Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples

Wang, Huimei; Zhang, Mingwei; Xie, Qiqi; Jin, Yu; Yan, Qi; Yue, Qiuyuan

doi:10.7717/peerj.7171

Cited by 14 publications

(12 citation statements)

References 45 publications

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“…Therefore, further elucidation of the pathogenesis of PC at the genetic level may help identify new diagnostic and prognostic indicators. With the rapid development of sequencing technology, microarray analyses, based on high-throughput platforms, have been widely used in biomedical and clinical research for screening genetic variants [12,13]. At present, there are many PC-related expression profile datasets of varying quality.…”

Section: Introductionmentioning

confidence: 99%

MCM2andNUSAP1Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer

Deng

Hao

et al. 2020

BioMed Research International

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Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.

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Section: Introductionmentioning

confidence: 99%

MCM2andNUSAP1Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer

Deng

Hao

et al. 2020

BioMed Research International

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“…Wang et al divided randomly into two nonoverlapping groups for cross-validation and found that MDD is mainly enriched in such pathways as ribonucleoprotein complex biogenesis, the Toll-like receptor signaling pathway, the apoptosis pathway, and the structural constituent of ribosomes. 33 Unlike the previous studies mentioned in this report, the present study applied a new approach, based on WGCNA, to investigate the molecular mechanisms underlying MDD samples compared with control samples. A total of 3276 DEGs were used to build coexpression networks and identify groups of highly coexpressed genes.…”

Section: Discussionmentioning

confidence: 99%

<p>Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression</p>

Zhang¹,

Xu²,

Yuan³

et al. 2020

NDT

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Purpose: Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease. Patients and Methods: Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well. Results: We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function. Conclusion: The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.

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“…With the rapid development of sequencing technologies and bioinformatics, microarray analysis has been widely used in biomedical research and clinical screening of genetic variation [7,8]. In this study, we downloaded a neuropathic painrelated gene expression dataset from the Gene Expression Omnibus (GEO) database [9], and we identified differentially expressed genes (DEGs) using the R software.…”

Section: Introductionmentioning

confidence: 99%

Protein Kinase A Is Involved in Neuropathic Pain by Activating the p38MAPK Pathway to Mediate Spinal Cord Cell Apoptosis

Deng

Yang

Xie

et al. 2020

Mediators of Inflammation

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Neuropathic pain is a serious clinical problem to be solved. This study is aimed at investigating protein kinase A (PKA) expression in neuropathic pain and its possible mechanisms of involvement. A neuropathic pain-related gene expression dataset was downloaded from Gene Expression Omnibus, and differentially expressed genes were screened using the R software. cytoHubba was used to screen for hub genes. A spared nerve injury (SNI) rat model was established, and the paw withdrawal threshold was determined using von Frey filaments. Western blotting and immunofluorescence were used to detect the expression and cellular localization, respectively, of key proteins in the spinal cord. Western blot, ELISA, and TUNEL assays were used to detect cell signal transduction, inflammation, and apoptosis, respectively. Pka was identified as a key gene involved in neuropathic pain. After SNI, mechanical allodynia occurred, PKA expression in the spinal cord increased, the p38MAPK pathway was activated, and spinal cord inflammation and apoptosis occurred in rats. PKA colocalized with neurons, astrocytes, and microglia, and apoptotic cells were mainly neurons. Intrathecal injection of a PKA inhibitor not only relieved mechanical hyperalgesia, inflammatory reaction, and apoptosis in SNI rats but also inhibited p38MAPK pathway activation. However, intrathecal injection of a p38MAPK inhibitor attenuated mechanical hyperalgesia, inflammation, and apoptosis, but did not affect PKA expression. In conclusion, PKA is involved in neuropathic pain by activating the p38MAPK pathway to mediate spinal cord cell apoptosis.

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Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples

Cited by 14 publications

References 45 publications

MCM2andNUSAP1Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer

MCM2andNUSAP1Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer

<p>Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression</p>

Protein Kinase A Is Involved in Neuropathic Pain by Activating the p38MAPK Pathway to Mediate Spinal Cord Cell Apoptosis

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