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Fold change based approach for identification of significant network markers in breast, lung and prostate cancer

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Title Fold change based approach for identification
of significant network markers in breast, lung
and prostate cancer
 
Creator Makhijani, Richa K.
Raut, Shital A.
Purohit, H. J.
 
Subject Microbiology
 
Description Cancer belongs to a class of highly aggressive diseases and a leading cause of death in the world. With more than
100 types of cancers, breast, lung and prostate cancer remain to be the most common types. To identify essential network
markers (NMs) and therapeutic targets in these cancers, the authors present a novel approach which uses gene expression
data from microarray and RNA-seq platforms and utilises the results from this data to evaluate protein–protein interaction (PPI)
network. Differentially expressed genes (DEGs) are extracted from microarray data using three different statistical methods in R,
to produce a consistent set of genes. Also, DEGs are extracted from RNA-seq data for the same three cancer types. DEG sets
found to be common in both platforms are obtained at three fold change (FC) cut-off levels to accurately identify the level of
change in expression of these genes in all three cancers. A cancer network is built using PPI data characterising gene sets at
log-FC (LFC)>1, LFC>1.5 and LFC>2, and interconnection between principal hub nodes of these networks is observed.
Resulting network of hubs at three FC levels highlights prime NMs with high confidence in multiple cancers as validated by
Gene Ontology functional enrichment and maximal complete subgraphs from CFinder.
 
Publisher Institution of Engineering and Technology
 
Date 2018
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://neeri.csircentral.net/1201/1/IET-SYB.2018.0012.pdf
Makhijani, Richa K. and Raut, Shital A. and Purohit, H. J. (2018) Fold change based approach for identification of significant network markers in breast, lung and prostate cancer. IET Systems Biology, 12 (5). pp. 213-218. ISSN 1751-8857
 
Relation http://www.ietdl.org
http://neeri.csircentral.net/1201/