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Network Theoretical Approach to Explore Factors Affecting Signal Propagation and Stability in Dementia’s Protein-Protein Interaction Network. Biomolecules 2022; 12:biom12030451. [PMID: 35327643 PMCID: PMC8946103 DOI: 10.3390/biom12030451] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 02/13/2022] [Accepted: 02/16/2022] [Indexed: 02/04/2023] Open
Abstract
Dementia—a syndrome affecting human cognition—is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer’s disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a whole is required. In this study, we try to find the high-impact core regulating processes and factors involved in dementia’s protein–protein interaction network. We also explore various aspects related to its stability and signal propagation. Using gene interaction databases such as STRING and GeneMANIA, a principal dementia network (PDN) consisting of 881 genes and 59,085 interactions was achieved. It was assortative in nature with hierarchical, scale-free topology enriched in various gene ontology (GO) categories and KEGG pathways, such as negative and positive regulation of apoptotic processes, macroautophagy, aging, response to drug, protein binding, etc. Using a clustering algorithm (Louvain method of modularity maximization) iteratively, we found a number of communities at different levels of hierarchy in PDN consisting of 95 “motif-localized hubs”, out of which, 7 were present at deepest level and hence were key regulators (KRs) of PDN (HSP90AA1, HSP90AB1, EGFR, FYN, JUN, CELF2 and CTNNA3). In order to explore aspects of network’s resilience, a knockout (of motif-localized hubs) experiment was carried out. It changed the network’s topology from a hierarchal scale-free topology to scale-free, where independent clusters exhibited greater control. Additionally, network experiments on interaction of druggable genome and motif-localized hubs were carried out where UBC, EGFR, APP, CTNNB1, NTRK1, FN1, HSP90AA1, MDM2, VCP, CTNNA1 and GRB2 were identified as hubs in the resultant network (RN). We finally concluded that stability and resilience of PDN highly relies on motif-localized hubs (especially those present at deeper levels), making them important therapeutic intervention candidates. HSP90AA1, involved in heat shock response (and its master regulator, i.e., HSF1), and EGFR are most important genes in pathology of dementia apart from KRs, given their presence as KRs as well as hubs in RN.
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Nersisyan S, Novosad V, Engibaryan N, Ushkaryov Y, Nikulin S, Tonevitsky A. ECM-Receptor Regulatory Network and Its Prognostic Role in Colorectal Cancer. Front Genet 2021; 12:782699. [PMID: 34938324 PMCID: PMC8685507 DOI: 10.3389/fgene.2021.782699] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 11/05/2021] [Indexed: 12/12/2022] Open
Abstract
Interactions of the extracellular matrix (ECM) and cellular receptors constitute one of the crucial pathways involved in colorectal cancer progression and metastasis. With the use of bioinformatics analysis, we comprehensively evaluated the prognostic information concentrated in the genes from this pathway. First, we constructed a ECM-receptor regulatory network by integrating the transcription factor (TF) and 5'-isomiR interaction databases with mRNA/miRNA-seq data from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD). Notably, one-third of interactions mediated by 5'-isomiRs was represented by noncanonical isomiRs (isomiRs, whose 5'-end sequence did not match with the canonical miRBase version). Then, exhaustive search-based feature selection was used to fit prognostic signatures composed of nodes from the network for overall survival prediction. Two reliable prognostic signatures were identified and validated on the independent The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) cohort. The first signature was made up by six genes, directly involved in ECM-receptor interaction: AGRN, DAG1, FN1, ITGA5, THBS3, and TNC (concordance index 0.61, logrank test p = 0.0164, 3-years ROC AUC = 0.68). The second hybrid signature was composed of three regulators: hsa-miR-32-5p, NR1H2, and SNAI1 (concordance index 0.64, logrank test p = 0.0229, 3-years ROC AUC = 0.71). While hsa-miR-32-5p exclusively regulated ECM-related genes (COL1A2 and ITGA5), NR1H2 and SNAI1 also targeted other pathways (adhesion, cell cycle, and cell division). Concordant distributions of the respective risk scores across four stages of colorectal cancer and adjacent normal mucosa additionally confirmed reliability of the models.
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Affiliation(s)
- Stepan Nersisyan
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
| | - Victor Novosad
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Narek Engibaryan
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
| | - Yuri Ushkaryov
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Medway School of Pharmacy, University of Kent, Chatham, United Kingdom
| | - Sergey Nikulin
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
- P. Hertsen Moscow Oncology Research Institute—Branch, National Medical Research Radiological Centre, Ministry of Health of Russian Federation, Moscow, Russia
- School of Biomedicine, Far Eastern Federal University, Vladivostok, Russia
| | - Alexander Tonevitsky
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- SRC Bioclinicum, Moscow, Russia
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Sistigu A, Musella M, Galassi C, Vitale I, De Maria R. Tuning Cancer Fate: Tumor Microenvironment's Role in Cancer Stem Cell Quiescence and Reawakening. Front Immunol 2020; 11:2166. [PMID: 33193295 PMCID: PMC7609361 DOI: 10.3389/fimmu.2020.02166] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/10/2020] [Indexed: 12/12/2022] Open
Abstract
Cancer cell dormancy is a common feature of human tumors and represents a major clinical barrier to the long-term efficacy of anticancer therapies. Dormant cancer cells, either in primary tumors or disseminated in secondary organs, may reawaken and relapse into a more aggressive disease. The mechanisms underpinning dormancy entry and exit strongly resemble those governing cancer cell stemness and include intrinsic and contextual cues. Cellular and molecular components of the tumor microenvironment persistently interact with cancer cells. This dialog is highly dynamic, as it evolves over time and space, strongly cooperates with intrinsic cell nets, and governs cancer cell features (like quiescence and stemness) and fate (survival and outgrowth). Therefore, there is a need for deeper insight into the biology of dormant cancer (stem) cells and the mechanisms regulating the equilibrium quiescence-versus-proliferation are vital in our pursuit of new therapeutic opportunities to prevent cancer from recurring. Here, we review and discuss microenvironmental regulations of cancer dormancy and its parallels with cancer stemness, and offer insights into the therapeutic strategies adopted to prevent a lethal recurrence, by either eradicating resident dormant cancer (stem) cells or maintaining them in a dormant state.
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Affiliation(s)
- Antonella Sistigu
- Istituto di Patologia Generale, Università Cattolica del Sacro Cuore, Rome, Italy.,Tumor Immunology and Immunotherapy Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Martina Musella
- Istituto di Patologia Generale, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Claudia Galassi
- Istituto di Patologia Generale, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Ilio Vitale
- IIGM - Italian Institute for Genomic Medicine, c/o IRCSS Candiolo (TO), Candiolo, Italy.,Candiolo Cancer Institute, FPO - IRCCS, Candiolo, Italy
| | - Ruggero De Maria
- Istituto di Patologia Generale, Università Cattolica del Sacro Cuore, Rome, Italy.,Fondazione Policlinico Universitario "A. Gemelli" - IRCCS, Rome, Italy
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4
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A novel method for predicting the progression rate of ALS disease based on automatic generation of probabilistic causal chains. Artif Intell Med 2020; 107:101879. [PMID: 32828438 DOI: 10.1016/j.artmed.2020.101879] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 04/17/2020] [Accepted: 05/12/2020] [Indexed: 01/22/2023]
Abstract
Causal discovery is considered as a major concept in biomedical informatics contributing to diagnosis, therapy, and prognosis of diseases. Probabilistic causality approaches in epidemiology and medicine is a common method for finding relationships between pathogen and disease, environment and disease, and adverse events and drugs. Bayesian Network (BN) is one of the common approaches for probabilistic causality, which is widely used in health-care and biomedical science. Since in many biomedical applications we deal with temporal dataset, the temporal extension of BNs called Dynamic Bayesian network (DBN) is used for such applications. DBNs define probabilistic relationships between parameters in consecutive time points in the form of a graph and have been successfully used in many biomedical applications. In this paper, a novel method was introduced for finding probabilistic causal chains from a temporal dataset with the help of entropy and causal tendency measures. In this method, first, Causal Features Dependency (CFD) matrix is created on the basis of parameters changes in consecutive events of a phenomenon, and then the probabilistic causal graph is constructed from this matrix based on entropy criteria. At the next step, a set of probabilistic causal chains of the corresponding causal graph is constructed by a novel polynomial-time heuristic. Finally, the causal chains are used for predicting the future trend of the phenomenon. The proposed model was applied to the Pooled Resource Open-Access Clinical Trials (PRO-ACT) dataset related to Amyotrophic Lateral Sclerosis (ALS) disease, in order to predict the progression rate of this disease. The results of comparison with Bayesian tree, random forest, support vector regression, linear regression, and multivariate regression show that the proposed algorithm can compete with these methods and in some cases outperforms other algorithms. This study revealed that probabilistic causality is an appropriate approach for predicting the future states of chronic diseases with unknown cause.
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Transcriptomics-based screening of molecular signatures associated with patients overall survival and their key regulators in subtypes of breast cancer. Cancer Genet 2019; 239:62-74. [PMID: 31569063 DOI: 10.1016/j.cancergen.2019.09.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 08/18/2019] [Accepted: 09/15/2019] [Indexed: 12/14/2022]
Abstract
Molecular subtypes of breast cancer are associated with differences in prognosis and strategies of molecular targeted therapies. Gene regulatory mechanisms as one of the reasons might modulate these differences. In the present study, we proposed a comprehensive data analysis and systems biology approach to explore molecular signatures which reduce the chance of patients overall survival and the possible mechanisms of their regulation by transcription factors (TFs) and microRNAs (miRNAs) in the main subtypes of breast tumor consist of Basal like, Her2 enriched, Luminal A and Luminal B breast cancer. In this regards, we used available microarray datasets to assess common differentially expressed genes (DEGs) in breast cancer subtypes. Using Kaplan-Meier curve analysis we identified common DEGs which are associated with decreasing in the overall survival of breast cancer patients. Furthermore, gene regulatory networks (GRNs) were depicted based on TFs and miRNAs with interest target genes. Then GRNs were analyzed and using five algorithms (Control centrality, Betweenness, Degree, Classification, and MCDS) the key regulators were identified for each subtype. In this study, we highlighted mechanisms underlying the regulation of breast cancer molecular signatures by TFs and miRNAs which their alteration reduce the chance of survival rate in each subtype of breast cancer. Our current study in a holistic insight revealed the importance of some genes and their regulators as potential prognostic markers and/or therapeutic targets in breast cancer patients.
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Suhail Y, Cain MP, Vanaja K, Kurywchak PA, Levchenko A, Kalluri R, Kshitiz. Systems Biology of Cancer Metastasis. Cell Syst 2019; 9:109-127. [PMID: 31465728 PMCID: PMC6716621 DOI: 10.1016/j.cels.2019.07.003] [Citation(s) in RCA: 208] [Impact Index Per Article: 41.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 04/29/2019] [Accepted: 06/28/2019] [Indexed: 12/12/2022]
Abstract
Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. The lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multiscale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.
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Affiliation(s)
- Yasir Suhail
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT, USA; Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA
| | - Margo P Cain
- Department of Cancer Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Kiran Vanaja
- Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA
| | - Paul A Kurywchak
- Department of Cancer Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Andre Levchenko
- Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA
| | - Raghu Kalluri
- Department of Cancer Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Kshitiz
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT, USA; Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA.
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Abstract
Background:Time series expression data of genes contain relations among different genes, which are difficult to model precisely. Slime-forming bacteria is one of the three major harmful bacteria types in industrial circulating cooling water systems.Objective:This study aimed at constructing gene regulation network(GRN) for slime-forming bacteria to understand the microbial fouling mechanism.Methods:For this purpose, an Adaptive Elman Neural Network (AENN) to reveal the relationships among genes using gene expression time series is proposed. The parameters of Elman neural network were optimized adaptively by a Genetic Algorithm (GA). And a Pearson correlation analysis is applied to discover the relationships among genes. In addition, the gene expression data of slime-forming bacteria by transcriptome gene sequencing was presented.Results:To evaluate our proposed method, we compared several alternative data-driven approaches, including a Neural Fuzzy Recurrent Network (NFRN), a basic Elman Neural Network (ENN), and an ensemble network. The experimental results of simulated and real datasets demonstrate that the proposed approach has a promising performance for modeling Gene Regulation Networks (GRNs). We also applied the proposed method for the GRN construction of slime-forming bacteria and at last a GRN for 6 genes was constructed.Conclusion:The proposed GRN construction method can effectively extract the regulations among genes. This is also the first report to construct the GRN for slime-forming bacteria.
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Affiliation(s)
- Shengxian Cao
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Yu Wang
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Zhenhao Tang
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
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8
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Ali S, Malik MZ, Singh SS, Chirom K, Ishrat R, Singh RKB. Exploring novel key regulators in breast cancer network. PLoS One 2018; 13:e0198525. [PMID: 29927945 PMCID: PMC6013121 DOI: 10.1371/journal.pone.0198525] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 05/21/2018] [Indexed: 12/18/2022] Open
Abstract
The breast cancer network constructed from 70 experimentally verified genes is found to follow hierarchical scale free nature with heterogeneous modular organization and diverge leading hubs. The topological parameters (degree distributions, clustering co-efficient, connectivity and centralities) of this network obey fractal rules indicating absence of centrality lethality rule, and efficient communication among the components. From the network theoretical approach, we identified few key regulators out of large number of leading hubs, which are deeply rooted from top to down of the network, serve as backbone of the network, and possible target genes. However, p53, which is one of these key regulators, is found to be in low rank and keep itself at low profile but directly cross-talks with important genes BRCA2 and BRCA3. The popularity of these hubs gets changed in unpredictable way at various levels of organization thus showing disassortive nature. The local community paradigm approach in this network shows strong correlation of nodes in majority of modules/sub-modules (fast communication among nodes) and weak correlation of nodes only in few modules/sub-modules (slow communication among nodes) at various levels of network organization.
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Affiliation(s)
- Shahnawaz Ali
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi-110025, India
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Md. Zubbair Malik
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi-110025, India
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Soibam Shyamchand Singh
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Keilash Chirom
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Romana Ishrat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi-110025, India
| | - R. K. Brojen Singh
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
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Khanteymoori AR, Olyaee MH, Abbaszadeh O, Valian M. A novel method for Bayesian networks structure learning based on Breeding Swarm algorithm. Soft comput 2017. [DOI: 10.1007/s00500-017-2557-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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10
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Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems. Adv Bioinformatics 2017; 2017:4827171. [PMID: 28250767 PMCID: PMC5303608 DOI: 10.1155/2017/4827171] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Revised: 10/10/2016] [Accepted: 10/19/2016] [Indexed: 11/17/2022] Open
Abstract
Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
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Hu Y, Hase T, Li HP, Prabhakar S, Kitano H, Ng SK, Ghosh S, Wee LJK. A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data. BMC Genomics 2016; 17:1025. [PMID: 28155657 PMCID: PMC5260093 DOI: 10.1186/s12864-016-3317-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a heterogeneous population of cells, one at a time. However, till date, there has not been a suitable computational methodology for the analysis of such intricate deluge of data, in particular techniques which will aid the identification of the unique transcriptomic profiles difference between the different cellular subtypes. In this paper, we describe the novel methodology for the analysis of single-cell RNA-seq data, obtained from neocortical cells and neural progenitor cells, using machine learning algorithms (Support Vector machine (SVM) and Random Forest (RF)). RESULTS Thirty-eight key transcripts were identified, using the SVM-based recursive feature elimination (SVM-RFE) method of feature selection, to best differentiate developing neocortical cells from neural progenitor cells in the SVM and RF classifiers built. Also, these genes possessed a higher discriminative power (enhanced prediction accuracy) as compared commonly used statistical techniques or geneset-based approaches. Further downstream network reconstruction analysis was carried out to unravel hidden general regulatory networks where novel interactions could be further validated in web-lab experimentation and be useful candidates to be targeted for the treatment of neuronal developmental diseases. CONCLUSION This novel approach reported for is able to identify transcripts, with reported neuronal involvement, which optimally differentiate neocortical cells and neural progenitor cells. It is believed to be extensible and applicable to other single-cell RNA-seq expression profiles like that of the study of the cancer progression and treatment within a highly heterogeneous tumour.
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Affiliation(s)
- Yongli Hu
- Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore, Singapore
- The Systems Biology Institute, Singapore Node hosted at the Institute for Infocomm Research, A*STAR, Singapore, Singapore
| | - Takeshi Hase
- The Systems Biology Institute, Falcon Building 5 F, 5-6-9 Shirokanedai, Minato, Tokyo, 108-0071 Japan
| | - Hui Peng Li
- Computational and Systems Biology, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Genome, #02-01, Singapore, 138672 Singapore
| | - Shyam Prabhakar
- Computational and Systems Biology, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Genome, #02-01, Singapore, 138672 Singapore
| | - Hiroaki Kitano
- The Systems Biology Institute, Falcon Building 5 F, 5-6-9 Shirokanedai, Minato, Tokyo, 108-0071 Japan
| | - See Kiong Ng
- Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore, Singapore
| | - Samik Ghosh
- The Systems Biology Institute, Falcon Building 5 F, 5-6-9 Shirokanedai, Minato, Tokyo, 108-0071 Japan
| | - Lawrence Jin Kiat Wee
- Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore, Singapore
- The Systems Biology Institute, Singapore Node hosted at the Institute for Infocomm Research, A*STAR, Singapore, Singapore
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de Anda-Jáuregui G, Velázquez-Caldelas TE, Espinal-Enríquez J, Hernández-Lemus E. Transcriptional Network Architecture of Breast Cancer Molecular Subtypes. Front Physiol 2016; 7:568. [PMID: 27920729 PMCID: PMC5118907 DOI: 10.3389/fphys.2016.00568] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 11/08/2016] [Indexed: 12/22/2022] Open
Abstract
Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.
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Affiliation(s)
| | | | - Jesús Espinal-Enríquez
- Computational Genomics, National Institute of Genomic MedicineMexico City, Mexico
- Complejidad en Biología de Sistemas, Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de MéxicoMexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics, National Institute of Genomic MedicineMexico City, Mexico
- Complejidad en Biología de Sistemas, Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de MéxicoMexico City, Mexico
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Liu L, Zhao T, Ma M, Wang Y. A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin. SPRINGERPLUS 2016; 5:1911. [PMID: 27867818 PMCID: PMC5095099 DOI: 10.1186/s40064-016-3526-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Accepted: 10/12/2016] [Indexed: 12/23/2022]
Abstract
Background Computer science and mathematical theories are combined to analyze the complex interactions among genes, which are simplified to a network to establish a theoretical model for the analysis of the structure, module and dynamic properties. In contrast, traditional model of gene regulatory networks often lack an effective method for solving gene expression data because of high durational and spatial complexity. In this paper, we propose a new model for constructing gene regulatory networks using back propagation (BP) neural network based on predictive function and network topology. Results Combined with complex nonlinear mapping and self-learning, the BP neural network was mapped into a complex network. Network characteristics were obtained from the parameters of the average path length, average clustering coefficient, average degree, modularity, and map’s density to simulate the real gene network by an artificial network. Through the statistical analysis and comparison of network parameters of Sea Urchin mRNA microarray data under different temperatures, the value of network parameters was observed. Differentially expressed Sea Urchin genes associated with temperature were determined by calculating the difference in the degree of each gene from different networks. Conclusion The new model we developed is suitable to simulate gene regulatory network and has capability of determining differentially expressed genes.
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Affiliation(s)
- Longlong Liu
- School of Mathematical Sciences, Ocean University of China, Qingdao, 266100 People's Republic of China
| | - Tingting Zhao
- School of Mathematical Sciences, Ocean University of China, Qingdao, 266100 People's Republic of China
| | - Meng Ma
- School of Mathematical Sciences, Ocean University of China, Qingdao, 266100 People's Republic of China
| | - Yan Wang
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101 People's Republic of China
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Time-Delayed Models of Gene Regulatory Networks. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:347273. [PMID: 26576197 PMCID: PMC4632181 DOI: 10.1155/2015/347273] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 08/31/2015] [Accepted: 09/14/2015] [Indexed: 11/17/2022]
Abstract
We discuss different mathematical models of gene regulatory networks as relevant to the onset and development of cancer. After discussion of alternative modelling approaches, we use a paradigmatic two-gene network to focus on the role played by time delays in the dynamics of gene regulatory networks. We contrast the dynamics of the reduced model arising in the limit of fast mRNA dynamics with that of the full model. The review concludes with the discussion of some open problems.
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Ali S, Majid A. Can–Evo–Ens: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences. J Biomed Inform 2015; 54:256-69. [DOI: 10.1016/j.jbi.2015.01.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Revised: 12/09/2014] [Accepted: 01/12/2015] [Indexed: 01/10/2023]
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Large differences in global transcriptional regulatory programs of normal and tumor colon cells. BMC Cancer 2014; 14:708. [PMID: 25253512 PMCID: PMC4182786 DOI: 10.1186/1471-2407-14-708] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Accepted: 09/17/2014] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Dysregulation of transcriptional programs leads to cell malfunctioning and can have an impact in cancer development. Our study aims to characterize global differences between transcriptional regulatory programs of normal and tumor cells of the colon. METHODS Affymetrix Human Genome U219 expression arrays were used to assess gene expression in 100 samples of colon tumor and their paired adjacent normal mucosa. Transcriptional networks were reconstructed using ARACNe algorithm using 1,000 bootstrap replicates consolidated into a consensus network. Networks were compared regarding topology parameters and identified well-connected clusters. Functional enrichment was performed with SIGORA method. ENCODE ChIP-Seq data curated in the hmChIP database was used for in silico validation of the most prominent transcription factors. RESULTS The normal network contained 1,177 transcription factors, 5,466 target genes and 61,226 transcriptional interactions. A large loss of transcriptional interactions in the tumor network was observed (11,585; 81% reduction), which also contained fewer transcription factors (621; 47% reduction) and target genes (2,190; 60% reduction) than the normal network. Gene silencing was not a main determinant of this loss of regulatory activity, since the average gene expression was essentially conserved. Also, 91 transcription factors increased their connectivity in the tumor network. These genes revealed a tumor-specific emergent transcriptional regulatory program with significant functional enrichment related to colorectal cancer pathway. In addition, the analysis of clusters again identified subnetworks in the tumors enriched for cancer related pathways (immune response, Wnt signaling, DNA replication, cell adherence, apoptosis, DNA repair, among others). Also multiple metabolism pathways show differential clustering between the tumor and normal network. CONCLUSIONS These findings will allow a better understanding of the transcriptional regulatory programs altered in colon cancer and could be an invaluable methodology to identify potential hubs with a relevant role in the field of cancer diagnosis, prognosis and therapy.
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Xie Y, Wang R, Zhu J. Construction of breast cancer gene regulatory networks and drug target optimization. Arch Gynecol Obstet 2014; 290:749-55. [DOI: 10.1007/s00404-014-3264-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Accepted: 04/17/2014] [Indexed: 01/27/2023]
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Winslow RL, Trayanova N, Geman D, Miller MI. Computational medicine: translating models to clinical care. Sci Transl Med 2013; 4:158rv11. [PMID: 23115356 DOI: 10.1126/scitranslmed.3003528] [Citation(s) in RCA: 119] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Because of the inherent complexity of coupled nonlinear biological systems, the development of computational models is necessary for achieving a quantitative understanding of their structure and function in health and disease. Statistical learning is applied to high-dimensional biomolecular data to create models that describe relationships between molecules and networks. Multiscale modeling links networks to cells, organs, and organ systems. Computational approaches are used to characterize anatomic shape and its variations in health and disease. In each case, the purposes of modeling are to capture all that we know about disease and to develop improved therapies tailored to the needs of individuals. We discuss advances in computational medicine, with specific examples in the fields of cancer, diabetes, cardiology, and neurology. Advances in translating these computational methods to the clinic are described, as well as challenges in applying models for improving patient health.
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Affiliation(s)
- Raimond L Winslow
- The Institute for Computational Medicine, Center for Cardiovascular Bioinformatics and Modeling, and Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA.
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