scholarly journals Network analysis of gene expression in mice provides new evidence of involvement of the mTOR pathway in antipsychotic-induced extrapyramidal symptoms

2015 ◽  
Vol 16 (3) ◽  
pp. 293-300 ◽  
Author(s):  
S Mas ◽  
P Gassó ◽  
D Boloc ◽  
N Rodriguez ◽  
F Mármol ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Mtor Pathway ◽  
Extrapyramidal Symptoms ◽  
New Evidence

scholarly journals Comprehensive high-throughput meta-analysis of differentially expressed microRNAs in transcriptomic datasets reveals significant disruption of MAPK/JNK signal transduction pathway in Adult T-cell leukemia/lymphoma

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Shahrzad Shadabi ◽  
Nargess Delrish ◽  
Mehdi Norouzi ◽  
Maryam Ehteshami ◽  
Fariba Habibian-Sezavar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signal Transduction ◽  
T Cell ◽  
Network Analysis ◽  
High Throughput ◽  
Target Gene ◽  
Differentially Expressed ◽  
Cell Leukemia ◽  
Adult T Cell Leukemia ◽  
Healthy Donors

Abstract Background Human T-lymphotropic virus 1 (HTLV-1) infection may lead to the development of Adult T-cell leukemia/lymphoma (ATLL). To further elucidate the pathophysiology of this aggressive CD4+ T-cell malignancy, we have performed an integrated systems biology approach to analyze previous transcriptome datasets focusing on differentially expressed miRNAs (DEMs) in peripheral blood of ATLL patients. Methods Datasets GSE28626, GSE31629, GSE11577 were used to identify ATLL-specific DEM signatures. The target genes of each identified miRNA were obtained to construct a protein-protein interactions network using STRING database. The target gene hubs were subjected to further analysis to demonstrate significantly enriched gene ontology terms and signaling pathways. Quantitative reverse transcription Polymerase Chain Reaction (RTqPCR) was performed on major genes in certain pathways identified by network analysis to highlight gene expression alterations. Results High-throughput in silico analysis revealed 9 DEMs hsa-let-7a, hsa-let-7g, hsa-mir-181b, hsa-mir-26b, hsa-mir-30c, hsa-mir-186, hsa-mir-10a, hsa-mir-30b, and hsa-let-7f between ATLL patients and healthy donors. Further analysis revealed the first 5 of DEMs were directly associated with previously identified pathways in the pathogenesis of HTLV-1. Network analysis demonstrated the involvement of target gene hubs in several signaling cascades, mainly in the MAPK pathway. RT-qPCR on human ATLL samples showed significant upregulation of EVI1, MKP1, PTPRR, and JNK gene vs healthy donors in MAPK/JNK pathway. Discussion The results highlighted the functional impact of a subset dysregulated microRNAs in ATLL on cellular gene expression and signal transduction pathways. Further studies are needed to identify novel biomarkers to obtain a comprehensive mapping of deregulated biological pathways in ATLL.

Abstract W P73: Feasibility and Preliminary Results of Whole Blood RNA-Sequencing Analysis in Patients With Intracranial Aneurysms

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Blake Haas ◽  
Nestor R Gonzalez ◽  
Elina Nikkola ◽  
Mark Connolly ◽  
William Hsu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Rna Sequencing ◽  
Whole Blood ◽  
Intracranial Aneurysms ◽  
Small Sample Size ◽  
Expression Patterns ◽  
Small Sample ◽  
Cellular Respiration ◽  
Sequencing Analysis

Introduction: Intracranial aneurysms (IA) growth and rupture have been associated with chronic remodeling of the arterial wall. However, the pathobiology of this process remains poorly understood. The objective of the present study was to evaluate the feasibility of analyzing gene expression patterns in peripheral blood of patients with ruptured and unruptured saccular IAs. Materials and Methods: We analyzed human whole blood transcriptomes by performing paired-end, 100 bp RNA-sequencing (RNAseq) using the Illumina platform. We used STAR to align reads to the genome, HTSeq to count reads, and DESeq to normalize counts across samples. Self-reported patient information was used to correct expression values for ancestry, age, and sex. We utilized weighted gene co-expression network analysis (WGCNA) to identify gene expression network modules associated with IA size and rupture. The DAVID tool was employed to search for Gene Ontology enrichment in relevant modules. Results: Samples from 12 patients (9 females, age 57.6 +/-12) with IAs were analyzed. Four had ruptured aneurysms. RNA isolation and application of the methodology described above was successful in all samples. Although the small sample size prevents us from drawing definite conclusions, we observed promising novel co-expression networks for IAs: WCGNA analysis showed down-regulation of two transcript modules associated with ruptured IA status (r=-0.78, p=0.008 and r=-0.77, p=0.009), and up-regulation of two modules associated with aneurysm size (r=0.86, p=0.002 and r=0.9, p=4e-04), respectively. DAVID analyses showed that genes upregulated in an IA size-associated module were enriched with genes involved in cellular respiration and translation, while genes involved in transcription were down-regulated in a module associated with ruptured IAs. Conclusions: Whole blood RNAseq analysis is a feasible tool to capture transcriptome dynamics and achieve a better understanding of the pathophysiology of IAs. Further longitudinal studies of patients with IAs using network analysis are justified.

Identification of Hub Genes Associated with Development of Lung Adenocarcinoma by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Jinglei Li ◽  
Wei Hou
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Network Analysis ◽  
Lung Adenocarcinoma ◽  
Normal Control ◽  
Gene Expression Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Differential Gene Expression Analysis ◽  
Differential Gene

Abstract Purpose: Lung adenocarcinoma (LUAD) has high heterogeneity and poor prognosis, posing a major challenge to human health worldwide. Therefore, it is necessary to improve our understanding of the molecular mechanism of LUAD in order to be able to better predict its prognosis and develop new therapeutic strategies for target genes.Methods: The Cancer Genome Atlas and Gene Expression Omnibus, were selected to comprehensively analyze and explore the differences between LUAD tumors and adjacent normal tissues. Critical gene information was obtained through weighted gene co-expression network analysis (WGCNA), differential gene expression analysis, and survival analysis.Results: Using WGCNA and differential gene expression analysis, 29 differentially expressed genes were screened. The functional annotation analysis showed these genes to be mainly concentrated in heart trabecula formation, regulation of inflammatory response, collagen-containing extracellular matrix, and metalloendopeptidase inhibitor activity. Also, in the protein–protein interaction network analysis, 10 central genes were identified using Cytoscape's CytoHubba plug-in. The expression of CDH5, TEK, TIMP3, EDNRB, EPAS1, MYL9, SPARCL1, KLF4, and TGFBR3 in LUAD tissue was found to be lower than that in the normal control group, while the expression of MMP1 in LUAD tissue was higher than that in the normal control group. According to survival analysis, the low expression of MYL9 and SPARCL1 was correlated with poor overall survival in patients with LUAD. Finally, through the verification of the Oncomine database, it was found that the expression levels of MYL9 and SPARCL1 were consistent with the mRNA levels in LUAD samples, and both were downregulated.Conclusion: Two survival-related genes, MYL9 and SPARCL1, were determined to be highly correlated with the development of LUAD. Both may play an essential role in the development LUAD and may be potential biomarkers for its diagnosis and treatment in the future.

scholarly journals Insight into Molecular Profile Changes after Skeletal Muscle Contusion Using Microarray and Bioinformatics Analyses

2020 ◽  
Author(s):  
Na Li ◽  
Ru-feng Bai ◽  
Chun Li ◽  
Li-hong Dang ◽  
Qiu-xiang Du ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Healing Process ◽  
Differentially Expressed ◽  
Molecular Profile ◽  
Wound Age ◽  
Functional Modules

Abstract Background: Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. This study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process.Methods: A total of 33 rats were divided randomly into control (n = 3), mild contusion (n = 15), and severe contusion (n = 15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n = 3 per subgroup). Then full genome microarray of RNA isolated from muscle tissue was performed to access the gene expression changes during healing process.Results: A total of 2,844 and 2,298 differentially expressed genes were identified in the mild and severe contusion groups, respectively. The analysis of the overlapping differentially expressed genes showed that there are common mechanisms of transcriptomic repair of mild and severe contusion within 48 h post-contusion. This was supported by the results of principal component analysis, hierarchical clustering, and weighted gene co‐expression network analysis of the 1,620 coexpressed genes in mildly and severely contused muscle. From these analyses, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. We then performed an analysis of the functions of genes (including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway annotation, and protein–protein interaction network analysis) in the functional modules and temporal clusters, and the hub genes in each module–cluster pair were identified. Interestingly, we found that genes downregulated within 24−48 h of the healing process were largely associated with metabolic processes, especially oxidative phosphorylation of reduced nicotinamide adenine dinucleotide phosphate, which has been rarely reported. Conclusions: These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.

scholarly journals The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Guanying Feng ◽  
Feifei Xue ◽  
Yingzheng He ◽  
Tianxiao Wang ◽  
Hua Yuan
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Network Analysis ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Risk Score ◽  
Squamous Cell ◽  
Neck Squamous Cell Carcinoma ◽  
Potential Biomarker ◽  
Score Model

ObjectivesThis study aimed to identify genes regulating cancer stemness of head and neck squamous cell carcinoma (HNSCC) and evaluate the ability of these genes to predict clinical outcomes.Materials and MethodsThe stemness index (mRNAsi) was obtained using a one-class logistic regression machine learning algorithm based on sequencing data of HNSCC patients. Stemness-related genes were identified by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis (LASSO). The coefficient of LASSO was applied to construct a diagnostic risk score model. The Cancer Genome Atlas database, the Gene Expression Omnibus database, Oncomine database and the Human Protein Atlas database were used to validate the expression of key genes. Interaction network analysis was performed using String database and DisNor database. The Connectivity Map database was used to screen potential compounds. The expressions of stemness-related genes were validated using quantitative real‐time polymerase chain reaction (qRT‐PCR).ResultsTTK, KIF14, KIF18A and DLGAP5 were identified. Stemness-related genes were upregulated in HNSCC samples. The risk score model had a significant predictive ability. CDK inhibitor was the top hit of potential compounds.ConclusionStemness-related gene expression profiles may be a potential biomarker for HNSCC.

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