基于双样本孟德尔随机化探究食源性致病菌与胃肠道癌症转移的相关性
Exploring the Correlation between Foodborne Pathogens and Gastrointestinal Cancer Metastasis Based on Two-Sample Mendelian Randomisation
DOI: 10.12677/amb.2025.141003, PDF, HTML, XML,    国家自然科学基金支持
作者: 郑浩东:上海理工大学健康科学与工程学院,上海;赵兴贺, 陈哲逸, 沈立松*, 杨俊瑶*:上海交通大学医学院附属新华医院检验科,上海
关键词: 食源性致病菌孟德尔随机化胃肠道癌症癌症转移Foodborne Pathogenic Bacteria Mendelian Randomization Gastrointestinal Cancer Cancer Metastasis
摘要: 研究利用孟德尔随机化法(Mendelian randomization, MR)分析了几种食源性病原体与白细胞分化抗原44 (Cluster of differentiation 44, CD44)之间的关联,CD44抗原是胃肠道癌症发展和转移的标志物。该研究从综合流行病学数据库(IEU)、芬兰基因数据库(FinnGen)和英国生物数据库(UK Biobank)获得了全基因组关联研究(Genome-wide association studies, GWAS)数据,并主要使用反方差加权(Inverse-variance weighting, IVW)等统计方法分析了相关性。同时还进行了敏感性分析,以提高结果的可靠性。研究结果表明,幽门螺旋杆菌与CD44抗原呈显著正相关,而小肠结肠耶尔森氏菌与CD44抗原呈显著性负相关,因此研究判断幽门螺旋杆菌会促进癌症的发生和转移,而小肠结肠耶尔森氏菌可抑制癌细胞的发展和转移。然而,其余细菌种类没有发现明显的相关性。部分食源性致病菌可能会促进胃肠道癌症的转移,如幽门螺旋杆菌。但还有些可能抑制了癌细胞的转移,表明背后可能有更复杂的生理机制。
Abstract: This study used Mendelian randomization (MR) to analyze the association between several foodborne pathogens and the Cluster of Differentiation 44 (CD44) antigen, a marker of gastrointestinal cancer development and metastasis. The study obtained genome-wide association studies (GWAS) data from Integrative Epidemiology Unit (IEU), FinnGen and UK Biobank and analyzed the correlation mainly using statistical methods such as inverse-variance weighted (IVW). Sensitivity analyses were also carried out to improve the reliability of the results. The results of the study showed that Helicobacter pylori showed a significant positive correlation with CD44 antigen; Yersinia enterocolitica showed a significant negative correlation with CD44 antigen; therefore, we judged that Helicobacter pylori promotes cancer development and metastasis, and Yersinia enterocolitica inhibits cancer cell development and metastasis. However, no significant correlation was found for the remaining bacterial species. Some foodborne pathogens, such as Helicobacter pylori, can promote the metastasis of gastrointestinal cancers. However, others can inhibit the metastasis of cancer cells, suggesting that there may be a more complex physiological mechanism involved.
文章引用:郑浩东, 赵兴贺, 陈哲逸, 沈立松, 杨俊瑶. 基于双样本孟德尔随机化探究食源性致病菌与胃肠道癌症转移的相关性[J]. 微生物前沿, 2025, 14(1): 16-24. https://doi.org/10.12677/amb.2025.141003

1. 引言

食源性致病菌是全球疾病的主要原因之一。据世界卫生组织统计,食物中有害致病菌导致超过200种疾病[1]。食品污染可能发生在食品生产的不同环节,如原料供应、储存、烹饪、加工和包装。食品工业中使用的不锈聚丙烯、聚乙烯、玻璃和钢材等常见材料都有可能造成食品污染[2]。最常见的食品来自家禽、肉类、动物产品、蔬菜、谷物等[3]。最常见的食源性致病细菌包括霍乱弧菌、小肠结肠耶尔森菌、志贺氏菌、单核细胞增生李斯特菌、沙门氏菌、大肠杆菌、产气荚膜梭菌、阪崎肠杆菌等[4]。这些细菌有能力在异常环境中抵抗和繁殖,例如,据观察,阪崎肠杆菌能在极端干燥的环境中存活很长时间[5]

胃肠道癌症,包括胃癌、结肠直肠癌等,容易发展为恶性和转移,最终导致死亡。胃肠道癌症占癌症相关死亡总数的28% [6],是全球关注的重大健康问题。胃肠道癌症的发展受多种因素的影响[7]。癌组织和胃肠道中栖息着多种细菌,它们共同组成了一个生物集群[8],有可能对癌症的发展和治疗产生多种不同的影响和作用。

抗原分化簇44 (cluster of differentiation, CD44)是出现在白细胞上的CD (分化簇)之一。CD44是一种粘附糖蛋白,在多种胚胎干细胞、淋巴细胞和某些类型的癌细胞中过度表达[9]。CD44以及各种亚型在胃肠道癌症中过度表达,并促进癌细胞的增殖和转移[10]-[12]。还有许多其他标记物与胃肠道癌症密切相关,如CD10、CD133 [13] [14]

虽然孟德尔随机化(Mendelian randomization, MR)发展已久,但近年来越来越流行[15]。如今,研究人员使用GWAS数据库和SNPs作为工具变量。MR是一种较好的方法,受混杂因素的影响较小。目前,MR不仅用于流行病学和癌症[16],还用于分子间相互作用和药物靶点筛选[17]。在本研究中,我们使用双样本MR方法评估常见食源性病原体与胃肠道肿瘤发生和转移标记物之间的关联。

2. 材料与方法

2.1. 研究设计

本研究采用双样本MR评估多种食源性致病菌和CD44抗原的相关性。在本研究中,我们将食源性致病菌作为暴露数据,将替代胃肠道癌症发病率和转移率的CD44抗原数据作为结果数据。暴露数据和结果数据来自不同的研究来源。实验设计和假设见图1

Figure 1. Experimental design and hypothesis: to investigate the impact of cancer metastasis using SNPs associated with gastrointestinal pathogenic bacterial infections and the tumour marker CD44

1. 实验设计和假设:利用与胃肠道致病菌感染和肿瘤标记物CD44相关SNPs研究癌症转移

2.2. 数据来源

食源性致病菌的数据来自IEU GWAS数据库、UK Biobank和FinnGen数据库。UK Biobank是一个功能强大的生物医学数据库,其中包括英国生物库50万名志愿者的血液样本、心脏和大脑扫描以及基因数据。FinnGen整理和分析了芬兰约50万人的基因组数据和生物医学信息,从而为科学界提供了疾病研究、诊断和预防的数据[18]。IEU (英国皇家研究理事会综合流行病学组)是一个多学科研究领域,由英国布里斯托尔大学建立[19]。疾病诊断与相应的细菌感染(如细菌性肠炎)或血液中存在相关病原体的抗体作为收集的依据。CD44抗原的数据来自Sun等人在2018年描述的关于蛋白质水平的GWAS汇总数据[20]

本研究收集的免疫系统蛋白质包括3301个样本和10,534,735个SNPs。数据库收集自欧洲地区的人群,不区分性别。在GWAS中发现的SNPs与感兴趣的性状独立相关,并尽可能排除连锁不平衡。上述基因组数据均可从UK Biobank (http://www.ukbiobank.ac.uk/)、IEU全球基因组数据库(https://gwas.mrcieu.ac.uk/)和FinnGen数据库(https://r9.finngen.fi/)中获得。表1列出了本研究中使用的暴露因素信息。

Table 1. Exposure factors and information sources utilized in this study

1. 本研究使用的暴露因素和数据

Bacteria type

GWAS ID

Sample size

NO. of SNPs

Population

Helicobacter pylori

ieu-b-4905

4683

7,247,045

European

V. cholera

AB1_CHOLERA

16,427

16,380,389

European

Campylobacter jejuni

AB1_CAMPYLOENTERITIS

6934

16,380,461

European

Listeria monocytogenes

AB1_LISTERIOSIS

1333

20,169,216

European

Salmonella spp.

AB1_SALMONELLA_OTH

12,276

16,380,388

European

Shigella spp.

AB1_SHIGELLOSIS

973

20,169,326

European

Yersinia enterocolitica

YERSINIAENTERI

1504

20,170,112

European

Staphylococccus aureus

ukb-b-4423

463,010

9,851,867

European

2.3. 工具变量选择

在本研究中,我们对来自FinnGen的数据进行了与暴露因素显著性(p < 5 × 105)相关的工具变量预筛选。应用一套标准化条件(p < 5 × 108)可能会导致获得的SNPs数量不足,无法进行后续分析。设置阈值(R2 < 0.1, clumping distance = 5000 kb)有助于去除潜在的强连锁不平衡(LD)。我们对来自UK Biobank的数据预先筛选了与暴露显著性(p < 5 × 108)相关的工具变量。设置阈值(R2 < 0.0001, clumping distance = 80,000 kb)。剔除严重影响结果的异常值。

2.4. 统计分析

我们对于暴露因素和结果变量采用MR分析中常用的五种方法。这五种方法具体为:逆方差加权(Inverse-variance weighting, IVW)检验、加权中位数Weighted median、MR Egger、Simple mode和Weighted mode。我们还使用MR Egger和IVW分析了异质性,并使用Egger_intercept方法分析了水平多效性。孟德尔随机化需要三个假设:(1) 基因与暴露因素高度相关;(2) 排除限制假设:工具变量(基因)不直接或间接影响暴露因素以外的结果;(3) 独立性假设:工具变量与混杂因素无关,应与人群随机分布[21]

IVW模型用于估计多个自变量的因果效应。虽然IVW模型容易产生多效性[22],但我们在处理过程中尽可能排除了多效性的存在。由于IVW与多个工具变量具有更好的一致性,因此将该方法作为判断的主要依据。研究结果将等位基因效应大小beta转换为几率比(OR)表示。根据beta值和标准差(SD)计算置信区间。p值小于0.05视为显著差异。所有双样本MR分析均使用4.3.3版R中的“TwoSampleMR”软件包进行。

3. 结果

3.1. 工具变量

本研究中使用的所有SNPs均为不相关。每种细菌的工具变量都经过预处理。预先去除回文SNP。

3.2. 食源性致病菌和胃肠道癌症转移标志物的相关性

对每种食源性病原体进行双样本MR分析,研究其与胃肠道癌症转移标志物的关联。研究结果如图2所示。在该结果中,OR值大于1表示正相关,OR小于1表示负相关。OR值与1的差值越大,表示效应越强。在IVW模型中,幽门螺旋杆菌(Helicobacter pylori)与CD44抗原呈显著正相关(OR = 1.156 (95%置信区间[CI] = 1.051~1.27),p值 = 0.003);IVW模型(OR = 0.972 (95%置信区间[CI] = 0.948~0.996),p值 = 0.022)和加权中位数模型(OR = 0.958 (95%置信区间[CI] = 0.927~0.991),p值 = 0.012)表明小肠耶尔森氏菌(Yersinia enterocolitica)和CD44抗原之间存在显著的负相关。其他几种细菌通过几种方法,尤其是IVW模型,并没有检测到与CD44抗原的相关性。因此,幽门螺旋杆菌可能会促进胃肠道癌症的发展和转移;而小肠结肠耶尔森式菌可能会抑制胃肠道癌症的发展和转移。我们的研究结果表明,其他细菌的感染似乎不会影响胃肠道癌症转移的进展。图3敏感性分析结果进一步证明了研究结果的可靠性。在逐个剔除SNP后,结果大于零或小于零表明可靠。

Figure 2. Five statistical methods for assessing the association between eight bacteria and CD44. Note: CI, confidence interval; OR, odds ratio

2. 五种统计方法检测八种细菌与CD44的关系。注:CI,置信区间;OR,比值比

Figure 3. Sensitivity analysis of eight foodborne pathogenic bacteria to markers of gastrointestinal cancer metastasis (leave-one-out). (A) V. cholera; (B) Campylobacter jejuni; (C) Helicobacter pylori; (D) Listeria monocytogenes; (E) Salmonella spp.; (F) Shigella spp.; (G) Yersinia enterocolitica; (H) Staphylococccus aureus

3. 八种食源性致病菌对胃肠道癌症转移标志物敏感性分析(留一法)。(A) 霍乱弧菌;(B) 空肠弯曲菌;(C) 幽门螺旋杆菌;(D) 单核细胞增生李斯特菌;(E) 沙门氏菌属;(F) 志贺氏菌属;(G) 小肠结肠炎耶尔森氏菌;(H) 金黄色葡萄球菌

4. 讨论

人体内微生物的存在会对各种生命活动产生直接或间接的影响[23]。如前所述,胃肠道癌症的研究是一个复杂的领域,其诱发因素众多,因此必须采取多样化的研究方法。先前的研究表明,胃肠道中的细菌可能有助于对癌症的免疫反应[24]。沙门氏菌可诱导肠道微生物群发生恶性改变,从而促进癌症病变的发展[25]。此外,幽门螺旋杆菌已被证实与胃癌密切相关[26]。虽然癌症转移是导致癌症相关死亡的主要原因,但与原发性癌症相比,癌症转移的预防和治疗更具挑战性。由于癌症转移是一个多步骤和涉及多组织器官的复杂过程[27],因此,显然需要对癌症转移进行进一步的研究。孟德尔随机化方法常用于癌症致病因素的研究。孟德尔随机法避免了反向因果关系问题和其他变量的混杂效应。这种方法有利于发现传统医学实验方法难以确定的因果关系。然而,在目前的MR研究中,并没有对癌症转移因素进行调查。因此,本研究利用与癌症转移相关的标志物对相关因素进行了集中研究。

在这项研究中,我们试图填补这一空白。我们发现幽门螺旋杆菌和小肠结肠炎耶尔森氏菌与CD44抗原的表达有显著的相关性。因此我们判断这两种细菌可能与肿瘤的形成,尤其是转移高度相关。具体来说,幽门螺旋杆菌会促进肿瘤的形成和转移,而小肠结肠耶尔森氏菌则可能通过生物机制阻碍肿瘤的形成和转移[28] [29]。以往的研究表明,幽门螺旋杆菌是胃癌和胃腺癌等胃肠道癌症的主要致病因素[30]。关于幽门螺旋杆菌促进胃肠道癌症发展和转移的机制,还有许多其他研究。如幽门螺旋杆菌感染导致的E-粘连蛋白基因启动子甲基化[31]

然而,对CD44抗原之间关系的基础研究目前较少。对于小肠结肠耶尔森氏菌来说,实际生理机制可能更复杂。对于胃肠道癌症,对小肠结肠耶尔森氏菌的研究不如幽门螺旋杆菌丰富。这项研究结果表明,小肠结肠耶尔森氏菌可抑制胃肠癌的发展和转移。最近发表的一篇论文表明,小肠结肠耶尔森氏菌可能是结直肠癌细胞死亡的原因[32],因此,与细菌有关的某些生物效应可能在癌症治疗中得到应用。由于细菌具有肿瘤靶向性和对人体环境的高度适应性,工程细菌平台在实体瘤治疗中具有很大的潜力[33]。但其他胃肠道癌症尚未进行研究。迄今为止,还没有关于小肠结肠耶尔森氏菌影响胃肠癌发病率和转移的研究。因此,本研究解决了这一领域的一些空白。但目前仍需要进行相关的基础实验研究。

就其他食源性致病菌而言,接种霍乱疫苗与降低结肠直肠癌死亡率有关[34],但霍乱弧菌与标记物之间没有相关性,也没有研究过该细菌与癌症的关系。然而,霍乱毒素已被证实可抑制多种癌症的生长并诱导细胞凋亡,包括结肠癌[35]和肺癌[36],因此背后可能有更复杂的生物学效应。对于其他结果为阴性的细菌,志贺氏杆菌属、空肠弯曲菌、伤寒沙门氏菌和大肠杆菌已被证实该因子可能促进癌细胞的生长[25] [37],但在癌症转移领域还缺乏进一步的研究。总之,在本研究结果的基础上,还需要进一步的探究。

以往的MR研究侧重于常见的暴露因素和结果。由于最初的GWAS研究缺乏分子水平的数据,开展此类研究具有挑战性。近年来,检测技术领域取得了重大进展。虽然本研究采用血液CD44抗原标记作为癌症的替代结果变量,但有可能会引入额外的混杂变量和误差。此外,CD44抗原有多种亚型,在结构和功能上存在一定的差异,可能对癌症产生不同的影响。就胃肠道癌症而言,主要与CD44v途径有关[38]。但本研究中使用的GWAS数据并未分类。若继续完善相关数据,可能有助于进行更深入的研究。另一方面,作为癌症标志物和CD44抗原的分子并不只有一种,因此,本研究并不能完全反映潜在现实的复杂性。此外,CD44与许多癌症和生理过程(如炎症和乳腺癌)都有关联[39] [40]。因此,如果与其他与癌症转移相关的标记物进行交叉验证,结果会更加可靠。另一方面,MR在实际应用中并不完全符合上述假设。尽管已尽最大努力消除混杂因素,但误差仍将不可避免地存在。这种方法取决于统计方法的进一步完善。未来的研究如果能结合更多的研究方法,如元分析、生物信息学和基础实验,可能会得出更精确、更详细的结果。本研究探讨了胃肠道癌症发展和转移的一个特定方面,为未来的癌症防治策略和进一步研究提供了理论基础。最后,本研究对暴露因素的选择也不够全面。还有其他种类的细菌有待研究。本研究考虑的人群均为欧洲人,结论可能不适用于东亚等其他地区的人群。

总之,在这项针对血液和癌症分子的前瞻性磁共振研究中,我们发现幽门螺旋杆菌被证明能促进胃肠道癌症的发展和转移,而小肠结肠耶尔森氏菌则被证明具有抑制的作用。但没有证据表明其他细菌种类有明显的影响。

基金项目

国家自然科学基金(81802082);上海市“医苑新星”青年医学人才培养资助计划–杰出青年医学人才类(2019016);上海市自然科学基金上海市科技创新行动计划(21ZR1441500)。

NOTES

*通讯作者。

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