al.).

表1 药物靶向蛋白对EC的MR因果效应估计值（Zheng等）。

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Table 2 MR causal effect estimates of druggable proteins on EC from DeCODE.

表2：DeCODE中可成药蛋白对EC的MR因果效应估计。

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Fig. 2

图2

The causal effect forest plot of MR analysis of proteins on EC. SNP, Single Nucleotide Polymorphism; OR, Odds Ratio; CI, Confidence Interval.

蛋白质对EC的MR分析的因果效应森林图。SNP，单核苷酸多态性；OR，比值比；CI，置信区间。

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Fig. 3

图 3

Scatter plots of effect estimates from the IVW model of MR analysis for EC proteinsA-L. (

EC蛋白A-L的MR分析IVW模型效应估计的散点图。

) ST3GAL1, (

) C9, (

C语言

) GNRH2, (

) PLA2R1, (

) HSP90B1, (

) MMP10, (

) KLK13, (

) CEL, (

我

) HPSE, (

) CTSF, (

) ADH4, (

) VIT. Scatter plot of Mendelian randomization IVW model effect estimation of (

) VIT. 孟德尔随机化IVW模型效应估计的散点图 (

–

) on EC. IVW, inverse variance weighted. Black dots: Represent SNP effect estimates, with position showing the size and direction. Error bars: The vertical lines above and below each black dot represent the effect estimate’s standard error (SE). Blue line: The IVW regression line shows the overall effect.

) 对EC的影响。IVW，逆方差加权。黑点：表示SNP效应估计值，位置显示大小和方向。误差线：每个黑点上下方的垂直线代表效应估计的标准误（SE）。蓝线：IVW回归线显示总体效应。

An upward slope suggests that higher protein levels may increase EC risk, while a downward slope indicates a decrease..

向上倾斜表明较高的蛋白质水平可能增加EC风险，而向下倾斜则表示减少。

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Sensitivity analysis of proteins for EC

蛋白质对EC的敏感性分析

First, we performed heterogeneity analysis on 12 proteins (ST3GAL1, C9, GNRH2, PLA2R1, HSP90B1, MMP10, KLK13, CEL, HPSE, CTSF, ADH4, VIT) and EC (Table

首先，我们对12种蛋白质（ST3GAL1、C9、GNRH2、PLA2R1、HSP90B1、MMP10、KLK13、CEL、HPSE、CTSF、ADH4、VIT）和EC进行了异质性分析（表）。

). ST3GAL1, GNRH2, PLA2R1, HSP90B1, MMP10, KLK13, HPSE, CTSF, and VIT showed no heterogeneity in the MR results (Cochran Q p-value > 0.05). The MR results of proteins C9 and CEL showed moderate heterogeneity (Cochran Q p-value < 0.05, 25% < I

ST3GAL1、GNRH2、PLA2R1、HSP90B1、MMP10、KLK13、HPSE、CTSF 和 VIT 在 MR 结果中未显示出异质性（Cochran Q p 值 > 0.05）。蛋白质 C9 和 CEL 的 MR 结果显示出中等程度的异质性（Cochran Q p 值 < 0.05，25% < I）。

< 50%). In contrast, the ADH4 showed high heterogeneity (Cochran Q p-value < 0.05, 50%< I

<50%)。相反，ADH4表现出高度异质性（Cochran Q p值<0.05，50%< I

). For proteins with heterogeneity, the IVW random-effects model (Table

). 对于具有异质性的蛋白质，IVW随机效应模型（表

) was used to estimate causal associations. It showed that C9, CEL, and ADH4 had a significant positive correlation with EC.

)被用来估计因果关联。结果显示，C9、CEL和ADH4与EC有显著的正相关关系。

As can be seen from the funnel plot of Fig.

从图的漏斗图可以看出。

A-L, the instrumental variables of 11 proteins (ST3GAL1, C9, GNRH2, PLA2R1, HSP90B1, MMP10, KLK13, CEL, HPSE, CTSF, VIT) were evenly distributed on the left and right sides of the IVW line without apparent heterogeneity. This suggested that the effect estimates of these proteins were relatively stable and had good reliability.

A-L，11种蛋白质（ST3GAL1、C9、GNRH2、PLA2R1、HSP90B1、MMP10、KLK13、CEL、HPSE、CTSF、VIT）的工具变量均匀分布在IVW线的两侧，没有明显的异质性。这表明这些蛋白质的效应估计相对稳定，具有良好的可靠性。

However, the instrumental variable for ADH4 was significantly unevenly distributed on both sides of the IVW line, showing evident heterogeneity, which suggests that other factors may influence its effect estimate and should be interpreted cautiously..

然而，ADH4 的工具变量在 IVW 线两侧分布显著不均，显示出明显的异质性，这表明其他因素可能影响其效应估计，应谨慎解读。

We then performed the pleiotropy test for the 12 proteins (ST3GAL1, C9, GNRH2, PLA2R1, HSP90B1, MMP10, KLK13, CEL, HPSE, CTSF, ADH4, VIT) and EC (Table

我们随后对12种蛋白质（ST3GAL1、C9、GNRH2、PLA2R1、HSP90B1、MMP10、KLK13、CEL、HPSE、CTSF、ADH4、VIT）和EC进行了多效性测试（表）。

). From Table

)。从表

, the p-value of the pleiotropy test for all proteins was greater than 0.05 (

，所有蛋白质的多效性检验的p值均大于0.05（

>0.05), and the intercept was close to 0. This indicates that horizontal pleiotropy did not affect these proteins’ causal inferences. This result further validates the stability and reliability of the causal relationships in this study.

>0.05），且截距接近于0。这表明水平多效性并未影响这些蛋白质的因果推断。这一结果进一步验证了本研究中因果关系的稳定性和可靠性。

We used the Steiger directional test for analysis to ensure that the causal direction of proteins affecting the incidence of EC was correct. We found that the p-value was far less than 0.05 (

我们使用Steiger方向性检验进行分析，以确保蛋白质影响EC发病率的因果方向正确。我们发现p值远小于0.05 (

<0.05), indicating the correct direction (Table

<0.05)，表明方向正确（表

）。

Table 3 The heterogeneity test for MR analysis of proteins on EC.

表3 蛋白质对EC的MR分析异质性检验。

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Table 4 Causal effect estimates from the MR IVW model of druggable proteins on EC from DeCODE.

表4 DeCODE中可成药蛋白对EC的MR IVW模型因果效应估计值。

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Table 5 Horizontal Pleiotropy tests of MR analysis for proteins on EC.

表5 蛋白质对EC的MR分析水平多效性检验。

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Table 6 Steiger directional test for MR analysis of proteins on EC.

表6 Steiger方向性检验用于EC上蛋白质的MR分析。

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Fig. 4

图4

Funnel plot of heterogeneity test in MR analysis of proteins on EC. A-L. (

MR分析中蛋白质对EC异质性检验的漏斗图。A-L。

) ST3GAL1, (

) C9, (

) GNRH2, (

) PLA2R1, (

) HSP90B1, (

) MMP10, (

) KLK13, (

) CEL, (

我

) HPSE, (

) CTSF, (

) ADH4, (

) VIT; Funnel plot of (

) VIT；漏斗图 (

–

) and EC in Mendelian randomization analysis.

) 和孟德尔随机化分析中的EC。

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SMR and colocalization analysis

SMR和共定位分析

A HEIDI test in SMR analysis was used to test the pleiotropy further. From Table

在SMR分析中，使用了HEIDI测试来进一步检验多效性。从表格中

, the p_HEIDI of HPSE, ST3GAL1, CEL, KLK13, and GNRH2 was greater than 0.05 (

，HPSE、ST3GAL1、CEL、KLK13 和 GNRH2 的 p_HEIDI 值大于 0.05（

>0.05). This indicated that there was no pleiotropy in the SNPs of these 5 proteins, further supporting the reliability of the results. Meanwhile, the SMR analysis results for these proteins showed p_SMR all less than 0.05 (

>0.05)。这表明这5种蛋白质的SNPs不存在多效性，进一步支持了结果的可靠性。同时，这些蛋白质的SMR分析结果显示p_SMR均小于0.05 (

<0.05). Thus, this indicated a statistically significant causal relationship between these proteins and EC. Furthermore, these findings collectively reinforce the importance of their potential impact on the development of EC.

<0.05）。因此，这表明这些蛋白质与EC之间存在统计学上显著的因果关系。此外，这些发现共同强调了它们对EC发展潜在影响的重要性。

Based on the coloc analysis (Table

基于共定位分析（表

), we found a moderate colocalization relationship between HPSE and EC (0.5 < PP.H4 < 0.8). This indicated that genetic variations in the levels of HPSE protein and the genetic variation in EC might be shared. Moreover, this suggests that changes in HPSE protein levels influence the risk of EC through a specific mechanism.

我们发现HPSE和EC之间存在中度共定位关系（0.5 < PP.H4 < 0.8）。这表明HPSE蛋白水平的遗传变异和EC的遗传变异可能存在共享。此外，这还表明HPSE蛋白水平的变化可能通过某种特定机制影响EC的风险。

Therefore, this finding provides novel insights for further investigating the role of HPSE in the development of EC and lays the groundwork for future treatment strategies..

因此，这一发现为进一步研究HPSE在EC发展中的作用提供了新的见解，并为未来的治疗策略奠定了基础。

Table 7 The SMR analysis results of proteins on EC.

表7 蛋白质在EC上的SMR分析结果。

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Table 8 The co-localization analysis for the proteins and EC.

表8 蛋白质和EC的共定位分析。

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Drug target analysis

药物靶点分析

We used the 5 druggable targets to analyze the potential drugs in the DURGBANK database. As shown in Table

我们使用5个可成药靶点来分析DURGBANK数据库中的潜在药物。如表所示

, GNRH2, KLK13 and ST3GAL1-encoded proteins had no corresponding available drugs, which could be further explored. In addition, the protein encoded by CEL corresponded to the drug DB04348, and the protein encoded by HPSE corresponded to the drugs DB01109 and DB06779.

，GNRH2、KLK13 和 ST3GAL1 编码的蛋白质没有相应的可用药物，这可以进一步探索。此外，CEL 编码的蛋白质对应于药物 DB04348，HPSE 编码的蛋白质对应于药物 DB01109 和 DB06779。

Table 9 Drug information of druggable targets in drugbank.

表9 DrugBank中可成药靶点的药物信息。

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Discussion

讨论

Despite the emergence of new therapies in recent years, the current treatment options for EC are primarily confined to surgery. However, there are no specific targeted drug therapies proven to be highly effective for the majority of EC patients. The human proteome is currently a major therapeutic target.

近年来，尽管出现了许多新的治疗方法，但目前子宫内膜癌的治疗选择主要局限于手术。然而，对于大多数子宫内膜癌患者，并没有特定的靶向药物疗法被证明非常有效。人类蛋白质组目前是一个主要的治疗靶点。

To our knowledge, this study is the first to combine plasma proteomics data with EC research, using two-sample MR analysis and Bayesian colocalization to assess causal proteins for EC. In our analysis, we assessed the pleiotropy of the selected proteins and evaluated heterogeneity using Cochran’s Q test.

据我们所知，本研究是首个将血浆蛋白质组学数据与子宫内膜癌（EC）研究相结合的研究，使用两样本孟德尔随机化分析和贝叶斯共定位来评估子宫内膜癌的因果蛋白。在我们的分析中，我们评估了所选蛋白的多效性，并使用Cochran's Q检验评估了异质性。

Additionally, we verified the correct causal direction using the Steiger directional test. The results showed that horizontal pleiotropy did not significantly influence the causal relationship between these proteins and EC. We also used cis-pQTLs as instruments and conducted sensitivity analyses, limiting bias from heterogeneity and horizontal pleiotropy.

此外，我们使用Steiger方向检验验证了正确的因果方向。结果表明，水平多效性并未显著影响这些蛋白质与EC之间的因果关系。我们还使用了顺式pQTL作为工具变量，并进行了敏感性分析，以限制异质性和水平多效性带来的偏差。

Additionally, SMR and colocalization analyses were used to exclude bias introduced by linkage disequilibrium (LD). Ultimately, we identified five proteins (HPSE, ST3GAL1, CEL, KLK13, and GNRH2) as the potential drug targets for EC and demonstrated the reliability of the results..

此外，使用SMR和共定位分析来排除连锁不平衡（LD）引入的偏差。最终，我们确定了五种蛋白质（HPSE、ST3GAL1、CEL、KLK13和GNRH2）作为EC的潜在药物靶点，并证明了结果的可靠性。

By combining large-scale plasma proteomics data with available druggable genomes, we investigated 511 potential gene targets and identified 12 proteins with strong causal relationships to EC. These proteins were ST3GAL1, C9, PLA2R1, HSP90B1, MMP10, KLK13, CEL, CTSF, ADH4, VIT, GNRH2, and HPSE. Subsequently, HPSE, ST3GAL1, CEL, KLK13, and GNRH2 were validated through colocalization analysis.

通过将大规模血浆蛋白质组学数据与现有的可成药基因组相结合，我们研究了511个潜在的基因靶点，并确定了12种与EC有强烈因果关系的蛋白质。这些蛋白质是ST3GAL1、C9、PLA2R1、HSP90B1、MMP10、KLK13、CEL、CTSF、ADH4、VIT、GNRH2和HPSE。随后，通过共定位分析验证了HPSE、ST3GAL1、CEL、KLK13和GNRH2。

Among these proteins, we found drugs corresponding to the CEL and HPSE proteins, indicating existing drugs available for these targets. However, the GNRH2, KLK13, and ST3GAL1 proteins currently have no known corresponding drugs, suggesting these may be under-researched or underutilized drug targets.

在这些蛋白质中，我们发现了与CEL和HPSE蛋白相对应的药物，表明这些靶点已有可用的药物。然而，GNRH2、KLK13和ST3GAL1蛋白目前尚无已知的对应药物，这表明它们可能是研究不足或未被充分利用的药物靶点。

Moreover, this data provides new possibilities and directions for future drug target development and design. If these drugs demonstrate safety and efficacy in future clinical trials, they could be repurposed or further optimized for EC treatment..

此外，这些数据为未来的药物靶点开发和设计提供了新的可能性和方向。如果这些药物在未来的临床试验中证明了安全性和有效性，它们可能会被重新定位或进一步优化用于子宫内膜癌的治疗。

Among the identified five proteins, heparanase (HPSE) showed a moderate colocalization relationship with EC (0.5 < PP.H4 < 0.8). This indicates that genetic variation in HPSE protein levels has a strong causal relationship with the risk of EC. Additionally, changes in HPSE protein levels may be an essential marker for increased risk of EC.

在鉴定出的五种蛋白质中，肝素酶（HPSE）与EC显示出中等共定位关系（0.5 < PP.H4 < 0.8）。这表明HPSE蛋白水平的遗传变异与EC风险具有很强的因果关系。此外，HPSE蛋白水平的变化可能是EC风险增加的重要标志。

HPSE is a key enzyme that degrades the extracellular matrix and promotes invasion and metastasis in various cancers.

HPSE是一种关键酶，可降解细胞外基质并促进多种癌症的侵袭和转移。

. Moreover, HPSE degrades heparan sulfate proteoglycans (HSPGs), leading to ECM degradation, remodeling, and angiogenesis, making it easier for tumor cells to invade and metastasize

此外，HPSE 降解硫酸乙酰肝素蛋白聚糖 (HSPGs)，导致细胞外基质降解、重塑和血管生成，使肿瘤细胞更容易侵袭和转移。

. Notably, it is extensively involved in biological and pathological processes such as tissue repair, inflammation, tumor angiogenesis, invasion, and metastasis

。特别是，它广泛参与组织修复、炎症、肿瘤血管生成、侵袭和转移等生理和病理过程。

. HPSE can also activate several pro-cancer signaling pathways, including ERK, AKT, and Wnt/β-catenin pathways, to enhance tumor cell proliferation, migration, and drug resistance

HPSE还可以激活多种促癌信号通路，包括ERK、AKT和Wnt/β-catenin通路，以增强肿瘤细胞的增殖、迁移和耐药性。

. In gastric and pancreatic cancers, overexpression of HPSE is associated with poor prognosis, and it potentiates the invasion and migration of pancreatic cancer cells via epithelial-to-mesenchymal transition through the Wnt/β-catenin pathway, while its specific mechanism in esophageal cancer requires further investigation.

在胃癌和胰腺癌中，HPSE的过表达与不良预后相关，并且它通过Wnt/β-连环蛋白途径，经由上皮-间质转化增强胰腺癌细胞的侵袭和迁移，而其在食管癌中的具体机制还需要进一步研究。

，

. Our study found a significant causal relationship between HPSE and EC, with no pleiotropy detected in the SMR analysis. We also observed that HPSE was inversely correlated with the risk of EC. This finding is consistent with the study of Wang et al.

我们的研究发现HPSE与EC之间存在显著的因果关系，SMR分析中未检测到多效性。我们还观察到HPSE与EC风险呈负相关。这一发现与Wang等人的研究一致。

, in which they suggested that HPSE expression was downregulated in ESCC tissues, and low levels of HPSE expression in tumor tissues were significantly associated with an increased risk of cancer-related death in ESCC patients. In addition, analysis of complete clinical and pathological information from ESCC tissues also indicates that HPSE expression in cancer tissues negatively correlates with the clinical pathological classification N stage and clinical stage.

，他们指出 HPSE 在食管鳞癌组织中表达下调，并且肿瘤组织中低水平的 HPSE 表达与食管鳞癌患者癌症相关死亡风险的增加显著相关。此外，对食管鳞癌组织的完整临床和病理信息的分析还表明，癌组织中的 HPSE 表达与临床病理分类 N 分期和临床分期呈负相关。

. However, HPSE is preferentially upregulated in the carcinogenesis of Barrett’s esophagus and intestinal-type gastric cancer

然而，在巴雷特食管和肠型胃癌的致癌过程中，HPSE优先上调。

, suggesting it plays a key role in the development of inflammation-related upper gastrointestinal adenocarcinomas. Therefore, HPSE may serve as a novel prognostic marker in EC tissues and suggests the potential effectiveness of HPSE-targeted therapies in treating EC. Some experimental evidence indicated that HPSE could release HS-bound growth factors by cleaving the heparan sulfate proteoglycan (HSPG) side chains in hepatocellular carcinoma (HCC).

，提示其在炎症相关上消化道腺癌的发病机制中起关键作用。因此，HPSE可能作为EC组织中的新型预后标志物，并提示HPSE靶向治疗在EC治疗中的潜在有效性。一些实验证据表明，在肝细胞癌（HCC）中，HPSE可通过切割硫酸乙酰肝素蛋白聚糖（HSPG）侧链释放与HS结合的生长因子。

These growth factors inhibited the proliferation and signal activation of tumor cells, such as melanoma and HCC.

这些生长因子抑制了肿瘤细胞的增殖和信号激活，例如黑色素瘤和肝细胞癌。

，

. Therefore, we hypothesized that low HPSE expression would enhance the ability of growth factors to bind to EC cells, leading to a poor prognosis. Our study revealed that Heparin and Dalteparin are the corresponding drugs targeting HPSE, as confirmed by the studies of Vlodavsky et al.

因此，我们假设低HPSE表达会增强生长因子与EC细胞结合的能力，导致预后不良。我们的研究显示，肝素和达肝素是针对HPSE的相应药物，这已由Vlodavsky等人的研究证实。

and Fairbrother et al.

和费尔布罗等。

Additionally, heparin inhibits the enzymatic activity of HPSE and blocks the production of VEGF. While heparin can inhibit its extracellular functions, it can’t prevent the regulatory effects of HPSE at the gene level

此外，肝素抑制HPSE的酶活性并阻止VEGF的产生。虽然肝素可以抑制其细胞外功能，但不能防止HPSE在基因水平的调控作用。

. Thus, targeting HPSE is considered a potential treatment strategy for cancer

因此，针对HPSE被认为是一种潜在的癌症治疗策略。

. At present, there are no studies combining Heparin or Dalteparin targeting HPSE for the treatment of EC. This may open up a new direction for future research.

目前尚无将肝素或达肝素靶向HPSE用于治疗EC的研究，这可能为未来的研究开辟新的方向。

In this study, we also identified GNRH2, gonadotropin-releasing hormone 2, as a potential target negatively associated with the risk of EC. GNRH2 is widely distributed in the central nervous system and expressed in peripheral tissues of mammals

在这项研究中，我们还鉴定了促性腺激素释放激素2（GNRH2）作为一个与EC风险负相关的潜在靶点。GNRH2广泛分布于中枢神经系统，并在哺乳动物的外周组织中表达。

. Studies have indicated that GnRH2 can reduce the migration and proliferation capacity of prostate cancer cells. Moreover, GnRH2 can inhibit the expression of ribosomal phosphorylation proteins in human cancer cells, which are key regulators of protein elongation

研究表明，GnRH2能够减少前列腺癌细胞的迁移和增殖能力。此外，GnRH2能够抑制人类癌细胞中核糖体磷酸化蛋白的表达，这些蛋白是蛋白质延伸的关键调节因子。

. GNRH2 has been proven to exhibit strong anti-proliferative and pro-apoptotic effects on various cancers, including breast, endometrial, ovarian, and prostate, through its receptors expressed on cancer cells

GNRH2 已被证明通过癌细胞上表达的受体对多种癌症（包括乳腺癌、子宫内膜癌、卵巢癌和前列腺癌）具有强烈的抗增殖和促凋亡作用。

. Importantly, its role in inhibiting cancer invasiveness is consistent with our conclusion. Although there is no direct evidence linking GNRH2 with EC, its similar role in tumor biology may offer insights into its potential clinical application. The most studied genotypes of GNRH2 in tumors included SNP rs3761243 and SNP rs6051545.

重要的是，它在抑制癌症侵袭性方面的作用与我们的结论一致。尽管没有直接证据将GNRH2与EC联系起来，但它在肿瘤生物学中的类似作用可能为其潜在的临床应用提供见解。在肿瘤中研究最多的GNRH2基因型包括SNP rs3761243和SNP rs6051545。

Notably, SNP rs3761243 is significantly associated with the survival status of osteosarcoma patients, providing crucial insights for the screening, diagnosis, and prognosis of osteosarcoma.

值得注意的是，SNP rs3761243与骨肉瘤患者的生存状态显著相关，为骨肉瘤的筛查、诊断和预后提供了重要的见解。

，

. SNP rs6051545 of GNRH2 is expressed in both normal prostate and prostate cancer, impacting prostate biology and the treatment of prostate cancer

GNRH2的SNP rs6051545在正常前列腺和前列腺癌中均有表达，影响前列腺生物学及前列腺癌的治疗。

. Our study indicated that the SNP rs6051545 of GNRH2 had a negative regulatory effect on EC. Regardless of whether it may be a direct or indirect target, it may inhibit the proliferation and survival of EC cells. Currently, there are no related studies in EC, and our research highlights the potential of this field.

我们的研究表明GNRH2的SNP rs6051545对EC具有负调控作用。无论其可能是直接还是间接靶点，它可能抑制EC细胞的增殖和存活。目前在EC中尚无相关研究，我们的研究凸显了这一领域的潜力。

The underlying mechanisms behind this phenomenon remain unclear and should be further elucidated in future studies..

这种现象背后的潜在机制仍然不清楚，应该在今后的研究中进一步阐明。

In our study, KLK13, ST3GAL1, and CEL were risk-associated proteins for EC. The human kallikrein-related peptidase (KLK) family encodes a series of secreted serine proteases involved in many biological functions, including skin desquamation, immune response, and enamel formation

在我们的研究中，KLK13、ST3GAL1 和 CEL 是与子宫内膜癌（EC）风险相关的蛋白质。人类激肽释放酶相关肽酶（KLK）家族编码一系列分泌型丝氨酸蛋白酶，这些酶参与许多生物学功能，包括皮肤脱屑、免疫反应和牙釉质形成。

. KLK is also involved in tumorigenesis, activating proteolytic processes, and is associated with tumor phenotypes. It is also known as KLK-L4, which has a DNA sequence of 10 kb and consists of five exons and four introns. Some studies suggested that KLK13, either alone or in combination with other biomarkers, could serve as a prognostic factor for ovarian cancer.

KLK还参与肿瘤发生，激活蛋白水解过程，并与肿瘤表型相关。它也被称为KLK-L4，其DNA序列为10 kb，由五个外显子和四个内含子组成。一些研究表明，KLK13单独或与其他生物标志物联合使用，可作为卵巢癌的预后因素。

, gastric cancer

，胃癌

, colorectal cancer

，结直肠癌

, oral cancer

口腔癌

四十三

, and bladder cancer

，以及膀胱癌

. However, the role of KLK13 in ESCC remains to be investigated. Research by Lin et al.

然而，KLK13在食管鳞状细胞癌（ESCC）中的作用仍有待研究。Lin等人的研究。

indicated that KLK13 was significantly downregulated in ESCC tissues. Lower KLK13 mRNA levels were associated with higher tumor grade, TNM (UICC, 2009) stage, and poorer survival rates. Additionally, overexpression of KLK13 was found to inhibit cell invasion and migration. However, Nohara et al.

表明KLK13在食管鳞状细胞癌组织中显著下调。较低的KLK13 mRNA水平与较高的肿瘤分级、TNM（UICC，2009）分期以及较差的存活率相关。此外，KLK13的过表达被发现能够抑制细胞侵袭和迁移。然而，Nohara等人。

found that patients with positive KLK13 expression in ESCC samples had significantly poorer prognoses than those with negative expressions. High levels of KLK13 expression were also associated with tumor progression, advanced tumor stage, and adverse prognosis, which was consistent with the findings of Shimomura et al..

发现ESCC样本中KLK13表达阳性的患者预后明显差于表达阴性的患者。KLK13的高表达水平还与肿瘤进展、晚期肿瘤分期和不良预后相关，这与Shimomura等人的研究结果一致。

Although their findings complemented those of Lin et al.

尽管他们的发现补充了林等人的研究结果。

, some discrepancies were likely due to variations in tumor stages, preoperative treatments affecting KLK13 expression levels, and differences in experimental methods. Compared to the studies by Nohara

，一些差异可能是由于肿瘤阶段的不同、术前治疗对KLK13表达水平的影响以及实验方法的差异所致。与Nohara的研究相比

and Shimomura et al.

和下村等人

, Lin’s

，林的

study only detected mRNA levels in ESCC tissues and conducted cell functional experiments, without performing immunohistochemical pathological diagnosis. From this perspective, their research is insufficient. Moreover, Shimomura’s

研究仅检测了ESCC组织中的mRNA水平并进行了细胞功能实验，未进行免疫组化病理诊断。从这个角度来看，他们的研究存在不足。此外，Shimomura的

research found that anticancer drugs could promote KLK13 demethylation through the upregulation of TET2/3, leading to the positive expression of KLK13 after preoperative treatment. This is also a likely reason for the upregulation of KLK13 in ESCC samples. Our findings are consistent with the conclusions of Nohara.

研究发现，抗癌药物可通过上调TET2/3促进KLK13去甲基化，导致术前治疗后KLK13的阳性表达。这也是ESCC样本中KLK13上调的可能原因。我们的发现与Nohara的结论一致。

and Shimomura

和下村

, indicating that the transition of KLK13 expression from negative to positive predicted poor prognosis in EC. Thus, KLK13 may be a unique novel molecule for monitoring ESCC progression, and elucidating the role of KLK13 in ESCC may provide new insights into the function of this molecule as a target for novel therapeutic drugs..

，表明KLK13表达从阴性到阳性的转变预示了EC的不良预后。因此，KLK13可能是监测ESCC进展的独特新颖分子，阐明KLK13在ESCC中的作用可能为该分子作为新型治疗药物靶点的功能提供新的见解。

Sialyltransferase ST3GAL1 is a key enzyme involved in glycosylation modifications. It catalyzes the α-2,3-sialylation of glycoproteins and glycolipids, influencing tumor progression

唾液酸转移酶ST3GAL1是一种参与糖基化修饰的关键酶。它催化糖蛋白和糖脂的α-2,3-唾液酸化，影响肿瘤进展。

. Consequently, glycosylation plays a critical role in tumorigenesis, and abnormal sialylation can alter cell adhesion properties, enhance immune evasion, and promote cancer cell invasion and metastasis. For example, in breast cancer and pancreatic cancer, ST3GAL1 promotes cancer cell survival and immune evasion by regulating adhesion molecules (such as MUC1 and integrins).

因此，糖基化在肿瘤发生中起着关键作用，异常的唾液酸化可以改变细胞粘附特性、增强免疫逃逸并促进癌细胞侵袭和转移。例如，在乳腺癌和胰腺癌中，ST3GAL1通过调节粘附分子（如MUC1和整合素）促进癌细胞存活和免疫逃逸。

and immune checkpoint molecules (such as PD-L1)

以及免疫检查点分子（如PD-L1）

. In addition, overexpression of ST3GAL1 can promote proliferation, migration, and invasion of intrahepatic cholangiocarcinoma (iCCA) cells, while inhibiting apoptosis

此外，ST3GAL1的过表达可以促进肝内胆管癌（iCCA）细胞的增殖、迁移和侵袭，同时抑制细胞凋亡。

. Clinically, high expression of ST3GAL1 was associated with poor outcomes in patients with breast cancer

临床研究表明，ST3GAL1的高表达与乳腺癌患者的不良预后相关。

and glioblastoma

和胶质母细胞瘤

, and it might increase tumor immune evasion through enhanced sialylation of CD55

，并且它可能通过增强CD55的唾液酸化来增加肿瘤免疫逃逸。

. Previous studies have indicated that abnormal glycosylation in esophageal squamous cell carcinoma may impact tumor growth and invasion

以前的研究表明，食管鳞状细胞癌中的异常糖基化可能影响肿瘤的生长和侵袭。

. As a key sialyltransferase, ST3GAL1 may promote EC progression by altering the structure of tumor-associated glycoproteins. Carboxyl ester lipase (CEL) is an important lipase that is primarily involved in lipid metabolism

作为一种关键的唾液酸转移酶，ST3GAL1可能通过改变肿瘤相关糖蛋白的结构来促进EC的进展。羧酸酯脂肪酶（CEL）是一种重要的脂肪酶，主要参与脂质代谢。

. It could hydrolyze dietary fats, cholesterol esters, and fat-soluble vitamins in the duodenum

它可以在十二指肠中水解膳食脂肪、胆固醇酯和脂溶性维生素。

. Beyond its digestive function, growing evidence indicated that this enzyme may have important extraintestinal roles. It could also cross the intestinal mucosa into the bloodstream and has been detected in the urine of healthy individuals

除了其消化功能外，越来越多的证据表明这种酶可能具有重要的肠外作用。它还可以穿过肠黏膜进入血液，并在健康个体的尿液中被检测到。

. Cui et al. demonstrated that high expression of CEL was an independent prognostic factor for poor survival rates in breast cancer

崔等人证实，CEL的高表达是乳腺癌生存率低的一个独立预后因素。

. Additionally, aberrant expression of CEL gene variants has been found in the pancreas of patients with chronic pancreatitis and pancreatic cancer

此外，在慢性胰腺炎和胰腺癌患者的胰腺中发现了CEL基因变异的异常表达。

，

. Recent studies have found that CEL may be regulated by metabolism in tumorigenesis

近期研究发现，在肿瘤发生过程中，CEL可能受到代谢的调控。

. By modulating fatty acid metabolism, CEL may drive alterations in the tumor microenvironment and inflammatory responses. Furthermore, lipid metabolism imbalance is closely associated with the development of gastrointestinal cancers, including EC

通过调节脂肪酸代谢，CEL可能驱动肿瘤微环境和炎症反应的改变。此外，脂质代谢失衡与包括食管癌在内的胃肠道癌症的发展密切相关。

. CEL also influences lipid metabolism and related inflammatory processes, which may indirectly contribute to the development of EC

CEL还影响脂质代谢和相关的炎症过程，这可能间接导致EC的发展。

六十二

. The co-localization of CEL in EC was relatively high in our study, suggesting a higher possibility of CEL acting as a pathogenic protein in EC. Furthermore, our research found that taurocholic acid could interact with the CEL gene, consistent with Fontbonne et al.‘s report

在我们的研究中，CEL在EC中的共定位相对较高，表明CEL作为EC致病蛋白的可能性较大。此外，我们的研究发现牛磺胆酸可以与CEL基因相互作用，这与Fontbonne等人的报告一致。

六十三

that taurocholate could serve as a reference CEL activator. As such, chronic exposure to bile acids, such as taurocholic acid, may stimulate esophageal epithelial cells, leading to cell damage and carcinogenesis, with specific bile acid metabolites acting as carcinogenic factors in EC

牛磺胆酸可以作为CEL激活剂的参考。因此，长期暴露于胆汁酸（如牛磺胆酸）可能会刺激食管上皮细胞，导致细胞损伤和致癌作用，其中特定的胆汁酸代谢物充当食管癌的致癌因素。

. CEL may regulate their metabolism, leading to adaptive cell changes in response to the bile acid environment, thereby promoting tumorigenesis

CEL可能调节其代谢，导致细胞在胆汁酸环境下发生适应性变化，从而促进肿瘤的发生。

. The accumulation of bile acids may also lead to increased inflammation, and the role of the CEL gene in inflammation or metabolic pathways may further affect the progression of EC. Although the specific mechanism of CEL in EC requires further investigation, its association with metabolic reprogramming and inflammation makes it a potential research focus..

胆汁酸的积累还可能导致炎症加剧，CEL基因在炎症或代谢途径中的作用可能进一步影响EC的进展。尽管CEL在EC中的具体机制还需要进一步研究，但其与代谢重编程和炎症的关联使其成为潜在的研究焦点。

This study has multiple critical advantages as follows. To our knowledge, this is the first study to explore the causal relationship between potential protein targets and EC by the two-sample MR method. This approach addressed the inherent flaws of previous traditional observational studies, such as reverse causality and confounding factors.

本研究具有多个重要优势，如下所示。据我们所知，这是首个采用两样本孟德尔随机化方法探索潜在蛋白质靶点与EC之间因果关系的研究。该方法解决了以往传统观察性研究的固有缺陷，如反向因果关系和混杂因素。

This study also provided new directions and theories for developing potential drug targets for EC. Firstly, by selecting plasma proteins and druggable gene-encoded proteins from different studies and taking their intersection, we further narrowed the scope of the study, making it more targeted. The results are more reliable as they are supported by the literature evidence.

本研究还为开发EC潜在药物靶点提供了新的方向和理论。首先，通过选取不同研究中的血浆蛋白和可成药基因编码蛋白并取其交集，进一步缩小了研究范围，使研究更具针对性，并且结果有文献证据支持，更加可靠。

Secondly, since each of the prioritized proteins had only one SNP, we downloaded their pQTL data for supplementation, thereby broadening the application of this analysis. The SNPs selected for protein pQTL were strong instruments, meeting the F-statistic greater than 10. PQTL dataset analysis focuses on genetic variation at the protein level.

其次，由于每个优先考虑的蛋白质只有一个SNP，我们下载了它们的pQTL数据进行补充，从而拓宽了该分析的应用。为蛋白质pQTL选择的SNP是强有力的工具，符合F统计量大于10的标准。pQTL数据集分析侧重于蛋白质水平的遗传变异。

Specifically, cis-pQTLs directly influence the expression levels of target proteins, providing clear insights into causal relationships. Therefore, the effects of common confounding factors in observational data do not need to be directly considered. Additionally, we have ensured the independence of genetic instruments through linkage disequilibrium (LD r² threshold < 0.1) to help reduce confounding bias due to correlations between the instruments.

具体来说，顺式pQTL直接影响力靶蛋白的表达水平，为因果关系提供了清晰的见解。因此，观察数据中常见混杂因素的影响无需直接考虑。此外，我们通过连锁不平衡（LD r² 阈值 < 0.1）确保了遗传工具的独立性，以帮助减少因工具间相关性导致的混杂偏倚。

Compared to the study by Zhu et al..

与朱等人的研究相比。

., which used proteomics and Mendelian randomization to identify therapeutic targets for hepatocellular carcinoma, our study offers a deeper analysis of the results. In addition to performing heterogeneity and pleiotropy tests, we also used the Steiger directional test to ensure the accuracy of the causal direction.

这项研究使用蛋白质组学和孟德尔随机化来确定肝细胞癌的治疗靶点，我们的研究对结果进行了更深入的分析。除了进行异质性和多效性检验外，我们还使用了Steiger方向检验来确保因果方向的准确性。

This multi-layered analytical approach effectively eliminates potential biases and provides stronger support for the reliability of the conclusions. Recent studies have focused on single GWAS or pQTL analyses.

这种多层次的分析方法有效消除了潜在的偏差，并为结论的可靠性提供了更强的支持。最近的研究集中在单个GWAS或pQTL分析上。

, lacking a comprehensive and systematic approach. This study introduces a novel interdisciplinary methodology by combining two-sample MR and SMR analyses to examine the causal relationship between gene expression and EC. We further enhanced the reliability of causal inference by performing cross-validation with HEIDI and colocalization, a relatively uncommon practice in the current literature..

，缺乏一个全面且系统的方法。本研究通过结合两样本MR和SMR分析，引入了一种新颖的跨学科方法来检验基因表达与EC之间的因果关系。我们进一步通过使用HEIDI和共定位进行交叉验证，增强了因果推断的可靠性，这是当前文献中相对少见的做法。

However, our study also has several limitations. The datasets were from European populations, which may limit the broader applicability of the causal inferences. Genetic variation frequencies differ across populations, and some pQTLs that are significant in the European population may have lower frequencies in East Asian or African populations, potentially affecting the robustness of gene-phenotype associations.

然而，我们的研究也存在一些局限性。数据集来自欧洲人群，这可能限制了因果推断的广泛适用性。不同人群中的基因变异频率有所不同，在欧洲人群中显著的某些pQTL在东亚或非洲人群中可能频率较低，这可能会影响基因与表型关联的稳健性。

Additionally, gene-environment interactions vary between ethnic groups. Effect attenuation may occur since the genetic instrumental variables in this study were selected based on European GWAS data. This means that SNP effects could be weaker in other populations, potentially impacting the generalizability of the results.

此外，基因与环境的交互作用在不同种族群体之间也存在差异。由于本研究中的遗传工具变量是基于欧洲人群的GWAS数据筛选的，因此可能会出现效应衰减。这意味着SNP效应在其他人群中可能较弱，从而可能影响结果的普适性。

Therefore, we plan to incorporate data from a more diverse population in future research to strengthen the generalizability and external validity of the results. Although we carefully selected pQTLs associated with the proteins as instrument variables, Mendelian randomization analysis may still be influenced by non-genetic effects (e.g., environmental factors), affecting the validity of causal inferences.

因此，我们计划在今后的研究中纳入更多样化人群的数据，以增强结果的普遍性和外部有效性。尽管我们仔细选择了与蛋白质相关的pQTL作为工具变量，孟德尔随机化分析仍可能受到非遗传效应（例如环境因素）的影响，从而影响因果推断的有效性。

Furthermore, despite performing heterogeneity tests, some proteins (ADH4, C9, CEL) exhibited moderate to high heterogeneity, which may be attributable to differences in the effects of SNPs, gene-gene interactions, or environmental factors. To minimize the impact of heterogeneity on our results, we applied the IVW random-effects model for proteins with higher heterogeneity and quantified heterogeneity levels using Cochran’s Q statistic to ensure the robustness of causal estimates further.

此外，尽管进行了异质性检验，但部分蛋白质（ADH4、C9、CEL）表现出中等到较高的异质性，这可能归因于SNP效应的差异、基因-基因相互作用或环境因素的影响。为了尽量减少异质性对结果的影响，我们对异质性较高的蛋白质应用了IVW随机效应模型，并使用Cochran’s Q统计量量化异质性水平，以进一步确保因果估计的稳健性。

Meanwhile, we assessed the distribution of SNP effects using funnel plots. The results indicated that the SNP effects for most proteins were balanced, but ADH.

同时，我们使用漏斗图评估了SNP效应的分布。结果表明，大多数蛋白质的SNP效应是平衡的，但ADH除外。

Conclusions

结论

In summary, our comprehensive analysis indicates that HPSE, ST3GAL1, CEL, KLK13, and GNRH2 proteins have a causal relationship with the risk of EC. The identified proteins may serve as attractive drug targets for EC, especially HPSE. Further research is needed to explore the roles of these candidate proteins in EC..

总之，我们的综合分析表明，HPSE、ST3GAL1、CEL、KLK13 和 GNRH2 蛋白与 EC 的风险存在因果关系。这些已识别的蛋白可能作为 EC 的潜在药物靶点，尤其是 HPSE。需要进一步研究来探索这些候选蛋白在 EC 中的作用。

Data availability

数据可用性

Data is provided within the manuscript or supplementary information files.

数据在手稿或补充信息文件中提供。

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Acknowledgements

致谢

We thank all contributors to the IEU open GWAS database and acknowledge all the investigators and participants of this study.

我们感谢所有对 IEU 开放 GWAS 数据库的贡献者，并对本研究的所有研究人员和参与者表示感谢。

Funding

资金

The research is supported by the Key Research and Development Program of Shaanxi (No. 2021ZDLSF02-06).

该研究得到了陕西省重点研发计划（编号：2021ZDLSF02-06）的支持。

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Department of Gastroenterology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, Shaanxi, China

中国陕西省西安市西安交通大学第一附属医院消化内科，邮编710061

Chengjun Li, Mudan Ren, Yan Yin & Shuixiang He

李成军、任牡丹、尹岩、何水祥

Department of Infectious Diseases, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710004, Shaanxi, China

中国陕西省西安市西安交通大学第二附属医院传染病科，邮编710004

Xiaomeng Cui

崔晓梦

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Chengjun Li

李成军

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崔晓梦

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Yan Yin

闫音

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Contributions

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Conceptualization: S.H. and C.L.; Methodology: C.L.; Formal analysis: C.L. and X.C.; Validation: Y.Y.; Investigation: M.R.; Resources: S.H.; Data curation: C.L. and X.C.; writing—original draft, review and editing: C.L.; Supervision: S.H.; Funding: S.H.. All authors have read and agreed to the published version of the manuscript..

概念化：S.H. 和 C.L.；方法论：C.L.；形式分析：C.L. 和 X.C.；验证：Y.Y.；调查：M.R.；资源：S.H.；数据管理：C.L. 和 X.C.；撰写—原始草稿、审查和编辑：C.L.；监督：S.H.；资金：S.H.。所有作者都已阅读并同意发表的手稿版本。

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水乡河

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The data and analysis of this study are publicly available and do not require ethical approval.

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Li, C., Cui, X., Ren, M.

李, C., 崔, X., 任, M.