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Transcriptomic Profiling for the Autophagy Pathway in Colorectal Cancer. Int J Mol Sci 2020; 21:ijms21197101. [PMID: 32993062 PMCID: PMC7582824 DOI: 10.3390/ijms21197101] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/22/2020] [Accepted: 09/24/2020] [Indexed: 12/13/2022] Open
Abstract
The role of autophagy in colorectal cancer (CRC) pathogenesis appears to be crucial. Autophagy acts both as a tumor suppressor, by removing redundant cellular material, and a tumor-promoting factor, by providing access to components necessary for growth, metabolism, and proliferation. To date, little is known about the expression of genes that play a basal role in the autophagy in CRC. In this study, we aimed to compare the expression levels of 46 genes involved in the autophagy pathway between tumor-adjacent and tumor tissue, employing large RNA sequencing (RNA-seq) and microarray datasets. Additionally, we verified our results using data on 38 CRC cell lines. Gene set enrichment analysis revealed a significant deregulation of autophagy-related gene sets in CRC. The unsupervised clustering of tumors using the mRNA levels of autophagy-related genes revealed the existence of two major clusters: microsatellite instability (MSI)-enriched and -depleted. In cluster 1 (MSI-depleted), ATG9B and LAMP1 genes were the most prominently expressed, whereas cluster 2 (MSI-enriched) was characterized by DRAM1 upregulation. CRC cell lines were also clustered according to MSI-enriched/-depleted subgroups. The moderate deregulation of autophagy-related genes in cancer tissue, as compared to adjacent tissue, suggests a prominent field cancerization or early disruption of autophagy. Genes differentiating these clusters are promising candidates for CRC targeting therapy worthy of further investigation.
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Search for novel STAT3-dependent genes reveals SERPINA3 as a new STAT3 target that regulates invasion of human melanoma cells. J Transl Med 2019; 99:1607-1621. [PMID: 31278347 DOI: 10.1038/s41374-019-0288-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 04/19/2019] [Accepted: 06/05/2019] [Indexed: 02/04/2023] Open
Abstract
Transcription factor signal transducer and activator of transcription 3 (STAT3) is constitutively activated in many cancers and promotes uncontrolled tumor growth and progression through multiple mechanisms. Compelling evidence shows tissue and cell-specific sets of STAT3 targets. Transcriptional targets of STAT3 in melanoma cells are largely unknown. Malignant melanoma is a deadly disease with highly aggressive and drug-resistant behavior. Less than 10% of patients with advanced melanomas reach the 5-year survival, partly due to the aggressive character of the tumor and ineffectiveness of current therapeutics for treating metastatic melanoma. STAT3 is constitutively activated in melanoma cells and plays important roles in its growth and angiogenesis in tumor xenograft studies. Moreover, highly metastatic melanoma cells have higher levels of active STAT3 than poorly metastatic ones. To identify genes that are driven by STAT3 in human melanoma cells, we performed JAK/STAT signaling specific and global gene expression profiling of human melanoma cells with silenced STAT3 expression. For selected genes, we performed computational identification of putative STAT3-binding sites and validated direct interactions STAT3 with defined promoters by using chromatin immunoprecipitation followed by qPCR. We found that STAT3 knockdown does not affect human melanoma cell viability, proliferation, or response to chemotherapeutics. We show that STAT3 regulates a discrete set of genes in melanoma cells, including SERPINA3, a novel STAT3 target gene, which is functionally involved in regulation of melanoma migration and invasion. Knockdown of STAT3 impaired cell migration and invasion, in part via regulation of its transcriptional target SERPINA3. Our results present novel targets and functions of STAT3 in melanoma cells.
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Nguyen T, Mitrea C, Draghici S. Network-Based Approaches for Pathway Level Analysis. ACTA ACUST UNITED AC 2019; 61:8.25.1-8.25.24. [PMID: 30040185 DOI: 10.1002/cpbi.42] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Identification of impacted pathways is an important problem because it allows us to gain insights into the underlying biology beyond the detection of differentially expressed genes. In the past decade, a plethora of methods have been developed for this purpose. The last generation of pathway analysis methods are designed to take into account various aspects of pathway topology in order to increase the accuracy of the findings. Here, we cover 34 such topology-based pathway analysis methods published in the past 13 years. We compare these methods on categories related to implementation, availability, input format, graph models, and statistical approaches used to compute pathway level statistics and statistical significance. We also discuss a number of critical challenges that need to be addressed, arising both in methodology and pathway representation, including inconsistent terminology, data format, lack of meaningful benchmarks, and, more importantly, a systematic bias that is present in most existing methods. © 2018 by John Wiley & Sons, Inc.
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Affiliation(s)
- Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, Nevada
| | - Cristina Mitrea
- Department of Computer Science, Wayne State University, Detroit, Michigan
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, Michigan.,Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan
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Ulanska-Poutanen J, Mieczkowski J, Zhao C, Konarzewska K, Kaza B, Pohl HB, Bugajski L, Kaminska B, Franklin RJ, Zawadzka M. Injury-induced perivascular niche supports alternative differentiation of adult rodent CNS progenitor cells. eLife 2018; 7:30325. [PMID: 30222103 PMCID: PMC6141235 DOI: 10.7554/elife.30325] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 09/03/2018] [Indexed: 01/06/2023] Open
Abstract
Following CNS demyelination, oligodendrocyte progenitor cells (OPCs) are able to differentiate into either remyelinating oligodendrocytes (OLs) or remyelinating Schwann cells (SCs). However, the signals that determine which type of remyelinating cell is generated and the underlying mechanisms involved have not been identified. Here, we show that distinctive microenvironments created in discrete niches within demyelinated white matter determine fate decisions of adult OPCs. By comparative transcriptome profiling we demonstrate that an ectopic, injury-induced perivascular niche is enriched with secreted ligands of the BMP and Wnt signalling pathways, produced by activated OPCs and endothelium, whereas reactive astrocyte within non-vascular area express the dual BMP/Wnt antagonist Sostdc1. The balance of BMP/Wnt signalling network is instructive for OPCs to undertake fate decision shortly after their activation: disruption of the OPCs homeostasis during demyelination results in BMP4 upregulation, which, in the absence of Socstdc1, favours SCs differentiation.
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Affiliation(s)
- Justyna Ulanska-Poutanen
- Laboratory of Molecular Neurobiology, Neurobiology Center, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Jakub Mieczkowski
- Laboratory of Molecular Neurobiology, Neurobiology Center, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Chao Zhao
- Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Katarzyna Konarzewska
- Laboratory of Molecular Neurobiology, Neurobiology Center, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Beata Kaza
- Laboratory of Molecular Neurobiology, Neurobiology Center, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Hartmut Bf Pohl
- Department of Biology, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Lukasz Bugajski
- Laboratory of Cytometry, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Bozena Kaminska
- Laboratory of Molecular Neurobiology, Neurobiology Center, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Robin Jm Franklin
- Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Malgorzata Zawadzka
- Laboratory of Molecular Neurobiology, Neurobiology Center, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
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5
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Hu X, Wei H, Zheng H. Identification of perturbed signaling pathways from gene expression data using information divergence. MOLECULAR BIOSYSTEMS 2017; 13:1797-1804. [PMID: 28702621 DOI: 10.1039/c7mb00285h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Abnormal regulation of signaling pathways is the key causative factor in several diseases. Although many methods have been proposed to identify significantly differential pathways between two conditions via microarray gene expression datasets, most of them concentrate on differences in the pathway components-either the differential expression or the correlation of genes in a given pathway. However, as biological functional units, signaling pathways may have diverse activity patterns across different biological contexts. In order to detect overall changes in pathways, we propose an analysis model called SPAID (Signaling Pathway Analysis based on Information Divergence). SPAID is based on the concept of information divergence, which can be used to compare two conditions by computing the differential probability distribution of the regulation capacity. We compared SPAID with several classical algorithms using different datasets, and the results indicate that SPAID produces higher repeatability, has better performance and universality, and extracts more comprehensive information regarding the underlying biological processes. In conclusion, by introducing the idea of information divergence, our study measures differences in pathways from an overall perspective and will provide a complementary analysis framework for pathway analysis.
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Affiliation(s)
- Xinying Hu
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, People's Republic of China.
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6
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Lee J, Jo K, Lee S, Kang J, Kim S. Prioritizing biological pathways by recognizing context in time-series gene expression data. BMC Bioinformatics 2016; 17:477. [PMID: 28155707 PMCID: PMC5259824 DOI: 10.1186/s12859-016-1335-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information. Results In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets. Conclusions Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1335-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jusang Lee
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Kyuri Jo
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sunwon Lee
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea. .,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. .,Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea.
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Jaber Z, Aouad P, Al Medawar M, Bahmad H, Abou-Abbass H, Kobeissy F. Application of Systems Biology to Neuroproteomics: The Path to Enhanced Theranostics in Traumatic Brain Injury. Methods Mol Biol 2016; 1462:139-155. [PMID: 27604717 DOI: 10.1007/978-1-4939-3816-2_9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The application of systems biology tools in analyzing heterogeneous data from multiple sources has become a necessity, especially in biomarker discovery. Such tools were developed with several approaches to address different types of research questions and hypotheses. In the field of neurotrauma and traumatic brain injury (TBI), three distinct approaches have been used so far as systems biology tools, namely functional group categorization, pathway analysis, and protein-protein interaction (PPI) networks. The databases allow for query of the system to identify candidate targets which can be further studied to elucidate potential downstream biomarkers indicative of disease progression, severity, and improvement. The various systems biology tools, databases, and strategies that can be implemented on available TBI data in neuroproteomic studies are discussed in this chapter.
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Affiliation(s)
- Zaynab Jaber
- Department of Biochemistry, Graduate School and University Center of CUNY, 365 Fifth Avenue, New York, NY, 10016, USA.
- Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon.
| | - Patrick Aouad
- Department of Biology, Faculty of Arts and Sciences, American University of Beirut, Beirut, Lebanon
| | - Mohamad Al Medawar
- Division of Vascular Endothelium and Microcirculation, Department of Medicine III, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, 01307, Dresden, Germany
| | - Hisham Bahmad
- Department of Anatomy, Cell Biology and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
- Faculty of Medicine, Beirut Arab University, Beirut, Lebanon
| | - Hussein Abou-Abbass
- Faculty of Medicine, Beirut Arab University, Beirut, Lebanon
- Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Firas Kobeissy
- Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon.
- Department of Psychiatry, Center for Neuroproteomics and Biomarkers Research, University of Florida, 4000 SW 23rd St., Apt. 5-204, Gainesville, FL, 32608, USA.
- Banyan Biomarkers, Inc, Alachua, FL, USA.
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8
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Ibrahim M, Jassim S, Cawthorne MA, Langlands K. A MATLAB tool for pathway enrichment using a topology-based pathway regulation score. BMC Bioinformatics 2014; 15:358. [PMID: 25367050 PMCID: PMC4255424 DOI: 10.1186/s12859-014-0358-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Accepted: 10/22/2014] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Handling the vast amount of gene expression data generated by genome-wide transcriptional profiling techniques is a challenging task, demanding an informed combination of pre-processing, filtering and analysis methods if meaningful biological conclusions are to be drawn. For example, a range of traditional statistical and computational pathway analysis approaches have been used to identify over-represented processes in microarray data derived from various disease states. However, most of these approaches tend not to exploit the full spectrum of gene expression data, or the various relationships and dependencies. Previously, we described a pathway enrichment analysis tool created in MATLAB that yields a Pathway Regulation Score (PRS) by considering signalling pathway topology, and the overrepresentation and magnitude of differentially-expressed genes (J Comput Biol 19:563-573, 2012). Herein, we extended this approach to include metabolic pathways, and described the use of a graphical user interface (GUI). RESULTS Using input from a variety of microarray platforms and species, users are able to calculate PRS scores, along with a corresponding z-score for comparison. Further pathway significance assessment may be performed to increase confidence in the pathways obtained, and users can view Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway diagrams marked-up to highlight impacted genes. CONCLUSIONS The PRS tool provides a filter in the isolation of biologically-relevant insights from complex transcriptomic data.
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Affiliation(s)
- Maysson Ibrahim
- Department of Applied Computing, the University of Buckingham, Buckingham, MK18 1EG, UK. .,The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK.
| | - Sabah Jassim
- Department of Applied Computing, the University of Buckingham, Buckingham, MK18 1EG, UK.
| | - Michael Anthony Cawthorne
- The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK.
| | - Kenneth Langlands
- The Buckingham Institute for Translational Medicine, the University of Buckingham, Buckingham, MK18 1EG, UK.
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9
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Goel G, Conway KL, Jaeger M, Netea MG, Xavier RJ. Multivariate inference of pathway activity in host immunity and response to therapeutics. Nucleic Acids Res 2014; 42:10288-306. [PMID: 25147207 PMCID: PMC4176341 DOI: 10.1093/nar/gku722] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Developing a quantitative view of how biological pathways are regulated in response to environmental factors is central for understanding of disease phenotypes. We present a computational framework, named Multivariate Inference of Pathway Activity (MIPA), which quantifies degree of activity induced in a biological pathway by computing five distinct measures from transcriptomic profiles of its member genes. Statistical significance of inferred activity is examined using multiple independent self-contained tests followed by a competitive analysis. The method incorporates a new algorithm to identify a subset of genes that may regulate the extent of activity induced in a pathway. We present an in-depth evaluation of specificity, robustness, and reproducibility of our method. We benchmarked MIPA's false positive rate at less than 1%. Using transcriptomic profiles representing distinct physiological and disease states, we illustrate applicability of our method in (i) identifying gene–gene interactions in autophagy-dependent response to Salmonella infection, (ii) uncovering gene–environment interactions in host response to bacterial and viral pathogens and (iii) identifying driver genes and processes that contribute to wound healing and response to anti-TNFα therapy. We provide relevant experimental validation that corroborates the accuracy and advantage of our method.
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Affiliation(s)
- Gautam Goel
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Kara L Conway
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Martin Jaeger
- Department of Internal Medicine and Nijmegen Institute for Infection, Inflammation and Immunity, Radboud University Nijmegen Medical Centre, Nijmegen 6525 GA, The Netherlands
| | - Mihai G Netea
- Department of Internal Medicine and Nijmegen Institute for Infection, Inflammation and Immunity, Radboud University Nijmegen Medical Centre, Nijmegen 6525 GA, The Netherlands
| | - Ramnik J Xavier
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA 02114, USA Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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10
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Song M, Yan Y, Jiang Z. Drug–pathway interaction prediction via multiple feature fusion. ACTA ACUST UNITED AC 2014; 10:2907-13. [DOI: 10.1039/c4mb00199k] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Abstract
High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTBD analysis. We have recently proposed an approach, called Bayesian Pathway Analysis (BPA), for analyzing HTBD using BNs in which known biological pathways are modeled as BNs and pathways that best explain the given HTBD are found. BPA uses the fold change information to obtain an input matrix to score each pathway modeled as a BN. Scoring is achieved using the Bayesian-Dirichlet Equivalent method and significance is assessed by randomization via bootstrapping of the columns of the input matrix. In this study, we improve on the BPA system by optimizing the steps involved in "Data Preprocessing and Discretization", "Scoring", "Significance Assessment", and "Software and Web Application". We tested the improved system on synthetic data sets and achieved over 98% accuracy in identifying the active pathways. The overall approach was applied on real cancer microarray data sets in order to investigate the pathways that are commonly active in different cancer types. We compared our findings on the real data sets with a relevant approach called the Signaling Pathway Impact Analysis (SPIA).
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12
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Periasamy J, Muthuswami M, Rao DB, Tan P, Ganesan K. Stratification and delineation of gastric cancer signaling by in vitro transcription factor activity profiling and integrative genomics. Cell Signal 2014; 26:880-94. [PMID: 24462706 DOI: 10.1016/j.cellsig.2014.01.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2013] [Revised: 01/10/2014] [Accepted: 01/13/2014] [Indexed: 01/12/2023]
Abstract
Integrative functional genomic approaches are helpful in delineating the complex dysregulations in cancers. In the present study, in vitro activity profiling of 45 signaling pathway driven transcription factors in eight gastric cancer cell lines and direct comparison with genome-wide profiles of gastric tumors were performed and the integration resulted in the identification of three categories of factors/pathways: i) highly activated signaling pathways that stem from mutations are the critical oncogenic drivers, ii) constitutively activated stress responsive pathways which are activated not due to genetic alterations, and iii) consistently down-regulated nuclear receptor responsive factors. This functional profiling helps in discriminating therapeutic targets and signaling interactions.
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Affiliation(s)
- Jayaprakash Periasamy
- Cancer Genetics Laboratory, Department of Genetics, Centre for Excellence in Genomic Sciences, School of Biological Sciences, Madurai Kamaraj University, Madurai, India
| | - Muthulakshmi Muthuswami
- Cancer Genetics Laboratory, Department of Genetics, Centre for Excellence in Genomic Sciences, School of Biological Sciences, Madurai Kamaraj University, Madurai, India
| | - Divya Bhaskar Rao
- Cancer Genetics Laboratory, Department of Genetics, Centre for Excellence in Genomic Sciences, School of Biological Sciences, Madurai Kamaraj University, Madurai, India
| | - Patrick Tan
- Duke-NUS Graduate Medical School Singapore, 8 College Road, Singapore
| | - Kumaresan Ganesan
- Cancer Genetics Laboratory, Department of Genetics, Centre for Excellence in Genomic Sciences, School of Biological Sciences, Madurai Kamaraj University, Madurai, India.
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Mitrea C, Taghavi Z, Bokanizad B, Hanoudi S, Tagett R, Donato M, Voichiţa C, Drăghici S. Methods and approaches in the topology-based analysis of biological pathways. Front Physiol 2013; 4:278. [PMID: 24133454 PMCID: PMC3794382 DOI: 10.3389/fphys.2013.00278] [Citation(s) in RCA: 136] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Accepted: 09/15/2013] [Indexed: 11/21/2022] Open
Abstract
The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as “third generation,” have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity.
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Affiliation(s)
- Cristina Mitrea
- Department of Computer Science, Wayne State University Detroit, MI, USA
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