scholarly journals Bioinformatics Analysis of Potential Biomarkers and Pathway Identification for Major Depressive Disorder

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dong Qi ◽  
Kui Chen
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Hedgehog Signaling ◽  
Epithelial Mesenchymal Transition ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Major Depressive ◽  
Mesenchymal Transition

Aiming at a more comprehensive understanding of the molecular biomarkers and potential mechanisms of major depressive disorder (MDD), from the Gene Expression Omnibus (GEO) database, we first obtained mRNA expression profiles and identified 585 differentially expressed genes (DEGs) through the R software, including 263 upregulated genes and 322 downregulated genes. Then, through the Kyoto Encyclopedia of Genome and Genome (KEGG) pathway and biological process (BP) analysis, we found that the upregulated and downregulated DEGs were abundant in different pathways, respectively. It was noteworthy that upregulated DEGs were the most significantly enriched in the mTOR signaling pathway. Subsequently, through the protein-protein interaction (PPI) network, we identified seven hub genes, namely, EXOSC2, CAMK2A, PRIM1, SMC4, TYMS, CDK6, and RPA2. Finally, through gene set enrichment analysis (GSEA), we obtained that hypoxia, epithelial-mesenchymal transition, hedgehog signaling, and reactive oxygen species pathway were the enriched pathways for MDD patients. The above data results would provide a new direction for the treatment of MDD patients.

scholarly journals Transthyretin as a Biomarker to Predict and Monitor Major Depressive Disorder Identified by Whole-Genome Transcriptomic Analysis in Mouse Models

Biomedicines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1124
Author(s):  
Sung-Liang Yu ◽  
Selina Shih-Ting Chu ◽  
Min-Hui Chien ◽  
Po-Hsiu Kuo ◽  
Pan-Chyr Yang ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Hedgehog Signaling ◽  
Stressful Life Events ◽  
Genomic Analysis ◽  
Enrichment Analysis ◽  
Transcriptomic Analysis ◽  
Molecular Networks ◽  
Mild Stress ◽  
Major Depressive

Background: Accumulations of stressful life events result in the onset of major depressive disorder (MDD). Comprehensive genomic analysis is required to elucidate pathophysiological changes and identify applicable biomarkers. Methods: Transcriptomic analysis was performed on different brain parts of a chronic mild stress (CMS)-induced MDD mouse model followed by systemic analysis. QPCR and ELISA were utilized for validation in mice and patients. Results: The highest numbers of genes with significant changes induced by CMS were 505 in the amygdala followed by 272 in the hippocampus (twofold changes; FDR, p < 0.05). Enrichment analysis indicated that the core-enriched genes in CMS-treated mice were positively enriched for IFN-γ response genes in the amygdala, and hedgehog signaling in the hippocampus. Transthyretin (TTR) was severely reduced in CMS-treated mice. In patients with diagnosed MDD, serum concentrations of TTR were reduced by 48.7% compared to controls (p = 0.0102). Paired samples from patients with MDD demonstrated a further 66.3% increase in TTR at remission compared to the acute phase (p = 0.0339). Conclusions: This study provides comprehensive information on molecular networks related to MDD as a basis for further investigation and identifies TTR for MDD monitoring and management. A clinical trial with bigger patient cohort should be conducted to validate this translational study.

scholarly journals Identification of a Novel Epithelial–Mesenchymal Transition Gene Signature Predicting Survival in Patients With HNSCC

2021 ◽  
Vol 27 ◽  
Author(s):  
Wei Xin ◽  
Chaoran Zhao ◽  
Longyang Jiang ◽  
Dongmei Pei ◽  
Lin Zhao ◽  
...  
Keyword(s):  
High Risk ◽  
Epithelial Mesenchymal Transition ◽  
Expression Profiles ◽  
Critical Role ◽  
Cell Cancer ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Mesenchymal Transition ◽  
Potential Biomarker

Head and neck squamous cell cancer (HNSCC) is one of the most common types of cancer worldwide. There have been many reports suggesting that biomarkers explored via database mining plays a critical role in predicting HNSCC prognosis. However, a single biomarker for prognostic analysis is not adequate. Additionally, there is growing evidence indicating that gene signature could be a better choice for HNSCC prognosis. We performed a comprehensive analysis of mRNA expression profiles using clinical information of HNSCC patients from The Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis (GSEA) was performed, and we found that a set of genes involved in epithelial mesenchymal transition (EMT) contributed to HNSCC. Cox proportional regression model was used to identify a four-gene (WIPF1, PPIB, BASP1, PLOD2) signature that were significantly associated with overall survival (OS), and all the four genes were significantly upregulated in tumor tissues. We successfully classified the patients with HNSCC into high-risk and low-risk groups, where in high-risk indicated poorer patient prognosis, indicating that this gene signature might be a novel potential biomarker for the prognosis of HNSCC. The prognostic ability of the gene signature was further validated in an independent cohort from the Gene Expression Omnibus (GEO) database. In conclusion, we identified a four-EMT-based gene signature which provides the potentiality to serve as novel independent biomarkers for predicting survival in HNSCC patients, as well as a new possibility for individualized treatment of HNSCC.

scholarly journals Epithelial–mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Yong’an Jiang ◽  
JingXing Leng ◽  
Qianxia Lin ◽  
Fang Zhou
Keyword(s):  
Network Analysis ◽  
Epithelial Mesenchymal Transition ◽  
Interaction Analysis ◽  
External Validation ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Mesenchymal Transition ◽  
Unruptured Aneurysms

AbstractIntracranial aneurysm (IA) can cause fatal subarachnoid hemorrhage (SAH) after rupture, and identifying patients with unruptured IAs is essential for reducing SAH fatalities. The epithelial–mesenchymal transition (EMT) may be vital to IA progression. Here, identified key EMT-related genes in aneurysms and their pathogenic mechanisms via bioinformatic analysis. The GSE13353, GSE75436, and GSE54083 datasets from Gene Expression Omnibus were analyzed with limma to identify differentially expressed genes (DEGs) among unruptured aneurysms, ruptured aneurysms, and healthy samples. The results revealed that three EMT-related DEGs (ADIPOQ, WNT11, and CCL21) were shared among all groups. Coexpression modules and hub genes were identified via weighted gene co-expression network analysis, revealing two significant modules (red and green) and 14 EMT-related genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses suggested that cytokine interactions were closely related. Gene set enrichment analysis revealed that unruptured aneurysms were enriched for the terms “inflammatory response” and “vascular endothelial growth”. Protein–protein interaction analysis identified seven key genes, which were evaluated with the GSE54083 dataset to determine their sensitivity and specificity. In the external validation set, we verified the differential expression of seven genes in unruptured aneurysms and normal samples. Together, these findings indicate that FN1, and SPARC may help distinguish normal patients from patients with asymptomatic IAs.

scholarly journals Identification of Epithelial–Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

2021 ◽  
Vol 9 ◽  
Author(s):  
Bo Xiao ◽  
Liyan Liu ◽  
Zhuoyuan Chen ◽  
Aoyu Li ◽  
Pingxiao Wang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

scholarly journals Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Regulated by KEAP1-NRF2 Pathway in Esophageal Carcinoma

2020 ◽  
Author(s):  
Mohamed Elshaer ◽  
Ahmed Hammad ◽  
Xiu Jun Wang ◽  
Xiuwen Tang
Keyword(s):  
Cell Lines ◽  
Epithelial Mesenchymal Transition ◽  
Cancer Cell Line ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Mesenchymal Transition ◽  
Gene Set ◽  
Over Expression

Abstract BackgroundKEAP1-NRF2 pathway alterations were identified in many cancers including, esophageal cancer (ESCA). Identifying biomarkers that are associated with mutations in this pathway will aid in defining this cancer subset; and hence in supporting precision and personalized medicine. MethodsIn this study, 182 tumor samples from the Cancer Genome Atlas (TCGA)-ESCA RNA-Seq V2 level 3 data were segregated into two groups KEAP1-NRF2-mutated (22) and wild-type (160).The two groups were subjected to differential gene expression analysis, and we performed Gene Set Enrichment Analysis (GSEA) to determine all significantly affected biological pathways. Then, the enriched gene set was integrated with the differentially expressed genes (DEGs) to identify a gene signature regulated by the KEAP1-NRF2 pathway in ESCA. Furthermore, we validated the gene signature using mRNA expression data of ESCA cell lines provided by the Cancer Cell Line Encyclopedia (CCLE). The identified signature was tested in 3 independent ESCA datasets to assess its prognostic value.ResultsWe identified 11 epithelial-mesenchymal transition (EMT) genes regulated by the KEAP1-NRF2 pathway in ESCA patients. Five of the 11 genes showed significant over-expression in KEAP1-NRF2-mutated ESCA cell lines. In addition, the over-expression of these five genes was significantly associated with poor survival in 3 independent ESCA datasets, including the TCGA-ESCA dataset.ConclusionAltogether, we identified a novel EMT 5-gene signature regulated by the KEAP1-NRF2 axis and this signature is strongly associated with metastasis and drug resistance in ESCA. These 5-genes are potential biomarkers and therapeutic targets for ESCA patients in whom the KEAP1-NRF2 pathway is altered.

scholarly journals HCK promotes glioblastoma progression by TGFβ signaling

2020 ◽  
Vol 40 (6) ◽  
Author(s):  
Zhenlin Wang ◽  
Chenting Ying ◽  
Anke Zhang ◽  
Houshi Xu ◽  
Yang Jiang ◽  
...  
Keyword(s):  
Tyrosine Kinases ◽  
B Lymphocyte ◽  
Epithelial Mesenchymal Transition ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Gene Set Enrichment Analysis ◽  
Tgfβ Signaling ◽  
Mesenchymal Transition

Abstract The hematopoietic cell kinase (HCK), a member of the Src family protein-tyrosine kinases (SFKs), is primarily expressed in cells of the myeloid and B lymphocyte lineages. Nevertheless, the roles of HCK in glioblastoma (GBM) remain to be examined. Thus, we aimed to investigate the effects of HCK on GBM development both in vitro and in vivo, as well as the underlying mechanism. The present study found that HCK was highly expressed in both tumor tissues from patients with GBM and cancer cell lines. HCK enhanced cell viability, proliferation, and migration, and induced cell apoptosis in vitro. Tumor xenografts results also demonstrated that HCK knockdown significantly inhibited tumor growth. Interestingly, gene set enrichment analysis (GSEA) showed HCK was closed associated with epithelial mesenchymal transition (EMT) and TGFβ signaling in GBM. In addition, we also found that HCK accentuates TGFβ-induced EMT, suggesting silencing HCK inhibited EMT through the inactivation of Smad signaling pathway. In conclusion, our findings indicated that HCK is involved in GBM progression via mediating EMT process, and may be served as a promising therapeutic target for GBM.

Stimulated Gene Expression Profiles as a Blood Marker of Major Depressive Disorder

2010 ◽  
Vol 68 (2) ◽  
pp. 179-186 ◽  
Author(s):  
Sabine Spijker ◽  
Jeroen S. Van Zanten ◽  
Simone De Jong ◽  
Brenda W.J.H. Penninx ◽  
Richard van Dyck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Major Depressive ◽  
Blood Marker

Identification of Crucial Genes and Diagnostic Value Analysis in Major Depressive Disorder Using Bioinformatics Analysis

2020 ◽  
Vol 23 ◽  
Author(s):  
Yao Gao ◽  
Huiliang Zhao ◽  
Teng Xu ◽  
Junsheng Tian ◽  
Xuemei Qin
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Major Depressive ◽  
Diagnostic Ability ◽  
Value Analysis

Aim and Objective:: Despite the prevalence and burden of major depressive disorder (MDD), our current understanding of the pathophysiology is still incomplete. Therefore, this paper aims to explore genes and evaluate their diagnostic ability in the pathogenesis of MDD. Methods:: Firstly, the expression profiles of mRNA and microRNA were downloaded from the gene expression database and analyzed by the GEO2R online tool to identify differentially expressed genes (DEGs) and differentially expressed microRNAs (DEMs). Then, the DAVID tool was used for functional enrichment analysis. Secondly, the comprehensive protein- protein interaction (PPI) network was analyzed using Cytoscape, and the network MCODE was applied to explore hub genes. Thirdly, the receiver operating characteristic (ROC) curve of the core gene was drawn to evaluate clinical diagnostic ability. Finally, mirecords was used to predict the target genes of DEMs. Results:: A total of 154 genes were identified as DEGs, and 14 microRNAs were identified as DEMs. Pathway enrichment analysis showed that DEGs were mainly involved in hematopoietic cell lineage, PI3K-Akt signaling pathway, cytokinecytokine receptor interaction, chemokine signaling pathway, and JAK-STAT signaling pathway. Three important modules are identified and selected by the MCODE clustering algorithm. The top 12 hub genes including CXCL16, CXCL1, GNB5, GNB4, OPRL1, SSTR2, IL7R, MYB, CSF1R, GSTM1, GSTM2, and GSTP1 were identified as important genes for subsequent analysis. Among these important hub genes, GSTM2, GNB4, GSTP1 and CXCL1 have good diagnostic ability. Finally, by combining these four genes, the diagnostic ability of MDD can be improved to 0.905, which is of great significance for the clinical diagnosis of MDD. Conclusion:: Our results indicate that GSTM2, GNB4, GSTP1 and CXCL1 have potential diagnostic markers and are of great significance in clinical research and diagnostic application of MDD. This result needs a large sample study to further confirm the pathogenesis of MDD.

scholarly journals Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Qingyu Liang ◽  
Gefei Guan ◽  
Xue Li ◽  
Chunmi Wei ◽  
Jianqi Wu ◽  
...  
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Treatment Strategies ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Risky Behaviors ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Mesenchymal Transition ◽  
Genome Atlas

Abstract Background Molecular classification has laid the framework for exploring glioma biology and treatment strategies. Pro-neural to mesenchymal transition (PMT) of glioma is known to be associated with aggressive phenotypes, unfavorable prognosis, and treatment resistance. Recent studies have highlighted that long non-coding RNAs (lncRNAs) are key mediators in cancer mesenchymal transition. However, the relationship between lncRNAs and PMT in glioma has not been systematically investigated. Methods Gene expression profiles from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, and Rembrandt with available clinical and genomic information were used for analyses. Bioinformatics methods such as weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), Cox analysis, and least absolute shrinkage and selection operator (LASSO) analysis were performed. Results According to PMT scores, we confirmed that PMT status was positively associated with risky behaviors and poor prognosis in glioma. The 149 PMT-related lncRNAs were identified by WGCNA analysis, among which 10 (LINC01057, TP73-AS1, AP000695.4, LINC01503, CRNDE, OSMR-AS1, SNHG18, AC145343.2, RP11-25K21.6, RP11-38L15.2) with significant prognostic value were further screened to construct a PMT-related lncRNA risk signature, which could divide cases into two groups with distinct prognoses. Multivariate Cox regression analyses indicated that the signature was an independent prognostic factor for high-grade glioma. High-risk cases were more likely to be classified as the mesenchymal subtype, which confers enhanced immunosuppressive status by recruiting macrophages, neutrophils, and regulatory T cells. Moreover, six lncRNAs of the signature could act as competing endogenous RNAs to promote PMT in glioblastoma. Conclusions We profiled PMT status in glioma and established a PMT-related 10-lncRNA signature for glioma that could independently predict glioma survival and trigger PMT, which enhanced immunosuppression.

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