瘢痕疙瘩是一种慢性、进行性的真皮肿瘤，其组织学特征为炎性网状真皮层中出现大量成纤维细胞及I型胶原蛋白的异常积聚[1]。在临床上，该病变以持续性生长为特征，常通过瘙痒、红斑浸润的前缘侵犯周围正常皮肤，不仅造成局部功能障碍，还严重影响患者的心理健康与生活质量[2-4]。目前常用的治疗手段包括手术切除、放射治疗、药物治疗及压力治疗等，然而疗效仍不尽如人意，复发率较高，尚缺乏能够根治该病的理想方案[5]。因此，深入揭示瘢痕疙瘩的发病机制，对于推动其有效治疗策略的开发具有重要的科学及临床意义。
免疫系统和炎症反应在瘢痕疙瘩的发生和发展中发挥关键作用[6]。据报道，瘢痕疙瘩组织中肿瘤坏死因子α(tumor necrosis factor-alpha，TNF-α)、白细胞介素(interleukin，IL)-6、IL-17、低氧诱导因子hypoxia-inducible factor-1 alpha，HIF-1α)和信号转导及转录激活因子3(signal transducer and activator of transcription 3，STAT3)等多种炎症相关循环细胞因子表达上调，且其表达水平与瘢痕疙瘩的临床表现及进展密切相关[7-10]。此外，炎症程度与瘢痕疙瘩的形成和发展密切相关，提示炎症微环境可能是瘢痕疙瘩持续增生的重要原因[2, 11-12]。尽管如此，瘢痕疙瘩的确切致病机制尚未完全阐明，尤其是循环细胞因子在其中的因果作用仍缺乏直接证据。
孟德尔随机化(Mendelian randomization，MR)是一种利用遗传变异作为工具变量(instrumental variables，IVs)，评估可变暴露因素与疾病结局之间因果关系的流行病学方法[13]。与随机对照试验(randomized controlled trial，RCT)相比，MR可有效减少混杂因素和反向因果的影响，已广泛应用于多种疾病与生物标志物的因果推断[14-16]。然而，目前尚无研究采用MR分析系统评估循环细胞因子与瘢痕疙瘩之间的因果关联。本研究拟采用两样本MR分析，探讨多种循环细胞因子与瘢痕疙瘩发生风险之间的潜在因果关系，以期为揭示瘢痕疙瘩的发病机制及寻找新的治疗靶点提供理论依据。
资料与方法
研究设计
在这项两样本MR研究(图1)中，采用单核苷酸多态性(single nucleotide polymorphism，SNPs)作为IVs。为确保数据的有效性，SNPs的选择基于3个主要假设：1)IVs与暴露因素显著相关(相关性假设)； 2)IVs仅通过暴露因素而非其他途径影响结局，这意味着不存在水平多效性(排他性假设)；3)IVs与任何混杂因素无关(独立性假设)[17]。
数据来源
循环细胞因子和瘢痕疙瘩的SNPs(附表1， https://doi.org/10.57760/sciencedb.xbyxb.00102)来自最新的全基因组关联分析(genome-wide association study，GWAS)数据。
| Circulating cytokines | Beta | SE | OR | 95% CI | P |
|---|---|---|---|---|---|
| CTACK levels | -0.004 | 0.199 | 0.996 | 0.674 to 1.472 | 0.985 |
| beta-nerve growth factor levels | 0.524 | 0.294 | 1.688 | 0.950 to 3.001 | 0.074 |
| Vascular endothelial growth factor levels | 0.003 | 0.172 | 1.003 | 0.717 to 1.404 | 0.984 |
| Macrophage migration inhibitory factor levels | 0.733 | 0.273 | 2.081 | 1.219 to 3.552 | 0.007 |
| TRAIL levels | -0.025 | 0.146 | 0.976 | 0.732 to 1.300 | 0.866 |
| Tumor necrosis factor beta levels | 0.124 | 0.170 | 1.131 | 0.810 to 1.580 | 0.468 |
| Tumor necrosis factor alpha levels | -0.377 | 0.302 | 0.686 | 0.379 to 1.240 | 0.212 |
| Stromal-cell-derived factor 1 alpha levels | 0.598 | 0.395 | 1.818 | 0.837 to 3.947 | 0.131 |
| Stem cell growth factor beta levels | -0.123 | 0.223 | 0.884 | 0.571 to 1.369 | 0.580 |
| Stem cell factor levels | -0.657 | 0.334 | 0.518 | 0.269 to 0.998 | 0.049 |
| Interleukin-16 levels | 0.116 | 0.193 | 1.123 | 0.770 to 1.638 | 0.546 |
| RANTES levels | -0.073 | 0.252 | 0.930 | 0.568 to 1.524 | 0.773 |
| Platelet-derived growth factor BB levels | -0.257 | 0.233 | 0.773 | 0.490 to 1.221 | 0.270 |
| Macrophage inflammatory protein 1b levels | -0.268 | 0.152 | 0.765 | 0.568 to 1.031 | 0.078 |
| Macrophage inflammatory protein 1a levels | 0.193 | 0.348 | 1.213 | 0.613 to 2.402 | 0.579 |
| Monokine induced by gamma interferon levels | -0.216 | 0.176 | 0.805 | 0.571 to 1.136 | 0.218 |
| Macrophage colony stimulating factor levels | -0.164 | 0.190 | 0.849 | 0.584 to 1.232 | 0.389 |
| Monocyte chemoattractant protein-3 levels | -0.362 | 0.291 | 0.696 | 0.393 to 1.232 | 0.213 |
| Monocyte chemoattractant protein-1 levels | 0.515 | 0.244 | 1.673 | 1.036 to 2.701 | 0.035 |
| Interleukin-12p70 levels | 0.039 | 0.215 | 1.040 | 0.682 to 1.585 | 0.857 |
| Interferon gamma-induced protein 10 levels | 0.031 | 0.237 | 1.031 | 0.648 to 1.641 | 0.897 |
| Interleukin-18 levels | 0.010 | 0.170 | 1.010 | 0.724 to 1.410 | 0.952 |
| Interleukin-17 levels | 0.051 | 0.321 | 1.053 | 0.561 to 1.974 | 0.873 |
| Interleukin-13 levels | 0.000 | 0.191 | 1.000 | 0.688 to 1.453 | 0.998 |
| Interleukin-10 levels | -0.436 | 0.255 | 0.647 | 0.393 to 1.066 | 0.087 |
| Interleukin-8 levels | 0.053 | 0.324 | 1.054 | 0.558 to 1.990 | 0.871 |
| Interleukin-6 levels | -0.234 | 0.661 | 0.791 | 0.217 to 2.891 | 0.723 |
| Interleukin-1-receptor antagonist levels | 0.261 | 0.380 | 1.299 | 0.616 to 2.738 | 0.492 |
| Interleukin-1-beta levels | 0.063 | 0.559 | 1.065 | 0.356 to 3.185 | 0.910 |
| Hepatocyte growth factor levels | -0.041 | 0.348 | 0.960 | 0.485 to 1.900 | 0.907 |
| Interleukin-9 levels | 0.021 | 0.279 | 1.021 | 0.590 to 1.765 | 0.941 |
| Interleukin-7 levels | 0.090 | 0.217 | 1.094 | 0.715 to 1.675 | 0.679 |
| Interleukin-5 levels | 0.048 | 0.334 | 1.050 | 0.546 to 2.019 | 0.885 |
| Interleukin-4 levels | 0.165 | 0.334 | 1.180 | 0.613 to 2.272 | 0.621 |
| Interleukin-2 receptor antagonist levels | -0.048 | 0.225 | 0.953 | 0.614 to 1.480 | 0.831 |
| Interleukin-2 levels | 0.255 | 0.244 | 1.290 | 0.799 to 2.083 | 0.297 |
| Interferon gamma levels | 0.325 | 0.372 | 1.384 | 0.668 to 2.867 | 0.382 |
| Growth-regulated protein alpha levels | 0.240 | 0.159 | 1.271 | 0.930 to 1.737 | 0.133 |
| Granulocyte-colony stimulating factor levels | 0.222 | 0.257 | 1.248 | 0.754 to 2.067 | 0.389 |
| Fibroblast growth factor basic levels | -0.043 | 0.483 | 0.958 | 0.372 to 2.466 | 0.929 |
| Eotaxin levels | 0.306 | 0.244 | 1.358 | 0.841 to 2.193 | 0.210 |
从芬兰青年心血管风险研究、FINRISK1997和FINRISK2002研究的样本中获得48种循环细胞因子的GWAS数据[18]，其中有7种循环细胞因子因缺失值超过90%而被排除，最终纳入41种循环细胞因子进行MR分析。研究样本覆盖芬兰的主要地理区域，年龄25~74岁，共计8 293例；均接受了EDTA血浆、肝素血浆和血液中循环细胞因子水平的测定。该GWAS汇总统计数据为公开、免费数据集，所有研究者均可通过IEU OpenGWAS Project获取。瘢痕疙瘩的GWAS数据集来自英国生物样本库，包括201例瘢痕疙瘩样本和456 147例对照样本[19]。该数据集同样为公开、免费资源，研究者可通过UK Biobank官方网站获取。循环细胞因子和瘢痕疙瘩的样本来自不同的队列，二者之间不存在样本重叠。
IVs的筛选
首先，以P<5×10-6为条件在循环细胞因子的GWAS数据中筛选与暴露强相关的SNPs[20-21]。然后，采用聚类法排除SNPs之间的连锁不平衡(linkage disequilibrium，LD)，具体参数设定为r2<0.001，聚类距离=10 000 kb。接着，在PhenoScanner V2平台上搜索筛选到的SNPs，排除可能影响循环细胞因子与瘢痕疙瘩之间关联的潜在混杂因素(如吸烟、糖尿病和焦虑症等)。PhenoScanner V2是一个综合信息基因型和表型关联的平台，可用于确定SNPs是否仅通过暴露影响结果[22]。最后，计算统计量F，计算公式为：
统计学处理
在两样本MR分析中，采用逆方差加权法(inverse-variance weighted，IVW)作为主要统计方法，评估暴露(循环细胞因子)对结局(瘢痕疙瘩)的因果效应。同时，采用加权中位数法(weighted median，WME)、MR-Egger回归法、加权模型、简单模型进行敏感性分析[24-25]。采用MR-Egger回归法、MR多效性残差和与异质性检验(MR pleiotropy residual sum and outlier，MR-PRESSO)法进一步评估可能的水平基因多效性[26-27]。在MR-Egger回归法中，截距项是判断水平多效性的关键指标[26]。使用Cochran’s Q检验评估SNP的异质性。使用留一法评估结果的稳健性和一致性。
此外，以瘢痕疙瘩为暴露，循环细胞因子为结局，进行反向MR分析，以排除反向因果关联，从而保证结果的稳健性。
所有分析和数据可视化均使用R 4.3.1软件中的TwoSampleMR包实现，检验水准α=0.05，P<0.05为差异有统计学意义。
结 果
MR分析结果
最终筛选得到4~19个SNPs作为循环细胞因子的IVs(附表1，https://doi.org/10.57760/sciencedb.xbyxb. 00102)，均为有效IVs(F>10)。
IVW分析结果显示：2种细胞因子与瘢痕疙瘩存在正向因果关联，分别为巨噬细胞迁移抑制因子(macrophage migration inhibitory factor，MIF)[比值比(odds ratio，OR)=2.081，95%置信区间(confidence interval，CI) 1.219~3.552，P=0.007]和单核细胞趋化蛋白-1(monocyte chemoattractant protein-1，MCP-1)(OR=1.673，95% CI 1.036~2.701，P=0.035)，而干细胞因子(stem cell factor，SCF)与瘢痕疙瘩存在负向因果关联(OR=0.518，95% CI 0.269~0.998，P=0.049；表1)。WME、MR-Egger回归法的结果与IVW一致(附表2，https://doi.org/10.57760/sciencedb.xbyxb.00102)。散点图显示了每种方法在每个结果数据库的具体效应(图2)。
| Circulating cytokines | Heterogenity | MR-Egger intercept | |||
|---|---|---|---|---|---|
| Q | P | Egger_intercept | SE | P | |
| CTACK levels | 7.972 | 0.632 | -0.115 | 0.102 | 0.288 |
| beta-nerve growth factor levels | 3.340 | 0.852 | 0.035 | 0.235 | 0.887 |
| Vascular endothelial growth factor levels | 1.737 | 0.998 | -0.026 | 0.065 | 0.700 |
| Macrophage migration inhibitory factor levels | 1.980 | 0.982 | -0.054 | 0.120 | 0.669 |
| TRAIL levels | 14.716 | 0.681 | 0.007 | 0.053 | 0.893 |
| Tumor necrosis factor beta levels | 2.910 | 0.573 | -0.033 | 0.093 | 0.744 |
| Tumor necrosis factor alpha levels | 1.016 | 0.907 | -0.077 | 0.114 | 0.545 |
| Stromal-cell-derived factor 1 alpha levels | 10.164 | 0.337 | 0.060 | 0.082 | 0.487 |
| Stem cell growth factor beta levels | 19.958 | 0.131 | 0.135 | 0.081 | 0.121 |
| Stem cell factor levels | 8.165 | 0.518 | 0.113 | 0.098 | 0.279 |
| Interleukin-16 levels | 14.793 | 0.140 | 0.142 | 0.093 | 0.161 |
| RANTES levels | 8.815 | 0.550 | 0.208 | 0.141 | 0.174 |
| Platelet-derived growth factor BB levels | 4.715 | 0.981 | 0.018 | 0.071 | 0.802 |
| Macrophage inflammatory protein 1b levels | 13.495 | 0.812 | 0.015 | 0.060 | 0.800 |
| Macrophage inflammatory protein 1a levels | 8.549 | 0.287 | -0.022 | 0.184 | 0.910 |
| Monokine induced by gamma interferon levels | 12.938 | 0.677 | -0.036 | 0.095 | 0.714 |
| Macrophage colony stimulating factor levels | 5.003 | 0.757 | -0.047 | 0.122 | 0.710 |
| Monocyte chemoattractant protein-3 levels | 4.229 | 0.238 | -0.347 | 0.214 | 0.246 |
| Monocyte chemoattractant protein-1 levels | 2.970 | 0.998 | -0.009 | 0.081 | 0.910 |
| Interleukin-12p70 levels | 4.775 | 0.906 | -0.043 | 0.060 | 0.491 |
MR-Egger回归法和MR-PRESSO法的结果显示不存在水平基因多效性(P>0.05；表2；附表3，https://doi. org/10.57760/sciencedb.xbyxb.00102)。Cochran’s Q检验结果(表2)显示：除IL-6的异质性检验具有统计学意义外(P=0.014)，其他细胞因子不存在异质性。留一法分析结果进一步证实了循环细胞因子与瘢痕疙瘩的因果关联(附图1~3，https://doi.org/10.57760/sciencedb. xbyxb.00102)。
| Circulating cytokines | Beta | SE | OR | 95% CI | P |
|---|---|---|---|---|---|
| CTACK levels | -0.007 | 0.022 | 0.993 | 0.951 to 1.037 | 0.753 |
| beta-nerve growth factor levels | 0.047 | 0.023 | 1.048 | 1.002 to 1.095 | 0.039 |
| Vascular endothelial growth factor levels | 0.006 | 0.021 | 1.006 | 0.966 to 1.047 | 0.785 |
| Macrophage migration inhibitory factor levels | -0.008 | 0.021 | 0.992 | 0.952 to 1.035 | 0.713 |
| TRAIL levels | 0.015 | 0.015 | 1.015 | 0.986 to 1.045 | 0.321 |
表2(续)
| Circulating cytokines | Heterogenity | MR-Egger intercept | |||
|---|---|---|---|---|---|
| Q | P | Egger_intercept | SE | P | |
| Interferon gamma-induced protein 10 levels | 3.952 | 0.949 | -0.036 | 0.089 | 0.697 |
| Interleukin-18 levels | 19.651 | 0.292 | -0.165 | 0.066 | 0.053 |
| Interleukin-17 levels | 3.541 | 0.981 | -0.098 | 0.109 | 0.391 |
| Interleukin-13 levels | 12.359 | 0.262 | -0.094 | 0.095 | 0.347 |
| Interleukin-10 levels | 5.774 | 0.888 | -0.131 | 0.068 | 0.083 |
| Interleukin-8 levels | 3.084 | 0.379 | 0.015 | 0.123 | 0.912 |
| Interleukin-6 levels | 16.016 | 0.014 | -0.178 | 0.163 | 0.325 |
| Interleukin-1-receptor antagonist levels | 8.166 | 0.226 | -0.336 | 0.160 | 0.090 |
| Interleukin-1-beta levels | 7.482 | 0.113 | 0.010 | 0.223 | 0.966 |
| Hepatocyte growth factor levels | 8.227 | 0.412 | 0.004 | 0.145 | 0.978 |
| Interleukin-9 levels | 5.571 | 0.591 | 0.049 | 0.146 | 0.747 |
| Interleukin-7 levels | 12.783 | 0.236 | -0.075 | 0.128 | 0.574 |
| Interleukin-5 levels | 3.630 | 0.604 | 0.036 | 0.146 | 0.819 |
| Interleukin-4 levels | 7.639 | 0.571 | -0.074 | 0.094 | 0.454 |
| Interleukin-2 receptor antagonist levels | 10.291 | 0.245 | -0.042 | 0.099 | 0.683 |
| Interleukin-2 levels | 7.247 | 0.611 | -0.099 | 0.080 | 0.252 |
| Interferon gamma levels | 13.278 | 0.276 | -0.089 | 0.096 | 0.372 |
| Growth-regulated protein alpha levels | 7.251 | 0.611 | -0.024 | 0.119 | 0.846 |
| Granulocyte-colony stimulating factor levels | 9.017 | 0.530 | -0.114 | 0.065 | 0.113 |
| Fibroblast growth factor basic levels | 4.438 | 0.728 | 0.054 | 0.129 | 0.687 |
| Eotaxin levels | 12.940 | 0.607 | 0.002 | 0.089 | 0.980 |
反向MR分析结果
选择7个独立的SNPs作为瘢痕疙瘩的IVs(附表2，https://doi.org/10.57760/sciencedb.xbyxb.00102)，均为有效IVs(F>10)
IVW分析结果显示：瘢痕疙瘩仅与β-神经生长因子(beta-nerve growth factor，β-NGF)存在因果关联(OR=1.048，95% CI 1.002~1.095，P=0.039；表3)。
此外，根据Cochran’s Q检验结果(表4)显示：除IL-5的异质性检验具有统计学意义(P=0.001)，其他细胞因子不存在异质性。MR-Egger回归法和MR-PRESSO法的结果显示不存在水平基因多效性(P>0.05；表4；附表4，https://doi.org/10.57760/sciencedb. xbyxb.00102)。
| Circulating cytokines | Heterogenity | MR-Egger intercept | |||
|---|---|---|---|---|---|
| Q | P | Egger_intercept | SE | P | |
| CTACK levels | 3.419 | 0.636 | 0.028 | 0.040 | 0.518 |
| beta-nerve growth factor levels | 3.809 | 0.577 | 0.076 | 0.041 | 0.135 |
| Vascular endothelial growth factor levels | 10.628 | 0.101 | 0.066 | 0.028 | 0.064 |
| Macrophage migration inhibitory factor levels | 3.350 | 0.764 | 0.010 | 0.038 | 0.807 |
| TRAIL levels | 1.654 | 0.895 | 0.003 | 0.027 | 0.914 |
| Tumor necrosis factor beta levels | 0.004 | 0.998 | -0.003 | 0.076 | 0.976 |
| Tumor necrosis factor alpha levels | 13.195 | 0.022 | 0.030 | 0.073 | 0.698 |
| Stromal-cell-derived factor 1 alpha levels | 5.970 | 0.309 | 0.049 | 0.028 | 0.149 |
| Stem cell growth factor beta levels | 4.370 | 0.497 | -0.024 | 0.040 | 0.580 |
| Stem cell factor levels | 5.551 | 0.352 | 0.000 | 0.031 | 0.989 |
| Interleukin-16 levels | 3.629 | 0.604 | 0.039 | 0.041 | 0.395 |
| RANTES levels | 7.379 | 0.287 | 0.004 | 0.047 | 0.939 |
| Platelet-derived growth factor BB levels | 5.340 | 0.376 | 0.044 | 0.027 | 0.176 |
| Macrophage inflammatory protein 1b levels | 3.990 | 0.551 | 0.015 | 0.027 | 0.606 |
| Macrophage inflammatory protein 1a levels | 12.783 | 0.026 | 0.049 | 0.069 | 0.518 |
| Monokine induced by gamma interferon levels | 1.869 | 0.931 | 0.012 | 0.037 | 0.755 |
| Macrophage colony stimulating factor levels | 9.372 | 0.154 | 0.008 | 0.065 | 0.908 |
| Monocyte chemoattractant protein-3 levels | 2.780 | 0.427 | 0.119 | 0.083 | 0.289 |
| Monocyte chemoattractant protein-1 levels | 7.823 | 0.166 | 0.055 | 0.027 | 0.110 |
| Interleukin-12p70 levels | 9.626 | 0.141 | 0.057 | 0.026 | 0.081 |
| Interferon gamma-induced protein 10 levels | 4.697 | 0.583 | -0.015 | 0.037 | 0.706 |
| Interleukin-18 levels | 1.097 | 0.982 | 0.014 | 0.039 | 0.741 |
| Interleukin-17 levels | 7.626 | 0.178 | 0.056 | 0.028 | 0.111 |
| Interleukin-13 levels | 6.556 | 0.111 | 0.044 | 0.066 | 0.540 |
| Interleukin-10 levels | 9.817 | 0.133 | 0.051 | 0.030 | 0.146 |
| Interleukin-8 levels | 8.912 | 0.113 | 0.045 | 0.056 | 0.466 |
| Interleukin-6 levels | 7.001 | 0.221 | 0.034 | 0.031 | 0.331 |
| Interleukin-1-receptor antagonist levels | 7.261 | 0.202 | 0.029 | 0.052 | 0.605 |
| Interleukin-1-beta levels | 11.692 | 0.111 | 0.025 | 0.041 | 0.561 |
| Hepatocyte growth factor levels | 0.715 | 0.982 | 0.017 | 0.027 | 0.554 |
| Interleukin-9 levels | 8.694 | 0.122 | -0.001 | 0.059 | 0.984 |
| Interleukin-7 levels | 15.872 | 0.054 | 0.028 | 0.067 | 0.692 |
| Interleukin-5 levels | 19.812 | 0.001 | 0.080 | 0.084 | 0.396 |
| Interleukin-4 levels | 8.739 | 0.120 | 0.040 | 0.035 | 0.318 |
| Interleukin-2 receptor antagonist levels | 2.966 | 0.705 | 0.055 | 0.040 | 0.236 |
| Interleukin-2 levels | 8.856 | 0.115 | 0.024 | 0.060 | 0.713 |
| Interferon gamma levels | 3.721 | 0.590 | 0.020 | 0.028 | 0.515 |
| Growth-regulated protein alpha levels | 3.425 | 0.635 | 0.064 | 0.041 | 0.194 |
| Granulocyte-colony stimulating factor levels | 12.262 | 0.056 | 0.061 | 0.031 | 0.107 |
| Fibroblast growth factor basic levels | 7.731 | 0.172 | 0.052 | 0.029 | 0.147 |
| Eotaxin levels | 2.672 | 0.750 | 0.036 | 0.027 | 0.251 |
表3(续)
| Circulating cytokines | Beta | SE | OR | 95% CI | P |
|---|---|---|---|---|---|
| Tumor necrosis factor beta levels | 0.063 | 0.040 | 1.065 | 0.984 to 1.153 | 0.118 |
| Tumor necrosis factor alpha levels | -0.019 | 0.037 | 0.981 | 0.913 to 1.055 | 0.610 |
| Stromal-cell-derived factor 1 alpha levels | 0.003 | 0.017 | 1.003 | 0.971 to 1.036 | 0.866 |
| Stem cell growth factor beta levels | -0.010 | 0.022 | 0.990 | 0.948 to 1.034 | 0.653 |
| Stem cell factor levels | 0.014 | 0.016 | 1.014 | 0.984 to 1.046 | 0.356 |
| Interleukin-16 levels | -0.027 | 0.023 | 0.973 | 0.931 to 1.018 | 0.232 |
| RANTES levels | 0.016 | 0.024 | 1.016 | 0.970 to 1.065 | 0.501 |
| Platelet-derived growth factor BB levels | 0.021 | 0.015 | 1.021 | 0.991 to 1.052 | 0.177 |
| Macrophage inflammatory protein 1b levels | 0.007 | 0.015 | 1.007 | 0.978 to 1.037 | 0.643 |
| Macrophage inflammatory protein 1a levels | 0.020 | 0.036 | 1.021 | 0.951 to 1.095 | 0.573 |
| Monokine induced by gamma interferon levels | 0.013 | 0.021 | 1.013 | 0.973 to 1.056 | 0.518 |
| Macrophage colony stimulating factor levels | -0.004 | 0.033 | 0.996 | 0.934 to 1.062 | 0.898 |
| Monocyte chemoattractant protein-3 levels | 0.031 | 0.044 | 1.032 | 0.947 to 1.125 | 0.475 |
| Monocyte chemoattractant protein-1 levels | 0.026 | 0.019 | 1.026 | 0.989 to 1.064 | 0.164 |
| Interleukin-12p70 levels | 0.018 | 0.018 | 1.018 | 0.982 to 1.055 | 0.326 |
| Interferon gamma-induced protein 10 levels | 0.014 | 0.021 | 1.014 | 0.973 to 1.056 | 0.506 |
| Interleukin-18 levels | 0.015 | 0.022 | 1.015 | 0.972 to 1.059 | 0.501 |
| Interleukin-17 levels | 0.008 | 0.019 | 1.008 | 0.971 to 1.046 | 0.687 |
| Interleukin-13 levels | 0.015 | 0.035 | 1.015 | 0.948 to 1.087 | 0.671 |
| Interleukin-10 levels | 0.027 | 0.019 | 1.027 | 0.990 to 1.067 | 0.157 |
| Interleukin-8 levels | 0.019 | 0.030 | 1.019 | 0.961 to 1.081 | 0.534 |
| Interleukin-6 levels | 0.018 | 0.018 | 1.019 | 0.984 to 1.054 | 0.297 |
| Interleukin-1-receptor antagonist levels | 0.005 | 0.027 | 1.005 | 0.953 to 1.059 | 0.861 |
| Interleukin-1-beta levels | -0.002 | 0.022 | 0.998 | 0.956 to 1.042 | 0.928 |
| Hepatocyte growth factor levels | 0.016 | 0.015 | 1.017 | 0.985 to 1.046 | 0.095 |
| Interleukin-9 levels | 0.018 | 0.029 | 1.018 | 0.962 to 1.078 | 0.528 |
| Interleukin-7 levels | 0.017 | 0.035 | 1.017 | 0.950 to 1.089 | 0.629 |
| Interleukin-5 levels | 0.029 | 0.046 | 1.029 | 0.941 to 1.126 | 0.530 |
| Interleukin-4 levels | 0.016 | 0.020 | 1.016 | 0.978 to 1.056 | 0.417 |
| Interleukin-2 receptor antagonist levels | 0.002 | 0.022 | 1.002 | 0.960 to 1.047 | 0.913 |
| Interleukin-2 levels | 0.006 | 0.030 | 1.006 | 0.948 to 1.067 | 0.839 |
| Interferon gamma levels | 0.017 | 0.015 | 1.017 | 0.987 to 1.049 | 0.262 |
| Growth-regulated protein alpha levels | 0.023 | 0.023 | 1.023 | 0.979 to 1.069 | 0.314 |
| Granulocyte-colony stimulating factor levels | 0.010 | 0.021 | 1.010 | 0.969 to 1.052 | 0.646 |
| Fibroblast growth factor basic levels | 0.015 | 0.019 | 1.015 | 0.977 to 1.054 | 0.449 |
| Eotaxin levels | 0.007 | 0.015 | 1.007 | 0.978 to 1.037 | 0.620 |
讨 论
本研究首次对循环细胞因子与瘢痕疙瘩之间因果关系进行MR分析，结果发现：较高水平的SCF可能会降低瘢痕疙瘩的发生风险，而高水平的MIF和MCP-1会升高瘢痕疙瘩的发生风险。此外，反向MR分析的结果显示只有循环细胞因子β-NGF水平的改变与瘢痕疙瘩存在轻度因果关联。因此，循环细胞因子与瘢痕疙瘩之间无双向遗传易感性。但应谨慎解读这一结果，因为MR是一项专注于基于遗传变异的数据资源的特定研究。
促炎性细胞因子MIF作为过度炎症反应中的关键介质，可直接上调胶原蛋白与蛋白多糖等细胞外基质成分的基因表达[28]。在瘢痕疙瘩成纤维细胞中，MIF刺激抗纤维化前列腺素E2(prostaglandin E2，PGE2)表达的能力显著低于正常成纤维细胞，从而导致PGE2合成不足，并进一步加剧胶原蛋白的过度产生[29]。此外，PGE2不仅能抑制瘢痕疙瘩成纤维细胞的迁移，还能拮抗转化生长因子(transforming growth factor，TGF)-β1介导的I型和III型胶原蛋白的生成[30]。因此，MIF的高表达可能是驱动瘢痕疙瘩病理过程中炎症反应增强与纤维化失控的关键因素。本研究通过MR研究提供了相关的人群遗传学证据：循环MIF水平每升高1个标准差，个体患瘢痕疙瘩的风险增加1.081倍，这进一步确立了MIF水平与瘢痕疙瘩发生风险之间的因果关系。
传统上，MCP-1主要被认为通过趋化作用招募单核细胞。然而，近期研究表明，除单核细胞趋化功能外，MCP-1还可能参与组织修复与愈合过程。在动脉粥样硬化[31]、硬皮病[32-33]和心肌梗死[34]等纤维化疾病中，MCP-1均认为是关键调控因子。动物实验进一步揭示，MCP-1缺失会导致梗死心脏区域巨噬细胞募集减少，对坏死心肌细胞的吞噬清除延迟，并伴随成纤维细胞浸润减少[35]。此外，MCP-1亦可直接调控成纤维细胞表型与功能，促进胶原蛋白表达[36]，从而参与瘢痕疙瘩中异常纤维化的形成。
SCF作为一种二聚体蛋白质，能够结合并激活 c-Kit受体，进而实现其生化功能。c-Kit受体的激活伴随着信号转导过程及其自身的自动磷酸化[37]。细胞内某些相互作用结构域，如SH2和PTB结构域，会优先识别并结合c-Kit胞内区的磷酸化酪氨酸残基，从而将下游信号蛋白质招募至活化的c-Kit受体周围[38]。依据细胞类型的不同，c-Kit信号的激活可促进细胞迁移、增殖与存活[39]。在生理条件下，c-Kit信号通路对正常造血、色素沉着、生殖功能、胃肠蠕动以及部分神经系统功能的维持至关重要[40-42]。目前，关于SCF与瘢痕疙瘩关联的临床观察研究或Meta分析较为有限。本研究通过MR分析发现，循环SCF水平升高与瘢痕疙瘩发生风险降低相关。SCF可能对瘢痕疙瘩形成具有保护作用。在瘢痕组织及血清刺激培养的成纤维细胞中，SCF及其受体c-Kit的表达均上调，说明二者均可能在伤口愈合早期阶段发挥重要作用[43]。进一步研究揭示，在模拟上皮-间充质相互作用的角质形成细胞与成纤维细胞共培养模型中，SCF的分泌及c-Kit胞外段脱落均有所增强，后者可能与肿瘤坏死因子转换酶的上调有关。值得注意的是，该酶在体内瘢痕疙瘩组织及体外共培养体系中均高表达。这些发现提示，SCF/c-Kit通路的调控异常可能与瘢痕疙瘩的发病机制密切相关。
本研究首次通过MR方法，揭示了MIF、MCP-1及SCF等循环细胞因子与瘢痕疙瘩之间的因果关系，具有重要的临床转化意义。瘢痕疙瘩是一种常见的皮肤纤维化疾病，具有较高的复发率，并对患者的生活质量产生显著影响，目前临床上缺乏有效的风险预测和治疗手段[44-45]。本研究为其临床管理提供了新方向：首先，外周血中MIF、MCP-1、SCF等细胞因子的水平可用于瘢痕疙瘩风险评估和早期筛查。其次，针对MIF和MCP-1等的靶向治疗已在相关领域展现潜力[32, 46-47]，其抑制剂或可用于瘢痕疙瘩的防治；此外，SCF的保护效应提示，活化SCF/c-Kit信号通路可能是促进正常愈合、抑制异常瘢痕形成的潜在策略。这些发现亟需前瞻性临床研究加以验证。
与流行病学研究不同，本研究最大限度地减少了混杂变量和反向因果关系的可能性。但本研究仍存在一定的局限性。首先，未能纳入瘢痕疙瘩并发症的分析，考虑到不同临床症状在病因学和预后方面的异质性，理想情况下应将并发症纳入研究。其次，所使用的瘢痕疙瘩GWAS数据集中病例样本量仅为201例，这在遗传学研究中属于较小规模，可能导致统计功效不足。一方面，这种限制可能使得真实存在但效应量较弱的因果关系未能被检出，从而产生假阴性结果；另一方面，当显著性水平接近阈值时(如本研究中观察到的SCF与瘢痕疙瘩之间的负向关联，P=0.049)，则可能存在假阳性风险。因此，我们认为该结果应被视为探索性发现，结论的可靠性有待在更大样本量的研究中得到进一步验证。
此外，尽管本研究在分析中剔除了与年龄显著相关的SNPs以减少混杂因素影响，但在部分分析中仍观察到IVs间的异质性。例如，IL-6与研究结局相关的分析中，Cochran’s Q检验提示显著异质性，提示所用SNP之间的效应估计并不完全一致。在反向MR分析中，从瘢痕疙瘩到IL-5的检验结果同样显示存在异质性。这些发现提示，相关IVs可能存在潜在的水平多效性或其他未被识别的混杂因素，从而导致效应估计的差异。虽然本研究采用了随机效应IVW模型，在一定程度上能够对异质性进行调整，但结果仍需谨慎解读。此外，值得注意的是，细胞因子水平具有动态变化的特性，而MR方法主要评估遗传因素对其长期平均水平的影响，难以反映其在不同时间点的波动。这一方法学局限也提示我们，在解释结果时应结合细胞因子的生物学特性，谨慎外推至实际临床情境。
综上所述，本研究最终确定了循环中的MIF、MCP-1和SCF水平与瘢痕疙瘩之间的因果关系。本研究加深了对瘢痕疙瘩发病机制的理解，并为临床制订有效的管理策略提供了依据。MIF、MCP-1和SCF可能是瘢痕疙瘩的潜在治疗靶点。
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陈璇, 邓可欣, 周建大, 刘灿. 循环细胞因子与瘢痕疙瘩的因果关系: 一项孟德尔随机化研究[J]. 中南大学学报(医学版), 2025, 50(7): 1145-1157. DOI:10.11817/j.issn.1672-7347.2025. 240359
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