scholarly journals Module Based Differential Coexpression Analysis Method for Type 2 Diabetes

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Lin Yuan ◽  
Chun-Hou Zheng ◽  
Jun-Feng Xia ◽  
De-Shuang Huang
Keyword(s):  
Type 2 Diabetes ◽  
Differential Expression Analysis ◽  
Maximum Clique ◽  
Analysis Method ◽  
Threshold Strategy ◽  
Coexpression Analysis ◽  
Differential Coexpression ◽  
Differential Coexpression Analysis ◽  
Gene Modules

More and more studies have shown that many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional biological pathway or network and are highly correlated. Differential coexpression analysis, as a more comprehensive technique to the differential expression analysis, was raised to research gene regulatory networks and biological pathways of phenotypic changes through measuring gene correlation changes between disease and normal conditions. In this paper, we propose a gene differential coexpression analysis algorithm in the level of gene sets and apply the algorithm to a publicly available type 2 diabetes (T2D) expression dataset. Firstly, we calculate coexpression biweight midcorrelation coefficients between all gene pairs. Then, we select informative correlation pairs using the “differential coexpression threshold” strategy. Finally, we identify the differential coexpression gene modules using maximum clique concept andk-clique algorithm. We apply the proposed differential coexpression analysis method on simulated data and T2D data. Two differential coexpression gene modules about T2D were detected, which should be useful for exploring the biological function of the related genes.

scholarly journals High-Throughput Sequencing and Exploration of the lncRNA-circRNA-miRNA-mRNA Network in Type 2 Diabetes Mellitus

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Fang Yang ◽  
Yang Chen ◽  
Zhiqiang Xue ◽  
Yaogai Lv ◽  
Li Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Regulatory Network ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Biological Functions ◽  
Ppi Network

Objective. Long noncoding RNA (lncRNA) and circular RNA (circRNA) are receiving increasing attention in diabetes research. However, there are still many unknown lncRNAs and circRNAs that need further study. The aim of this study is to identify new lncRNAs and circRNAs and their potential biological functions in type 2 diabetes mellitus (T2DM). Methods. RNA sequencing and differential expression analysis were used to identify the noncoding RNAs (ncRNAs) and mRNAs that were expressed abnormally between the T2DM and control groups. The competitive endogenous RNA (ceRNA) regulatory network revealed the mechanism of lncRNA and circRNA coregulating gene expression. The biological functions of lncRNA and circRNA were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The candidate hub mRNAs were selected by the protein-protein interaction (PPI) network and validated by using the Gene Expression Omnibus (GEO) database. Results. Differential expression analysis results showed that 441 lncRNAs (366 upregulated and 75 downregulated), 683 circRNAs (354 upregulated and 329 downregulated), 93 miRNAs (63 upregulated and 30 downregulated), and 2923 mRNAs (1156 upregulated and 1779 downregulated) were identified as remarkably differentially expressed in the T2DM group. The ceRNA regulatory network showed that a single lncRNA and circRNA can be associated with multiple miRNAs, and then, they coregulate more mRNAs. Functional analysis showed that differentially expressed lncRNA (DElncRNA) and differentially expressed circRNA (DEcircRNA) may play important roles in the mTOR signaling pathway, lysosomal pathway, apoptosis pathway, and tuberculosis pathway. In addition, PIK3R5, AKT2, and CLTA were hub mRNAs screened out that were enriched in an important pathway by establishing the PPI network. Conclusions. This study is the first study to explore the molecular mechanisms of lncRNA and circRNA in T2DM through the ceRNA network cofounded by lncRNA and circRNA. Our study provides a novel insight into the T2DM from the ceRNA regulatory network.

scholarly journals A Review of Lactoferrin Inflammatory Role in Type 2 Diabetes Mellitus with Neutrophil Dysfunction

2021 ◽  
pp. 377-390
Author(s):  
Amani Y. Alhalwani
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Early Diagnosis ◽  
Analysis Method ◽  
First Line ◽  
Important Sign ◽  
Diabetic Population ◽  
Diagnostic Work ◽  
Neutrophil Dysfunction

Lactoferrin (LF) is a protein that plays important roles in many diseases including diabetes mellitus (DM). DM is one of the most challenging health concerns of the 21st century. At least 30% of the diabetic population is undiagnosed at any one time, so effective and early diagnosis is of critical concern. Several of the body’s chemicals, such as enzymes, electrolytes, and proteins, have been used as biomarkers in the diagnosis of diabetic diseases. Detection of LF is considered an important sign of type 2 diabetes (T2DM), due to its activity as an anti-inflammatory agent and in the down-regulation of pro-inflammation. LF is produced by glandular epithelial cells and neutrophils, and a decrease in its concentration is linked with the dysfunction of neutrophils in many diseases. Neutrophils are the first line of defence against pathogens that invade the human body during inflammation. Therefore, the health of neutrophils can be employed as a biomarker in the diagnosis of diseases such as diabetes. A decrease in LF concentrations in T2DM could result in increased levels of inflammatory markers that are associated with the inflammation activity. Increased understanding of the link between LF concentration and development of T2DM should improve early diagnosis and treatment outcomes. LF is identified through use of various techniques such as immunoassay, proteomics, and spectrometry. The aim of this review is to summarise each pathway and some of the most relevant LF biomarkers that may be used to monitor the development or progression of diabetes and its complications, and the link between levels of LF and neutrophil dysfunction in T2DM. Moreover, the objective of this review is to show the most common LF analysis that may be useful in the clinical diagnosis of T2DM and discuss to what extent this analysis method can be a tool for prognostic and diagnostic work.

scholarly journals Identification of Transcriptional Modules and Key Genes in Chickens Infected with Salmonella enterica Serovar Pullorum Using Integrated Coexpression Analyses

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Bao-Hong Liu ◽  
Jian-Ping Cai
Keyword(s):  
Salmonella Enterica ◽  
Differential Expression Analysis ◽  
Molecular Response ◽  
Cytoskeleton Organization ◽  
Differential Coexpression ◽  
Differential Coexpression Analysis ◽  
Salmonella Enterica Serovar Pullorum ◽  
Causes Of Mortality ◽  
Coexpressed Genes ◽  
G Protein Coupled

Salmonella enterica Pullorum is one of the leading causes of mortality in poultry. Understanding the molecular response in chickens in response to the infection by S. enterica is important in revealing the mechanisms of pathogenesis and disease progress. There have been studies on identifying genes associated with Salmonella infection by differential expression analysis, but the relationships among regulated genes have not been investigated. In this study, we employed weighted gene coexpression network analysis (WGCNA) and differential coexpression analysis (DCEA) to identify coexpression modules by exploring microarray data derived from chicken splenic tissues in response to the S. enterica infection. A total of 19 modules from 13,538 genes were associated with the Jak-STAT signaling pathway, the extracellular matrix, cytoskeleton organization, the regulation of the actin cytoskeleton, G-protein coupled receptor activity, Toll-like receptor signaling pathways, and immune system processes; among them, 14 differentially coexpressed modules (DCMs) and 2,856 differentially coexpressed genes (DCGs) were identified. The global expression of module genes between infected and uninfected chickens showed slight differences but considerable changes for global coexpression. Furthermore, DCGs were consistently linked to the hubs of the modules. These results will help prioritize candidate genes for future studies of Salmonella infection.

scholarly journals SUBSPACE DIFFERENTIAL COEXPRESSION ANALYSIS:

2009 ◽  
pp. 145-156 ◽  
Author(s):  
GANG FANG ◽  
RUI KUANG ◽  
GAURAV PANDEY ◽  
MICHAEL STEINBACH ◽  
CHAD L. MYERS ◽  
...  
Keyword(s):  
Coexpression Analysis ◽  
Differential Coexpression ◽  
Differential Coexpression Analysis
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