Application Potential Of Blood-brain Congruent Differential Genes In Patients With Major Depressive Disorder

Objectives: Major depressive disorder(depressive disorder)is a common mental disorder whose cause and underlying mechanism are not yet fully defined,and its high rate of disability and suicide is a great challenge to global health.The diagnosis of MDD mainly relies on the self-reported symptoms of patients and the evaluation of psychological and psychiatric scales.The lack of objective diagnostic markers results in a very high rate of missed diagnosis and misdiagnosis.In this study,differential expression genes(DEGs)with potential diagnostic value in brain tissue and peripheral blood were identified by bioinformatics methods and verified by molecular biology experiments.Methods: The brain tissue and peripheral blood sample data of MDD were downloaded from Gene Expression Omnibus(GEO),and corresponding DEGs analysis was conducted.In the MDD brain data set,Weighted Gene Co-Expression Network Analysis(WGCNA)was used to extract the most disease-related modules and construct the co-expression gene network.In addition,Venn diagram was used to identify the shared DEGs of modules and peripheral blood sample data sets.Functional pathway prediction was used to identify the DEGs most associated with the disease,and some DEGs were selected as candidate diagnostic genes.Their potential diagnostic value was determined by receiver operating characteristic curve(ROC curve)analysis and histogram analysis.Peripheral blood samples were collected from MDD patients with a male to female ratio and age matching and healthy controls,and phenol was used: The RNA in peripheral blood leukocytes was extracted by chloroform extraction.Real-time quantitative reverse transcription polymerase chain reaction(RT-q PCR)test was used to detect the expression of candidate diagnostic genes in the disease group and the healthy control group.The expression of the gene in the brain tissue data set was compared with that in the brain tissue data set.Finally,meaningful diagnostic genes were determined by ROC curve analysis.Results:(1)The MDD brain tissue dataset GSE53987 and peripheral blood tissue dataset GSE98793 were downloaded from GEO database.WGCNA was used to identify the module(MEgrey)most associated with MDD in GSE53987,and 623 DEGs were extracted from this module.The intersection of MEgrey and GSE98793 was obtained by Venn diagram,and 163 common DEGs were identified.The co-expression network of shared DEGs was reconstructed,all hub genes were identified according to the degree of connectivity,and gene function annotation of hub genes was performed.17 candidate genes were screened out according to the enrichment results.Regression analysis and subject working characteristic curve showed that CEP350,HSPG2 and SMAD5 candidate central genes had high auxiliary value in the diagnosis of MDD.(2)RT-q PCR results showed that CEP350 and SMAD55 were less expressed in MDD than the control group,and the expression of HSPG2 was the same as that of GSE53987 ROC curve analysis showed that CEP350 and SMAD5 had the efficacy of single diagnosis of MDD,and the efficacy of combined diagnosis was improved compared with single gene diagnosis.Conclusion(s): In this study,bioinformatics and molecular biological methods were combined to identify DEGs shared in brain tissue and peripheral blood of patients with MDD,and objective biomarkers for the diagnosis of MDD were extracted.Gene functional annotation indicated that candidate diagnostic genes may play an important role in the pathogenesis of MDD.The experimental results indicated that CEP350 and SMAD5 combined detection have certain diagnostic value for MDD,and this gene combination has a potential important role in clinical practice.