Analyzing gene expression profiles with preliminary validations in cardiac hypertrophy induced by pressure overload

The aim of this study was to identify the key genes involved in the cardiac hypertrophy (CH) induced by pressure overload. mRNA microarray data sets GSE5500 and GSE18801 were downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were screened using the Limma package; then, functional and pathway enrichment analysis were performed for common DEGs using the Database for Annotation, Visualization and Integrated Discovery database. Furthermore, the top DEGs were further validated using quantitative PCR in the hypertrophic heart tissue induced by isoprenaline. A total of 113 common DEGs with absolute fold change > 0.5, including 60 significantly upregulated DEGs and 53 downregulated DEGs, were obtained. Gene ontology term enrichment analysis suggested that common upregulated DEG were mainly enriched in neutrophil chemotaxis, extracellular fibril organization, and cell proliferation; and the common downregulated genes were significantly enriched in ion transport, endoplasmic reticulum, and dendritic spine. Kyoto Encyclopedia of Genes and Genomes pathway analysis found that the common DEGs were mainly enriched in extracellular matrix receptor interaction, phagosome, and focal adhesion. Additionally, the expression of Mfap4, Ltbp2, Aspn, Serpina3n, and Cnksr1 were upregulated in the model of CH, while the expression of Anp32a was downregulated. The current study identified the key deregulated genes and pathways involved in the CH, which could shed new light to understand the mechanism of CH.

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In Silico Discovery of Candidate Drugs against Covid-19

Viruses ◽

10.3390/v12040404 ◽

2020 ◽

Vol 12 (4) ◽

pp. 404 ◽

Cited By ~ 42

Author(s):

Claudia Cava ◽

Gloria Bertoli ◽

Isabella Castiglioni

Keyword(s):

Gene Expression ◽

In Silico ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Basic Mechanism ◽

Cell Receptor ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis

Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.

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Bcl-2 Is Involved in Cardiac Hypertrophy through PI3K-Akt Pathway

BioMed Research International ◽

10.1155/2021/6615502 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Xianwei Meng ◽

Jun Cui ◽

Guibin He

Keyword(s):

Gene Expression ◽

Cardiac Hypertrophy ◽

Signaling Pathway ◽

Enrichment Analysis ◽

B Cell Lymphoma ◽

Akt Signaling ◽

Receptor Interaction ◽

Pathway Enrichment Analysis ◽

Akt Signaling Pathway ◽

Upregulated Genes

Cardiac hypertrophy (CH) is a common cause of sudden cardiac death and heart failure, resulting in a significant medical burden. The present study is aimed at exploring potential CH-related pathways and the key downstream effectors. The gene expression profile of GSE129090 was obtained from the Gene Expression Omnibus database (GEO), and 1325 differentially expressed genes (DEGs) were identified, including 785 upregulated genes and 540 downregulated genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway enrichment analysis of DEGs were then performed. Although there were no pathways enriched by downregulated genes, many CH-related pathways were identified by upregulated genes, including PI3K-Akt signaling pathway, extracellular matrix- (ECM-) receptor interaction, regulation of actin cytoskeleton, and hypertrophic cardiomyopathy (HCM). In the deeper analysis of PI3K-Akt signaling pathway, we found all the signaling transduction pointed to B cell lymphoma-2- (Bcl-2-) mediated cell survival. We then demonstrated that PI3K-Akt signaling pathway was indeed activated in cardiac hypertrophy. Furthermore, no matter LY294002, an inhibitor of the PI3K/AKT signaling pathway, or Venetoclax, a selective Bcl-2 inhibitor, protected against cardiac hypertrophy. In conclusion, these data indicate that Bcl-2 is involved in cardiac hypertrophy as a key downstream effector of PI3K-Akt signaling pathway, suggesting a potential therapeutic target for the clinical management of cardiac hypertrophy.

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Diurnal profiling of neuroendocrine genes in murine heart, and shift in proopiomelanocortin gene expression with pressure-overload cardiac hypertrophy

Journal of Molecular Endocrinology ◽

10.1677/jme-08-0050 ◽

2008 ◽

Vol 41 (3) ◽

pp. 117-124 ◽

Cited By ~ 18

Author(s):

Jennifer A Chalmers ◽

Shuo-Yen J Lin ◽

Tami A Martino ◽

Sara Arab ◽

Peter Liu ◽

...

Keyword(s):

Gene Expression ◽

Smooth Muscle ◽

Vascular Smooth Muscle ◽

Cardiac Hypertrophy ◽

Mrna Expression ◽

Leptin Receptor ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Pressure Overload ◽

Pcr Analysis

Neuroendocrine peptides express biologic activity relevant to the cardiovascular system, including regulating heart rate and blood pressure, though little is known about the mechanisms involved. Here, we investigated neuroendocrine gene expression underlying diurnal physiology of the heart. We first used microarray and RT-PCR analysis and demonstrate the simultaneous expression of neuroendocrine genes in normal murine heart, including POMC, GnRH, neuropeptide Y, leptin receptor, GH-releasing hormone, cocaine- and amphetamine-regulated transcript, proglucagon, and galanin. We examined diurnal gene expression profiles, with cosinar bioinformatics to evaluate statistically significant rhythms. The POMC gene exhibits a day/night, circadian or diurnal, pattern of expression in heart, and we postulated that this may be important to cardiac growth and renewal. POMC diurnal gene rhythmicity is altered in pressure-overload cardiac hypertrophy, when compared with control heart, and levels increased at the dark-to-light transition times. These findings are also consistent with the proposal that neuropeptides mediate adverse remodeling processes, such as occur in pathologic hypertrophy. To investigate cellular responses, we screened three cell lines representing fibroblasts, cardiac myocytes, and vascular smooth muscle cells (NIH3T3, heart line 1, and mouse vascular smooth muscle cell line 1 (Movas-1) respectively). POMC mRNA expression is the most notable in Movas-1 cells and, furthermore, exhibits rhythmicity with culture synchronization. Taken together, these results highlight the diverse neuroendocrine mRNA expression profiles in cardiovasculature, and provide a novel model vascular culture system to research the role these neuropeptides play in organ health, integrity, and disease.

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Identification of key genes and pathways of BMP-9-induced osteogenic differentiation of mesenchymal stem cells by integrated bioinformatics analysis

Journal of Orthopaedic Surgery and Research ◽

10.1186/s13018-021-02390-w ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Jia-qi Wu ◽

Lin-bo Mao ◽

Ling-feng Liu ◽

Yong-mei Li ◽

Jian Wu ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Mesenchymal Stem Cells ◽

Osteogenic Differentiation ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Key Genes

Abstract Background The purpose of present study was to identify the differentially expressed genes (DEGs) associated with BMP-9-induced osteogenic differentiation of mesenchymal stem cells (MSCs) by using bioinformatics methods. Methods Gene expression profiles of BMP-9-induced MSCs were compared between with GFP-induced MSCs and BMP-9-induced MSCs. GSE48882 containing two groups of gene expression profiles, 3 GFP-induced MSC samples and 3 from BMP-9-induced MSCs, was downloaded from the Gene Expression Omnibus (GEO) database. Then, DEGs were clustered based on functions and signaling pathways with significant enrichment analysis. Pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that the identified DEGs were potentially involved in cytoplasm, nucleus, and extracellular exosome signaling pathway. Results A total of 1967 DEGs (1029 upregulated and 938 downregulated) were identified from GSE48882 datasets. R/Bioconductor package limma was used to identify the DEGs. Further analysis revealed that there were 35 common DEGs observed between the samples. GO function and KEGG pathway enrichment analysis, among which endoplasmic reticulum, protein export, RNA transport, and apoptosis was the most significant dysregulated pathway. The result of protein-protein interaction (PPI) network modules demonstrated that the Hspa5, P4hb, Sec61a1, Smarca2, Pdia3, Dnajc3, Hyou1, Smad7, Derl1, and Surf4 were the high-degree hub nodes. Conclusion Taken above, using integrated bioinformatical analysis, we have identified DEGs candidate genes and pathways in BMP-9 induced MSCs, which could improve our understanding of the key genes and pathways for BMP-9-induced osteogenic of MSCs.

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Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

Public Health Genomics ◽

10.1159/000517308 ◽

2021 ◽

Vol 24 (5-6) ◽

pp. 267-279

Author(s):

Xianyang Zhu ◽

Wen Guo

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Meta Analysis ◽

Wnt Signaling Pathway ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

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Identification and Verification of Core Genes in Colorectal Cancer

BioMed Research International ◽

10.1155/2020/8082697 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Houxi Xu ◽

Yuzhu Ma ◽

Jinzhi Zhang ◽

Jialin Gu ◽

Xinyue Jing ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Malignant Neoplasm ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Core Genes

Colorectal cancer, a malignant neoplasm that occurs in the colorectal mucosa, is one of the most common types of gastrointestinal cancer. Colorectal cancer has been studied extensively, but the molecular mechanisms of this malignancy have not been characterized. This study identified and verified core genes associated with colorectal cancer using integrated bioinformatics analysis. Three gene expression profiles (GSE15781, GSE110223, and GSE110224) were downloaded from the Gene Expression Omnibus (GEO) databases. A total of 87 common differentially expressed genes (DEGs) among GSE15781, GSE110223, and GSE110224 were identified, including 19 upregulated genes and 68 downregulated genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for common DEGs using clusterProfiler. These common DEGs were significantly involved in cancer-associated functions and signaling pathways. Then, we constructed protein-protein interaction networks of these common DEGs using Cytoscape software, which resulted in the identification of the following 10 core genes: SST, PYY, CXCL1, CXCL8, CXCL3, ZG16, AQP8, CLCA4, MS4A12, and GUCA2A. Analysis using qRT-PCR has shown that SST, CXCL8, and MS4A12 were significant differentially expressed between colorectal cancer tissues and normal colorectal tissues (P<0.05). Gene Expression Profiling Interactive Analysis (GEPIA) overall survival (OS) has shown that low expressions of AQP8, ZG16, CXCL3, and CXCL8 may predict poor survival outcome in colorectal cancer. In conclusion, the core genes identified in this study contributed to the understanding of the molecular mechanisms involved in colorectal cancer development and may be targets for early diagnosis, prevention, and treatment of colorectal cancer.

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Identification of the potential biomarkers in patients with glioma: a weighted gene co-expression network analysis

Carcinogenesis ◽

10.1093/carcin/bgz194 ◽

2019 ◽

Vol 41 (6) ◽

pp. 743-750 ◽

Cited By ~ 1

Author(s):

Ting-Yu Chen ◽

Yang Liu ◽

Liang Chen ◽

Jie Luo ◽

Chao Zhang ◽

...

Keyword(s):

Gene Expression ◽

Current Knowledge ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Module Preservation ◽

New Biomarkers

Abstract Glioma is the most common brain tumor with high mortality. However, there are still challenges for the timely and accurate diagnosis and effective treatment of the tumor. One hundred and twenty-one samples with grades II, III and IV from the Gene Expression Omnibus database were used to construct gene co-expression networks to identify hub modules closely related to glioma grade, and performed pathway enrichment analysis on genes from significant modules. In gene co-expression network constructed by 2345 differentially expressed genes from 121 gene expression profiles for glioma, we identified the black and blue modules that associated with grading. The module preservation analysis based on 118 samples indicates that the two modules were replicable. Enrichment analysis showed that the extracellular matrix genes were enriched for blue module, while cell division genes were enriched for black module. According to survival analysis, 21 hub genes were significantly up-regulated and one gene was significantly down-regulated. What’s more, IKBIP, SEC24D, and FAM46A are the genes with little attention among the 22 hub genes. In this study, IKBIP, SEC24D, and FAM46A related to glioma were mentioned for the first time to the current knowledge, which might provide a new idea for us to study the disease in the future. IKBIP, SEC24D and FAM46A among the 22 hub genes identified that are related to the malignancy degree of glioma might be used as new biomarkers to improve the diagnosis, treatment and prognosis of glioma.

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Identification of Significant Genes And Pathways In ARDS Via Bioinformatical Analysis

10.21203/rs.3.rs-457902/v1 ◽

2021 ◽

Author(s):

Weina Lu ◽

Ran Ji

Keyword(s):

Cellular Response ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Integrated Analysis ◽

Pathway Enrichment Analysis ◽

Signal Pathways ◽

Ppi Network ◽

Hub Genes

Abstract Background and Aims: Acute respiratory distress syndrome (ARDS) is one of the most common acute thoracopathy with complicated pathogenesis in ICU. The study is to explore the differentially expressed genes (DEGs) in the lung tissue and underlying altering mechanisms in ARDS.Methods: Gene expression profiles of GSE2411 and GSE130936 were available from GEO database, both of them included in GPL 339. Then, an integrated analysis of these genes was performed, including gene ontology (GO) and KEGG pathway enrichment analysis, protein-protein interaction (PPI) network construction, Transcription Factors (TFs) forecasting, and their expression in varied organs.Results: A total of 39 differential expressed genes were screened from the datasets, including 39 up-regulated genes and 0 down-regulated genes. The up-regulated genes were mainly enriched in the biological process, such as immune system process, innate immune response, inflammatory response, cellular response to interferon-beta and also involved in some signal pathways, including cytokine-cytokine receptor interaction, salmonella infection, legionellosis, chemokine, and Toll-like receptor signal pathway. GBP2, IFIT2 and IFIT3 were identified as hub genes in the lung by PPI network analysis with MCODE plug-in, as well as GO and KEGG re-enrichment. All of the three hub genes were regulated by the predictive common TFs, including STAT1, E2F1, IRF1, IRF2, and IRF9. Conclusions: This study implied that hub gene GBP2, IFIT2 and IFIT3, which might be regulated by STAT1, E2F1, IRF1, IRF2, or IRF9, played significant roles in ARDS. They could be potential diagnostic or therapeutic targets for ARDS patients.

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