Bidirectional two-sample MR analysis of causal relationships between human inflammatory proteins and HS and keloids
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摘要:
目的 探讨人炎症蛋白与增生性瘢痕（HS）、瘢痕疙瘩之间的因果关系。 方法 该研究为基于双向双样本孟德尔随机化（MR）分析的研究。从全基因组关联分析数据库中获取人炎症蛋白、HS和瘢痕疙瘩的数据,采用逆方差加权（IVW）法评估91种炎症蛋白与HS、瘢痕疙瘩之间的因果关系,即正向MR分析。针对前述关联,采用Cochran Q检验评估异质性,采用MR-Egger回归检验和MR-PRESSO离群值检验评估水平多效性,采用留一法分析结果的稳健性。采用IVW法评估增生性瘢痕、瘢痕疙瘩与前述正向MR分析筛选出的炎症蛋白之间是否存在反向因果关系。 结果 CD6、白血病抑制因子（LIF）、肿瘤坏死因子配体超家族成员12（TNFSF12）、程序性死亡配体1（PD-L1）、白细胞介素-17C（IL-17C）、LIF受体（LIFR）、骨保护蛋白（OPG）、成纤维细胞生长因子23（FGF23）与HS之间均存在显著因果关系（OR分别为1.365、0.506、1.567、1.683、0.621、1.375、0.623、0.553,95%CI分别为1.100~1.693、0.289~0.887、1.081~2.273、1.090~2.599、0.408~0.947、1.025~1.845、0.402~0.966、0.315~0.971,P<0.05）,其中CD6、TNFSF12、PD-L1、LIFR为HS的风险性因素,LIF、IL-17C、OPG、FGF23为HS的保护性因素；CD5、IL-10受体α亚基（IL-10RA）、IL-5、LIF、OPG与瘢痕疙瘩之间均存在显著因果关系（OR分别为0.744、1.303、0.686、0.603、0.715,95%CI分别为0.573~0.965、1.024~1.660、0.472~0.996、0.431~0.842、0.553~0.924,P<0.05）,其中IL-10RA为瘢痕疙瘩的风险性因素,CD5、IL-5、LIF、OPG为瘢痕疙瘩的保护性因素。上述关联均不存在显著异质性或显著水平多效性（P>0.05）,结果的稳健性未受单个单核苷酸多态性驱动。HS与前述正向MR分析筛选出的8种炎症蛋白中的TNFSF12、LIFR之间均存在反向因果关系（OR分别为0.972、0.968,95%CI分别为0.949~0.997、0.942~0.994,P<0.05）,瘢痕疙瘩与前述正向MR分析筛选出的5种炎症蛋白之间均不存在反向因果关系（P>0.05）。 结论 CD6、TNFSF12、PD-L1、LIFR可能增加HS患病风险,OPG、FGF23可能降低HS患病风险；IL-10RA可能增加瘢痕疙瘩患病风险,CD5、IL-5、LIF、OPG可能降低瘢痕疙瘩患病风险。 Abstract:Objective To explore the causal relationships between human inflammatory proteins and hypertrophic scars (HS) and keloids. Methods This study was conducted based on bidirectional two-sample Mendelian randomization (MR) analysis. Data of human inflammatory proteins, HS, and keloids were acquired from genome-wide association study database. The inverse variance weighted (IVW) method was adopted to evaluate the causal relationships between 91 kinds of human inflammatory proteins and HS and keloids, i.e., forward MR analysis. For the above associations, Cochran's Q test was used to assess heterogeneity, MR-Egger regression and MR-PRESSO outlier tests were performed to evaluate horizontal pleiotropy, and the leave-one-out method was applied to analyze the robustness of the results. The IVW method was also used to evaluate whether there was a reverse causal relationship between HS, keloids and inflammatory proteins screened out by aforementioned forward MR analysis. Results CD6, leukemia inhibitory factor (LIF), tumor necrosis factor ligand superfamily member 12 (TNFSF12), programmed death-ligand 1 (PD-L1), interleukin-17C (IL-17C), LIF receptor (LIFR), osteoprotegerin (OPG), and fibroblast growth factor 23 (FGF23) had significant causal relationships with HS (with ORs of 1.365, 0.506, 1.567, 1.683, 0.621, 1.375, 0.623, and 0.553, respectively, 95%CIs of 1.100-1.693, 0.289-0.887, 1.081-2.273, 1.090-2.599, 0.408-0.947, 1.025-1.845, 0.402-0.966, and 0.315-0.971, respectively, P<0.05). Among them, CD6, TNFSF12, PD-L1, and LIFR were risk factors for HS, while LIF, IL-17C, OPG, and FGF23 were protective factors for HS. CD5, IL-10 receptor subunit alpha (IL-10RA), IL-5, LIF, and OPG had significant causal relationships with keloids (with ORs of 0.744, 1.303, 0.686, 0.603, and 0.715, respectively, 95%CIs of 0.573-0.965, 1.024-1.660, 0.472-0.996, 0.431-0.842, and 0.553-0.924, respectively, P<0.05). Among them, IL-10RA was a risk factor for keloids, whereas CD5, IL-5, LIF, and OPG were protective factors for keloids. No significant heterogeneity or horizontal pleiotropy was observed in the above associations (P>0.05), and the robustness of the results was not driven by any single nucleotide polymorphism. Significant reverse causal relationships existed between HS and TNFSF12 and LIFR of the 8 inflammatory proteins screened out by aforementioned forward MR analysis (with ORs of 0.972 and 0.968, respectively, 95%CIs of 0.949-0.997 and 0.942-0.994, respectively, P<0.05). No reverse causal relationship was found between keloids and the 5 inflammatory proteins screened out by aforementioned forward MR analysis (P>0.05). Conclusions CD6, TNFSF12, PD-L1, and LIFR may increase the risk of HS, while OPG and FGF23 may decrease the risk of HS. IL-10RA may increase the risk of keloids, while CD5, IL-5, LIF, and OPG may decrease the risk of keloids. -
参考文献
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图 1 人炎症蛋白与增生性瘢痕及瘢痕疙瘩之间因果关系的双向双样本孟德尔随机化分析的核心假设和分析流程图
注：“√”表示存在关联或有效路径,“×”表示该路径不存在或已被排除；①表示关联性假设,即假设工具变量与暴露因素之间存在稳固的强相关；②表示独立性假设,即假设工具变量独立于混杂因素；③表示排他性假设,即假设工具变量只能通过暴露因素对结局产生影响
图 2 留一法分析的与8种人炎症蛋白存在显著因果关系的增生性瘢痕SNP的可靠性。2A.SNP与CD6的因果关系；2B.SNP与白血病抑制因子的因果关系；2C.SNP与肿瘤坏死因子配体超家族成员12的因果关系；2D.SNP与程序性死亡配体1的因果关系；2E.SNP与白细胞介素-17C的因果关系；2F.SNP与白血病抑制因子受体的因果关系；2G.SNP与骨保护蛋白的因果关系；2H.SNP与成纤维细胞生长因子23的因果关系
注：SNP为单核苷酸多态性
图 3 留一法分析的与5种人炎症蛋白存在显著因果关系的瘢痕疙瘩SNP的可靠性。3A.SNP与CD5的因果关系；3B.SNP与白细胞介素-10受体α亚基的因果关系；3C.SNP与白细胞介素-5的因果关系；3D.SNP与白血病抑制因子的因果关系；3E.SNP与骨保护蛋白的因果关系
注：SNP为单核苷酸多态性
Table 1. 逆方差加权法分析显示8种人炎症蛋白与增生性瘢痕之间存在显著因果关系
炎症蛋白 SNP数（个） OR 95%CI P值 CD6 11 1.365 1.100~1.693 0.005 成纤维细胞生长因子23 10 0.553 0.315~0.971 0.039 白细胞介素-17C 13 0.621 0.408~0.947 0.027 白血病抑制因子 7 0.506 0.289~0.887 0.017 白血病抑制因子受体 11 1.375 1.025~1.845 0.034 骨保护蛋白 15 0.623 0.402~0.966 0.034 程序性死亡配体1 12 1.683 1.090~2.599 0.019 肿瘤坏死因子配体超家族成员12 18 1.567 1.081~2.273 0.018 注：SNP为单核苷酸多态性 
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Table 2. 与增生性瘢痕之间存在显著因果关系的8种人炎症蛋白SNP的异质性和水平多效性分析结果
炎症蛋白 SNP数（个） Cochran Q检验 MR-Egger回归检验 MR-PRESSO离群值检验 Q值 P值 截距 P值 RSSobs P值 CD6 11 6.990 0.726 -0.011 0.746 7.479 0.810 成纤维细胞生长因子23 10 2.451 0.982 -0.026 0.699 3.072 0.987 白细胞介素-17C 13 5.230 0.950 -0.042 0.541 6.451 0.947 白血病抑制因子 7 5.573 0.473 -0.043 0.544 7.422 0.502 白血病抑制因子受体 11 3.849 0.954 0.015 0.728 4.246 0.978 骨保护蛋白 15 22.231 0.074 0.030 0.645 26.590 0.092 程序性死亡配体1 12 7.124 0.789 -0.027 0.560 8.285 0.798 肿瘤坏死因子配体超家族成员12 18 11.064 0.853 -0.025 0.592 12.030 0.868 注：SNP为单核苷酸多态性,MR为孟德尔随机化；Cochran Q检验评估异质性,另2种检验评估水平多效性
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Table 3. 增生性瘢痕与8种人炎症蛋白之间反向因果关系的逆方差加权法分析
炎症蛋白 SNP数（个） OR 95%CI P值 CD6 13 0.989 0.965~1.014 0.391 成纤维细胞生长因子23 13 0.995 0.970~1.021 0.713 白细胞介素-17C 13 0.998 0.971~1.026 0.892 白血病抑制因子 13 0.983 0.949~1.017 0.327 白血病抑制因子受体 13 0.968 0.942~0.994 0.016 骨保护蛋白 13 0.998 0.974~1.021 0.842 程序性死亡配体1 13 1.013 0.982~1.044 0.419 肿瘤坏死因子配体超家族成员12 13 0.972 0.949~0.997 0.026 注：SNP为单核苷酸多态性
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Table 4. 逆方差加权法分析得出5种人炎症蛋白与瘢痕疙瘩之间存在显著因果关系
炎症蛋白 SNP数（个） OR 95%CI P值 CD5 22 0.744 0.573~0.965 0.026 白细胞介素-10受体α亚基 10 1.303 1.024~1.660 0.032 白细胞介素-5 11 0.686 0.472~0.996 0.048 白血病抑制因子 12 0.603 0.431~0.842 0.003 骨保护蛋白 21 0.715 0.553~0.924 0.010 注：SNP为单核苷酸多态性
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Table 5. 补充双样本MR分析方法得出3种人炎症蛋白与瘢痕疙瘩之间存在潜在因果关系
分析方法及炎症蛋白 SNP数（个） OR 95%CI P值 MR-Egger法 骨保护蛋白 21 0.537 0.311~0.927 0.038 加权中位数法 CD5 22 0.679 0.467~0.986 0.042 白细胞介素-5 11 0.604 0.372~0.980 0.041 骨保护蛋白 21 0.688 0.502~0.944 0.020 简单模式法 CD5 22 0.502 0.273~0.924 0.038 骨保护蛋白 21 0.546 0.326~0.917 0.033 注：MR为孟德尔随机化,SNP为单核苷酸多态性
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Table 6. 与瘢痕疙瘩之间存在显著因果关系的5种人炎症蛋白SNP的异质性和水平多效性分析结果
炎症蛋白 SNP数（个） Cochran Q检验 MR-Egger回归检验 MR-PRESSO离群值检验 Q值 P值 截距 P值 RSSobs P值 CD5 22 15.487 0.798 0.018 0.626 16.842 0.826 白细胞介素-10受体α亚基 10 8.551 0.480 0.044 0.185 14.737 0.469 白细胞介素-5 11 11.178 0.344 -0.017 0.713 13.570 0.363 白血病抑制因子 12 6.504 0.838 -0.020 0.608 7.321 0.864 骨保护蛋白 21 24.469 0.223 0.038 0.260 26.167 0.305 注：SNP为单核苷酸多态性,MR为孟德尔随机化；Cochran Q检验评估异质性,另2种检验评估水平多效性
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Table 7. 瘢痕疙瘩与5种人炎症蛋白之间反向因果关系的逆方差加权法分析
炎症蛋白 SNP数（个） OR 95%CI P值 CD5 23 0.988 0.969~1.008 0.245 白细胞介素-10受体α亚基 23 0.994 0.972~1.017 0.593 白细胞介素-5 23 1.016 0.993~1.039 0.171 白血病抑制因子 23 1.006 0.979~1.033 0.664 骨保护蛋白 23 0.995 0.975~1.015 0.638 注：SNP为单核苷酸多态性
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