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Hegde M, Girisa S, Kunnumakkara AB. A compilation of bioinformatic approaches to identify novel downstream targets for the detection and prophylaxis of cancer. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2023; 134:75-113. [PMID: 36858743 DOI: 10.1016/bs.apcsb.2022.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
The paradigm of cancer genomics has been radically changed by the development in next-generation sequencing (NGS) technologies making it possible to envisage individualized treatment based on tumor and stromal cells genome in a clinical setting within a short timeframe. The abundance of data has led to new avenues for studying coordinated alterations that impair biological processes, which in turn has increased the demand for bioinformatic tools for pathway analysis. While most of this work has been concentrated on optimizing certain algorithms to obtain quicker and more accurate results. Large volumes of these existing algorithm-based data are difficult for the biologists and clinicians to access, download and reanalyze them. In the present study, we have listed the bioinformatics algorithms and user-friendly graphical user interface (GUI) tools that enable code-independent analysis of big data without compromising the quality and time. We have also described the advantages and drawbacks of each of these platforms. Additionally, we emphasize the importance of creating new, more user-friendly solutions to provide better access to open data and talk about relevant problems like data sharing and patient privacy.
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Affiliation(s)
- Mangala Hegde
- Cancer Biology Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology (IIT) Guwahati, Guwahati, Assam, India
| | - Sosmitha Girisa
- Cancer Biology Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology (IIT) Guwahati, Guwahati, Assam, India
| | - Ajaikumar B Kunnumakkara
- Cancer Biology Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology (IIT) Guwahati, Guwahati, Assam, India.
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Iliopoulos A, Beis G, Apostolou P, Papasotiriou I. Complex Networks, Gene Expression and Cancer Complexity: A Brief Review of Methodology and Applications. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191017093504] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
In this brief survey, various aspects of cancer complexity and how this complexity can
be confronted using modern complex networks’ theory and gene expression datasets, are described.
In particular, the causes and the basic features of cancer complexity, as well as the challenges
it brought are underlined, while the importance of gene expression data in cancer research
and in reverse engineering of gene co-expression networks is highlighted. In addition, an introduction
to the corresponding theoretical and mathematical framework of graph theory and complex
networks is provided. The basics of network reconstruction along with the limitations of gene
network inference, the enrichment and survival analysis, evolution, robustness-resilience and cascades
in complex networks, are described. Finally, an indicative and suggestive example of a cancer
gene co-expression network inference and analysis is given.
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Affiliation(s)
- A.C. Iliopoulos
- Research and Development Department, Research Genetic Cancer Centre S.A., Florina, Greece
| | - G. Beis
- Research and Development Department, Research Genetic Cancer Centre S.A., Florina, Greece
| | - P. Apostolou
- Research and Development Department, Research Genetic Cancer Centre S.A., Florina, Greece
| | - I. Papasotiriou
- Research Genetic Cancer Centre International GmbH, Zug, Switzerland
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Stein-O'Brien GL, Arora R, Culhane AC, Favorov AV, Garmire LX, Greene CS, Goff LA, Li Y, Ngom A, Ochs MF, Xu Y, Fertig EJ. Enter the Matrix: Factorization Uncovers Knowledge from Omics. Trends Genet 2018; 34:790-805. [PMID: 30143323 PMCID: PMC6309559 DOI: 10.1016/j.tig.2018.07.003] [Citation(s) in RCA: 111] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 06/01/2018] [Accepted: 07/16/2018] [Indexed: 12/20/2022]
Abstract
Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.
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Affiliation(s)
- Genevieve L Stein-O'Brien
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA; Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Raman Arora
- Department of Computer Science, Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD, USA
| | - Aedin C Culhane
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Alexander V Favorov
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA; Vavilov Institute of General Genetics, Moscow, Russia
| | | | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, PA, USA; Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, PA, USA
| | - Loyal A Goff
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Yifeng Li
- Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON, Canada
| | - Aloune Ngom
- School of Computer Science, University of Windsor, Windsor, ON, Canada
| | - Michael F Ochs
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA
| | - Yanxun Xu
- Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Elana J Fertig
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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Senger S, Ingano L, Freire R, Anselmo A, Zhu W, Sadreyev R, Walker WA, Fasano A. Human Fetal-Derived Enterospheres Provide Insights on Intestinal Development and a Novel Model to Study Necrotizing Enterocolitis (NEC). Cell Mol Gastroenterol Hepatol 2018; 5:549-568. [PMID: 29930978 PMCID: PMC6009798 DOI: 10.1016/j.jcmgh.2018.01.014] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2017] [Accepted: 01/18/2018] [Indexed: 12/29/2022]
Abstract
BACKGROUND & AIMS Untreated necrotizing enterocolitis (NEC) can lead to massive inflammation resulting in intestinal necrosis with a high mortality rate in preterm infants. Limited access to human samples and relevant experimental models have hampered progress in NEC pathogenesis. Earlier evidence has suggested that bacterial colonization of an immature and developing intestine can lead to an abnormally high inflammatory response to bacterial bioproducts. The aim of our study was to use human fetal organoids to gain insights into NEC pathogenesis. METHODS RNA sequencing analysis was performed to compare patterns of gene expression in human fetal-derived enterospheres (FEnS) and adult-derived enterospheres (AEnS). Differentially expressed genes were analyzed using computational techniques for dimensional reduction, clustering, and gene set enrichment. Unsupervised cluster analysis, Gene Ontology, and gene pathway analysis were used to predict differences between gene expression of samples. Cell monolayers derived from FEnS and AEnS were evaluated for epithelium function and responsiveness to lipopolysaccharide and commensal bacteria. RESULTS Based on gene expression patterns, FEnS clustered according to their developmental age in 2 distinct groups: early and late FEnS, with the latter more closely resembling AEnS. Genes involved in maturation, gut barrier function, and innate immunity were responsible for these differences. FEnS-derived monolayers exposed to either lipopolysaccharide or commensal Escherichia coli showed that late FEnS activated gene expression of key inflammatory cytokines, whereas early FEnS monolayers did not, owing to decreased expression of nuclear factor-κB-associated machinery. CONCLUSIONS Our results provide insights into processes underlying human intestinal development and support the use of FEnS as a relevant human preclinical model for NEC. Accession number of repository for expression data: GSE101531.
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Key Words
- AD, adult duodenal
- AEnS, adult-derived enterospheres
- CLDN, claudin
- CXCL, chemokine (C-X-C motif) ligand
- DMEM, Dulbecco's modified Eagle medium
- EGF, epidermal growth factor
- Enteroids
- FDR, false discovery rate
- FEnS, fetal-derived enterospheres
- FITC, fluorescein isothiocyanate
- Fetal Organoids
- HIO, human intestinal organoid
- HS, Escherichia coli human commensal isolate
- IFN, interferon
- IL, interleukin
- LPS, lipopolysaccharide A
- MAMP, microbe-associated molecular pattern
- NEC, necrotizing enterocolitis
- NF-κB, nuclear factor-κB
- Necrotizing Enterocolitis
- PBS, phosphate-buffered saline
- PCR, polymerase chain reaction
- PGE2, prostaglandin E2
- RPKM, reads per kilobase of transcript per million
- RT-PCR, reverse-transcription polymerase chain reaction
- TEER, transepithelial electrical resistance
- TLR, Toll-like receptor
- TNF, tumor necrosis factor
- WAE, wound-associated epithelial cells
- ΔΔCT, relative threshold cycle
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Affiliation(s)
- Stefania Senger
- Department of Pediatrics, Mucosal Immunology and Biology Research Center, Massachusetts General Hospital, Boston, Massachusetts,Harvard Medical School, Boston, Massachusetts
| | - Laura Ingano
- Department of Pediatrics, Mucosal Immunology and Biology Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Rachel Freire
- Department of Pediatrics, Mucosal Immunology and Biology Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Antony Anselmo
- Department of Molecular Biology, Cancer Center and Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Weishu Zhu
- Department of Pediatrics, Mucosal Immunology and Biology Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Ruslan Sadreyev
- Department of Molecular Biology, Cancer Center and Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - William Allan Walker
- Department of Pediatrics, Mucosal Immunology and Biology Research Center, Massachusetts General Hospital, Boston, Massachusetts,Harvard Medical School, Boston, Massachusetts
| | - Alessio Fasano
- Department of Pediatrics, Mucosal Immunology and Biology Research Center, Massachusetts General Hospital, Boston, Massachusetts,Harvard Medical School, Boston, Massachusetts,Correspondence Address correspondence to: Alessio Fasano, MD, Mucosal Immunology and Biology Research Center - MGHfC Harvard Medical School 114 16th Street (114-3501), Charlestown, Massachusetts 02129-4404. fax: (617) 724-1731.
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Ma H, Zhao H. Drug target inference through pathway analysis of genomics data. Adv Drug Deliv Rev 2013; 65:966-72. [PMID: 23369829 DOI: 10.1016/j.addr.2012.12.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Revised: 12/21/2012] [Accepted: 12/22/2012] [Indexed: 10/27/2022]
Abstract
Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues in this field, covering methodological developments, their pros and cons, as well as future research directions.
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Holmans P. Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits. ADVANCES IN GENETICS 2010; 72:141-79. [PMID: 21029852 DOI: 10.1016/b978-0-12-380862-2.00007-2] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
A number of statistical methods have been developed to test for associations between pathways (collections of genes related biologically) and complex genetic traits. Pathway analysis methods were originally developed for analyzing gene expression data, but recently methods have been developed to perform pathway analysis on genome-wide association study (GWAS) data. The purpose of this review is to give an overview of these methods, enabling the reader to gain an understanding of what pathway analysis involves, and to select the method most suited to their purposes. This review describes the various types of statistical methods for pathway analysis, detailing the strengths and weaknesses of each. Factors influencing the power of pathway analyses, such as gene coverage and choice of pathways to analyze, are discussed, as well as various unresolved statistical issues. Finally, a list of computer programs for performing pathway analysis on genome-wide association data is provided.
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Affiliation(s)
- Peter Holmans
- Biostatistics and Bioinformatics Unit, MRC Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Neurology, Cardiff University School of Medicine, Heath Park, Cardiff, United Kingdom
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Janga SC, Tzakos A. Structure and organization of drug-target networks: insights from genomic approaches for drug discovery. MOLECULAR BIOSYSTEMS 2009; 5:1536-48. [DOI: 10.1039/b908147j] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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