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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Blood Cells Molecule...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Blood Cells Molecules and Diseases
Article . 2015 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Identification of key genes affecting disease free survival time of pediatric acute lymphoblastic leukemia based on bioinformatic analysis

Authors: Haiyan Gao; Xin-Guo Luo; Jing-Hua Wang; Xi Chen;

Identification of key genes affecting disease free survival time of pediatric acute lymphoblastic leukemia based on bioinformatic analysis

Abstract

The poor prognosis of pediatric acute lymphoblastic leukemia (ALL) indicates the existence of key candidate genes that affect pediatric ALL and its prognosis. The limma package in R was applied to screen differentially expressed genes (DEGs), and the Survival package and KMsurv package in R were used to screen disease free survival time related genes (prognosis genes). Then, based on latent pathway identification analysis (LPIA), latent pathways were identified, and pathway-pathway interaction network was constructed and visualized by Cytoscape. Based on the expression values of 8284 genes in 126 chips, 2796 DEGs and 353 prognosis genes were screened out. After overlapping DEGs and prognosis genes, 75 key genes were identified, which were most significantly enriched in 25 GO functions and chronic myeloid leukemia pathway. For the 75 key genes, 27 disease risk sub-pathways were identified, and HK3, HNMT, SULT2B1, KYNU, and PTGS2 were the significant key genes which were enriched in these sub-pathways. Furthermore, based on pathway-pathway interaction analysis, HK3 and PTGS2 were predicted as the most important genes. Through glycolysis and arachidonic acid metabolism, HK3 and PTGS2 might play important roles in pediatric ALL and its prognosis, and thus, might be potential targets for therapeutic intervention to suppress pediatric ALL.

Related Organizations
Keywords

Male, Adolescent, Gene Expression Regulation, Leukemic, Computational Biology, Infant, Precursor Cell Lymphoblastic Leukemia-Lymphoma, Disease-Free Survival, Neoplasm Proteins, Survival Rate, Child, Preschool, Humans, Female, Child, Genes, Neoplasm

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    9
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Average
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