- Open Access
A comparative study of cancer proteins in the human protein-protein interaction network
© Sun and Zhao; licensee BioMed Central Ltd. 2010
- Published: 01 December 2010
Cancer is a complex disease. So far, many genes have been reported to involve in the development of cancer. Rather than the traditional approach to studying individual genes or loci, a systematic investigation of cancer proteins in the human protein-protein interaction network may provide important biological information for uncovering the molecular mechanisms of cancer and, potentially, other complex diseases.
We explored global and local network characteristics of the proteins encoded by cancer genes (cancer proteins) in the human interactome. We found that the network topology of the cancer proteins was much different from that of the proteins encoded by essential genes (essential proteins) or control genes (control proteins). Relative to the essential proteins or control proteins, cancer proteins tended to have higher degree, higher betweenness, shorter shortest-path distance, and weaker clustering coefficient in the human interactome. We further separated the cancer proteins into two groups (recessive and dominant cancer proteins) and compared their topological features. Recessive cancer proteins had higher betweenness than dominant cancer proteins, while their degree distribution and characteristic shortest path distance were also significantly different. Finally, we found that cancer proteins were not randomly distributed in the human interactome and they connected strongly with each other.
Our study revealed much stronger protein-protein interaction characteristics of cancer proteins relative to the essential proteins or control proteins in the whole human interactome. We also found stronger network characteristics of recessive than dominant cancer proteins. The results are helpful for cancer candidate gene prioritization and verification, biomarker discovery, and, ultimately, understanding the etiology of cancer at the systems biological level.
- Degree Distribution
- Cluster Coefficient
- Cancer Gene
- Protein Interaction Network
- Control Protein
Cancer is a common complex disease. Many genetic factors and genes have been reported to play an important role in its pathogenesis. Identification of genes that activate or accelerate the development of cancer (referred as ‘cancer genes’) has been one of the major goals in cancer research. During the past three decades, a great number of cancer genes have been reported. Recently, a census of human cancer genes was conducted based on the published literature  and the annotated genes were stored in the Cancer Gene Census database . Although this collection of genes is still likely incomplete or error-prone, it offers us a great opportunity to examine the general features of many cancer genes, rather than individual gene, at one time. Indeed, several important features have been found based on these genes, such as sequence alteration, mutations in different cancers, protein domains and partial network properties [1, 3].
Recently, investigation of interactions between proteins encoded by disease genes in the human protein-protein interaction network (i.e., the human interactome) has become one of the major and powerful approaches to elucidating the molecular mechanisms underlying the complex diseases [4–10]. Cancer is the most severe human disease, costing us many billions of dollars each year. Thus, a systematic examination of the proteins encoded by cancer genes (cancer proteins) in the human interactome may help us identify new candidate genes, improve candidate gene prioritization methods, and have a deeper understanding of the genetic landscape of cancer. This research topic emerged recently. For example, we have seen recent reports on the exploration of cancer related networks including the topological analysis [3, 11], prediction of cancer candidate genes  and their dynamic modularity . Meanwhile, several studies reported that essential genes tended to encode hubs [14, 15], but another study revealed that vast majority of disease genes are nonessential and have no tendency toward higher degree in the human protein-protein interaction network. Such incongruence of disease and essential gene properties in the human interactome requires further investigations. Moreover, so far these studies have explored only limited network properties or focused on one specific type of cancers. To have a global view of the interactions of cancer proteins, it is necessary to systematically investigate the network properties of cancer proteins in the human interactome, to compare properties of the subtypes of cancer proteins, and also to compare cancer proteins with essential proteins.
In this study, we explored the global and local network characteristics of cancer proteins through mapping the cancer proteins into the human interactome. For the global properties, we performed four different topological measurements, i.e., degree, betweenness, clustering coefficient and shortest-path distance. To assess the observed topological features of cancer proteins, we compared them to other proteins encoded by essential genes (essential proteins) or control genes (control proteins). For local network characteristics, we extracted subnetworks for the cancer proteins and compared them with the randomly generated networks. Our results revealed that (1) cancer proteins display a global topology that is significantly different from that of essential proteins or control proteins, (2) cancer proteins could form non-random networks, and (3) recessive cancer proteins had even stronger network characteristics than dominant cancer proteins.
Construction of the human interactome
Investigation of the protein interaction characteristics for a set of proteins requires a complete and accurate whole protein interaction network. In this study, we integrated all the available human protein-protein interactions from six major protein interaction databases but restricted only those with experimental evidence. The six databases are Human Protein Reference Database (HPRD, release 7) , Biomolecular Interaction Network Database (BIND, 20060525)  , IntAct (downloaded on 2007-09-28) , Molecular INTeration database (MINT, downloaded on 2007-06-28) , Reactome (version 24)  and the Database of Interacting Proteins (DIP, version Hsapi20070707) . After removing the redundancy and self-interactions, we constructed a comprehensive and reliable human protein-protein interaction network, which contained 53,255 unique interactions among 10,549 proteins. We considered these interactions as an approximation of a full set of human protein-protein interactions, or, the human interactome.
Cancer genes, essential genes and control genes
We obtained 384 cancer genes and their detailed annotations from the Cancer Gene Census database (2009-08-04 version) . The Cancer Gene Census database is updated regularly based on the original census data summarized in Nature Review Cancer . Among the 384 cancer genes, 382 could be mapped to genes with official gene symbols in the National Center for Biotechnology Information (NCBI) . We considered these genes as cancer candidate genes. Among them, there were 342 genes whose proteins could be found in the human interactome. A cancer gene may act in a dominant or recessive manner . Therefore, we separated these 342 cancer genes into two groups: recessive group, which had 69 cancer genes, and dominant group, which had 273 cancer genes, according to the annotations in the Cancer Gene Census database.
Summary of network properties for five protein sets in the human interactome
No. of genes
No. of proteinsa
Network properties (average value)
Recessive cancer proteins
Dominant cancer proteins
Global network analysis
For each node in each protein set, we applied four topological measures to assess its role in the network : degree and degree distribution, clustering coefficient, betweenness, and shortest-path distance. First, the degree or connectivity of a node, k, is the count (number) of the direct links of this node in the network. For a set of proteins, we plotted degree distribution for more detailed information. Based on the distribution, we had P(k), which is the probability that a node has exactly k links in the set of proteins. The more links a node has, the more important it is in terms of network stability . Second, the clustering coefficient, C, of a node is the ratio of the observed number of direct connections between the node’s immediate network neighbors over the maximum possible number of such connections. Third, the betweenness of a node, B, is defined as the number of shortest paths between all possible pairs of nodes in the network that traverse the node. In a biological network, betweenness measures the ways in which signals can pass through the interaction network . Fourth, for a pair of selected nodes in the network, there are many alternative paths between them. The path with the smallest number of links is defined as the shortest path. The number of links passing through in the shortest path is defined as shortest-path distance (L). Because the two nodes in a pair might belong to the same protein set or to different protein sets, we extended the shortest paths into two categories. The first one is the shortest-path between one node belonging to one protein set and another node not belonging to that protein set. The distance was defined as the general shortest-path distance (abbreviated as gSPD). The second one is the shortest-path between two nodes from the same protein set. The distance was defined as the characteristic shortest-path distance (abbreviated as cSPD). For a protein set, we calculated the mean value of all proteins, i.e., average degree, average betweenness, average clustering coefficient or average shortest-path distance.
Construction and randomization of cancer-specific networks
To generate cancer-specific network, we started from the 342 cancer proteins and extracted the protein-protein interactions between these proteins. To test whether the observed cancer-specific networks are not random, we applied an empirical approach. First, we generated 1000 randomized networks with the same number of nodes and links as in the cancer-specific networks using the Erdos-Renyi model  in R igraph package. For each random network, we calculated the average values for three measurements: betweenness (B), clustering coefficient (C), and general shortest-path distance (L). Next, we counted the number of the randomized networks whose average betweenness (n B ) or clustering coefficient (n C ) was higher than the observed betweenness or clustering coefficient, respectively. For general shortest-path distance, we counted the number (n L ) of the randomized networks whose shortest-path distance is shorter than that of the cancer-specific networks. Finally, we calculated empirical P value = n B /1000, n C /1000, or n L /1000, respectively, for these three network topological measurements.
Functional analysis of top cancer proteins
We examined the functional features of cancer proteins that were top ranked in network analysis. We ranked the cancer proteins and selected the top 20 cancer proteins for each network measure. This resulted in a total of 44 cancer proteins after removing the redundancy. We examined the biological significance of these top genes in KEGG database by pathway enrichment analysis. KEGG is a database of biological system that integrates genomic, chemical and functional information through the process of PATHWAY mapping . KEGG PATHWAY database includes about 120 existing pathways and KEGG DISEASE links disease genes, pathways, drugs and diagnostic markers . We calculated P values using the Fisher’s exact test using the human genome as the background and then adjusted the P values by FDR using Benjamini- Hochberg procedure . We applied the following two criteria to identify cancer-enriched pathways: 1) FDR P value was less than 0.001 and 2) the number of cancer proteins involved in a pathway was at least 5.
Global properties of cancer proteins
In a biological network, topological properties of nodes are important for understanding the underlying biological mechanisms . In this study, we examined four important network measurements: degree, betweenness, clustering coefficient and shortest-path distance. Table 1 summarizes the global properties of the five protein sets.
Cancer proteins tend to have higher degree
The average degree of the 342 cancer proteins was 25.41, which was significantly higher than that of the essential proteins (18.39, Wilcoxon test, P = 1.2 × 10–6) or that of the control proteins (8.25, P < 2.2 × 10–16). The average degree of the cancer proteins is approximately 3.1 times that of the control proteins. The ratio is higher than that (2.1) based on the predicted protein-protein interactions in a previous study . Thus, proteins encoded by cancer genes tend to interact strongly with other proteins and have higher connectivity in the whole network. This observation of higher degree in the cancer proteins than the control proteins supports a recent report that disease genes tend to have higher degree than non-disease genes .
Interestingly, comparing the essential proteins, the cancer proteins had stronger connectivity and tended to be hubs in the human interactome. This observation might reflect that the cancer genes mainly play important role in cell proliferation, cell differentiation and cell death , or might be due to the data bias of the cancer genes toward more studies, as cancer is the most studied disease . Our preliminary study using the homologous genes in yeast and unbiased yeast protein-protein interaction data revealed that this data bias is not the primary factor for the cancer protein features and the conclusion above could still hold in the yeast interactome (data not shown). More work is needed to elucidate this difference.
Cancer proteins tend to have higher betweenness
Cancer proteins tend to have lower clustering coefficient
Cancer proteins tend to have shorter shortest-path distance
Similarly, we found that the average characteristic shortest-path distance (cSPD) of the cancer proteins (3.20) was significantly shorter than that of the essential proteins (3.55, Wilcoxon test, P < 2.2 × 10–16) or that of the control proteins (4.10, P < 2.2 × 10–16) (Figure 4B). For the cancer proteins, the average cSPD (3.20) was significantly shorter than the average gSPDs (3.63) (P < 2.2 × 10–16). Specifically, when distance equaled to 3, the proportion of the cancer proteins for gSPD (38.3%) was much lower than that of the cancer proteins for cSPD (51.3%). Conversely, when distance equaled to 4, the proportion of the cancer proteins for gSPD (42.2%) was much larger than that of the cancer proteins for cSPD (28.5%). This comparison indicates that the efficiency of cancer proteins contacting with each other might be higher than that of cancer proteins contacting with non-cancer proteins in the human protein interaction network.
Strong positive correlation between degree and betweenness of cancer proteins
Weak negative correlation between degree and clustering coefficient of the cancer proteins
Clustering coefficient is based on degree . Similarly, we examined the correlation between degree and clustering coefficient for cancer proteins. The correlation coefficient (r) was -0.14 (Pearson’s product-moment correlation test, P = 0.01) between these two measurements. This negative correlation confirms our previous observation that, although cancer proteins themselves have high connectivity, relative to other proteins and cancer proteins’ degree, their neighbors do not have high density of clusters (Table 1).
Global properties of the recessive and dominant cancer proteins
Among the 72 recessive and 310 dominant cancer genes, 69 and 310 whose proteins could be mapped into the human interactome, respectively. We analyzed their global properties. The results were summarized in Table 1. For average degree, average clustering coefficient and general shortest-path distance (gSPD), no significant difference was detected between the recessive and dominant cancer proteins. The average degree of the recessive cancer proteins (33.03) was higher than that of recessive cancer proteins (23.44). Although the average degree was not significantly different between these two cancer protein sets (Wilcoxon test, P = 0.11), we found that their degree distributions were significantly different (Wilcoxon test, P = 3.1 × 10–8). Moreover, the average betweenness (9.82 × 104) of the recessive cancer proteins was higher than that of the dominant cancer proteins (5.33 × 104, Wilcoxon test, P = 0.049). Finally, the average cSPD of the recessive cancer proteins (3.05) was significantly lower than that of the dominant cancer proteins (3.21, Wilcoxon test, P < 2.2 × 10–16). These comparisons revealed that recessive cancer proteins have even stronger network topological properties, suggesting different inheritable mechanisms of the recessive and dominant cancer genes in cancer.
Nonrandomness of cancer-specific subnetworks
We next assessed whether the cancer-specific subnetworks are non-random. We generated 1000 random subnetworks with the same number of nodes and links in the cancer-specific networks, and then compared their average degree, average shortest-path distance and average clustering coefficient. Among the 254 nodes in cancer specific subnetworks, 62 (22.4%) had high connectivity (degree > 5), while among the 254,000 nodes in the 1000 random subnetworks, only 0.2% of the nodes had high connectivity (degree > 5). Among 1000 random networks, the average shortest-path distance was 3.71 and the average clustering coefficient was 0.02. The empirical P values for shortest-path distance and clustering coefficient were zero. This evaluation revealed that the cancer-specific networks are not random.
It is worth noting that, in the cancer subnetworks, several cancer proteins are critical in the network topology: BRCA1, EP300, PIK3R1 and TP53. All of them are recessive cancer proteins. When we removed them from the cancer-specific network, the network centralization decreased from 0.141 to 0.126. Here, network centralization measures the degree of the effect when removing some central nodes in the whole network . The result indicates that after removing the four proteins from the cancer-specific network, the cancer-specific network become much looser. This network feature supports the experimental evidence of these genes being critical in the pathology of cancer.
Pathways enriched in top cancer proteins
Based on the comparison of the basic network properties among cancer proteins, essential proteins and control proteins, we selected the top 20 cancer proteins for each network measurement (connectivity, betweenness, clustering coefficient, gSPD and cSPD). This resulted in a total of 44 non-redundant cancer proteins. The strong overlap of top cancer proteins among these five network measurements suggests that these network properties are effective in detecting cancer protein features.
Pathways significantly enriched in the top cancer proteins ranked by network properties
No. of genea
FDR P valuec
1.8 × 10 –19
4.5 × 10–18
Chronic myeloid leukemia
1.3 × 10 –15
1.6 × 10–14
1.3 × 10 –13
1.1 × 10–12
1.3 × 10 –12
8.4 × 10–12
Non-small cell lung cancer
8.2 × 10–11
4.1 × 10–10
2.0 × 10–10
8.4 × 10–10
4.6 × 10–10
1.7 × 10–9
8.1 × 10–10
2.5 × 10–9
1.0 × 10–9
2.9 × 10–9
1.2 × 10–9
3.0 × 10–9
Acute myeloid leukemia
4.4 × 10–9
1.0 × 10–8
ErbB signaling pathway
6.7 × 10–9
1.4 × 10–8
Renal cell carcinoma
2.1 × 10–8
4.0 × 10–8
1.1 × 10–7
1.9 × 10–7
Small cell lung cancer
1.2 × 10–7
2.0 × 10–7
Jak-STAT signaling pathway
1.0 × 10–6
1.6 × 10–6
1.1 × 10–6
1.6 × 10–6
6.6 × 10–6
9.1 × 10–6
Wnt signaling pathway
9.6 × 10–6
1.3 × 10–5
T cell receptor signaling pathway
4.9 × 10–5
6.2 × 10–5
7.8 × 10–5
9.3 × 10–5
Natural killer cell mediated cytotoxicity
3.7 × 10–4
4.2 × 10–4
Ubiquitin mediated proteolysis
4.0 × 10–4
4.3 × 10–4
MAPK signaling pathway
5.6 × 10–4
5.9 × 10–4
In this study, we explored the global characteristics of cancer proteins in the human protein- protein interaction network. Based on the four topological measurements, we found that the cancer proteins had significantly different topological properties compared to the essential or control proteins. Specifically, cancer proteins tended to have higher connectivity and betweenness, shorter shortest-path distance, and weaker clustering coefficient than other proteins. We also found recessive cancer proteins had even stronger network characteristics than dominant cancer proteins. Our results suggest that protein-protein interaction features of cancer genes are important for our understanding the etiology of cancers and, potentially, for other complex diseases.
This work was partially supported by NIH research grants (R21AA017437 and R03LM009598), Vanderbilt-Ingram Cancer Center and the SPORE in GI Cancer Grant (50CA95103), and an NARSAD 2008 Young Investigator Award to Z.Z. Publication of thissupplement was made possible with support from the International Society of Intelligent Biological Medicine (ISIBM).
This article has been published as part of BMC Genomics Volume 11 Supplement 3, 2010: The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2164/11?issue=S3.
- Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR: A census of human cancer genes. Nat Rev Cancer. 2004, 4: 177-183. 10.1038/nrc1299.PubMed CentralView ArticlePubMedGoogle Scholar
- Cancer Gene Census. [http://www.sanger.ac.uk/genetics/CGP/Census/]
- Jonsson PF, Bates PA: Global topological features of cancer proteins in the human interactome. Bioinformatics. 2006, 22: 2291-2297. 10.1093/bioinformatics/btl390.PubMed CentralView ArticlePubMedGoogle Scholar
- Ideker T, Sharan R: Protein networks in disease. Genome Res. 2008, 18: 644-65. 10.1101/gr.071852.107.PubMed CentralView ArticlePubMedGoogle Scholar
- Oti M, Snel B, Huynen MA, Brunner HG: Predicting disease genes using protein- protein interactions. J Med Genet. 2006, 43: 691-698. 10.1136/jmg.2006.041376.PubMed CentralView ArticlePubMedGoogle Scholar
- Feldman I, Rzhetsky A, Vitkup D: Network properties of genes harboring inherited disease mutations. Proc Natl Acad Sci U S A. 2008, 105: 4323-4328. 10.1073/pnas.0701722105.PubMed CentralView ArticlePubMedGoogle Scholar
- Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL: The human disease network. Proc Natl Acad Sci U S A. 2007, 104: 8685-8690. 10.1073/pnas.0701361104.PubMed CentralView ArticlePubMedGoogle Scholar
- Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, Cho RJ, Chen RO, Brownstein BH, Cobb JP, Tschoeke SK: A network-based analysis of systemic inflammation in humans. Nature. 2005, 437: 1032-1037. 10.1038/nature03985.View ArticlePubMedGoogle Scholar
- Sun J, Jia P, Fanous AH, Webb BT, van den Oord EJCG, Kendler KS, Zhao Z: A multi-dimensional evidence-based candidate gene prioritization approach for complex diseases. Bioinformatics. 2009, 25: 2595-2602. 10.1093/bioinformatics/btp428.PubMed CentralView ArticlePubMedGoogle Scholar
- Guo AY, Sun J, Riley BP, Thiselton DL, Kendler KS, Zhao Z: The dystrobrevin binding protein 1 (DTNBP1) gene: features and networks. Mol Psychiatry. 2009, 14: 18-29. 10.1038/mp.2008.88.PubMed CentralView ArticlePubMedGoogle Scholar
- Platzer A, Perco P, Lukas A, Mayer B: Characterization of protein-interaction networks in tumors. BMC Bioinformatics. 2007, 8: 224-10.1186/1471-2105-8-224.PubMed CentralView ArticlePubMedGoogle Scholar
- Aragues R, Sander C, Oliva B: Predicting cancer involvement of genes from heterogeneous data. BMC Bioinformatics. 2008, 9: 172-10.1186/1471-2105-9-172.PubMed CentralView ArticlePubMedGoogle Scholar
- Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL: Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009, 27: 199-204. 10.1038/nbt.1522.View ArticlePubMedGoogle Scholar
- Jeong H, Mason SP, Barabasi AL, Oltvai ZN: Lethality and centrality in protein networks. Nature. 2001, 411: 41-42. 10.1038/35075138.View ArticlePubMedGoogle Scholar
- Yu H, Greenbaum D, Xin Lu H, Zhu X, Gerstein M: Genomic analysis of essentiality within protein networks. Trends Genet. 2004, 20: 227-231. 10.1016/j.tig.2004.04.008.View ArticlePubMedGoogle Scholar
- Peri S, Navarro JD, Amanchy R, Kristiansen TZ, Jonnalagadda CK, Surendranath V, Niranjan V, Muthusamy B, Gandhi TK, Gronborg M: Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 2003, 13: 2363-2371. 10.1101/gr.1680803.PubMed CentralView ArticlePubMedGoogle Scholar
- Bader GD, Donaldson I, Wolting C, Ouellette BF, Pawson T, Hogue CW: BIND--The Biomolecular Interaction Network Database. Nucleic Acids Res. 2001, 29: 242-245. 10.1093/nar/29.1.242.PubMed CentralView ArticlePubMedGoogle Scholar
- Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A: IntAct: an open source molecular interaction database. Nucleic Acids Res. 2004, 32: D452-455. 10.1093/nar/gkh052.PubMed CentralView ArticlePubMedGoogle Scholar
- Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G: MINT: a Molecular INTeraction database. FEBS Lett. 2002, 513: 135-140. 10.1016/S0014-5793(01)03293-8.View ArticlePubMedGoogle Scholar
- Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L: Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 2005, 33: D428-432. 10.1093/nar/gki072.PubMed CentralView ArticlePubMedGoogle Scholar
- Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D: The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 2004, 32: D449-451. 10.1093/nar/gkh086.PubMed CentralView ArticlePubMedGoogle Scholar
- NCBI. [http://www.ncbi.nlm.nih.gov/]
- Volinia S, Mascellani N, Marchesini J, Veronese A, Ormondroyd E, Alder H, Palatini J, Negrini M, Croce CM: Genome wide identification of recessive cancer genes by combinatorial mutation analysis. PLoS ONE. 2008, 3: e3380-10.1371/journal.pone.0003380.PubMed CentralView ArticlePubMedGoogle Scholar
- Blake JA, Eppig JT, Bult CJ, Kadin JA, Richardson JE: The Mouse Genome Database (MGD): updates and enhancements. Nucleic Acids Res. 2006, 34: D562-567. 10.1093/nar/gkj085.PubMed CentralView ArticlePubMedGoogle Scholar
- Mouse Genome Informatics. [http://www.informatics.jax.org/]
- Barabasi AL, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004, 5: 101-113. 10.1038/nrg1272.View ArticlePubMedGoogle Scholar
- Albert R, Jeong H, Barabasi AL: Error and attack tolerance of complex networks. Nature. 2000, 406: 378-382. 10.1038/35019019.View ArticlePubMedGoogle Scholar
- Erdős P, Rényi A: On the evolution of random graphs. Publ Math Inst Hung Acad Sci. 1960, 5: 17-60.Google Scholar
- Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008, 36: D480-484. 10.1093/nar/gkm882.PubMed CentralView ArticlePubMedGoogle Scholar
- KEGG PATHWAY. [http://www.genome.jp/kegg/pathway.html]
- Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. JR Statist Soc B. 1995, 57: 289-300.Google Scholar
- Dong J, Horvath S: Understanding network concepts in modules. BMC Syst Biol. 2007, 1: 24-10.1186/1752-0509-1-24.PubMed CentralView ArticlePubMedGoogle Scholar
- Hanahan D, Weinberg RA: The hallmarks of cancer. Cell. 2000, 100: 57-70. 10.1016/S0092-8674(00)81683-9.View ArticlePubMedGoogle Scholar
- Bunz F: Principles of cancer genetics. 2008, New York: SpringerView ArticleGoogle Scholar
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