Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases’ lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.

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Identification of Hub Genes in Anaplastic Thyroid Carcinoma: Evidence From Bioinformatics Analysis

Technology in Cancer Research & Treatment ◽

10.1177/1533033820962135 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096213

Author(s):

Liqi Li ◽

Mingjie Zhu ◽

Hu Huang ◽

Junqiang Wu ◽

Dong Meng

Keyword(s):

Thyroid Carcinoma ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Quantitative Polymerase Chain Reaction ◽

Interaction Network ◽

Anaplastic Thyroid Carcinoma ◽

Rank Aggregation ◽

Differentially Expressed ◽

Hub Genes ◽

Rare Type

Anaplastic thyroid carcinoma (ATC) is a rare type of thyroid cancer that results in fatal clinical outcomes; the pathogenesis of this life-threatening disease has yet to be fully elucidated. This study aims to identify the hub genes of ATC that may play key roles in ATC development and could serve as prognostic biomarkers or therapeutic targets. Two microarray datasets (GSE33630 and GSE53072) were obtained from the Gene Expression Omnibus database; these sets included 16 ATC and 49 normal thyroid samples. Differential expression analyses were performed for each dataset, and 420 genes were screened as common differentially expressed genes using the robust rank aggregation method. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to explore the potential bio-functions of these differentially expressed genes (DEGs). The terms and enriched pathways were primarily associated with cell cycle, cell adhesion, and cancer-related signaling pathways. Furthermore, a protein-protein interaction network of DEG expression products was constructed using Cytoscape. Based on the whole network, we identified 7 hub genes that included CDK1, TOP2A, CDC20, KIF11, CCNA2, NUSAP1, and KIF2C. The expression levels of these hub genes were validated using quantitative polymerase chain reaction analyses of clinical specimens. In conclusion, the present study identified several key genes that are involved in ATC development and provides novel insights into the understanding of the molecular mechanisms of ATC development.

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PSVI-36 Transcriptome analysis of porcine tonsils following PRRS virus infection

Journal of Animal Science ◽

10.1093/jas/skz258.417 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 202-203

Author(s):

Haesu Ko ◽

Won-Il Kim ◽

Han-Hwa Chae ◽

Won-Chul Park ◽

Jong-Eun Park ◽

...

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Post Infection ◽

Cellular Response ◽

Interleukin 1 ◽

Clinical Signs ◽

Negative Regulation ◽

Differentially Expressed ◽

Genome Replication ◽

Go Terms

Abstract The major concern for the pig industry is disease control and prevention, and porcine reproductive and respiratory syndrome (PRRS) is one of the main threats to it. Tonsil is a part of the immune system and its role is to recognize and reject foreign antigens to prevent them from invading the lungs. Interestingly, porcine tonsil can harbor PRRS viruses over 150 days post infection (dpi) with no clinical signs, causing PRRS re-break. This study was conducted to find out variation in levels of gene expression in tonsils and investigate functional roles of differentially expressed genes in tonsils depending on days post infection. We used porcine tonsils from weaned pigs following experimental infection with PRRSV-2 (JA142 strain) at 3, 10, 21, 28, 35 dpi. Based on RNA-seq analysis pipeline, differentially expressed genes (DEGs) were considered significant at False Discovery Rate (FDR) ≤ 0.05 level and above the two-fold change. Comparative analyses of 3 dpi vs. 10 dpi, 3 dpi vs. 21 dpi, 3 dpi vs. 28 dpi, and 3 dpi vs. 35 dpi produced 368, 315, 754, and 336 DEGs, respectively. Then we annotated the functions of each DEG set using DAVID tool. Significant GO terms of the MF, BP, and CC ontologies and KEGG pathways were selected within the limit of FDR < 0.05. Common overrepresented GO terms of all DEG sets were mainly negative regulation of viral genome replication, defense response to virus, negative regulation of type Ⅰ interferon production, and type Ⅰ interferon signaling pathway. Specific GO terms were found such as cellular response to interleukin-1, interferon-gamma-mediated signaling pathway, growth factor activity, and acute-phase response from each DEG set, respectively. It suggested tonsil responded to protect itself from PRRSV infection but further study is required to understand PRRSV persistence in tonsils.

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Network Analyses of Differentially Expressed Genes in Osteoarthritis to Identify Hub Genes

BioMed Research International ◽

10.1155/2019/8340573 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Zhaoyan Li ◽

Lei Zhong ◽

Zhenwu Du ◽

Gaoyang Chen ◽

Jing Shang ◽

...

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Receptor Interaction ◽

Hub Genes ◽

Expression Levels ◽

Network Analyses ◽

Synovial Membranes

Background. Osteoarthritis (OA) is the most common degenerative disease in orthopedics. However, the cause and underlying molecular mechanisms are not clear. This study aims to identify the hub genes and pathways involved in the occurrence of osteoarthritis. Methods. The raw data of GSE89408 were downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by R software. The DAVID database was used for pathway and gene ontology analysis, and p<0.05 and gene count >2 were set as the cut-off point. Moreover, protein-protein interaction (PPI) network construction was applied for exploring the hub genes in osteoarthritis. The expression levels of the top ten hub genes in knee osteoarthritis synovial membranes and controls were detected by quantitative real-time PCR system. Results. A total of 229 DEGs were identified in osteoarthritis synovial membranes compared with normal synovial membranes, including 145 upregulated and 84 downregulated differentially expressed genes. The KEGG pathway analysis results showed that up-DEGs were enriched in proteoglycans in cytokine-cytokine receptor interaction, chemokine signaling pathway, rheumatoid arthritis, and TNF signaling pathway, whereas down-DEGs were enriched in the PPAR signaling pathway and AMPK signaling pathway. The qRT-PCR results showed that the expression levels of ADIPOQ, IL6, and CXCR1 in the synovium of osteoarthritis were significantly increased (p <0.05).

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Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

10.21203/rs.3.rs-564869/v1 ◽

2021 ◽

Author(s):

Yanzhi Ge ◽

Zuxiang Chen ◽

Yanbin Fu ◽

Li Zhou ◽

Haipeng Xu ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Differentially Expressed Genes ◽

Large Scale ◽

Molecular Mechanisms ◽

Immune Cell ◽

Interaction Network ◽

Differentially Expressed ◽

Specific Gene ◽

Hub Genes

Abstract Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with partially common phenotypes and genotypes. This study aimed to determine the mechanistic similarities and differences between osteoarthritis and rheumatoid arthritis by analyzing the differentially expressed genes and signaling pathways. Microarray data of osteoarthritis and rheumatoid arthritis were obtained from the Gene Expression Omnibus. By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified in synovial membrane samples from patients and healthy donations. Then, the Gene ontology significant functions annotation, Kyoto Encyclopedia of Genes and Genomes pathways and protein-protein interaction network analysis were conducted. Moreover, CIBERSORT was used to further distinguish OA and RA in immune infiltration. Finally, animal experimentation was conducted and the establishment of model, which was verified using PCR in the mouse. As an overlapping process, we identified 1116 DEGs between OA and RA. It was indicated that specific gene signatures differed significantly between OA and RA connected with the distinct pathways. Of identified DEGs, 9 immune cell types among 22 were identified to distinguish from each other. The qRT-PCR result showed that the eight-tenths expression levels of the hub genes were significantly increased in OA samples (P < 0.05). This large-scale gene expression study provided new insights for disease-associated genes and molecular mechanisms as well as their associated function in osteoarthritis and rheumatoid arthritis, which simultaneously offer a new direction for biomarker development and the distinguishment of gene-level mechanisms between osteoarthritis and rheumatoid arthritis.

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Elucidating the Key Biomarkers and Immune Infiltration in Acne by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-209484/v1 ◽

2021 ◽

Author(s):

Lu Yang ◽

Yan-hong Shou ◽

Yong-sheng Yang ◽

Jin-hua Xu

Keyword(s):

Mast Cells ◽

Signaling Pathway ◽

Differentially Expressed Genes ◽

Acne Vulgaris ◽

Interaction Network ◽

Microarray Dataset ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Immune Infiltration

Abstract Background Acne vulgaris is a common inflammatory condition of skin. However, the landscape of immune infiltration in acne has not been entirely described. Objectives This study used a bioinformatics approach to investigate the inflammatory acne-related key biomarkers and signaling pathways, and immune infiltration in the acne lesion. Methods Two microarray datasets (GSE108110 and GSE53795) were downloaded from Gene Expression Omnibus. We used “limma” package from R software to identify the differentially expressed genes (DEGs) and perform the functional enrichment analyses. Then we built a protein-protein interaction network (PPI), performed the hub genes’ identification through STRING and Cytoscape. We applied the CIBERSORT algorithm to describe the immune infiltration in acne, and explored the correlation between biomarkers and immune infiltration. In the end, our findings in the study were verified by analyzing microarray dataset GSE6475. Results The differentially expressed genes (DEGs) including 292 upregulated genes and 150 downregulated genes in acne compared with non-lesional skin. The hub genes FPR1, C3AR1, CXCL1, CXCL8, FPR2, C3, CCR7, ITGB2 and pivotal pathways JAK-STAT signaling pathway, Toll-like receptor and NOD-like receptor signaling pathway were the most significantly associated with raising neutrophils, monocytes, activated mast cells, as well as reducing resting mast cells and Tregs. Conclusions Our study provides new insights into the pathogenesis and the targets which might be immunomodulatory potential for acne.

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Bioinformatics analysis of differentially expressed genes and identification of an miRNA–mRNA network associated with entorhinal cortex and hippocampus in Alzheimer’s disease

Hereditas ◽

10.1186/s41065-021-00190-0 ◽

2021 ◽

Vol 158 (1) ◽

Author(s):

Haoming Li ◽

Linqing Zou ◽

Jinhong Shi ◽

Xiao Han

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Differentially Expressed Genes ◽

Entorhinal Cortex ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Neurodegenerative Disorder ◽

Early Stage ◽

Differentially Expressed ◽

Hub Genes

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

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A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

10.21203/rs.3.rs-19035/v1 ◽

2020 ◽

Author(s):

Yanjie Han ◽

Xinxin Li ◽

Jiliang Yan ◽

Chunyan Ma ◽

Xin Wang ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Growth Factor Receptor ◽

Principal Component ◽

Distant Metastases ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Hub Genes ◽

Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

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A study on regional differences in decidualization of the mouse uterus

Reproduction ◽

10.1530/rep-16-0486 ◽

2017 ◽

Vol 153 (5) ◽

pp. 645-653 ◽

Cited By ~ 3

Author(s):

Miao Zhao ◽

Wen-Qian Zhang ◽

Ji-Long Liu

Keyword(s):

Differentially Expressed Genes ◽

Regional Differences ◽

Molecular Mechanisms ◽

Cell Metabolism ◽

Gene Expression Pattern ◽

Differentially Expressed ◽

Hub Genes ◽

Promoter Regions ◽

Mouse Uterus ◽

Region Gene

Although regional differences in mouse decidualization have been recognized for decades, the molecular mechanisms remain understudied. In the present study, by using RNA-seq, we compared transcriptomic differences between the anti-mesometrial (AM) region and the mesometrial (M) region of mouse uterus on day 8 of pregnancy. A total of 1423 differentially expressed genes were identified, of which 811 genes were upregulated and 612 genes were downregulated in the AM region compared to those in the M region. Gene ontology analysis showed that upregulated genes were generally involved in cell metabolism and differentiation, whereas downregulated genes were associated with lymphocyte themes and immune response. Through network analysis, we identified a total of 6 hub genes. These hub genes are likely more important than other genes due to their key positions in the network. We also examined the promoter regions of differentially expressed genes for the enrichment of transcription factor-binding sites. In the end, we demonstrated that a similar regional gene expression pattern can be observed in the artificial decidualization model. Our study contributes to an increase in the knowledge on the molecular mechanisms underlying regional decidualization in mice.

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Multiple-Tissue and Multilevel Analysis on Differentially Expressed Genes and Differentially Correlated Gene Pairs for HFpEF

Frontiers in Genetics ◽

10.3389/fgene.2021.668702 ◽

2021 ◽

Vol 12 ◽

Author(s):

Guofeng Zhou ◽

Shaoyan Sun ◽

Qiuyue Yuan ◽

Run Zhang ◽

Ping Jiang ◽

...

Keyword(s):

Adipose Tissue ◽

Differentially Expressed Genes ◽

Complex Disease ◽

Cerebral Arteries ◽

Interaction Network ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Tissue Specific ◽

Gene Pairs

Heart failure with preserved ejection fraction (HFpEF) is a complex disease characterized by dysfunctions in the heart, adipose tissue, and cerebral arteries. The elucidation of the interactions between these three tissues in HFpEF will improve our understanding of the mechanism of HFpEF. In this study, we propose a multilevel comparative framework based on differentially expressed genes (DEGs) and differentially correlated gene pairs (DCGs) to investigate the shared and unique pathological features among the three tissues in HFpEF. At the network level, functional enrichment analysis revealed that the networks of the heart, adipose tissue, and cerebral arteries were enriched in the cell cycle and immune response. The networks of the heart and adipose tissues were enriched in hemostasis, G-protein coupled receptor (GPCR) ligand, and cancer-related pathway. The heart-specific networks were enriched in the inflammatory response and cardiac hypertrophy, while the adipose-tissue-specific networks were enriched in the response to peptides and regulation of cell adhesion. The cerebral-artery-specific networks were enriched in gene expression (transcription). At the module and gene levels, 5 housekeeping DEGs, 2 housekeeping DCGs, 6 modules of merged protein–protein interaction network, 5 tissue-specific hub genes, and 20 shared hub genes were identified through comparative analysis of tissue pairs. Furthermore, the therapeutic drugs for HFpEF-targeting these genes were examined using molecular docking. The combination of multitissue and multilevel comparative frameworks is a potential strategy for the discovery of effective therapy and personalized medicine for HFpEF.

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PO-046 Gene expression profiling of peripheral blood mononuclear cells in young male trampoline athletes

Exercise Biochemistry Review ◽

10.14428/ebr.v1i3.10113 ◽

2018 ◽

Vol 1 (3) ◽

Author(s):

Li Gao ◽

Yong Jie Yang ◽

En Qi Li ◽

Jia Ning Mao

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Bone Health ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Interaction Network ◽

Differentially Expressed ◽

Signal Pathways ◽

Healthy Controls ◽

Mineral Density

Objective Evidence indicates that physical activity influence bone health. However, the molecular mechanisms mediating the beneficial adaptations to exercise are not well understood. The purpose of this study was to examine the differentially expressed genes in PBMC between athletes and healthy controls, and to analyze the important functional genes and signal pathways that cause increased bone mineral density in athletes, in order to further reveal the molecular mechanisms of exercise promoting bone health. Methods Five professional trampoline athletes and five age-matched untrained college students participated in this study. Used the human expression Microarray V4.0 expression profiling chip to detect differentially expressed genes in the two groups, and performed KEGG Pathway analysis and application of STRING database to construct protein interaction Network; Real-Time PCR technology was used to verify the expression of some differential genes. Results Compared with healthy controls, there were significant improvement in lumbar spine bone mineral density, and 236 up-regulated as well as 265 down-regulated in serum samples of athletes. The differentially expressed genes involved 28 signal pathways, such as cell adhesion molecules. Protein interaction network showed that MYC was at the core node position. Real-time PCR results showed that the expression levels of CD40 and ITGα6 genes in the athletes were up-regulated compared with the healthy controls, the detection results were consistent with that of the gene chip. Conclusions The findings highlight that long-term high-intensity trampoline training could induce transcriptional changes in PBMC of the athletes. These data suggest that gene expression fingerprints can serve as a powerful research tool to design novel strategies for monitoring exercise. The findings of the study also provide support for the notion that PBMC could be used as a substitute to study exercise training that affects bone health.

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