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血细胞与乳腺癌风险之间的因果关系和免疫介质:一项孟德尔随机化研究
血细胞与乳腺癌风险之间的因果关系和免疫介质:一项孟德尔随机化研究

1:宋梅子,海南医科大学第一附属医院普外科songmeizi789@163.com
1:宋梅子, 海南医科大学附属第一附属医院普外科 海口市 songmeizi789@163.com

2:刘 宇 海南医科大学附属第一医院乳腺外科 口 海南 15218046@qq.com
2: 刘宇 海南医科大学第一附属医院乳腺外科 口 海南 15218046@qq.com

3.董佳旭海南医科大学第一附属医院普外科海南省海口市1023090179@qq.com
3. 董佳旭 海南医科大学第一附属医院普外科 海南省海口市 1023090179@qq.com

4.徐佳芳,海南医科大学第一附属医院乳腺外科海南省海口市,xujiafangyuyu@163.com
4. 徐佳芳, 海南医科大学第一附属医院乳腺外科 海南省海口市 ,xujiafangyuyu@163.com

5.杨明浩,海南医科大学第一附属医院普外科海南省海口市956040450@qq.com
5. 杨明浩, 海南医科大学第一附属医院普外科 海南省海口市 956040450@qq.com

6.南医科大学第一附属医院生殖医学中心庆杰胡海南省海口市,543835000@qq.com
6.南医科大学第一附属医院生殖医学中心 ,海南省海口市 杰胡 Qingjie 543835000@qq.com

7.尹思琪 , 海南医科大学附属第一医院乳腺外科,海南省海口市18745277463@163.com
7. 尹思琪 , 海南医科大学附属第一医院乳腺外科 海口 18745277463@163.com

8.,海医科大学附属第一医院普外科中国海南省海口市龙华路31570102,中国 205489529@qq.com
8.昕,海医科大学附属第一医院普外科 海南省海口市华路 31 号 570102, 中国 205489529@qq.com

*通信:
* 通信:

彦,海南医科大学第一附属医院普外科中国海南省海口市龙华路 31 号570102,中国 205489529@qq.com
n 海南医科大学附属第一医院普外科 ,海南省海口 570102 市龙华路 31 号, 中国 205489529@qq.com

利益争夺
利益争夺

作者声明没有利益冲突
作者声明没有利益冲突

道德规范:
道德规范:

我们使用公共数据库,不需要获得道德声明
我们使用公共数据库,不需要获得道德声明

关键字:
关键字:

乳腺癌、免疫细胞孟德尔随机化
乳腺癌、 免疫细胞 孟德尔随机化

抽象:
抽象:

背景: 先前的研究表明血细胞与乳腺癌风险之间存在潜在关联,但涉及特定血细胞指标和免疫细胞介质作用的因果关系仍不清楚。本研究采用孟德尔随机化来探索不同血细胞谱与乳腺癌风险之间的因果关系,同时也寻求确定免疫细胞指标中的潜在中介因素。
背景: 既往研究表明血细胞与乳腺癌风险之间存在潜在关联,但涉及特定血细胞指标和免疫细胞介质作用的因果关系仍不清楚。本研究采用孟德尔随机化来探索不同血细胞谱与乳腺癌风险之间的因果关系,同时也寻求确定免疫细胞指标中的潜在中介因素。

方法: 我们利用孟德尔随机化来探索 91 种不同血细胞类型对乳腺癌风险的因果影响,使用遗传变异作为工具变量。该分析采用显著性阈值为 0.05 的逆方差加权 (GWAS) 方法来评估因果关系。进行多变量分析以确定免疫细胞在血细胞与乳腺癌之间关联的中介作用。进行异质性测试和多小尺寸测试以完成敏感性分析,确保结果的稳定性和可靠性。
方法: 我们利用孟德尔随机化来探索 91 种不同血细胞类型对乳腺癌风险的因果影响,使用遗传变异作为工具变量。该分析采用显著性阈值为 0.05 的逆方差加权 (GWAS) 方法来评估因果关系。进行多变量分析以确定免疫细胞在血细胞与乳腺癌之间关联的中介作用。 进行异质性测试和多小尺寸测试以完成敏感性分析,确保结果的稳定性和可靠性。

结果: 我们确定了血细胞与乳腺癌风险之间的显着因果关系。具体来说,发现中性粒细胞扰动反应 (中性粒细胞 2 与中性粒细胞 4 响应 WDF 染料测量的 KCl 扰动的比率)与乳腺癌风险有因果关系。此外,CD45RA-CD4+ T 细胞绝对计数被确定为这种关系中的介质。CD45RA-CD4+ T 细胞绝对计数的中介作用为 -0.055 (P=0.022),表明中性粒细胞扰动反应对乳腺癌风险的影响是通过 CD45RA-CD4+ T 细胞绝对计数介导的。
结果: 我们确定了血细胞与乳腺癌风险之间的显着因果关系。具体来说,发现中性粒细胞扰动反应 (中性粒细胞 2 与中性粒细胞 4 响应 WDF 染料测量的 KCl 扰动的比率)与乳腺癌风险有因果关系。此外,CD45RA-CD4+ T 细胞绝对计数被确定为这种关系中的介质。CD45RA-CD4+ T 细胞绝对计数的中介作用为 -0.055 (P=0.022),表明中性粒细胞扰动反应对乳腺癌风险的影响是通过 CD45RA-CD4+ T 细胞绝对计数介导的。

结论: 我们的研究强调了中性粒细胞扰动反应与乳腺癌风险之间的因果关系,其中 CD45RA-CD4+ T 细胞绝对计数起着重要的介质。这些发现为免疫细胞在血细胞指标与乳腺癌风险之间关系中的作用提供了新的见解,为进一步研究和干预提供了潜在的目标。

介绍
介绍

乳腺癌是全球女性发病、残疾和死亡的主要原因。 根据最近的数据,预计到 2023 年,全球将有大约 230 万例新诊断乳腺癌,使其成为仅次于肺癌的第二大常见癌症占癌症总数的 11.6%。 与此同时,预计将有约 670,000 人死于这种疾病,占全球癌症死亡人数的 6.9%,使乳腺癌成为继肺癌、结直肠癌和胃癌之后癌症相关死亡的第四大原因。 它的死亡率明显超过宫颈癌和卵巢癌等妇科癌症。1 超过 770 万女性在诊断后至少存活五年,乳腺导管癌也是全球最普遍的恶性肿瘤2.早期发现是降低死亡率的关键因素3-5,强调需要加强旨在预防的公共卫生工作。确定致病风险因素对于改进乳腺癌的预防策略至关重要。
乳腺癌是全球女性发病、残疾和死亡的主要原因。 根据最近的数据,预计到 2023 年,全球将有大约 230 万例新诊断乳腺癌,使其成为仅次于肺癌的第二大常见癌症 占癌症总数的 11.6%。 与此同时 ,预计将有约 670,000 人死于这种疾病, 占全球癌症死亡人数的 6.9%, 使乳腺癌成为继肺癌、结直肠癌和胃癌之后癌症相关死亡的第四大原因。 它的死亡率明显超过宫颈癌和卵巢癌等妇科癌症。1. 超过 770 万女性在诊断后至少存活 5 年,乳腺癌也是全球最普遍的恶性肿瘤 2。早期发现是降低死亡率 3-5 的关键因素 ,强调需要加强旨在预防的公共卫生工作。确定致病风险因素对于改进乳腺癌的预防策略至关重要。

循环血细胞,包括中性粒细胞、淋巴细胞亚群和血小板,可通过常规实验室检查轻松测量6.这些成分的异常可以为各种疾病提供关键的诊断见解6.几项研究调查了血细胞成分与乳腺癌之间的关联。例如,观察性研究探讨了中性粒细胞之间的关系7-10、 中性粒细胞与淋巴细胞比值11-14、 淋巴细胞亚群15-17和血小板参数18-20和乳腺癌。尽管进行了广泛的观察性研究、动物研究和临床试验,但确定血细胞成分相关的乳腺癌风险因素仍然具有挑战性。 传统的研究方法容易出现混淆偏见和反向因果关系。此外,随机对照试验 (RCT) 昂贵、耗时且通常不切实际,尤其是对于乳腺癌等疾病,这些疾病在暴露于风险因素和疾病发作之间可能存在明显的延迟。
循环血细胞,包括中性粒细胞、淋巴细胞亚群和血小板,很容易通过常规实验室检查进行测量 6。这些成分的异常可以为各种疾病提供关键的诊断见解 6。几项研究调查了血细胞成分与乳腺癌之间的关联。例如,观察性研究探讨了中性粒细胞 7-10、中性粒细胞与淋巴细胞比值 11-14、淋巴细胞亚群 15-17 和血小板参数 18-20 与乳腺癌之间的关系。尽管进行了广泛的观察性研究、动物研究和临床试验, 但确定血细胞成分相关的乳腺癌风险因素仍然具有挑战性。 传统的研究方法容易出现混淆偏见和反向因果关系。此外,随机对照试验 (RCT) 昂贵、耗时且通常不切实际,尤其是对于乳腺癌等疾病,这些疾病在暴露于风险因素和疾病发作之间可能存在明显的延迟。

孟德尔随机化 (MR) 是一种遗传流行病学方法,它通过使用遗传变异作为工具变量 (IV) 来评估暴露(通常是风险因素)与结果(如疾病)之间的因果关系21.通过模拟自然发生的随机临床试验的结构,MR 提供了一种强大的工具来识别因果关系,与传统观察性研究相比,混杂因素更少22.此外,MR 研究消除了反向因果关系,因为用作 IV 的遗传变异是在大多数结果开始之前建立的23.
孟德尔随机化 (MR) 是一种遗传流行病学方法,它通过使用遗传变异作为工具变量 (IV) 来评估暴露(通常是风险因素)与结果(如疾病)之间的因果关系 21。通过模拟自然发生的随机临床试验的结构,MR 提供了一种强大的工具来识别因果关系,与传统观察性研究相比,混杂因素更少 22。此外,MR 研究消除了反向因果关系,因为用作 IV 的遗传变异是在大多数结果开始之前建立23

最近的 MR 研究揭示了乳腺癌与包括免疫细胞在内的多种因素之间的潜在因果关系24饮酒25、 系统性红斑狼疮 (SLE) 的遗传易感性26、 骨密度27、高密度脂蛋白胆固醇和乙酸盐等血液代谢物28、微量营养素消耗量和循环浓度29和饮食因素30最近,双向孟德尔随机化31已经报道了探索血细胞与乳腺癌之间的关联。通过严格的分析方法,本研究成功地建立了某些血细胞表型与乳腺癌之间具有统计学意义的相关性,这些发现对进一步的研究和潜在的临床应用具有重大意义。在先前研究的基础上,我们的研究结合了免疫介导,增强了研究重点和针对性。这些发现可能为乳腺癌治疗提供新的策略,特别是对于免疫治疗反应不佳的患者。
最近的 MR 研究揭示了乳腺癌与包括免疫细胞在内的多种因素之间的潜在因果关系 24 饮酒 25、 系统性红斑狼疮 (SLE) 的遗传易感性 26、 骨密度 27、高密度脂蛋白胆固醇和乙酸盐等血液代谢物 28、微量营养素消耗量和循环浓度 29 和饮食因素 30. 最近 ,一种探索血细胞与乳腺癌之间关联的双向孟德尔随机化 31 已经报道。通过严格的分析方法,本研究成功地建立了某些血细胞表型与乳腺癌之间具有统计学意义的相关性,这些发现对进一步的研究和潜在的临床应用具有重大意义。在先前研究的基础上,我们的研究结合了免疫介导,增强了研究重点和针对性。这些发现可能为乳腺癌治疗提供新的策略,特别是对于免疫治疗反应不佳的患者。

Materials and methods
材料和方法

Study design
研究设计

This study employed two-sample and multivariate MR analyses to investigate the mediating causal relationship between 91 blood cell traits and breast cancer risk. We utilized data from 731 immune cells to evaluate these relationships. MR leverages genetic variation as IVs to infer causal relationships, requiring the fulfillment of three essential assumptions: (1) The genetic variation must be directly associated with the exposure; (2) The genetic variation must be independent of potential confounding factors that could affect the relationship between exposure and outcome; (3) The genetic variation must influence the outcome solely through its effect on the exposure, without affecting the outcome through other pathways. Breast cancer data were sourced from the IEU openGWAS database, comprising 17,389 cases and 240,341 controls.
本研究采用双样本和多变量 MR 分析来探讨 91 种血细胞性状与乳腺癌风险之间的中介因果关系。我们利用来自 731 个免疫细胞的数据来评估这些关系。MR 利用遗传变异作为 IV 来推断因果关系,需要满足三个基本假设:(1) 遗传变异必须与暴露直接相关;(2) 遗传变异必须独立于可能影响暴露和结果之间关系的潜在混杂因素;(3) 遗传变异必须仅通过其对暴露的影响来影响结果,而不通过其他途径影响结果。乳腺癌数据来自 IEU openGWAS 数据库,包括 17,389 例病例和 240,341 例对照。

GWAS data for all blood cells and immune cells
所有血细胞和免疫细胞的 GWAS 数据

The data on blood cells were obtained from the GWAS Catalog, which identified potential genetically determined variations in human blood cells through perturbative phenotype analysis and linked these variations to various common diseases. This approach involved utilizing genome-wide association studies (GWAS) to examine gene variations associated with changes in blood cell responses under different perturbation conditions32.
血细胞数据来自 GWAS 目录,该目录通过扰动表型分析确定了人类血细胞中潜在的遗传决定变异,并将这些变异与各种常见疾病联系起来。这种方法涉及利用全基因组关联研究 (GWAS) 来检查在不同扰动条件下与血细胞反应变化相关的基因变异 32

The immune cell data were sourced from IEU openGWAS and included a total of 731 immune phenotypes. These phenotypes encompass absolute cell counts (AC) (n=118), median fluorescence intensity (MFI) reflecting surface antigen levels (n=389), morphological parameters (MP) (n=32), and relative cell counts (RC) (n=192). Specifically, MFI, AC, and RC features cover a range of cell types including B cells, common dendritic cells (CDCs), mature T cells, monocytes, bone marrow cells, TBNK cells (T cells, B cells, natural killer cells), and regulatory T cells (Treg). The MP features include CDC and TBNK cells. The initial GWAS for immune cells utilized data from 3,757 individuals of European descent, ensuring no overlap in the dataset. Approximately 22 million high-density array SNP genotypes were analyzed using a reference panel based on Sardinian population sequences. Correlations were examined after adjusting for covariates such as gender and age33.
免疫细胞数据来源于 IEU openGWAS,共包括 731 种免疫表型。这些表型包括绝对细胞计数 (AC) (n=118)、反映表面抗原水平的中位荧光强度 (MFI) (n=389)、形态参数 (MP) (n=32) 和相对细胞计数 (RC) (n=192)。具体来说,MFI、AC 和 RC 特征涵盖一系列细胞类型,包括 B 细胞、常见树突状细胞 (CDC)、成熟 T 细胞、单核细胞、骨髓细胞、TBNK 细胞(T 细胞、B 细胞、自然杀伤细胞)和调节性 T 细胞 (Treg)。MP 特征包括 CDC 和 TBNK 细胞。免疫细胞的初始 GWAS 利用了来自 3,757 名欧洲血统个体的数据,确保数据集中没有重叠。使用基于撒丁岛种群序列的参考面板分析了大约 2200 万个高密度阵列 SNP 基因型。在调整了性别和年龄 33 等协变量后检查了相关性

Selection of Instrumental Variables (IVs)
工具变量 (IV) 的选择

To select IVs for each blood cell and immune cell, we set the significance level at 1×10-5, ensuring a strong correlation between genetic variation and exposure. To obtain independent IVs, we applied a linkage disequilibrium (LD) threshold of R2<0.001 using the "TwoSampleMR" package, with an aggregation distance of 10,000 kb. For breast cancer, we adjusted the significance level to 5×10-8 , a standard threshold for genome-wide significance in GWAS studies. We used the same LD threshold of R2<0.001 and an aggregation distance of 10,000 kb. We calculated the F-value for each SNP and excluded single nucleotide polymorphisms (SNPs) with an F-value < 10 to ensure the robustness of our IVs.For blood cells, there were 987 SNPs in 91 kinds of blood cells after screening, 18728 SNPs in 731 immune cells after screening, and 46 SNPs in breast cancer after screening for reverse MR analysis.
为了为每个血细胞和免疫细胞选择 IV,我们将显着性水平设置为 1×10-5,确保遗传变异和暴露之间的强相关性。为了获得独立的 IV,我们使用 “TwoSampleMR” 包应用了 R2<0.001 的连锁不平衡 (LD) 阈值 ,聚集距离为 10,000 kb。对于乳腺癌,我们将显着性水平调整为 5×10-8,这是 GWAS 研究中全基因组显着性的标准阈值。我们使用相同的 LD 阈值 R2<0.001 和 10,000 kb 的聚集距离。我们计算了每个 SNP 的 F 值,并排除了 F 值为 < 10 的单核苷酸多态性 (SNP), 以确保我们 IV 的稳健性。 血细胞方面,筛选后 91 种血细胞中有 987 个 SNP,筛选后 731 个免疫细胞中有 18728 个 SNP,筛选后乳腺癌中有 46 个 SNP。

Statistical Analysis
统计 A 分析

All analyses were performed using R software version 4.3.3, a widely used environment for statistical computing and graphical analysis (http://www.Rproject.org). The "TwoSampleMR" package (version 0.6.6) was employed within R for conducting MR analyses. This package is specifically tailored for MR analysis, providing tools for estimating, testing, and conducting sensitivity analyses of causal effects. We utilized the inverse variance weighted (IVW) method, a standard approach in MR that aggregates Wald estimates from multiple genetic variations. This method combines the ratio of SNP associations with exposure to SNP associations with outcomes, weighted by the inverse variance of each SNP result. Additionally, we employed weighted median and pattern-based methods as supplementary techniques, which offer robust causal estimates even if some IVs are invalid, provided certain assumptions are satisfied. To ensure the reliability and accuracy of the results, we conducted rigorous sensitivity analyses, including Cochran's Q-test, to assess heterogeneity among IVs. This comprehensive statistical evaluation helps validate the robustness of our findings based on the available data.
所有分析均使用 R 软件版本 4.3.3 进行,这是一种广泛用于统计计算和图形分析 (http://www.Rproject.org) 的环境。在 R 中使用了 “TwoSampleMR” 软件包(版本 0.6.6)进行 MR 分析。该软件包专为 MR 分析量身定制,提供用于估计、测试和进行因果效应敏感性分析的工具。我们利用了逆方差加权 (IVW) 方法,这是 MR 中的一种标准方法,它汇总了来自多个遗传变异的 Wald 估计值。该方法将 SNP 关联与 SNP 关联的暴露与结果的比率相结合,由每个 SNP 结果的逆方差加权。此外,我们采用加权中位数和基于模式的方法作为补充技术,即使某些 IV 无效,只要满足某些假设,也能提供稳健的因果估计。为了确保结果的可靠性和准确性,我们进行了严格的敏感性分析,包括 Cochran Q 检验,以评估 IV 之间的异质性。这种全面的统计评估有助于验证我们基于现有数据的研究结果的稳健性。

Mendelian Mediation Analysis
孟德尔 MA 分析

Firstly, we analyzed the causal relationship between blood cells and breast cancer, ensuring that reverse causation was not influencing the results. Following this, we examined the causal relationship between immune cells and breast cancer. Positive associations identified in both blood cells and immune cells were selected for further analysis to determine the mediating pathways.
首先,我们分析了血细胞与乳腺癌之间的因果关系,确保反向因果关系不会影响结果。在此之后,我们研究了免疫细胞与乳腺癌之间的因果关系。选择在血细胞和免疫细胞中发现的正关联用于进一步分析以确定介导途径。

In our analysis, the overall impact of blood cells on breast cancer was decomposed into direct and indirect effects. The indirect effect was calculated by multiplying the β value for the association between blood cells and immune cells by the β value for the association between immune cells and breast cancer. The direct effect was then determined by subtracting the indirect effect from the total effect.
在我们的分析中,血细胞对乳腺癌的总体影响被分解为直接和间接影响。通过将血细胞与免疫细胞之间关联的 β 值乘以免疫细胞与乳腺癌之间关联的 β 值来计算间接效应。然后,通过从总效应中减去间接效应来确定直接效应。

Result
结果

The Causal Relationship between Blood Cells and Breast Cancer
Blood Cells 和 Breast Cancer 之间的 Causal R 兴高采烈

We utilized the IVW method with a significance level of 0.05 to investigate the causal relationships between blood cells and breast cancer. Our analysis identified nine significant causal relationships (Supplementary Material 1). To ensure robustness, we excluded results where MR Egger, Weighted median, and IVW methods yielded conflicting directions. Ultimately, consistent results across all three methods confirmed nine positive associations. Among these, five associations were linked to an increased risk of breast cancer, while four were associated with a decreased risk (Figure 1). Additionally, we observed a reverse causal relationship between Neutrophil perturbation response (median of neutrophil 1 at baseline measured by WDF dye) and breast cancer (Supplementary Material 2). We also found pleiotropy between Neutrophil perturbation response (coefficient of variation of neutrophil 1 in response to Pam3CSK4 perturbation measured by WDF dye) and breast cancer outcomes (Supplementary Material 3). After using MR egger, we found that all positive results did not have level pleiotropy (supplementary material 4), and cochran's Q-test showed that all positive results had no heterogeneity (supplementary material 5).
We 使用显着性水平为 0.05 的 IVW 方法来研究血细胞与乳腺癌之间的因果关系。我们的分析确定了 9 个重要的因果关系(补充材料 1)。为了确保稳定性,我们排除了 MR Egger、加权中位数和 IVW 方法产生冲突方向的结果。最终,所有三种方法的一致结果证实了 9 个阳性关联。其中,5 种关联与患乳腺癌风险增加有关,而 4 种与风险降低有关(图 1)。此外,我们观察到中性粒细胞扰动反应 (通过 WDF 染料测量的基线中性粒细胞 1 的中位数) 与乳腺癌 (补充材料 2) 之间存在反向因果关系。我们还发现中性粒细胞扰动反应 (中性粒细胞 1 响应 Pam3CSK4 扰动的变异系数通过 WDF 染料测量) 与乳腺癌结局 (补充材料 3) 之间存在多效性。 使用 MR egger 后,我们发现所有阳性结果均不具有水平多效性 (补充材料 4),cochran 的 Q 检验显示所有阳性结果均无异质性 (补充材料 5)。

Figure 1:Forest plots depicting the causal associations between Breast cancer and blood cells.
图 1:描述乳腺癌和血细胞之间因果关系的森林图。

*Abbreviations: IVW, inverse variance weighting; CI, confidence interval.
*缩写:IVW,逆方差加权;CI,置信区间。

The Causal Relationship between Immune Cells and Breast Cancer
Immune Cells 和 Breast Cancer 之间的 Causal Relationship

Using the IVW method, we identified 27 immune cell types with a significant association with breast cancer at a p-value threshold of 0.05 (Supplementary Material 6). All positive findings showed consistency across MR Egger, weighted regression, and IVW methodologies. Among these, 9 immune cell types were associated with an increased risk of breast cancer, while 18 were associated with a decreased risk (Figure 2). Additionally, we observed a reverse causal relationship involving CD45 on CD33+ HLA DR+ CD14dim cells (Supplementary Material 7). Pleiotropy were noted between HLA DR++ monocyte %monocyte, CD4+/CD8+ T cells, and CD8 on Terminally Differentiated CD8+ T cells in relation to the breast cancer outcome (Supplementary Material 8), which were therefore excluded from further analysis. After using MR egger, we found that all positive results did not have level pleiotropy (supplementary material 9), and cochran's Q-test showed that all positive results had no heterogeneity (supplementary material 10).
使用 IVW 方法,我们以 0.05 的 p 值阈值确定了 27 种与乳腺癌显著相关的免疫细胞类型(补充材料 6)。所有阳性结果均显示 MR Egger 、加权回归和 IVW 方法的一致性。其中,9 种免疫细胞类型与乳腺癌风险增加有关,而 18 种免疫细胞类型与风险降低有关(图 2)。此外,我们观察到 CD45 在 CD33+ HLA DR+ CD14dim 细胞上存在反向因果关系 (补充材料 7)。 HLA DR++ 单核细胞 %单核细胞、CD4+/CD8+ T 细胞和终末分化 CD8 + T 细胞上的 CD8 与乳腺癌结局相关 P 趋向性 (补充材料 8),因此被排除在进一步分析之外。 使用 MR egger 后,我们发现所有阳性结果均不具有水平多效性 (补充材料 9),cochran 的 Q 检验显示所有阳性结果均无异质性 (补充材料 10)。

Figure 2:Forest plots depicting the causal associations between Breast cancer and immune cells.
图 2:描述乳腺癌和免疫细胞之间因果关系的森林图。

*Abbreviations: IVW, inverse variance weighting; CI, confidence interval.
*缩写:IVW,逆方差加权;CI,置信区间。

Immune Cells act as Mediators between Blood Cells and Breast Cancer
免疫 Cells 在 Blood Cells 和 Breast Cancer 之间充当 M 编辑者

Using the IVW method as the primary analytical approach, we conducted MR analysis on positive blood cells and immune cells, applying a significance threshold of 0.05. After performing multivariate consistency screening for the overall direction of action, we identified that the Neutrophil perturbation response (the ratio of neutrophil 2 to neutrophil 4 in response to KCl perturbation measured by WDF dye) serves as the pathway characteristic of exposure. The CD45RA-CD4+ T cell Absolute Count was determined to be the mediator influencing breast cancer risk. The mediating effect of CD45RA-CD4+ T cell Absolute Count was found to be -0.055 (P=0.022).
使用 IVW 方法作为主要分析方法,我们对阳性血细胞和免疫细胞进行了 MR 分析,显着性阈值为 0.05。在对总体作用方向进行多变量一致性筛选后,我们确定中性粒细胞扰动反应 (中性粒细胞 2 与中性粒细胞 4 响应 WDF 染料测量的 KCl 扰动的比率)是暴露的途径特征。CD45RA-CD4+ T 细胞绝对计数被确定为影响乳腺癌风险的介质。CD45RA-CD4 + T 细胞绝对计数的介导作用为 -0.055 (P=0.022)。

Discussion
讨论

Through MR analysis, we found that among 91 types of human blood cells, an increased neutrophil perturbation response (measured by the neutrophil 2/neutrophil 4 ratio in response to KCl perturbation using WDF dye) raises the risk of breast cancer. Additionally, the absolute count of CD45RA-CD4+ T cells plays a mediating role, amplifying this effect. Our MR analysis of the relationships between neutrophil perturbation response, CD45RA-CD4+ T cell absolute count, and breast cancer provides evidence supporting a causal link between neutrophil perturbation response and breast cancer, with CD45RA-CD4+ T cell absolute count acting as a mediator. MR is considered a form of naturally occurring RCT. Compared to traditional RCTs, the advantage of MR lies in using (SNPs) significantly associated with the exposure variable as IVs. This approach minimizes the impact of confounding factors on the results.
通过 MR 分析,我们发现在 91 种人类血细胞中,中性粒细胞扰动反应增加(通过响应使用 WDF 染料的 KCl 扰动的中性粒细胞 2/中性粒细胞 4 比率来衡量)会增加患乳腺癌的风险。此外,CD45RA-CD4+ T 细胞的绝对计数起中介作用,放大了这种效应。我们对中性粒细胞扰动反应、CD45RA-CD4+ T 细胞绝对计数和乳腺癌之间关系的 MR 分析提供了证据支持中性粒细胞扰动反应与乳腺癌之间的因果关系,其中 CD45RA-CD4+ T 细胞绝对计数起中介作用。MR 被认为是自然发生的 RCT 的一种形式。与传统 RCT 相比,MR 的优势在于使用与暴露变量显著相关的 (SNP) 作为 IV。这种方法可以最大限度地减少混杂因素对结果的影响。

Neutrophil perturbation response refers to the series of biological changes neutrophils undergo upon stimulation, including the activation, migration, phagocytosis, and cytotoxicity of different neutrophil subsets34. Neutrophils are a critical component of the innate immune system, primarily responsible for engulfing and digesting bacteria, fungi, and clearing damaged or dead cells. They play a key role in combating infections and mediating inflammatory responses by rapidly responding to infections or tissue damage and releasing various inflammatory mediators to recruit and activate other immune cells35. However, in certain conditions, such as chronic inflammation or immunosuppressive states, excessive activation or abnormal responses of neutrophils may contribute to the onset and progression of diseases, including breast cancer36,37. Chronic inflammation can promote tumor cell proliferation, invasion, and metastasis, and increased neutrophil numbers or dysfunctional neutrophil activity may be associated with an elevated risk of breast cancer38. The tumor microenvironment in breast cancer contains large numbers of immune cells, including neutrophils, which can influence tumor growth and metastasis by releasing cytokines and chemokines39-41. Some studies suggest that tumor-associated neutrophils (TANs) may promote tumor growth and metastasis in breast cancer42. High neutrophil counts in peripheral blood or tumor tissues of breast cancer patients have been linked to poor prognosis, possibly due to the pro-tumor effects of neutrophils within the tumor microenvironment42. However, neutrophils are not a homogeneous cell population. Neutrophil perturbation response leads to abnormal changes in the numbers or functions of different neutrophil subsets, which may increase breast cancer risk by promoting chronic inflammation or altering the tumor microenvironment. Currently, there is no direct clinical evidence elucidating the precise mechanisms by which neutrophil perturbation response increases breast cancer risk. Therefore, further research on the relationship between neutrophil perturbation response and breast cancer risk is of significant importance.
中性粒细胞扰动反应是指中性粒细胞在受到刺激时经历的一系列生物学变化,包括不同中性粒细胞亚群的激活、迁移、吞噬作用和细胞毒性 34。中性粒细胞是先天免疫系统的关键组成部分,主要负责吞噬和消化细菌、真菌以及清除受损或死细胞。它们通过快速响应感染或组织损伤并释放各种炎症介质来募集和激活其他免疫细胞,在对抗感染和介导炎症反应中发挥关键作用 35。然而,在某些情况下,例如慢性炎症或免疫抑制状态,中性粒细胞的过度激活或异常反应可能会导致疾病的发生和进展,包括乳腺癌 36,37。慢性炎症可促进肿瘤细胞增殖、侵袭和转移,中性粒细胞数量增加或中性粒细胞活性功能障碍可能与乳腺癌风险升高有关 38。乳腺癌中的肿瘤微环境包含大量免疫细胞,包括中性粒细胞,它们可以通过释放细胞因子和趋化因子来影响肿瘤生长和转移 39-41。一些研究表明,肿瘤相关中性粒细胞 (TAN) 可能促进乳腺癌的肿瘤生长和转移 42。乳腺癌患者外周血或肿瘤组织中的高中性粒细胞计数与不良预后有关,这可能是由于肿瘤微环境中中性粒细胞的促肿瘤作用 42。然而,中性粒细胞不是一个同质的细胞群。 中性粒细胞扰动反应导致不同中性粒细胞亚群的数量或功能发生异常变化,这可能通过促进慢性炎症或改变肿瘤微环境来增加乳腺癌风险。目前,没有直接的临床证据阐明中性粒细胞扰动反应增加乳腺癌风险的确切机制。因此,进一步研究中性粒细胞扰动反应与乳腺癌风险之间的关系具有重要意义。

CD45RA-CD4+ T cells are a subset of CD4+ memory T cells with specific immune functions and phenotypic characteristics. These cells are typically considered part of the memory T cell population, formed after an initial immune response. They are capable of long-term survival and can rapidly respond to antigen stimulation, swiftly initiating an immune reaction upon re-exposure to the same antigen24,43-45. Within the immune system, CD4+ T cells play diverse roles, including assisting in the activation and differentiation of other immune cells, such as CD8+ T cells and B cells, as well as directly participating in immune responses46. Current research primarily focuses on the role of CD4+ T cells in breast cancer development, progression, and immunotherapy. Studies have shown that CD4+ T cells can assist other immune cells, like CD8+ T cells, in killing tumor cells47,48. Additionally, they can directly suppress tumor cell growth and division by secreting cytokines such as IFN-γ and TNF-α, which inhibit the cell cycle of tumor cells and ultimately limit tumor growth49-51. In the tumor microenvironment (TME) of breast cancer, the number and functional state of CD4+ T cells may influence tumor progression and patient prognosis through various mechanisms52. Although the role of CD4+ T cells in tumor immune surveillance and anti-tumor immunity is widely recognized, there is limited literature specifically addressing the association between CD45RA-CD4+ T cell Absolute Count and breast cancer45. CD45RA-CD4+ T cells may be involved in the immune response of breast cancer patients45, but the precise mechanisms remain unclear. These cells may influence breast cancer occurrence and progression by either enhancing or suppressing the activity of other immune cells. Future research should further explore the relationship between the quantity and functional state of CD45RA-CD4+ T cells in breast cancer patients and disease prognosis. Additionally, investigating their potential role in immunotherapy could provide insights into whether these cells can serve as therapeutic targets or predictive markers, offering more precise and effective treatment strategies for patients.
CD45RA-CD4+ T 细胞是 CD4+ 记忆 T 细胞的一个亚群,具有特定的免疫功能和表型特征。这些细胞通常被认为是记忆 T 细胞群的一部分,在初始免疫反应后形成。它们能够长期存活,并且可以对抗原刺激做出快速反应,在再次暴露于相同抗原时迅速启动免疫反应 24,43-45。在免疫系统中,CD4+ T 细胞发挥着多种作用,包括协助其他免疫细胞(如 CD8+ T 细胞和 B 细胞)的激活和分化,以及直接参与免疫应答 46。目前的研究主要集中在 CD4+ T 细胞在乳腺癌发生、进展和免疫治疗中的作用。研究表明,CD4+ T 细胞可以帮助其他免疫细胞(如 CD8+ T 细胞)杀死肿瘤细胞 47,48。此外,它们可以通过分泌 IFN-γ 和 TNF-α 等细胞因子直接抑制肿瘤细胞的生长和分裂,从而抑制肿瘤细胞的细胞周期并最终限制肿瘤生长 49-51。在乳腺癌的肿瘤微环境 (TME) 中,CD4+ T 细胞的数量和功能状态可能通过各种机制影响肿瘤进展和患者预后 52。尽管 CD4+ T 细胞在肿瘤免疫监视和抗肿瘤免疫中的作用已得到广泛认可,但专门讨论 CD45RA-CD4+ T 细胞绝对计数与乳腺癌之间关联的文献有限 45。 CD45RA-CD4+ T 细胞可能参与乳腺癌患者的免疫反应 45,但确切的机制尚不清楚。这些细胞可能通过增强或抑制其他免疫细胞的活性来影响乳腺癌的发生和进展。未来的研究应进一步探讨乳腺癌患者 CD45RA-CD4+ T 细胞数量和功能状态与疾病预后之间的关系。此外,研究它们在免疫治疗中的潜在作用可以深入了解这些细胞是否可以作为治疗靶点或预测标志物,从而为患者提供更精确和有效的治疗策略。

To date, no research has specifically explored the association between Neutrophil perturbation response and CD45RA-CD4+ T cell Absolute Count. This study is the first comprehensive investigation based on publicly available GWAS data to examine this relationship. Metagenomic sequencing studies have established a link between Neutrophil perturbation response and CD45RA-CD4+ T cell Absolute Count. Although there is no direct evidence yet to confirm the mediating role of CD45RA-CD4+ T cells between Neutrophil perturbation response and breast cancer risk, the development and progression of breast cancer are closely associated with immune regulation and inflammatory responses within the tumor microenvironment
迄今为止,还没有研究专门探讨中性粒细胞扰动反应与 CD45RA-CD4+ T 细胞绝对计数之间的关联。本研究是首次基于公开可用的 GWAS 数据进行全面调查,以检验这种关系。宏基因组测序研究已经确定了中性粒细胞扰动反应与 CD45RA-CD4+ T 细胞绝对计数之间的联系。虽然目前还没有直接证据证实 CD45RA-CD4+ T 细胞在中性粒细胞扰动反应和乳腺癌风险之间的中介作用,但乳腺癌的发生和发展与肿瘤微环境中的免疫调节和炎症反应密切相关
53,54. Based on the function and mechanisms of CD45RA-CD4+ T cells as immune mediators, it is plausible to hypothesize that they may interact with Neutrophil perturbation response in breast cancer development. Neutrophils and CD45RA-CD4+ T cells typically act through different pathways in immune responses—neutrophils primarily participate in innate immunity, while CD45RA-CD4+ T cells are more involved in adaptive immunity. The Neutrophil perturbation response may enhance inflammatory responses within the tumor microenvironment, thereby promoting breast cancer progression. At the same time, CD45RA-CD4+ T cells, through their immune-regulatory functions, may influence the activation state of neutrophils. For example, CD45RA-CD4+ T cells can secrete cytokines such as IFN-γ and IL-2, which regulate neutrophil migration, phagocytosis, and cytotoxic abilities, as well as modulate the inflammatory response within the tumor microenvironment
.根据 CD45RA-CD4+ T 细胞作为免疫介质的功能和机制,可以合理地假设它们可能与乳腺癌发展中的中性粒细胞扰动反应相互作用。中性粒细胞和 CD45RA-CD4+ T 细胞通常通过不同的免疫反应途径发挥作用——中性粒细胞主要参与先天免疫,而 CD45RA-CD4+ T 细胞更多地参与适应性免疫。中性粒细胞扰动反应可能会增强肿瘤微环境中的炎症反应,从而促进乳腺癌进展。同时,CD45RA-CD4+ T 细胞通过其免疫调节功能可能影响中性粒细胞的活化状态。例如,CD45RA-CD4+ T 细胞可以分泌 IFN-γ 和 IL-2 等细胞因子,这些细胞因子调节中性粒细胞迁移、吞噬作用和细胞毒能力,以及调节肿瘤微环境中的炎症反应
55-57. Through these actions, CD45RA-CD4+ T cells may serve as mediators between Neutrophil perturbation response and breast cancer risk.
.通过这些作用,CD45RA-CD4+ T 细胞可能作为中性粒细胞扰动反应和乳腺癌风险之间的介质。
At the same time, other studies have shown the opposite results, namely, a negative association between CD4+T cells and breast cancer risk (Discovery: OR, 0.996; P = 0.030. Validation: OR, 0.843; P = 4.09E-07), this relationship was mainly mediated by Caspase 8
与此同时,其他研究显示了相反的结果,即 CD4+T 细胞与乳腺癌风险之间存在负相关(发现:OR,0.996;P = 0.030。验证:OR,0.843;P = 4.09E-07),这种关系主要由 Caspase 8 介导
58.,There is also evidence that
,还有证据表明
the immune cell phenotypes CD3 on CD28+ CD4-CD8- T cells and HLA DR on CD33- HLA DR+ protect against BC. This protective effect may be achieved through various mechanisms, including enhancing immune surveillance to recognize and eliminate tumor cells; secreting cytokines to inhibit tumor cell proliferation and growth directly; triggering apoptotic pathways in tumor cells to reduce their number; modulating the tumor microenvironment to make it unfavorable for tumor growth and spread; activating other immune cells to boost the overall immune response; and inhibiting angiogenesis to reduce the tumor's nutrient supply
CD28+ CD4-CD8-T 细胞上的免疫细胞表型 CD3 和 CD33-HLA DR+ 上的 HLA DR 可预防 BC。这种保护作用可以通过多种机制来实现,包括增强免疫监视以识别和消除肿瘤细胞;分泌细胞因子直接抑制肿瘤细胞增殖和生长;触发肿瘤细胞中的凋亡途径以减少其数量;调节肿瘤微环境,使其不利于肿瘤生长和扩散;激活其他免疫细胞以增强整体免疫反应;抑制血管生成以减少肿瘤的营养供应
59. This is a complex biological process involving multiple cellular and molecular interactions, and its precise mechanisms require further investigation.
这是一个复杂的生物过程,涉及多种细胞和分子相互作用,其确切机制需要进一步研究。
At the same time, through the continuous mining of the database and the full use of Mendelian randomization, other blood cells also have unexpected effects, such as CD24+CD27+B cells are associated with a reduced risk of breast cancer (OR = 0.9978,95% CI: 0.996-0.999, p = 0.001), while IgD-CD38 B cells were associated with an increased risk of breast cancer (OR = 1.002,95% CI: 1.001-1.004, p = 0.005). CD14+CD16+ monocytes were associated with an increased risk of breast cancer (OR = 1.000,95% CI: 1.000-1.001, p = 0.005)
同时,通过对数据库的不断挖掘和充分利用孟德尔随机化,其他血细胞也产生了意想不到的效果,如 CD24+CD27+B 细胞与乳腺癌风险降低相关 (OR = 0.9978,95% CI: 0.996-0.999, p = 0.001),而 IgD-CD38 B 细胞与乳腺癌风险增加相关 (OR = 1.002,95% CI: 1.001-1.004,p = 0.005)。CD14+CD16+ 单核细胞与乳腺癌风险增加相关 (OR = 1.000,95% CI: 1.000-1.001,p = 0.005)
60.Future research in this area will help us better understand the immunopathology of breast cancer and provide a theoretical foundation for developing new strategies for the prevention and treatment of breast cancer.
该领域的未来研究将帮助我们更好地了解乳腺癌的免疫病理学,并为开发预防和治疗乳腺癌的新策略提供理论基础。

Limitations
局限性

While our study provides valuable insights, several limitations must be acknowledged. Firstly, MR analysis offers robust methods for evaluating causal relationships, but it reflects lifetime genetic exposure rather than short-term effects. This limitation means that MR might not fully capture the benefits of short-term interventions on Neutrophil perturbation response. Secondly, our research relies on genetic data from European populations due to the limited availability of GWAS data for Asian populations. This reliance introduces potential limitations in generalizability, as genetic distributions can vary significantly between ethnic groups. Therefore, our findings may not be fully representative and could be influenced by racial and regional genetic differences. Future studies should include diverse ethnic groups to validate and extend these results. Finally, the lack of specific clinical data, such as age and underlying health conditions, for the study populations restricts further analysis and understanding. Addressing these gaps in future GWAS studies and incorporating molecular experimental validations will be crucial for refining our conclusions and enhancing the applicability of our findings.
虽然我们的研究提供了有价值的见解,但必须承认一些局限性。首先,MR 分析为评估因果关系提供了可靠的方法,但它反映了终生遗传暴露而不是短期影响。这种局限性意味着 MR 可能无法完全捕捉到短期干预对中性粒细胞扰动反应的益处。其次,由于亚洲人群的 GWAS 数据有限,我们的研究依赖于欧洲人群的遗传数据。这种依赖在泛化性方面引入了潜在的限制,因为种族群体之间的基因分布可能有很大差异。因此,我们的研究结果可能不完全具有代表性,并且可能受到种族和地区遗传差异的影响。未来的研究应包括不同的种族群体,以验证和扩展这些结果。最后,缺乏研究人群的具体临床数据,例如年龄和潜在的健康状况,限制了进一步的分析和理解。在未来的 GWAS 研究中解决这些差距并结合分子实验验证对于完善我们的结论和提高我们研究结果的适用性至关重要。

Conclusion
结论

Our study reveals that Neutrophil perturbation response (the ratio of neutrophil 2 to neutrophil 4 in response to KCl perturbation measured by WDF dye) increases the risk of breast cancer, with CD45RA-CD4+ T cell Absolute Count acting as a mediator that amplifies this effect. Specifically, Neutrophil perturbation response elevates breast cancer risk through its interaction with CD45RA-CD4+ T cell Absolute Count. This research provides a novel perspective on the mechanisms underlying breast cancer development. Future investigations should further explore the immunomodulatory mechanisms involved in breast cancer pathogenesis and identify potential intervention targets, thereby guiding future therapeutic strategies.
我们的研究表明,中性粒细胞扰动反应(中性粒细胞 2 与中性粒细胞 4 响应 KCl 扰动的比率由 WDF 染料测量)会增加患乳腺癌的风险,CD45RA-CD4+ T 细胞绝对计数充当放大这种效应的介质。具体来说,中性粒细胞扰动反应通过与 CD45RA-CD4+ T 细胞绝对计数的相互作用而增加乳腺癌风险。这项研究为乳腺癌发展的潜在机制提供了新的视角。未来的研究应进一步探索乳腺癌发病机制所涉及的免疫调节机制,并确定潜在的干预靶点,从而指导未来的治疗策略。

Abbreviations

MR
先生

IVs
静脉注射

GWAS
GWAS 认证

IVW
体外编织

SNPs
SNP

Mendelian randomization
孟德尔随机化

Instrumental variables
工具变量

Genome-wide association studies
全基因组关联研究

Inverse variance weighted
逆方差加权

Single nucleotide polymorphisms
单核苷酸多态性

Statements and Declarations
声明和声明

Availability of data and materials
数据和材料的可用性

Datasets collected and analyzed during this study are available from the corresponding authors upon reasonable request.
本研究期间收集和分析的数据集可应合理要求从通讯作者处获得。

Competing interests
利益争夺

The authors declare no competing interests.
作者声明没有利益冲突

Consent for publication
同意发布

All authors have read and agreed to the published version of the manuscript.
所有作者均已阅读并同意手稿的已发表版本。

Author contributions
作者贡献

Meizi Song contributed to study conception and design, data collection and drafting of the manuscript. Qingjie Hu and Jiaxu Dong contributed to data collection. Jiafang Xu and Siqi Yin contributed to data analysis and interpretation. Yu Liu and Xun Bi contributed to study conception and design, analysis and interpretation of the data, and revision of the final manuscript. All authors gave final approval of the version to be published.
宋梅子为研究构思和设计、数据收集和手稿起草做出了贡献。 Qingjie 胡 Jiaxu Dong 为数据收集做出了贡献。Jiafang Xu 和 Siqi Yin 对数据分析和解释做出了贡献 YuLiu Xun Bi 研究构思和设计、数据分析和解释以及最终手稿的修改做出了贡献 。所有作者都对要发布的版本给予了最终批准。

ACKNOWLEDGMENTS
确认

No.

Funding
资金

This work was supported by the Health Science and Technology Innovation Joint Project of Hainan Province (WSJK2024MS197) and the Key Research and Development Program of Hainan Province (ZDYF2020139).
这项工作得到了海南省健康科技创新联合项目 (WSJK2024MS197) 和海南省重点研发计划 (ZDYF2020139) 的支持。

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Figure 1: Results of MR positive analysis of blood cells and breast cancer.
图 1:血细胞和乳腺癌的 MR 阳性分析结果。

Figure 2: Results of MR positive analysis of immune cells and breast cancer.
图 2:免疫细胞和乳腺癌的 MR 阳性分析结果。

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