Qingbin Li

Qingbin Li


Research Focus & Metrics

Academic Career Length

31Years (1996-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 August 2016 in Computational Materials Science
Verified
Journal Article
Closed access
GLGuowei Liu
Guowei Liu
QLQingbin Li
Qingbin Li
MMMohammed A. Msekh
Mohammed A. Msekh
ZZZheng Zuo
Zheng Zuo

1 November 2014 in Construction and Building Materials
Verified
Journal Article
Closed access
YHYu Hu
Yu Hu
DLDanni Luo
Danni Luo
PLPenghui Li
Penghui Li
QLQingbin Li
Qingbin Li
GSGuoqiang Sun
Guoqiang Sun

1 March 2007 in International Journal of Solids and Structures
Verified
Journal Article
Closed access
HWHailong Wang
Hailong Wang
QLQingbin Li
Qingbin Li

1 October 2006 in Engineering Structures
Verified
Journal Article
Closed access
ZDZongcai Deng
Zongcai Deng
QLQingbin Li
Qingbin Li
HSHongjun Sun
Hongjun Sun

1 March 2012 in Applied Thermal Engineering
Verified
Journal Article
Closed access
JYJian Yang
Jian Yang
YHYu Hu
Yu Hu
ZZZheng Zuo
Zheng Zuo
FJFeng Jin
Feng Jin
QLQingbin Li
Qingbin Li

15 May 2015 in PLOS ONE
Verified
Journal Article
Open access
PDF file
JCJinquan Cai
Jinquan Cai
WZWei Zhang
Wei Zhang
PYPei Yang
Pei Yang
YWYinyan Wang
Yinyan Wang
MLMingyang Li
Mingyang Li
CJchuanlu jiang
chuanlu jiang
Glioblastomas (GBM) are comprised of a heterogeneous population of tumor cells, immune cells, and extracellular matrix. Interactions among these different cell types and pro-/anti-inflammatory cytokines may promote tumor development and progression. The objective of this study was to develop a cytokine-related gene signature to improve outcome prediction for patients with primary GBM. Here, we used Cox regression and risk-score analysis to develop a cytokine-related gene signature in primary GBMs from the whole transcriptome sequencing profile of the Chinese Glioma Genome Atlas (CGGA) database (n=105). We also examined differences in immune cell phenotype and immune factor expression between the high-risk and low-risk groups. Cytokine-related genes were ranked based on their ability to predict survival in the CGGA database. The six genes showing the strongest predictive value were CXCL10, IL17R, CCR2, IL17B, IL10RB, and CCL2. Patients with a high-risk score had poor overall survival and progression-free survival. Additionally, the high-risk group was characterized by increased mRNA expression of M2 microglia/macrophage markers and elevated levels of IL10 and TGFβ1. The six cytokine-related gene signature is sufficient to predict survival and to identify a subgroup of primary GBM exhibiting the M2 cell phenotype.