Revealing pathway maps of renal cell carcinoma by gene expression change

Fei Hung Hung, Hung Wen Chiu, Yo-Cheng Chang

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Protein-protein interactions (PPIs) and gene expression profiles interact with each other in the regulation of a pathway. Many studies have expressed the feasibility of deriving the pathway from the PPI network or gene expression information. However, previous researches are still limited to a small region of large-scale genomics and whole-proteomics. Furthermore, the gene information induced by diseases had not been considered yet in such researches. In this study, we propose an approach to find potential fragments of active pathways related to various stages of diseases by a top-rank score-based method, integrating PPI network and gene expression change information. Validation of produced pathway maps is performed by mapping with KEGG renal cell carcinoma (RCC) map. The pathway maps of RCC are built and three key genes are found. The accuracies of coverage ratio of the produced pathway map are 50% and 48.48%. In this case, the hubs that link the nodes from RCC provide a valuable guide for further studies for understanding RCC. In conclusion, the pathway map co-constructed by this proposed method can provide more insight than limited subnetwork biomarkers.

Original languageEnglish
Pages (from-to)111-121
Number of pages11
JournalComputers in Biology and Medicine
Volume51
DOIs
Publication statusPublished - Aug 1 2014

Fingerprint

Renal Cell Carcinoma
Gene expression
Cells
Proteins
Protein Interaction Maps
Gene Expression
Genomics
Genes
Transcriptome
Research
Proteomics
Biomarkers

Keywords

  • Cancer
  • Computational method
  • Gene expression
  • Protein-protein interaction
  • Renal cell carcinoma
  • Signaling pathway

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Medicine(all)

Cite this

Revealing pathway maps of renal cell carcinoma by gene expression change. / Hung, Fei Hung; Chiu, Hung Wen; Chang, Yo-Cheng.

In: Computers in Biology and Medicine, Vol. 51, 01.08.2014, p. 111-121.

Research output: Contribution to journalArticle

@article{6846c1531382402baaf1d17aacf85b58,
title = "Revealing pathway maps of renal cell carcinoma by gene expression change",
abstract = "Protein-protein interactions (PPIs) and gene expression profiles interact with each other in the regulation of a pathway. Many studies have expressed the feasibility of deriving the pathway from the PPI network or gene expression information. However, previous researches are still limited to a small region of large-scale genomics and whole-proteomics. Furthermore, the gene information induced by diseases had not been considered yet in such researches. In this study, we propose an approach to find potential fragments of active pathways related to various stages of diseases by a top-rank score-based method, integrating PPI network and gene expression change information. Validation of produced pathway maps is performed by mapping with KEGG renal cell carcinoma (RCC) map. The pathway maps of RCC are built and three key genes are found. The accuracies of coverage ratio of the produced pathway map are 50{\%} and 48.48{\%}. In this case, the hubs that link the nodes from RCC provide a valuable guide for further studies for understanding RCC. In conclusion, the pathway map co-constructed by this proposed method can provide more insight than limited subnetwork biomarkers.",
keywords = "Cancer, Computational method, Gene expression, Protein-protein interaction, Renal cell carcinoma, Signaling pathway",
author = "Hung, {Fei Hung} and Chiu, {Hung Wen} and Yo-Cheng Chang",
year = "2014",
month = "8",
day = "1",
doi = "10.1016/j.compbiomed.2014.04.023",
language = "English",
volume = "51",
pages = "111--121",
journal = "Computers in Biology and Medicine",
issn = "0010-4825",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Revealing pathway maps of renal cell carcinoma by gene expression change

AU - Hung, Fei Hung

AU - Chiu, Hung Wen

AU - Chang, Yo-Cheng

PY - 2014/8/1

Y1 - 2014/8/1

N2 - Protein-protein interactions (PPIs) and gene expression profiles interact with each other in the regulation of a pathway. Many studies have expressed the feasibility of deriving the pathway from the PPI network or gene expression information. However, previous researches are still limited to a small region of large-scale genomics and whole-proteomics. Furthermore, the gene information induced by diseases had not been considered yet in such researches. In this study, we propose an approach to find potential fragments of active pathways related to various stages of diseases by a top-rank score-based method, integrating PPI network and gene expression change information. Validation of produced pathway maps is performed by mapping with KEGG renal cell carcinoma (RCC) map. The pathway maps of RCC are built and three key genes are found. The accuracies of coverage ratio of the produced pathway map are 50% and 48.48%. In this case, the hubs that link the nodes from RCC provide a valuable guide for further studies for understanding RCC. In conclusion, the pathway map co-constructed by this proposed method can provide more insight than limited subnetwork biomarkers.

AB - Protein-protein interactions (PPIs) and gene expression profiles interact with each other in the regulation of a pathway. Many studies have expressed the feasibility of deriving the pathway from the PPI network or gene expression information. However, previous researches are still limited to a small region of large-scale genomics and whole-proteomics. Furthermore, the gene information induced by diseases had not been considered yet in such researches. In this study, we propose an approach to find potential fragments of active pathways related to various stages of diseases by a top-rank score-based method, integrating PPI network and gene expression change information. Validation of produced pathway maps is performed by mapping with KEGG renal cell carcinoma (RCC) map. The pathway maps of RCC are built and three key genes are found. The accuracies of coverage ratio of the produced pathway map are 50% and 48.48%. In this case, the hubs that link the nodes from RCC provide a valuable guide for further studies for understanding RCC. In conclusion, the pathway map co-constructed by this proposed method can provide more insight than limited subnetwork biomarkers.

KW - Cancer

KW - Computational method

KW - Gene expression

KW - Protein-protein interaction

KW - Renal cell carcinoma

KW - Signaling pathway

UR - http://www.scopus.com/inward/record.url?scp=84901840263&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901840263&partnerID=8YFLogxK

U2 - 10.1016/j.compbiomed.2014.04.023

DO - 10.1016/j.compbiomed.2014.04.023

M3 - Article

C2 - 24907414

AN - SCOPUS:84901840263

VL - 51

SP - 111

EP - 121

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

SN - 0010-4825

ER -