Open Access Research article

Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network

Hui Liu1, Jianzhong Su13, Junhua Li2, Hongbo Liu1, Jie Lv1, Boyan Li1, Hong Qiao2* and Yan Zhang1*

Author Affiliations

1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China

2 The Second Affiliated Hospital, Harbin Medical University, Harbin, China

3 The Academy of Fundamental and Interdisciplinary Science, Harbin Institute of Technology, Harbin, China

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BMC Systems Biology 2011, 5:158  doi:10.1186/1752-0509-5-158

Published: 11 October 2011

Additional files

Additional file 1:

Category of seed genes. The first column is the Entrez Gene ID for the seed genes. In columns 2 to 5, 1 represents a seed gene that is related with this type of cancers and 0 represents a seed gene that is not related with this type of cancers. The last column shows the types of the seed genes. The seed genes were classified into 4 types; genes related with one type of cancers are marked with 1, genes related with two types of cancers are marked with 2, genes related with three types of cancers are marked with 3, and the genes related with all the four types of cancers are marked with 4.

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Additional file 2:

Comparison of CASN and random subnetworks. Comparison between degree and clustering coefficient of CASN and the three kinds of random subnetworks.

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Additional file 3:

Scores of the optimized genes. The first column is the Entrez Gene ID for the optimized gene. The second and the third columns are the true and random scores respectively, for the optimized genes by the neighborhood-weighting decision rule.

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Additional file 4:

Category of optimized genes. The first column is the Entrez Gene ID for the optimized gene. From columns 2 to 5, 1 represents an optimized gene that interacts with the seed genes related with the type of cancers and 0 represents an optimized gene that does not interact with the seed genes related with the type of cancers. The last column shows the types of the optimized genes. The optimized genes were classified into 4 types, the genes related with one type of cancer are marked with 1, the genes related with two types of cancers are marked with 2, the genes related with three types of cancers are marked with 3, and the genes related with all the four types of cancers are marked with 4.

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Additional file 5:

Degree of the genes in WHPN and CASN. The genes are on the x axes and the degree of the genes is on the y axes. (A) The degrees of the CASN genes (red dots) and non-CASN genes (blue dots) in WHPN; (B) The degrees of the seed genes (red dots), optimized genes (blue dots) and rest potential genes (yellow dots) in CASN.

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Additional file 6:

Comparison of CASN and non-CASN genes. Comparisons are based on mean, median, minimum, maximum and the percentiles 25, 50 and 75.

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Additional file 7:

Comparison of seed genes, optimized genes and rest potential genes. Comparisons are based on mean, median, minimum, maximum and the percentiles 25, 50 and 75.

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Additional file 8:

GO enrichment analysis for WHPN genes. The GO enrichment analysis of CASN genes and non-CASN genes in WHPN are shown in Additional file 8. A P value of < 0.05 was taken to be significant.

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Additional file 9:

GO enrichment analysis for CASN genes. The GO enrichment analysis of seed genes, optimized genes and rest potential genes are shown in Additional file 9. A P value of < 0.05 was taken to be significant.

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Additional file 10:

KEGG enrichment analysis for WHPN genes. The KEGG enrichment analysis of CASN genes and non-CASN genes in WHPN are shown in Additional file 10. A P value of < 0.05 was taken to be significant.

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Additional file 11:

KEGG enrichment analysis for CASN genes. The KEGG enrichment analysis of seed genes, optimized genes and rest potential genes are shown in Additional file 11. A P value of < 0.05 was taken to be significant.

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Additional file 12:

SAM score for the differentially expressed genes. Of the 154 optimized genes, 52 differentially expressed genes and they are ranked by their diff_scorei.

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Additional file 13:

PubMed co-citations of the optimized genes. The first column is the Entrez Gene ID for the optimized genes. Of the 154 optimized genes, 43 genes that were found from a preliminary analysis to be associated with cancers and aberrant methylation in PubMed. The star (*) in the first column marks a gene that was subsequently validated to be methylated aberrantly in cancers by text mining the literature in PubMed.

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Additional file 14:

Diagnostic, prognostic and drug marker validation of optimized genes. After searching PubMed manually, 27 genes were identified as diagnostic markers and 20 genes were identified as prognostic markers for cancers and other complex diseases. Mapped into DrugBank target list, 31 genes can be target as drug response markers.

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