Graph Theoretic Concepts as the Building Blocks for Disease Initiation and Progression at Protein Network Level: Identification and Challenges

Mondal, Ananda Mohan; Schultz, Cornelia Ada; Sheppard, Markea; Carson, Jasmine; Tanvir, Raihanul Bari; Aqila, Tasmia

doi:10.1109/bibm.2018.8621417

Cited by 4 publications

(3 citation statements)

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“…The advancement in research and technology has been slowly shifting the focus of lung cancer diagnosis, prognosis and treatment towards understanding the underlying cause of disease progression using protein-protein interaction (PPI) networks, gene co-expression networks and molecular pathways. Though the PPI and co-expression networks are static in nature, these come with rich information about the dynamic processes such as behavior of genetic networks in response to DNA damage [6], prediction of protein subcellular localization [7][8][9][10][11][12], protein function [13], genetic interaction [14], process of aging [15], and protein network biomarkers [16][17][18][19][20][21][22]. The networks are of special interest because the genes do not act alone.…”

Section: Introductionmentioning

confidence: 99%

“…They act as a group to achieve a collective goal. In their recent work, Mondal et al [19] showed that proteins or genes achieved their collective goals by forming clique-like and bipartite graphs, which could be the building blocks for disease initiation and progression. Using these building blocks, Tanvir et al [20] discovered network modules related to cancers from gene co-expression networks.…”

Section: Introductionmentioning

confidence: 99%

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Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

et al. 2020

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Background Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. Results The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. Conclusion A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

et al. 2020

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“…The main goal of this paper is to explore the existence of clique-bipartite-like network modules in actual gene network for cancer. Mondal et al [32] showed that clique-like structures and bipartite graphs could be the building blocks for disease progression, Figure 2 in [32]. The rationale is that a group of proteins or genes work together by forming a network (a clique-like structure) to accomplish a specific function, which could be related to a disease stage [32] and bipartite structure represents the cross-talk among genes between two disease stages.…”

Section: Introductionmentioning

confidence: 99%

Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer

Tanvir

Aqila

Maharjan

et al. 2019

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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.

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Cancer Biomarker Discovery from Gene Co-expression Networks Using Community Detection Methods

Tanvir

Mondal

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Graph Theoretic Concepts as the Building Blocks for Disease Initiation and Progression at Protein Network Level: Identification and Challenges

Cited by 4 publications

References 23 publications

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer

Cancer Biomarker Discovery from Gene Co-expression Networks Using Community Detection Methods

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