scholarly journals Identification of potential key genes for SARS-CoV-2 infected human bronchial organoids based on bioinformatics analysis

2020 ◽  
Author(s):  
Hanming Gu ◽  
Gongsheng Yuan
Keyword(s):  
Signaling Pathways ◽  
Bioinformatics Analysis ◽  
Homo Sapiens ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Receptor Interaction ◽  
Functional Categories ◽  
P53 Signaling ◽  
Key Genes ◽  
P53 Signaling Pathway

AbstractThere is an urgent need to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) that leads to COVID-19 and respiratory failure. Our study is to discover differentially expressed genes (DEGs) and biological signaling pathways by using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profiles of the GSE150819 datasets were originally produced using an Illumina NextSeq 500 (Homo sapiens). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) were utilized to identify functional categories and significant pathways. KEGG and GO results suggested that the Cytokine-cytokine receptor interaction, P53 signaling pathway, and Apoptosis are the main signaling pathways in SARS-CoV-2 infected human bronchial organoids (hBOs). Furthermore, NFKBIA, C3, and CCL20 may be key genes in SARS-CoV-2 infected hBOs. Therefore, our study provides further insights into the therapy of COVID-19.

scholarly journals Identification of key genes in SARS-CoV-2 patients on bioinformatics analysis

2020 ◽  
Author(s):  
Hanming Gu ◽  
Gongsheng Yuan
Keyword(s):  
Acid Metabolism ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Biological Pathways ◽  
Biological Processes ◽  
Functional Categories ◽  
Biochemical Pathways ◽  
Key Genes ◽  
Underlying Pathogenesis

AbstractThe COVID-19 pandemic has infected millions of people and overwhelmed many health systems globally. Our study is to identify differentially expressed genes (DEGs) and associated biological processes of COVID-19 using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profiles of the GSE152075 datasets were originally produced by using the high-throughput Illumina NextSeq 500. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed to identify functional categories and biochemical pathways. GO and KEGG results suggested that several biological pathways such as “Fatty acid metabolism” and “Cilium morphogenesis” are mostly involved in the development of COVID-19. Moreover, several genes are critical for virus invasion and adhesion including FLOC, DYNLL1, FBXL3, and FBXW11 and show significant differences in COVID-19 patients. Thus, our study provides further insights into the underlying pathogenesis of COVID-19.

scholarly journals Identification of key genes and pathways in the hPSC-derived lungs infected by the SARS-CoV-2

2020 ◽  
Author(s):  
Hanming Gu ◽  
Gongsheng Yuan
Keyword(s):  
Gene Expression ◽  
Severe Acute Respiratory Syndrome ◽  
Signaling Pathways ◽  
Gene Expression Profile ◽  
Homo Sapiens ◽  
Brain Disorders ◽  
Functional Categories ◽  
Mitochondrial Dysfunctions ◽  
The World ◽  
Key Genes

Abstract Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has led to numerous infections and deaths in the world. Our research is to explore the differentially expressed genes (DEGs) and signaling pathways in hPSC-derived lungs by using a bioinformatics method to clarify their potential pathogenesis. The gene expression profile of GSE155241 dataset was originally created by using an Illumina NovaSeq 6000 (Homo sapiens) platform. Functional categories and significant pathways were identified by the KEGG and GO analysis. The results suggested that brain disorders and mitochondrial dysfunctions are the main signaling pathways affected by the SARS-CoV-2 infection. Furthermore, key genes e.g. CDC20, NCBP1 and inhibitors e.g. MEK1-2-inhibitor, tivozanib may paly critical roles in COVID-19. Therefore, our study provides insights into the treatment of COVID-19 and related disorders.

scholarly journals Analysis of Transcription Factor-Related Regulatory Networks Based on Bioinformatics Analysis and Validation in Hepatocellular Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Shui Liu ◽  
Xiaoxiao Yao ◽  
Dan Zhang ◽  
Jiyao Sheng ◽  
Xin Wen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Signaling Pathway ◽  
Gene Network ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Mrna Levels ◽  
Qrt Pcr ◽  
P53 Signaling ◽  
Tf Gene ◽  
P53 Signaling Pathway

Hepatocellular carcinoma (HCC) accounts for a significant proportion of liver cancer, which has become the second most common cause of cancer-related mortality worldwide. To investigate the potential mechanisms of invasion and progression of HCC, bioinformatics analysis and validation by qRT-PCR were performed. We found 237 differentially expressed genes (DEGs) including EGR1, FOS, and FOSB, which were three cancer-related transcription factors. Subsequently, we constructed TF-gene network and miRNA-TF-mRNA network based on data obtained from mRNA and miRNA expression profiles for analysis of HCC. We found that 42 key genes from the TF-gene network including EGR1, FOS, and FOSB were most enriched in the p53 signaling pathway. The qRT-PCR data confirmed that mRNA levels of EGR1, FOS, and FOSB all were decreased in HCC tissues. In addition, we confirmed that the mRNA levels of CCNB1, CCNB2, and CHEK1, three key markers of the p53 signaling pathway, were all increased in HCC tissues by bioinformatics analysis and qRT-PCR validation. Therefore, we speculated that miR-181a-5p, which was upregulated in HCC tissues, could regulate FOS and EGR1 to promote the invasion and progression of HCC by p53 signaling pathway. Overall, the study provides support for the possible mechanisms of progression in HCC.

scholarly journals Bioinformatics analysis and experimental validation of TTK as a biomarker for prognosis in non-small cell lung cancer

2020 ◽  
Vol 40 (10) ◽  
Author(s):  
Jiajia Chen ◽  
Rong Wu ◽  
Ying Xuan ◽  
Min Jiang ◽  
Yuecan Zeng
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cell Cycle ◽  
Small Cell Lung Cancer ◽  
Bioinformatics Analysis ◽  
Small Cell ◽  
Small Cell Lung ◽  
P53 Signaling ◽  
Key Genes ◽  
P53 Signaling Pathway

Abstract Background: Despite the prominent development of medical technology in recent years, the prognosis of non-small cell lung cancer (NSCLC) is still not optimistic. It is crucial to identify more reliable diagnostic biomarkers for the early diagnosis and personalized therapy of NSCLC and clarify the molecular mechanisms underlying NSCLC progression. Methods: In the present study, bioinformatics analysis was performed on three datasets obtained from the Gene Expression Omnibus to identify the NSCLC-associated differentially expressed genes (DEGs). Immunohistochemistry-based tissue microarray of human NSCLC was used to experimental validating the potential targets obtained from bioinformatics analysis. Results: By using protein–protein interaction (PPI) network analysis, Kaplan–Meier plotter, and Gene Expression Profiling Interactive Analysis, we selected 40 core DEGs for further study. Then, a re-analysis of 40 selected genes via Kyoto Encyclopedia of Genes and Genomes pathway enrichment showed that nine key genes involved in the cell cycle and p53 signaling pathway participated in the development of NSCLC. Then, we checked the protein level of nine key genes by semi-quantitative of IHC and checked the distribution at a single-cell level. Finally, we validated dual-specificity protein kinase TTK as a biomarker for prognosis in a tissue microarray. High TTK expression associated with a higher histological stage, advanced TNM stage, high frequency of positive lymph nodes, and worse 5-year overall survival. Conclusions: We found nine key genes were enriched in the cell cycle and p53 signaling pathway. TTK could be considered as a potential therapeutic target and for the prognosis biomarker of NSCLC. These findings will provide new insights for the development of individualized therapeutic targets for NSCLC.

scholarly journals Identifications of Candidate Genes Significantly Associated With Rectal Cancer by Integrated Bioinformatics Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382097327
Author(s):  
Zhili Xu ◽  
Yan Li ◽  
Yiyi Cui ◽  
Yong Guo
Keyword(s):  
Gene Expression ◽  
Rectal Cancer ◽  
Candidate Genes ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Receptor Interaction ◽  
Cancer Tissue ◽  
Cancer Tissues ◽  
Core Genes

Introduction: Rectal cancer ranks as the eighth in cancer-related morbidity and the tenth in the cancer-related mortality. A few studies have explored several biomarkers for colorectal cancer. However, there is still a great need for us to excavate novel biomarkers with effective and efficient diagnostic and prognostic values to discover the etiology and pathogenesis of rectal cancer separately. Therefore, we aimed to identify more novel candidate genes that were significantly associated with rectal cancer through integrated bioinformatics analysis. Methods: We analyzed the gene expression profiles of GSE15781 and GSE20842 from Gene Expression Omnibus database to identify differentially expressed genes between normal rectal tissue and rectal cancer tissue. Results: We searched for core genes, carried out survival analysis and analyzed the expressions of core genes. We found that 142 genes were significantly upregulated, and 229 genes were significantly downregulated in all 3 independent studies. In KEGG analysis, the upregulated genes were significantly enriched in cytokine-cytokine receptor interaction, IL-17 signaling pathway, cell cycle, etc. The downregulated genes were primarily enriched in nitrogen metabolism, mineral absorption and pentose and glucuronate interconversions. Inhibin subunit beta B (INHBB) expressed markedly higher in rectal cancer tissues compared with normal tissues, and claudins (CLDN) 23 expressed significantly lower in rectal cancer tissues. Conclusion: In conclusion, we discovered that INHBB could provide a great significant diagnostic and prognostic values for rectal cancer.

scholarly journals AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1037.2-1038
Author(s):  
X. Sun ◽  
S. X. Zhang ◽  
S. Song ◽  
T. Kong ◽  
C. Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Shanxi Province ◽  
Receptor Interaction ◽  
Term Therapy ◽  
Pathway Enrichment Analysis ◽  
Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

Genomic Fabric Remodeling in Prostate Cancer

2021 ◽  
Author(s):  
Sanda Iacobas ◽  
Dumitru A. Iacobas
Keyword(s):  
Prostate Cancer ◽  
Normal Tissue ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mathematical Object ◽  
P53 Signaling ◽  
Large Populations ◽  
Genomic Characterization ◽  
Meta Analyses

Prostate cancer is a leading cause of death among men but its genomic characterization and best therapeutic strategy are still under debate. The Genomic Fabric Paradigm (GFP) considers the transcriptome as a multi-dimensional mathematical object subjected to a dynamic set of expression correlations among the genes. Here, GFP is applied to gene expression profiles of three (one primary, and two secondary) cancer nodules and the surrounding normal tissue from a surgically removed prostate tumor. GFP was used to determine the regulation and rewiring of the P53 signaling, apoptosis, prostate cancer and several other pathways involved in survival and proliferation of the cancer cells. Genes responsible for the block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy and sustained angiogenesis were found as differently regulated in the three cancer nodules with respect to the normal tissue. The analysis indicates that even histo-pathologically equally graded cancer nodules from the same tumor have substantially different transcriptomic organizations, raising legitimate questions about the validity of meta-analyses comparing large populations of healthy and cancer humans. The study suggests that GFP may provide a personalized alternative to the biomarkers’ approach of cancer genomics.

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