scholarly journals Identification of Key Genes and Underlying Mechanisms in Acute Kawasaki Disease Based on Bioinformatics Analysis

2021 ◽  
Vol 27 ◽  
Author(s):  
Side Gao ◽  
Wenjian Ma ◽  
Xuze Lin ◽  
Sizhuang Huang ◽  
Mengyue Yu
Keyword(s):  
Kawasaki Disease ◽  
Bioinformatics Analysis ◽  
Acute Kawasaki Disease ◽  
Key Genes ◽  
Underlying Mechanisms

Efficacy of Intravenous Immunoglobulin Combined with Prednisolone Following Resistance to Initial Intravenous Immunoglobulin Treatment of Acute Kawasaki Disease

2013 ◽  
Vol 163 (2) ◽  
pp. 521-526.e1 ◽  
Author(s):  
Tohru Kobayashi ◽  
Tomio Kobayashi ◽  
Akihiro Morikawa ◽  
Kentaro Ikeda ◽  
Mitsuru Seki ◽  
...  
Keyword(s):  
Kawasaki Disease ◽  
Intravenous Immunoglobulin ◽  
Acute Kawasaki Disease ◽  
Immunoglobulin Treatment

Therapy of acute Kawasaki disease

1991 ◽  
Vol 1 (3) ◽  
pp. 254-255
Author(s):  
Jane W. Newburger
Keyword(s):  
Myocardial Infarction ◽  
Early Childhood ◽  
Kawasaki Disease ◽  
Sudden Death ◽  
Cervical Lymphadenopathy ◽  
Unknown Etiology ◽  
Acute Kawasaki Disease ◽  
Arterial Insufficiency ◽  
Infancy And Early Childhood ◽  
Arterial Aneurysms

Kawasaki disease is an acute vasculitis of unknown etiology that occurs predominantly in infancy and early childhood. It is characterize by fever, bilateral nonexudative conjunctivitis, erythema of the lips and oral mucosa, changes in the extremities, rash, and cervical lymphadenopathy.1,2 Coronary arterial aneurysms, or ectasia, develop in approximately 15 to 25% of children with the disease, and may lead to myocardial infarction, sudden death, or chronic coronary arterial insufficiency.2–4

scholarly journals Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Hao Long ◽  
Chaofeng Liang ◽  
Xi’an Zhang ◽  
Luxiong Fang ◽  
Gang Wang ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Prediction Model ◽  
Basic Science ◽  
Prediction Accuracy ◽  
Genetic Basis ◽  
Bioinformatics Analysis ◽  
Clinical Treatment ◽  
Structural Level ◽  
Biotechnological Application ◽  
Key Genes

Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment. In the present study, bioinformatics analysis was performed to reveal and identify the key genes of glioblastoma multiforme (GBM). The results obtained in the present study signified the importance of some genes, such as COL3A1, FN1, and MMP9, for glioblastoma. Based on the selected genes, a prediction model was built, which achieved 94.4% prediction accuracy. These findings might provide more insights into the genetic basis of glioblastoma.

Identification of potential key genes and key pathways related to clear cell renal cell carcinoma through bioinformatics analysis

2020 ◽  
Vol 52 (8) ◽  
pp. 853-863
Author(s):  
Wenxin Zhai ◽  
Haijiao Lu ◽  
Shenghua Dong ◽  
Jing Fang ◽  
Zhuang Yu
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Phosphoenolpyruvate Carboxykinase ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Interaction Network ◽  
Polymerase Chain Reaction Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Key Genes

Abstract Clear cell renal cell carcinoma (ccRCC) is a common malignancy of the genitourinary system and is associated with high mortality rates. However, the molecular mechanism of ccRCC pathogenesis is still unclear, which translates to few effective diagnostic and prognostic biomarkers. In this study, we conducted a bioinformatics analysis on three Gene Expression Omnibus datasets and identified 437 differentially expressed genes (DEGs) related to ccRCC development and prognosis, of which 311 and 126 genes are respectively down-regulated and up-regulated. The protein–protein interaction network of these DEGs consists of 395 nodes and 1872 interactions and 2 prominent modules. The Staphylococcus aureus infection and complement and coagulation cascades are significantly enriched in module 1 and are likely involved in ccRCC progression. Forty-two hub genes were screened, of which von Willebrand factor, TIMP metallopeptidase inhibitor 1, plasminogen, formimidoyltransferase cyclodeaminase, solute carrier family 34 member 1, hydroxyacid oxidase 2, alanine-glyoxylate aminotransferase 2, phosphoenolpyruvate carboxykinase 1, and 3-hydroxy-3-methylglutaryl-CoA synthase 2 are possibly related to the prognosis of ccRCC. The differential expression of all nine genes was confirmed by quantitative real-time polymerase chain reaction analysis of the ccRCC and normal renal tissues. These key genes are potential biomarkers for the diagnosis and prognosis of ccRCC and warrant further investigation.

scholarly journals Screening and verifying key genes with poor prognosis in colon cancer through bioinformatics analysis

2020 ◽  
Vol 9 (11) ◽  
pp. 6720-6732
Author(s):  
Buyuan Dong ◽  
Mengyu Chai ◽  
Hao Chen ◽  
Qian Feng ◽  
Rong Jin ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Key Genes

scholarly journals Identification of key genes for predicting colorectal cancer prognosis by integrated bioinformatics analysis

2019 ◽  
Author(s):  
Gong‑Peng Dai ◽  
Li‑Ping Wang ◽  
Yu‑Qing Wen ◽  
Xue‑Qun Ren ◽  
Shu‑Guang Zuo
Keyword(s):  
Colorectal Cancer ◽  
Bioinformatics Analysis ◽  
Cancer Prognosis ◽  
Key Genes
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