CN116904578B - 线粒体差异化表达特征基因在制备重度抑郁症诊断剂中的应用 - Google Patents
线粒体差异化表达特征基因在制备重度抑郁症诊断剂中的应用 Download PDFInfo
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Abstract
本发明涉及线粒体差异化表达特征基因的表达量检测剂在制备重度抑郁症诊断剂中的应用。所述线粒体差异化表达特征基因选自PMPCB、MRPS28、LYRM2、MGST1、COX20、PTPMT1和STX17中的一种或多种组合。本发明研究了一些线粒体相关基因与重度抑郁症之间的关联，并从中挑选了7个线粒体差异化表达特征基因作为重度抑郁症的生物标志物，在此基础上，本发明进一步对这7个特征基因的表达量进行了相应地赋分，并基于上述赋分构建了重度抑郁症诊断模型，用于预测受试者患有重度抑郁症的风险。该诊断模型具有相对较好的诊断性能和临床应用价值。
Description
技术领域
本发明属于抑郁症诊断领域，更特别地，涉及线粒体差异化表达特征基因的表达量检测剂在制备重度抑郁症诊断剂中的应用。
背景技术
重度抑郁症(MDD)是一种高发和复发性的精神障碍，其特点是长期情绪低落、兴趣减退、无法体验快乐，并伴有睡眠或食欲紊乱。尽管在了解抑郁症的发病方面做了大量的实验和临床研究工作，如遗传、生物、心理和社会原因，但对MDD的病因学的认识仍然是零散的。由于该疾病的异质性表现和多因素决定因素，MDD的诊断和治疗仍然是一个重大挑战。在过去的几十年里，人们做了许多尝试，以确定诊断MDD的生物标志物。然而，由于特异性和疗效不足，其中只有少数可以应用于临床实践。此外，抗抑郁治疗的疗效不足，大约30％的患者对一线抗抑郁治疗没有充分或满意的反应。因此，迫切需要加强我们对MDD生物学基础的理解，确定新的诊断生物标志物和潜在的治疗目标。
在这项研究中，我们通过梳理Gene Expression Omnibus(GEO)的转录组测序基因数据和从MitoCarta3.0数据库获得的线粒体相关基因列表，确定了MDD患者和健康对照组之间差异表达的线粒体相关基因(DeMRG)。接下来，应用三种机器学习算法来筛选用于MDD诊断的特征基因。基于从31个DeMRGs中发现的7个特征基因，我们构建了一个诊断模型，并评估了多变量模型的诊断能力。随后，通过免疫分析评估了MDD患者和对照组之间免疫细胞浸润的免疫景观。利用一致性聚类分析，我们进一步将MDD患者根据七个特征性DeMRGs表达模式分为两个集群。最后，我们进一步分析了两个集群之间免疫细胞浸润的变化，以及这些特征性DeMRGs和免疫细胞浸润之间的相关性。我们关于线粒体功能障碍及其与MDD中免疫学浸润相关性的发现，为更好地理解MDD发病的潜在分子机制提供了一个新的视角，并有助于确定有前景的生物标志物以及治疗靶点。
发明内容
为解决以上问题，本发明提供了线粒体差异化表达特征基因的表达量检测剂在制备重度抑郁症诊断剂中的应用。
在一个具体实施方案中，所述线粒体差异化表达特征基因选自PMPCB、MRPS28、LYRM2、MGST1、COX20、PTPMT1和STX17中的一种或多种组合。
本发明研究了一些线粒体相关基因与重度抑郁症之间的关联，并从中挑选了7个线粒体差异化表达特征基因作为重度抑郁症的生物标志物，在此基础上，本发明进一步对这7个特征基因的表达量进行了相应地赋分，并基于上述赋分构建了重度抑郁症诊断模型，用于预测受试者患有重度抑郁症的风险。该诊断模型具有相对较好的诊断性能和在临床应用价值。
附图说明
图1为数据集GSE98793中差异表达的线粒体相关基因的热图。
图2为基因PMPCB、MRPS28、LYRM2、MGST1、COX20、PTPMT1和STX17构建的评估重度抑郁症风险的列线图(A)及该列线图模型的校准曲线(B)。
图3为上述重度抑郁症风险评估模型数据集GSE98793中的验证结果，其中A-G为7个特征基因在MDD和对照样本中的表达差异对比图，H为7个特征基因的ROC曲线分析，I为重度抑郁症风险评估模型的ROC曲线分析。
图4为免疫细胞浸润分析的结果，其中A和B分别为描述MDD组和对照组之间免疫浸润差异的叠加条形图和小提琴图，C为22种免疫细胞比例的相关矩阵。
图5为基于线粒体相关特征基因的MDD亚型分类结果，其中A-D显示MDD样本被分为两个亚组是最佳的聚类数目，E为MDD集群A和集群B之间的免疫细胞含量的差异，F为7个线粒体相关特征基因与免疫细胞浸润之间的相关性。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。此外，下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。
1.数据收集和预处理
MDD患者和健康对照组的RNA芯片数据是从GEO数据集(https://www.ncbi.nlm.nih.gov/geo/)中获取的。利用表达谱数据集GSE98793(包含128名MDD患者和64名健康对照者)作为训练集，以确定与MDD相关的枢纽基因并构建诊断模型。
结果如图1所示，共发现31个线粒体相关基因的差异表达，其中MDD样本上调基因6个，下调基因25个，并且大部分差异表达的线粒体相关基因都是相互关联的，并表现出较强的、明显的关联程度。进一步地研究显示，这些基因与线粒体内膜、线粒体基质、含线粒体蛋白复合物等重要的细胞成分相关。
2.生物标志物的筛选和模型的构建
结合选择算子的逻辑回归(LASSO)、支持向量机递归特征消除(SVM-RFE)和随机森林(RF)来筛选用于最终构建MDD诊断模型的特征生物标志物。通过LASSO逻辑回归算法确定了18个基因作为潜在的MDD相关生物标志物，SVM-RFE算法确定了26个基因，RF算法确定了17个基因。我们在这三个算法的基础上取交集，确定了七个重叠基因，包括PMPCB、MRPS28、LYRM2、MGST1、COX20、PTPMT1和STX17。
在NCBI中，这些基因序列获取号如下：
PMPCB的获取号为mRNA NCBI reference(Gene accession number):NM_004279.3；
MRPS28的获取号为mRNA NCBI reference(Gene accession number):NM_014018.3；
LYRM2的获取号为mRNA NCBI reference(Gene accession number):NM_020466.5；
MGST1的获取号为mRNA NCBI reference(Gene accession number):NM_001260511.2；
COX20的获取号为mRNA NCBI reference(Gene accession number):NM_001312871.1
PTPMT1的获取号为mRNA NCBI reference(Gene accession number):NM_001143984.2。
STX17的获取号为mRNA NCBI reference(Gene accession number):NM_017919.3。
然后，我们结合上述七个特征基因构建了一个列线图来预测疾病的风险(图2A)。利用校准图来显示列线图的性能，结果如图2B所示，校准图证实了我们模型的性能。
3.模型的验证
在GSE98793中对七个特征基因的表达水平进行了验证。如图3A-G所示，MGST1的表达水平在MDD中明显高于对照组；COX20、LYRM2、MRPS28、PMPCB、PTPMT1和STX17的表达水平在MDD患者中明显降低。此外，还绘制了ROC曲线，并计算了AUC来区分MDD和对照组。7个特征基因的诊断能力显示了良好的诊断价值，COX20和STX17的AUC为0.665，PMPCB的AUC为0.649，MGST1的AUC为0.635，MRPS28的AUC为0.620，PTPMT1的AUC为0.619，LYRM2的AUC为0.614(图3H)。我们进一步通过多变量逻辑回归分析将多个特征生物标志物的测量结果结合起来，以提高诊断效率。我们通过将每个基因的基因表达值与其对应的系数相乘来构建了一种新的诊断风险评分，该系数是通过多元逻辑回归分析获得的。诊断评分如下所示：Z＝(-0.3594*PMPCB基因表达水平)+(-0.9036*MRPS28基因表达水平)+(-1.1714*LYRM2基因表达水平)+(1.5690*MGST1基因表达水平)+(-0.9499*COX20基因表达水平)+(-1.1624*PTPMT1基因表达水平)+(-1.3670*STX17基因表达水平)。最佳诊断风险模型＝1/(1+e^-Z)。区分MDD和对照组的多变量模型的诊断能力显示出良好的诊断价值，AUC为0.772(95％CI＝0.702-0.842)，明显高于七个特征基因的最高AUC(图3I)。
4.免疫细胞浸润分析
为了进一步探索MDD组和对照组的免疫细胞组成，我们通过使用CIBERSORT算法调查了训练集每个样本中22个免疫细胞浸润的比例(图4A)。然后我们构建了一个小提琴图来比较两组之间免疫细胞浸润的差异。如图4B所示，与对照组样本相比，MDD样本中T细胞CD8(p＝0.022)和T细胞γδ(p＝0.006)的比例明显降低，而MDD组中NK细胞静止期(p＝0.007)、巨噬细胞M0(p＝0.015)和中性粒细胞(p＝0.043)的比例明显高于对照组的比例。因此，这些类型的免疫细胞可能是与MDD有关的潜在核心免疫细胞。此外，各免疫细胞之间的相关热图显示，B细胞记忆与树突状细胞静止期(Cor＝0.46)、巨噬细胞M2(Cor＝0.43)和T细胞CD4记忆静止期(Cor＝0.43)呈正相关关系。相反，T细胞CD8与中性粒细胞(Cor＝-0.66)和静止的T细胞CD4记忆(Cor＝-0.56)呈负相关(图4C)。综上所述，上述研究结果显示，MDD和对照组样本之间的免疫细胞浸润特征存在很大差异。
5.聚类分析确定了基于线粒体相关特征基因的两种MDD亚型
最后，基于MDD组中7个线粒体相关特征基因的表达水平，我们通过无监督聚类方法对128个样本进行聚类，以进一步阐明亚组之间的生物学差异。如图5A和5B所示，K＝2是最佳的聚类数目，组内相关性最高，组间干扰最小。因此，MDD样本被分为两个亚组：A组(n＝35)和B组(n＝93)，群组间7个线粒体相关特征基因的表达水平存在差异(图5C)，PCA图所示集群A和集群B之间的基因表达模式是不同的(图5D)。进一步进行ssGSEA来评估MDD簇和免疫细胞浸润水平之间的关系，结果显示，B群中的几种免疫细胞明显较高，表明B群患者的免疫浸润更大(图5E)。通过对7个线粒体相关特征基因与浸润免疫细胞的相关性分析，发现线粒体相关特征基因与免疫细胞浸润之间存在着密切而全面的联系，它们之间相互作用，影响MDD的免疫特征。
以上验证结果表明，本研究中选择的基于多种生物标志物的风险评分对MDD患者表现出很高的诊断价值。
本领域的技术人员容易理解，以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。
Claims (1)
1.线粒体差异化表达特征基因的表达量检测剂在制备重度抑郁症诊断剂中的应用，所述线粒体差异化表达特征基因为PMPCB、MRPS28、LYRM2、MGST1、COX20、PTPMT1和STX17的组合,利用所述线粒体差异化表达特征基因的表达水平来构建重度抑郁症的诊断风险模型；
所述线粒体差异化表达特征基因的诊断评分如下：Z= (-0.3594 × PMPCB基因表达水平) + (-0.9036 ×MRPS28基因表达水平) + (-1.1714 × LYRM2基因表达水平) +(1.5690 × MGST1基因表达水平) + (-0.9499× COX20基因表达水平) + (-1.1624 ×PTPMT1基因表达水平) + (-1.3670 × STX17基因表达水平)；
所述诊断风险模型=1/(1+e^-Z)。
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