scholarly journals Identification of MicroRNA as Sepsis Biomarker Based on miRNAs Regulatory Network Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jie Huang ◽  
Zhandong Sun ◽  
Wenying Yan ◽  
Yujie Zhu ◽  
Yuxin Lin ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Expression Profiles ◽  
Delayed Diagnosis ◽  
Interaction Network ◽  
High Sensitivity ◽  
Regulate Gene Expression ◽  
Protein Protein Interaction ◽  
Small Noncoding Rnas ◽  
Mirna Expression Profiles ◽  
Pathways Analysis

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers.

scholarly journals Three novel circRNAs upregulated in tissue and plasma from hepatocellular carcinoma patients and their regulatory network

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lianghai Wang ◽  
Lisha Zhou ◽  
Jun Hou ◽  
Jin Meng ◽  
Ke Lin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regulatory Network ◽  
Expression Profiles ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Circular Rnas ◽  
Pathway Enrichment Analysis ◽  
Plasma Samples ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background The regulatory roles of circular RNAs (circRNAs) in tumorigenesis have attracted increasing attention. However, novel circRNAs with the potential to be used as serum/plasma biomarkers and their regulatory mechanism in the pathogenesis of hepatocellular carcinoma (HCC) remain explored. Methods CircRNA expression profiles of tumor tissues and plasma samples from HCC patients were compiled and jointly analyzed. CircRNA–miRNA–mRNA interactions were predicted by bioinformatics tools. The expression of interacting miRNAs and mRNA was verified in independent datasets. Survival analysis and pathway enrichment analysis were conducted on hub genes. Results We identified three significantly up-regulated circRNAs (hsa_circ_0009910, hsa_circ_0049783, and hsa_circ_0089172) both in HCC tissues and plasma samples. Two of them were validated to be indeed circular and could be excreted from hepatoma cells. We further revealed four miRNAs (hsa-miR-455-5p, hsa-miR-615-3p, hsa-miR-18a-3p, hsa-miR-4524a-3p) that targeting circRNAs and expressed in human HCC samples, and 95 mRNAs targeted by miRNAs and significantly up-regulated in two HCC cohorts. A protein-protein interaction network revealed 19 hub genes, 12 of them (MCM6, CCNB1, CDC20, NDC80, ZWINT, ASPM, CENPU, MCM3, MCM5, ECT2, CDC7, and DLGAP5) were associated with reduced survival in two HCC cohorts. KEGG, Reactome, and Wikipathway enrichment analysis indicated that the hub genes mainly functioned in DNA replication and cell cycle. Conclusions Our study uncovers three novel deregulated circRNAs in tumor and plasma from HCC patients and provides an insight into the pathogenesis from the circRNA–miRNA–mRNA regulatory network.

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.

scholarly journals Control Analysis of Protein-Protein Interaction Network Reveals Potential Regulatory Targets for MYCN

2021 ◽  
Vol 11 ◽  
Author(s):  
Chunyu Pan ◽  
Yuyan Zhu ◽  
Meng Yu ◽  
Yongkang Zhao ◽  
Changsheng Zhang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Regulatory Network ◽  
Interaction Network ◽  
Control Analysis ◽  
Potential Drug ◽  
Driver Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network ◽  
Network Controllability

BackgroundMYCN is an oncogenic transcription factor of the MYC family and plays an important role in the formation of tissues and organs during development before birth. Due to the difficulty in drugging MYCN directly, revealing the molecules in MYCN regulatory networks will help to identify effective therapeutic targets.MethodsWe utilized network controllability theory, a recent developed powerful tool, to identify the potential drug target around MYCN based on Protein-Protein interaction network of MYCN. First, we constructed a Protein-Protein interaction network of MYCN based on public databases. Second, network control analysis was applied on network to identify driver genes and indispensable genes of the MYCN regulatory network. Finally, we developed a novel integrated approach to identify potential drug targets for regulating the function of the MYCN regulatory network.ResultsWe constructed an MYCN regulatory network that has 79 genes and 129 interactions. Based on network controllability theory, we analyzed driver genes which capable to fully control the network. We found 10 indispensable genes whose alternation will significantly change the regulatory pathways of the MYCN network. We evaluated the stability and correlation analysis of these genes and found EGFR may be the potential drug target which closely associated with MYCN.ConclusionTogether, our findings indicate that EGFR plays an important role in the regulatory network and pathways of MYCN and therefore may represent an attractive therapeutic target for cancer treatment.

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.

scholarly journals Chromosome mapping of five differently expressed miRNAs in porcine skeletal muscle development (Brief Report)

2010 ◽  
Vol 53 (6) ◽  
pp. 734-736
Author(s):  
H. B. He ◽  
S. H. Zhao ◽  
X. Y. Li
Keyword(s):  
Skeletal Muscle ◽  
Muscle Development ◽  
Expression Profiles ◽  
Chromosome Mapping ◽  
Differentially Expressed ◽  
Skeletal Muscle Development ◽  
Regulate Gene Expression ◽  
Mirna Expression Profiles ◽  
Differentially Expressed Mirnas ◽  
Regulatory Rnas

Abstract. MicroRNAs (miRNAs) are a class of short, non-coding regulatory RNAs, which are approximately 22 nucleotides in length. Typically, miRNAs negatively regulate gene expression by binding with the 3' untranslated region (UTR) of its regulatory target mRNAs. MicroRNAs are known to play diverse roles in fundamental biological processes, such as proliferation, differentiation and apoptosis (Bartel 2004, 2009). It has been reported that miR-1, miR-133, miR-181 and miR-206 play important roles in skeletal muscle proliferation and hypertrophy (Callis et al. 2007, McCarthy -Esser 2007). We have detected porcine miRNA expression profiles during different stage of skeletal muscle development and a total of 140 miRNAs were differentially expressed (HUANG et al. 2008). In this study, we mapped five differentially expressed miRNAs (mir-29c, mir-103-1, mir-127, mir-193b and mir-218-1) using the radiation hybrid (IMpRH) panel (YERLE et al. 1998).

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 Identification of lncRNA–mRNA Regulatory Network Associated With Isolated Systolic Hypertension and Atherosclerotic Cerebral Infarction

2021 ◽  
Author(s):  
jie Yang ◽  
Yan-Nan Tao ◽  
Fang-Xiao Hu ◽  
Yong-Zhi Chen ◽  
Xue-Song Yang ◽  
...  
Keyword(s):  
Cerebral Infarction ◽  
Regulatory Network ◽  
Systolic Hypertension ◽  
Expression Profiles ◽  
Pearson Correlation ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Isolated Systolic Hypertension ◽  
Hub Genes ◽  
Qrt Pcr

Abstract Background: Increasing evidences uncover that lncRNAs play an important role in Isolated systolic hypertension (ISH). However, a systematic lncRNA-mRNA regulatory network is still absent in isolated systolic hypertension and atherosclerotic cerebral infarction patients (ISH & ACI).Aim:This research aims to establish a lncRNA-mRNA co-expression network in patients with ISH & ACI, to probe into the potential functions of lncRNA in those patients.Design and Setting:Expression profiles of lncRNA and mRNAs are collected and compared respectively from 8 patients with ISH and 8 patients with ISH & ACI by RNA-seq data.Methods: Differentially expressed lncRNAs and mRNAs were screened out via high-throughput sequencing in the plasma of ISH/ACI patients and control ISH patients. Then, a lncRNA-mRNA interaction network was built using the Pearson correlation coefficient by Cytoscape software. The expression levels of the hub genes and lncRNAs were verified by qRT-PCR in another 10 ISH/ACI patients and 10 control patients. Results: 2768 differentially expressed lncRNAs and 747 differentially expressed mRNAs were identified. 2 hub genes (CD226 and PARVB) and 11 lncRNAs were identified in the lncRNA-mRNA interaction network. qRT-PCR and cell assay results verified that lncRNAs ENST00000590604 and CD226 are highly expressed in patients of ISH & ACI. CD226 was associated with vascular endothelial cells growth and stability through platelet activation and focal adhesion pathway.Conclusion: We established a novel mRNA-lncRNA interaction network. lncRNAs ENST00000590604 and CD226 might be the potential biomarkers of ISH & ACI.

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 Identification of Key Genes Related with Alzheimer’s Disease Treatment Through Bioinformatics Analysis

2018 ◽  
Vol 9 (1) ◽  
pp. 78
Author(s):  
Liqun Wang ◽  
Hongjia Qian ◽  
Liqun Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Amyloid Β ◽  
Interaction Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Tg2576 Mice

T0901317, a live X receptor agonist, can reduce amyloid β generation in vitro and in a mouse Alzheimer’s disease (AD) model. To investigate the global molecular effects of T0901317 in mouse hippocampus, we downloaded public GSE31624 generated from the hippocampus of wild-type mice, Tg2576 mice and T0901317-treated Tg2576 mice. Differentially-expressed genes (DEGs) were identified on LIMMA of R software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment were analyzed through DAVID. Protein- protein interaction and hub genes were obtained based on STRING and Cytoscape. Nine downregulated and 68 upregulated DEGs in T0901317-treated Tg2576 were identified in comparison with untreated Tg2576 mice. Annotation analyses showed these DEGs correlated with transport (BP), membrane (CC) and binding (MF) terms and the dopaminergic synapse pathway. Protein-protein interaction network was built to find out some hub genes by maximal clique centrality. Discs large homolog 4 (Dlg4), the most outstanding gene, was associated with cognition improvement in aged AD mice. T0901317 may impact the development by regulating the Dlg4 expression. In conclusion, we investigated effects of T0901317 therapy on gene expression profiles in the hippocampus of Tg2576 mice and found Dlg4 may serve as putative therapeutics target for AD treatment.

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