scholarly journals Identification of Key Genes and Pathways in Myeloma side population cells by Bioinformatics Analysis

2020 ◽  
Vol 17 (14) ◽  
pp. 2063-2076 ◽  
Author(s):  
Qin Yang ◽  
Kaihu Li ◽  
Xin Li ◽  
Jing Liu
Keyword(s):  
Bioinformatics Analysis ◽  
Side Population ◽  
Side Population Cells ◽  
Key Genes

scholarly journals Combination of quantification and observation methods for study of “Side Population” cells in their “in vitro” microenvironment

2007 ◽  
Vol 71A (4) ◽  
pp. 251-257 ◽  
Author(s):  
Rachid Benchaouir ◽  
Julien Picot ◽  
Nicolas Greppo ◽  
Philippe Rameau ◽  
Daniel Stockholm ◽  
...  
Keyword(s):  
Side Population ◽  
Side Population Cells ◽  
Observation Methods

Side Population Cells in the Human Vocal Fold

2007 ◽  
Vol 116 (11) ◽  
pp. 847-852 ◽  
Author(s):  
Masaru Yamashita ◽  
Shigeru Hirano ◽  
Shin-Ichi Kanemaru ◽  
Shunichiro Tsuji ◽  
Atsushi Suehiro ◽  
...  
Keyword(s):  
Vocal Fold ◽  
Side Population ◽  
Side Population Cells

Using ABCG2-molecule-expressing side population cells to identify cancer stem-like cells in a human ovarian cell line

2011 ◽  
Vol 35 (3) ◽  
pp. 227-234 ◽  
Author(s):  
Jun Dou ◽  
Cuilian Jiang ◽  
Jing Wang ◽  
Xian Zhang ◽  
Fengshu Zhao ◽  
...  
Keyword(s):  
Cell Line ◽  
Side Population ◽  
Side Population Cells ◽  
Ovarian Cell

Characterization of cardiac side population cells

2006 ◽  
Vol 40 (6) ◽  
pp. 993
Author(s):  
Kenichi Yamahara ◽  
Steven R. Coppen ◽  
Anabel Varela_Carver ◽  
Satsuki Fukushima ◽  
Alexander E. Ermakov ◽  
...  
Keyword(s):  
Side Population ◽  
Side Population Cells

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.

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