Prediction and Testing of Biological Networks Underlying Intestinal Cancer

Patel, Vishal; Bebek, Gürkan; Mariadason, John M.; Wang, Donghai; Augenlicht, Leonard H.; Chance, Mark R.

doi:10.1371/journal.pone.0012497

Cited by 11 publications

(10 citation statements)

References 54 publications

(62 reference statements)

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“…After imputing interaction edges using sequence homology [ 11 ], this number was increased to 65. However, filtering out paths whose ( i ) average mRNA coexpression was low ( r < |0.6|, a significance threshold validated in similar studies [ 11 , 17 ]) and ( ii ) support of GO annotation association rules based on known signaling pathways and functional annotations [ 11 ] was weak ( p - value > 0.05), the number of Apc -CAN-gene petals was reduced to 24 (Figure 2 ). The petals identified vary in the number of nodes (from 3 - 35) and edges (from 2 - 80) they contain, with some nodes beings shared among multiple petals.…”

Section: Resultsmentioning

confidence: 86%

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PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling

2010

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BackgroundColon cancer is driven by mutations in a number of genes, the most notorious of which is Apc. Though much of Apc's signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in Apc.Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in Apc is observed.ResultsWe developed a pipeline, PETALS, to predict and test likely signaling pathways connecting Apc to other CAN-genes, where the interaction network originating at Apc is defined as a "blossom," with each Apc-CAN-gene subnetwork referred to as a "petal." Known and predicted protein interactions are used to identify an Apc blossom with 24 petals. Then, using a novel measure of bimodality, the coexpression of each petal is evaluated against proteomic (2 D differential In Gel Electrophoresis, 2D-DIGE) measurements from the Apc1638N+/-mouse to test the network-based hypotheses.ConclusionsThe predicted pathways linking Apc and Hapln1 exhibited the highest amount of bimodal coexpression with the proteomic targets, prioritizing the Apc-Hapln1 petal over other CAN-gene pairs and suggesting that this petal may be involved in regulating the observed proteome-level effects. These results not only demonstrate how functional 'omics data can be employed to test in silico predictions of CAN-gene pathways, but also reveal an approach to integrate models of upstream genetic interference with measured, downstream effects.

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Section: Resultsmentioning

confidence: 86%

“…As our current knowledge of molecular networks is incomplete [ 16 ], we use sequence homology to infer these missing data. The details of the Blossom algorithm follow below (see Methods for additional details; refer to Figure 1 in [ 17 ] for a diagram). First, likely false positives from the underlying PPI network are filtered out.…”

Section: Resultsmentioning

confidence: 99%

PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling

2010

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“…AKT1 has also been associated with the APC–CDKN1A network [ 33 ]. Our results showed the negative expression of the cell cycle regulator CDKN1A and WNT signalling pathway regulator APC in a subset of the CTCs, suggesting the involvement of the canonical WNT pathway in colorectal carcinoma.…”

Section: Discussionmentioning

confidence: 99%

Genetic Heterogeneity of Single Circulating Tumour Cells in Colorectal Carcinoma

Hamid

Gopalan

Matos

et al. 2020

IJMS

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The aim of the present study was to isolate and investigate the genetic heterogeneities in single circulating tumour cells (CTCs) from patients with colorectal carcinoma (CRC). Twenty-eight single CTCs were collected from eight patients with CRC using a negative immunomagnetic enrichment method. After validation with glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene expression in 3 colon cancer cell lines, a panel of 19 genes were used to analyse the single CTCs (n = 28), primary colorectal carcinoma tissues (n = 8) and colon carcinoma cells (n = 6) using real-time qPCR. Genetic heterogeneities were assessed by comparing gene expression profiles of single CTCs from the different patients and in the same patient, respectively. Genetic profiling of the single CTCs showed extensive heterogeneities of the selected genes among the CTCs. Hierarchical clustering analyses exhibited two clusters of CTCs with differentially expressed genes, which highlighted different modifications from the primary carcinomas. Further, the genetic heterogeneities were observed between different patients or in the same patient. Finally, AKT1 expression was significantly (p = 0.0129) higher in single CTCs from CRC of advanced pathological stages (III or IV) CRC than in CTCs from CRC of early stages (I or II). Our findings suggest that single-cell genetic analysis can monitor the genetic heterogeneities and guide the personalised therapeutic targets in clinical sectors.

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“…Information Flow Patel et al, 2010)-when mutated with APC, increase the tumor burden. In a recent study, Bebek et al, (2010) present a pipeline where bimodality of coexpresssion is used to prioritize proteomics targets identified in a mouse model of colorectal cancer.…”

Section: Introductionmentioning

confidence: 99%

Vavien: An Algorithm for Prioritizing Candidate Disease Genes Based on Topological Similarity of Proteins in Interaction Networks

Erten

Bebek

Koyutürk

2011

Journal of Computational Biology

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Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow-based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the ''topological profile'' of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, Vavien, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. Vavien outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.

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Prediction and Testing of Biological Networks Underlying Intestinal Cancer

Cited by 11 publications

References 54 publications

PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling

PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling

Genetic Heterogeneity of Single Circulating Tumour Cells in Colorectal Carcinoma

Vavien: An Algorithm for Prioritizing Candidate Disease Genes Based on Topological Similarity of Proteins in Interaction Networks

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