FYN and CD247: key Genes for Septic Shock Based on Bioinformatics and Meta-Analysis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Jiang ◽  
Qian Miao ◽  
Lin Hu ◽  
Tingyan Zhou ◽  
Yingchun Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Survival Analysis ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Key Genes ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background: Septic shock is sepsis accompanied by hemodynamic instability and high clinical mortality. Material and Methods: GSE95233, GSE57065, GSE131761 gene-expression profiles of healthy control subjects and septic shock patients were downloaded from the Gene-Expression Omnibus (GEO) database, and differences of expression profiles and their intersection were analysed using GEO2R. Function and pathway enrichment analysis was performed on common differentially expressed genes (DEG), and key genes for septic shock were screened using a protein-protein interaction network created with STRING. Also, data from the GEO database were used for survival analysis for key genes, and a meta-analysis was used to explore expression trends of core genes. Finally, high-throughput sequencing using the blood of a murine sepsis model was performed to analyse the expression of CD247 and FYN in mice. Results: A total of 539 DEGs were obtained (p < 0.05). Gene ontology analysis showed that key genes were enriched in functions, such as immune response and T cell activity, and DEGs were enriched in signal pathways, such as T cell receptors. FYN and CD247 are in the centre of the protein-protein interaction network, and survival analysis found that they are positively correlated with survival from sepsis. Further, meta-analysis results showed that FYN could be useful for the prognosis of patients, and CD247 might distinguish between sepsis and systemic inflammatory response syndrome patients. Finally, RNA sequencing using a mouse septic shock model showed low expression of CD247 and FYN in this model. Conclusion: FYN and CD247 are expected to become new biomarkers of septic shock.

scholarly journals Supervised machine learning models and protein-protein interaction network analysis of gene expression profiles induced by omega-3 polyunsaturated fatty acids

2022 ◽  
Vol 02 ◽  
Author(s):  
Sergey Shityakov ◽  
Jane Pei-Chen Chang ◽  
Ching-Fang Sun ◽  
David Ta-Wei Guu ◽  
Thomas Dandekar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Supervised Machine Learning ◽  
Omega 3 ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Background: Omega-3 polyunsaturated fatty acids (PUFAs), such as eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids, have beneficial effects on human health, but their effect on gene expression in elderly individuals (age ≥ 65) is largely unknown. In order to examine this, the gene expression profiles were analyzed in the healthy subjects (n = 96) at baseline and after 26 weeks of supplementation with EPA+DHA to determine up-regulated and down-regulated dif-ferentially expressed genes (DEGs) triggered by PUFAs. The protein-protein interaction (PPI) networks were constructed by mapping these DEGs to a human interactome and linking them to the specific pathways. Objective: This study aimed to implement supervised machine learning models and protein-protein interaction network analysis of gene expression profiles induced by PUFAs. Methods: The transcriptional profile of GSE12375 was obtained from the Gene Expression Om-nibus database, which is based on the Affymetrix NuGO array. The probe cell intensity data were converted into the gene expression values, and the background correction was performed by the multi-array average algorithm. The LIMMA (Linear Models for Microarray Data) algo-rithm was implemented to identify relevant DEGs at baseline and after 26 weeks of supplemen-tation with a p-value < 0.05. The DAVID web server was used to identify and construct the en-riched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Finally, the construction of machine learning (ML) models, including logistic regression, naïve Bayes, and deep neural networks, were implemented for the analyzed DEGs associated with the specific pathways. Results: The results revealed that up-regulated DEGs were associated with neurotrophin/MAPK signaling, whereas the down-regulated DEGs were linked to cancer, acute myeloid leukemia, and long-term depression pathways. Additionally, ML approaches were able to cluster the EPA/DHA-treated and control groups by the logistic regression performing the best. Conclusion: Overall, this study highlights the pivotal changes in DEGs induced by PUFAs and provides the rationale for the implementation of ML algorithms as predictive models for this type of biomedical data.

scholarly journals FRI0584 SCREENING AND IDENTIFICATION OF BIOMARKERS IN MYOSITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 896-897
Author(s):  
W. Liu ◽  
X. Zhang
Keyword(s):  
Gene Expression ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background:Myositis, including dermatomyositis and polymyositis, is autoimmune disorders that is characterized by muscle degeneration in the proximal extremities, with the complications of weakness of muscles, interstitial lung disease and vascular lesions, even leading to death in an acute progressive process[1,2]. However, the molecular mechanisms of myositis are rarely understood.Objectives:Identify the candidate genes in myositis.Methods:Microarray datasets GSE128470, GSE48280 and GSE39454 were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and function enrichment analyses were conducted. The protein-protein interaction network and the analyses of hub genes were performed with STRING and Cytoscape.Results:There were 98 DEGs, of which the function and pathways enrichment analyses showed defense response, immune response, response to virus, inflammatory response, response to wounding, cell adhesion, cell proliferation, cell death and macromolecule metabolic process. 20 hub genes were identified, of which 7 including IRF9 TRIM22 MX2 IFITM1 IFI6 IFI44 IFI44L had not been reported in the literature, related to the response to virus, immune response, transcription from RNA polymerase II promoter, cell apoptosis, cell death. The verification analysis about the 7 genes in GSE128314 showed significant differences in myositis.Conclusion:In conclusion, DEGs and hub genes identified in our study showed the potential molecular mechanisms in myositis, providing the helpful targets for diagnosis and clinical strategy of myositis.References:[1] Wu H, Geng D, Xu J. An approach to the development of interstitial lung disease in dermatomyositis: a study of 230 cases in China[J]. Journal of International Medical Research. 2013;41(2):493–501.[2] Fathi M, Dastmalchi M, Rasmussen E, Lundberg IE, Tornling G. Interstitial lung disease, a common manifestation of newly diagnosed polymyositis and dermatomyositis[J]. Annals of the Rheumatic Diseases. 2004;63(3):297–301.Figure 1.The protein-protein interaction network of 20 hub genesFigure 2.7 genes in GSE128314 showed significant differences in myositisAcknowledgments:The authors acknowledge the efforts of the Gene Expression Omnibus (GEO) database. The interpretation and reporting of these data are the sole responsibility of the authors.Disclosure of Interests:None declared

scholarly journals Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhengqing Zhu ◽  
Lei Zhong ◽  
Ronghang Li ◽  
Yuzhe Liu ◽  
Xiangrun Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Protein Protein Interaction ◽  
Polymerase Chain ◽  
Upregulated Genes ◽  
Protein Protein Interaction Network

Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold‐change∣>1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA.

scholarly journals Construction and investigation of a combined hypoxia and stemness index lncRNA-associated ceRNA regulatory network in lung adenocarcinoma

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Lili Guo ◽  
Hongxia Li ◽  
Weiying Li ◽  
Junfang Tang
Keyword(s):  
Network Analysis ◽  
Tumor Cell ◽  
Lung Adenocarcinoma ◽  
Regulatory Network ◽  
Expression Profiles ◽  
Interaction Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Key Genes ◽  
Protein Protein Interaction Network

Abstract Hypoxia and stemness are important factors in tumor progression. We aimed to explore the ncRNA classifier associated with hypoxia and stemness in lung adenocarcinoma (LUAD). We found that the prognosis of LUAD patients with high hypoxia and stemness index was worse than that of patients with low hypoxia and stemness index. RNA expression profiles of these two clusters were analyzed, and 6867 differentially expressed (DE) mRNAs were screened. Functional analysis showed that DE mRNAs were associated with cell cycle and DNA replication. Protein–protein interaction network analysis revealed 20 hub genes, among which CENPF, BUB1, BUB1B, KIF23 and TTK had significant influence on prognosis. In addition, 807 DE lncRNAs and 243 DE miRNAs were identified. CeRNA network analysis indicated that AC079160.1-miR-539-5p-CENPF may be an important regulatory axis that potentially regulates the progression of LUAD. The expression of AC079160.1 and CENPF were positively correlated with hypoxia and stemness index, while miR-539-5p expression level was negatively correlated with hypoxia and stemness index. Overall, we identified CENPF, BUB1, BUB1B, KIF23 and TTK as potentially key genes involved in regulating hypoxia-induced tumor cell stemness, and found that AC079160.1-miR-539-5p-CENPF axis may be involved in regulating hypoxia induced tumor cell stemness in LUAD.

scholarly journals Exploring Heat-Response Mechanisms of MicroRNAs Based on Microarray Data of Rice Post-meiosis Panicle

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yan Peng ◽  
Xianwen Zhang ◽  
Yuewu Liu ◽  
Xinbo Chen
Keyword(s):  
Heat Stress ◽  
Target Genes ◽  
Expression Profiles ◽  
Time Lag ◽  
Interaction Network ◽  
Biological Processes ◽  
Functional Identification ◽  
Protein Protein Interaction ◽  
Heat Response ◽  
Protein Protein Interaction Network

To explore heat response mechanisms of mircoRNAs (miRNAs) in rice post-meiosis panicle, microarray analysis was performed on RNA isolated from rice post-meiosis panicles which were treated at 40°C for 0 min, 10 min, 20 min, 60 min, and 2 h. By integrating paired differentially expressed (DE) miRNAs and mRNA expression profiles, we found that the expression levels of 29 DE-miRNA families were negatively correlated to their 178 DE-target genes. Further analysis showed that the majority of miRNAs in 29 DE-miRNA families resisted the heat stress by downregulating their target genes and a time lag existed between expression of miRNAs and their target genes. Then, GO-Slim classification and functional identification of these 178 target genes showed that (1) miRNAs were mainly involved in a series of basic biological processes even under heat conditions; (2) some miRNAs might play important roles in the heat resistance (such as osa-miR164, osa-miR166, osa-miR169, osa-miR319, osa-miR390, osa-miR395, and osa-miR399); (3) osa-miR172 might play important roles in protecting the rice panicle under the heat stress, but osa-miR437, osa-miR418, osa-miR164, miR156, and miR529 might negatively affect rice fertility and panicle flower; and (4) osa-miR414 might inhibit the flowering gene expression by downregulation of LOC_Os 05g51830 to delay the heading of rice. Finally, a heat-induced miRNA-PPI (protein-protein interaction) network was constructed, and three miRNA coregulatory modules were discovered.

Uncovering potential lncRNAs and nearby mRNAs in systemic lupus erythematosus from the Gene Expression Omnibus dataset

Epigenomics ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 1795-1809 ◽  
Author(s):  
Haiyu Cao ◽  
Dong Li ◽  
Huixiu Lu ◽  
Jing Sun ◽  
Haibin Li
Keyword(s):  
Gene Expression ◽  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Noncoding Rnas ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Systemic Lupus ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Aim: The aim of this study was to find potential differentially expressed long noncoding RNAs (lncRNAs) and mRNAs in systemic lupus erythematosus. Materials & methods: Differentially expressed lncRNAs and mRNAs were obtained in the Gene Expression Omnibus dataset. Functional annotation of differentially expressed mRNAs was performed, followed by protein–protein interaction network analysis. Then, the interaction network of lncRNA-nearby targeted mRNA was built. Results: Several interaction pairs of lncRNA-nearby targeted mRNA including NRIR-RSAD2, RP11-153M7.5-TLR2, RP4-758J18.2-CCNL2, RP11-69E11.4-PABPC4 and RP11-496I9.1-IRF7/ HRAS/ PHRF1 were identified. Measles and MAPK were significantly enriched signaling pathways of differentially expressed mRNAs. Conclusion: Our study identified several differentially expressed lncRNAs and mRNAs. And their interactions may play a crucial role in the process of systemic lupus erythematosus.

scholarly journals Identification of differentially expressed genes in hepatocellular carcinoma by integrated bioinformatic analysis

2019 ◽  
Author(s):  
Guangxin Yan ◽  
Zhaoyu Liu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Dna Replication ◽  
Liver Cancer ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

AbstractHepatocellular carcinoma is one of the most common tumors in the world and has a high mortality rate. This study elucidates the mechanism of hepatocellular carcinoma- (HCC) related development. The HCC gene expression profile (GSE54238, GSE84004) was downloaded from Gene Expression Omnibus for comprehensive analysis. A total of 359 genes were identified, of which 195 were upregulated and 164 were downregulated. Analysis of the condensed results showed that “extracellular allotrope” is a substantially enriched term. “Cell cycle”, “metabolic pathway” and “DNA replication” are three significantly enriched Kyoto Encyclopedia of Genes and Genomespathways. Subsequently, a protein-protein interaction network was constructed. The most important module in the protein-protein interaction network was selected for path enrichment analysis. The results showed thatCCNA2, PLK1, CDC20, UBE2CandAURKAwere identified as central genes, and the expression of these five hub genes in liver cancer was significantly increased in The Cancer Genome Atlas. Univariate regression analysis was also performed to show that the overall survival and disease-free survival of patients in the high expression group were longer than in the expression group. In addition, genes in important modules are mainly involved in “cell cycle”, “DNA replication” and “oocyte meiosis” signaling pathways. Finally, through upstream miRNA analysis, mir-300 and mir-381-3p were found to coregulateCCNA2,AURKAandUBE2C. These results provide a set of targets that can help researchers to further elucidate the underlying mechanism of liver cancer.

scholarly journals Similarity of regulatory network between leukemia stem cells and normal hemopoietic stem cells

2018 ◽  
Vol 6 (4) ◽  
pp. 129-140
Author(s):  
Zhi-Jian Li ◽  
Xing-Ling Sui ◽  
Xue-Bo Yang ◽  
Wen Sun
Keyword(s):  
Stem Cells ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Impact Factors ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

AbstractTo reveal the biology of AML, we compared gene-expression profiles between normal hematopoietic cells from 38 healthy donors and leukemic blasts (LBs) from 26 AML patients. We defined the comparison of LB and unselected BM as experiment 1, LB and CD34+ isolated from BM as experiment 2, LB and unselected PB as experiment 3, and LB and CD34+ isolated from PB as experiment 4. Then, protein–protein interaction network of DEGs was constructed to identify critical genes. Regulatory impact factors were used to identify critical transcription factors from the differential co-expression network constructed via reanalyzing the microarray profile from the perspective of differential co-expression. Gene ontology enrichment was performed to extract biological meaning. The comparison among the number of DEGs obtained in four experiments showed that cells did not tend to differentiation and CD34+ was more similar to cancer stem cells. Based on the results of protein–protein interaction network,CREBBP,F2RL1,MCM2, andTP53were respectively the key genes in experiments 1, 2, 3, and 4. From gene ontology analysis, we found that immune response was the most common one in four stages. Our results might provide a platform for determining the pathology and therapy of AML.

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