Exploring Kawasaki disease-specific hub genes revealing a striking similarity of expression profile to bacterial infections using weighted gene co-expression network analysis (WGCNA) and co-expression modules identification tool (CEMiTool): An integrated bioinformatics and experimental study

Immunobiology ◽  
2020 ◽  
Vol 225 (4) ◽  
pp. 151980 ◽  
Author(s):  
Sajjad Esmaeili ◽  
Amir Mehrgou ◽  
Naser Kakavandi ◽  
Yazdan Rahmati
Keyword(s):  
Experimental Study ◽  
Network Analysis ◽  
Kawasaki Disease ◽  
Expression Profile ◽  
Bacterial Infections ◽  
Striking Similarity ◽  
Hub Genes ◽  
Disease Specific ◽  
Identification Tool

scholarly journals Diagnostic biomarkers for invasive aspergillosis utilizing weighted gene co-expression network analysis

2020 ◽  
Author(s):  
Weiwei Deng ◽  
Yubo Ma ◽  
Panpan Liang ◽  
Chen Huang ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Invasive Aspergillosis ◽  
Bacterial Infections ◽  
High Sensitivity ◽  
Roc Curves ◽  
Microarray Dataset ◽  
High Expression ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Hematological Patients

Abstract Background: Invasive aspergillosis (IA) has a significant mortality in immunocompromised patients. In recent years, with more aggressive immunosuppressed therapies, the incidence of IA was increasing. However, diagnostic biomarkers with high sensitivity and specificity remain rare. To get new diagnostic biomarkers, microarray dataset GSE78000 was analyzed. Methods: Weighted gene co-expression network analysis (WGCNA) was used to identify hub genes. Roc curves were employed for investigating diagnostic biomarkers for IA.Results: Hub genes were TLR4, TP53I3/PIG3, TMTC1, ITGAM, CYSTM1, FAR1, GAS7 and MKNK1. However, after we compared gene expression of hematological patients suffering from IA with non-IA patients, only TLR4, TP53I3/PIG3 and TMTC1 were significantly high expression in IA patients. At the optimal cut‐off value, TLR4 can diagnose patients with IA with 78.3% sensitivity and 72.7% specificity. TP53I3/PIG3 can diagnose patients with IA with 91.3% sensitivity and 54.5% specificity. TMTC1 can diagnose patients with IA with 78.3% sensitivity and 81.8% specificity. In addition, the data of hematological patients suffering from Staphylococcus aureus (S. aureus) and Escherichia coli (E.coli) infections were also analyzed. The results showed that TLR4 and TP53I3/PIG3 were also significantly high expression in S. aureus and E.coli infections, while only TP53I3/PIG3 was obviously higher expression in patients with bacterial infections compared with IA. As for TMTC1, we cannot annotate the gene from the microarray data. Conclusions: our results suggested that TLR4, TP53I3/PIG3 and TMTC1 might be used for the diagnosis of IA, and TP53I3/PIG3 can also be used to discriminate hematological aspergillosis and bacterial infections.

scholarly journals Weighted gene co-expression network analysis identified underlying hub genes and mechanisms in the occurrence and development of viral myocarditis

2020 ◽  
Vol 8 (21) ◽  
pp. 1348-1348
Author(s):  
Zetao Ma ◽  
Zhida Shen ◽  
Yingchao Gong ◽  
Jiaqi Zhou ◽  
Xiaoou Chen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Viral Myocarditis ◽  
Hub Genes

scholarly journals Incidence of Kawasaki disease before and during the COVID-19 pandemic: a retrospective cohort study in Japan

2021 ◽  
Vol 5 (1) ◽  
pp. e001034
Author(s):  
Kyohei Iio ◽  
Kousaku Matsubara ◽  
Chisato Miyakoshi ◽  
Kunitaka Ota ◽  
Rika Yamaoka ◽  
...  
Keyword(s):  
Cohort Study ◽  
Kawasaki Disease ◽  
Infectious Diseases ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Bacterial Infections ◽  
Epidemiological Studies ◽  
Primary Objective ◽  
Temporal Association ◽  
Difference In Difference

BackgroundEpidemiological studies in Kawasaki disease (KD) have suggested infectious aetiology. During the COVID-19 pandemic, measures for mitigating SARS-CoV-2 transmission also suppress the circulation of other contagious microorganisms. The primary objective is to compare the number and incidence of KD before and during the COVID-19 pandemic in Japan, and the secondary objective is to investigate temporal association between the KD epidemiology and activities of SARS-CoV-2 and other viral and bacterial infections.MethodsA retrospective cohort study was conducted between 2016 and 2020 in Kobe, Japan. We collected information of hospitalised KD children in Kobe. Child population was identified through the resident registry system. Activity of COVID-19 and 11 other infectious diseases was derived from a public health monitoring system. Monthly change of KD incidence was analysed using a difference-in-difference regression model.ResultsThroughout the study period, 1027 KD children were identified. KD had begun to decline in April 2020, coinciding with the beginning of the COVID-19 pandemic. The number of KD cases (n=66) between April and December 2020 was 40% of the average in the same period in 2016–2019 (165/year). Annual KD incidence was 315, 300, 353, 347 and 188/100 000 children aged 0–4 years in 2016–2020, respectively. The difference-in-difference value of KD incidence was significantly reduced in the fourth quarter in 2020 (−15.8, 95% CI −28.0 to −3.5), compared with that in 2016–2019. Sentinel surveillance showed a marked decrease of all infectious diseases except exanthema subitum after the beginning of the COVID-19 pandemic. There were 86 COVID-19 cases aged <10 years and no KD children associated with COVID-19.ConclusionThis study showed that the number and incidence of KD was dramatically reduced during the COVID-19 pandemic in Japan. This change was temporally associated with decreased activities of various infectious diseases other than COVID-19, supporting the hypothesis of infection-triggered pathogenesis in KD.

scholarly journals Expression profile analysis reveals hub genes that are associated with immune system dysregulation in primary myelofibrosis

Hematology ◽  
2021 ◽  
Vol 26 (1) ◽  
pp. 478-490
Author(s):  
Haotian Ma ◽  
Jincen Liu ◽  
Zilong Li ◽  
Huaye Xiong ◽  
Yulei Zhang ◽  
...  
Keyword(s):  
Immune System ◽  
Expression Profile ◽  
Profile Analysis ◽  
Primary Myelofibrosis ◽  
Hub Genes ◽  
Expression Profile Analysis

scholarly journals Identification of key modules and hub genes in glioblastoma multiforme based on co‐expression network analysis

FEBS Open Bio ◽  
2021 ◽  
Author(s):  
Chun Li ◽  
Bangming Pu ◽  
Long Gu ◽  
Mingwei Zhang ◽  
Hongping Shen ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Network Analysis ◽  
Hub Genes

scholarly journals Trends in Nursing Research on Infections: Semantic Network Analysis and Topic Modeling

2021 ◽  
Vol 18 (13) ◽  
pp. 6915
Author(s):  
Jongsoon Won ◽  
Kyunghee Kim ◽  
Kyeong-Yae Sohng ◽  
Sung-Ok Chang ◽  
Seung-Kyo Chaung ◽  
...  
Keyword(s):  
Urinary Tract ◽  
Network Analysis ◽  
Infection Control ◽  
Urinary Tract Infections ◽  
Bacterial Infections ◽  
Semantic Network ◽  
People Living With Hiv ◽  
Transmitted Infection ◽  
Sexually Transmitted ◽  
Tract Infections

Background: Many countries around the world are currently threatened by the COVID-19 pandemic, and nurses are facing increasing responsibilities and work demands related to infection control. To establish a developmental strategy for infection control, it is important to analyze, understand, or visualize the accumulated data gathered from research in the field of nursing. Methods: A total of 4854 articles published between 1978 and 2017 were retrieved from the Web of Science. Abstracts from these articles were extracted, and network analysis was conducted using the semantic network module. Results: ‘wound’, ‘injury’, ‘breast’, “dressing”, ‘temperature’, ‘drainage’, ‘diabetes’, ‘abscess’, and ‘cleaning’ were identified as the keywords with high values of degree centrality, betweenness centrality, and closeness centrality; hence, they were determined to be influential in the network. The major topics were ‘PLWH’ (people living with HIV), ‘pregnancy’, and ‘STI’ (sexually transmitted infection). Conclusions: Diverse infection research has been conducted on the topics of blood-borne infections, sexually transmitted infections, respiratory infections, urinary tract infections, and bacterial infections. STIs (including HIV), pregnancy, and bacterial infections have been the focus of particularly intense research by nursing researchers. More research on viral infections, urinary tract infections, immune topic, and hospital-acquired infections will be needed.

scholarly journals The potential role of chemotaxis and the complement system in the formation and progression of thoracic aortic aneurysms inferred from the weighted gene coexpression network analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chuxiang Lei ◽  
Dan Yang ◽  
Wenlin Chen ◽  
Haoxuan Kan ◽  
Fang Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Clinical Significance ◽  
Immune Cells ◽  
Complement System ◽  
Aortic Wall ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
The Complement System ◽  
Coexpression Network Analysis

Abstract Background Thoracic aortic aneurysm (TAA) can be life-threatening due to the progressive weakening and dilatation of the aortic wall. Once the aortic wall has ruptured, no effective pharmaceutical therapies are available. However, studies on TAA at the gene expression level are limited. Our study aimed to identify the driver genes and critical pathways of TAA through gene coexpression networks. Methods We analyzed the genetic data of TAA patients from a public database by weighted gene coexpression network analysis (WGCNA). Modules with clinical significance were identified, and the differentially expressed genes (DEGs) were intersected with the genes in these modules. Gene Ontology and pathway enrichment analyses were performed. Finally, hub genes that might be driving factors of TAA were identified. Furthermore, we evaluated the diagnostic accuracy of these genes and analyzed the composition of immune cells using the CIBERSORT algorithm. Results We identified 256 DEGs and two modules with clinical significance. The immune response, including leukocyte adhesion, mononuclear cell proliferation and T cell activation, was identified by functional enrichment analysis. CX3CR1, C3, and C3AR1 were the top 3 hub genes in the module correlated with TAA, and the areas under the curve (AUCs) by receiver operating characteristic (ROC) analysis of all the hub genes exceeded 0.7. Finally, we found that the proportions of infiltrating immune cells in TAA and normal tissues were different, especially in terms of macrophages and natural killer (NK) cells. Conclusion Chemotaxis and the complement system were identified as crucial pathways in TAA, and macrophages with interactive immune cells may regulate this pathological process.

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