Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer
Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiat...
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doaj-bbf5ae2a6540477ebad0954cad612f992020-11-25T01:51:03ZengMDPI AGData2306-57292019-06-01428110.3390/data4020081data4020081Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for CancerRaihanul Bari Tanvir0Tasmia Aqila1Mona Maharjan2Abdullah Al Mamun3Ananda Mohan Mondal4School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USASchool of Computing and Information Sciences, Florida International University, Miami, FL 33199, USASchool of Computing and Information Sciences, Florida International University, Miami, FL 33199, USASchool of Computing and Information Sciences, Florida International University, Miami, FL 33199, USASchool of Computing and Information Sciences, Florida International University, Miami, FL 33199, USATwo graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)—are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient ≥ 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed—maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer.https://www.mdpi.com/2306-5729/4/2/81bipartite graphcliquenetwork biomarkerPearson correlation coefficient (PCC)gene co-expression network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Raihanul Bari Tanvir Tasmia Aqila Mona Maharjan Abdullah Al Mamun Ananda Mohan Mondal |
spellingShingle |
Raihanul Bari Tanvir Tasmia Aqila Mona Maharjan Abdullah Al Mamun Ananda Mohan Mondal Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer Data bipartite graph clique network biomarker Pearson correlation coefficient (PCC) gene co-expression network |
author_facet |
Raihanul Bari Tanvir Tasmia Aqila Mona Maharjan Abdullah Al Mamun Ananda Mohan Mondal |
author_sort |
Raihanul Bari Tanvir |
title |
Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer |
title_short |
Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer |
title_full |
Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer |
title_fullStr |
Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer |
title_full_unstemmed |
Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer |
title_sort |
graph theoretic and pearson correlation-based discovery of network biomarkers for cancer |
publisher |
MDPI AG |
series |
Data |
issn |
2306-5729 |
publishDate |
2019-06-01 |
description |
Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)—are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient ≥ 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed—maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer. |
topic |
bipartite graph clique network biomarker Pearson correlation coefficient (PCC) gene co-expression network |
url |
https://www.mdpi.com/2306-5729/4/2/81 |
work_keys_str_mv |
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