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Abstract：
OBJECTIVE: To find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD).
METHODS: Differential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated.
RESULTS: Differential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed.
CONCLUSIONS: Among all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.
METHODS: Differential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated.
RESULTS: Differential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed.
CONCLUSIONS: Among all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.
摘要：
目的：了解N6-甲基腺苷（m6A）基因与重度抑郁症（MDD）的关系。
方法：初步鉴定了正常和MDD样品之间m6A相关基因的差异表达。随后对这些基因的功能及其可能影响的途径进行了分析。利用这些差异基因的表达矩阵构建诊断模型,并使用列线图可视化。同时,根据这些m6A相关基因的表达，采用无监督分类方法对所有患者进行分类。在此之后,计算了不同簇之间的共同差异基因。通过分析簇之间常见差异表达基因的功能，阐明了m6A相关基因在MDD患者发病中的作用。
结果：在MDD组和对照组的ELAVL1和YTHDC2中观察到差异表达。ELAVL1与MDD患者并发焦虑相关。基于这两个基因的线性回归模型可以准确预测GSE98793数据集中的患者是否患有MDD，并可以为临床决策提供净收益。根据ELAVL1和YTHDC2的表达矩阵，将MDD患者分为三个簇。在这些集群中，共有937个常见的差异基因。还对这些基因进行了富集分析。应用ssGSEA方法预测GSE98793数据集样品中23个免疫细胞的含量。剖析了这些免疫细胞与ELAVL1、YTHDC2和分歧簇之间的关系。
结论：在所有m6A基因中，ELAVL1和YTHDC2与MDD密切相关，ELAVL1与MDD共病焦虑相关。ELAVL1和YTHDC2与MDD中的免疫细胞具有相反的关联。
方法：初步鉴定了正常和MDD样品之间m6A相关基因的差异表达。随后对这些基因的功能及其可能影响的途径进行了分析。利用这些差异基因的表达矩阵构建诊断模型,并使用列线图可视化。同时,根据这些m6A相关基因的表达，采用无监督分类方法对所有患者进行分类。在此之后,计算了不同簇之间的共同差异基因。通过分析簇之间常见差异表达基因的功能，阐明了m6A相关基因在MDD患者发病中的作用。
结果：在MDD组和对照组的ELAVL1和YTHDC2中观察到差异表达。ELAVL1与MDD患者并发焦虑相关。基于这两个基因的线性回归模型可以准确预测GSE98793数据集中的患者是否患有MDD，并可以为临床决策提供净收益。根据ELAVL1和YTHDC2的表达矩阵，将MDD患者分为三个簇。在这些集群中，共有937个常见的差异基因。还对这些基因进行了富集分析。应用ssGSEA方法预测GSE98793数据集样品中23个免疫细胞的含量。剖析了这些免疫细胞与ELAVL1、YTHDC2和分歧簇之间的关系。
结论：在所有m6A基因中，ELAVL1和YTHDC2与MDD密切相关，ELAVL1与MDD共病焦虑相关。ELAVL1和YTHDC2与MDD中的免疫细胞具有相反的关联。
