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A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network.

Ruan X, Li H, Liu B, Chen J, Zhang S, Sun Z, Liu S, Sun F, Liu Q - Oncol. Rep. (2015)

Bottom Line: Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method.Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC.The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China.

ABSTRACT
The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The differentially co-expressed links were detected by Pearson's correlation, the empirical Bayesian (EB) approach and Weighted Gene Co-expression Network Analysis (WGCNA). The co-expressed gene pairs were merged by a rank-based algorithm. We obtained 842; 371; 2,883 and 1,595 co-expressed gene pairs from the co-expression networks of the STRING database, Pearson's correlation EB method and WGCNA, respectively. Two hundred and eighty-one differentially co-expressed (DC) gene pairs were obtained from the merged network using this novel method. Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method. Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC. The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis.

No MeSH data available.


Related in: MedlinePlus

Co-expression network of 281 DC gene pairs of RCC from the merged matrix. Genes (nodes) are connected by edges if their vectors are sufficiently similar. Black edge is associated with a pair of genes with q-value correction (P<0.1). DC, differentially co-expressed; RCC, renal cell carcinoma.
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f2-or-34-02-0567: Co-expression network of 281 DC gene pairs of RCC from the merged matrix. Genes (nodes) are connected by edges if their vectors are sufficiently similar. Black edge is associated with a pair of genes with q-value correction (P<0.1). DC, differentially co-expressed; RCC, renal cell carcinoma.

Mentions: We merged all the co-expressed gene pairs identified from the four methods utilizing RP algorithm, and 13,945 genes were assessed after merging. Two hundred and eighty one DC gene pairs were obtained after q-value correction (P<0.1) and their co-expression network is shown in Fig. 2. There were 154 nodes and 281 edges in the co-expression network.


A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network.

Ruan X, Li H, Liu B, Chen J, Zhang S, Sun Z, Liu S, Sun F, Liu Q - Oncol. Rep. (2015)

Co-expression network of 281 DC gene pairs of RCC from the merged matrix. Genes (nodes) are connected by edges if their vectors are sufficiently similar. Black edge is associated with a pair of genes with q-value correction (P<0.1). DC, differentially co-expressed; RCC, renal cell carcinoma.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4487672&req=5

f2-or-34-02-0567: Co-expression network of 281 DC gene pairs of RCC from the merged matrix. Genes (nodes) are connected by edges if their vectors are sufficiently similar. Black edge is associated with a pair of genes with q-value correction (P<0.1). DC, differentially co-expressed; RCC, renal cell carcinoma.
Mentions: We merged all the co-expressed gene pairs identified from the four methods utilizing RP algorithm, and 13,945 genes were assessed after merging. Two hundred and eighty one DC gene pairs were obtained after q-value correction (P<0.1) and their co-expression network is shown in Fig. 2. There were 154 nodes and 281 edges in the co-expression network.

Bottom Line: Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method.Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC.The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China.

ABSTRACT
The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The differentially co-expressed links were detected by Pearson's correlation, the empirical Bayesian (EB) approach and Weighted Gene Co-expression Network Analysis (WGCNA). The co-expressed gene pairs were merged by a rank-based algorithm. We obtained 842; 371; 2,883 and 1,595 co-expressed gene pairs from the co-expression networks of the STRING database, Pearson's correlation EB method and WGCNA, respectively. Two hundred and eighty-one differentially co-expressed (DC) gene pairs were obtained from the merged network using this novel method. Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method. Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC. The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis.

No MeSH data available.


Related in: MedlinePlus