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Komorowski A, Murgaš M, Vidal R, Singh A, Gryglewski G, Kasper S, Wiltfang J, Lanzenberger R, Goya‐Maldonado R. Regional gene expression patterns are associated with task-specific brain activation during reward and emotion processing measured with functional MRI. Hum Brain Mapp 2022; 43:5266-5280. [PMID: 35796185 PMCID: PMC9812247 DOI: 10.1002/hbm.26001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 06/02/2022] [Accepted: 06/06/2022] [Indexed: 01/15/2023] Open
Abstract
The exploration of the spatial relationship between gene expression profiles and task-evoked response patterns known to be altered in neuropsychiatric disorders, for example depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in cellular localization processes, metabolism, translation, and synapse regulation. An overlap of these genes with risk genes from major depressive disorder genome-wide association studies revealed the involvement of the master regulators TCF4 and PAX6 in emotion and reward processing. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.
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Affiliation(s)
- Arkadiusz Komorowski
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH)Medical University of ViennaVienna
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH)Medical University of ViennaVienna
| | - Ramon Vidal
- Max Delbrück Center for Molecular MedicineBerlinGermany
| | - Aditya Singh
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP‐Lab), Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG)Georg‐August UniversityGoettingenGermany
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH)Medical University of ViennaVienna
- Child Study CenterYale UniversityNew HavenConnecticutUSA
| | - Siegfried Kasper
- Center for Brain ResearchMedical University of ViennaViennaAustria
| | - Jens Wiltfang
- Department of Psychiatry and PsychotherapyUniversity Medical Center Goettingen (UMG), Georg‐August UniversityGoettingenGermany
- German Center for Neurodegenerative Diseases (DZNE)GoettingenGermany
- Neurosciences and Signalling Group, Institute of Biomedicine (iBiMED), Department of Medical SciencesUniversity of AveiroAveiroPortugal
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH)Medical University of ViennaVienna
| | - Roberto Goya‐Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP‐Lab), Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG)Georg‐August UniversityGoettingenGermany
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2
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Komorowski A, Weidenauer A, Murgaš M, Sauerzopf U, Wadsak W, Mitterhauser M, Bauer M, Hacker M, Praschak-Rieder N, Kasper S, Lanzenberger R, Willeit M. Association of dopamine D 2/3 receptor binding potential measured using PET and [ 11C]-(+)-PHNO with post-mortem DRD 2/3 gene expression in the human brain. Neuroimage 2020; 223:117270. [PMID: 32818617 PMCID: PMC7610745 DOI: 10.1016/j.neuroimage.2020.117270] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 08/12/2020] [Accepted: 08/12/2020] [Indexed: 01/11/2023] Open
Abstract
Open access post-mortem transcriptome atlases such as the Allen Human Brain Atlas (AHBA) can inform us about mRNA expression of numerous proteins of interest across the whole brain, while in vivo protein binding in the human brain can be quantified by means of neuroreceptor positron emission tomography (PET). By combining both modalities, the association between regional gene expression and receptor distribution in the living brain can be approximated. Here, we compare the characteristics of D2 and D3 dopamine receptor distribution by applying the dopamine D2/3 receptor agonist radioligand [11C]-(+)-PHNO and human gene expression data. Since [11C]-(+)-PHNO has a higher affinity for D3 compared to D2 receptors, we hypothesized that there is a stronger relationship between D2/3 non-displaceable binding potentials (BPND) and D3 mRNA expression. To investigate the relationship between D2/3 BPND and mRNA expression of DRD2 and DRD3 we performed [11C]-(+)-PHNO PET scans in 27 healthy subjects (12 females) and extracted gene expression data from the AHBA. We also calculated D2/D3 mRNA expression ratios to imitate the mixed D2/3 signal of [11C]-(+)-PHNO. In accordance with our a priori hypothesis, a strong correlation between [11C]-(+)-PHNO and DRD3 expression was found. However, there was no significant correlation with DRD2 expression. Calculated D2/D3 mRNA expression ratios also showed a positive correlation with [11C]-(+)-PHNO binding, reflecting the mixed D2/3 signal of the radioligand. Our study supports the usefulness of combining gene expression data from open access brain atlases with in vivo imaging data in order to gain more detailed knowledge on neurotransmitter signaling.
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Affiliation(s)
- Arkadiusz Komorowski
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Ana Weidenauer
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Ulrich Sauerzopf
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Wolfgang Wadsak
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria; Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | - Markus Mitterhauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria; Ludwig Boltzmann Institute for Applied Diagnostics, Vienna, Austria
| | - Martin Bauer
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Nicole Praschak-Rieder
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
| | - Siegfried Kasper
- Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria.
| | - Matthäus Willeit
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
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3
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McCartney AM, Hyland EM, Cormican P, Moran RJ, Webb AE, Lee KD, Hernandez-Rodriguez J, Prado-Martinez J, Creevey CJ, Aspden JL, McInerney JO, Marques-Bonet T, O'Connell MJ. Gene Fusions Derived by Transcriptional Readthrough are Driven by Segmental Duplication in Human. Genome Biol Evol 2020; 11:2678-2690. [PMID: 31400206 PMCID: PMC6764479 DOI: 10.1093/gbe/evz163] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/17/2019] [Indexed: 12/14/2022] Open
Abstract
Gene fusion occurs when two or more individual genes with independent open reading frames becoming juxtaposed under the same open reading frame creating a new fused gene. A small number of gene fusions described in detail have been associated with novel functions, for example, the hominid-specific PIPSL gene, TNFSF12, and the TWE-PRIL gene family. We use Sequence Similarity Networks and species level comparisons of great ape genomes to identify 45 new genes that have emerged by transcriptional readthrough, that is, transcription-derived gene fusion. For 35 of these putative gene fusions, we have been able to assess available RNAseq data to determine whether there are reads that map to each breakpoint. A total of 29 of the putative gene fusions had annotated transcripts (9/29 of which are human-specific). We carried out RT-qPCR in a range of human tissues (placenta, lung, liver, brain, and testes) and found that 23 of the putative gene fusion events were expressed in at least one tissue. Examining the available ribosome foot-printing data, we find evidence for translation of three of the fused genes in human. Finally, we find enrichment for transcription-derived gene fusions in regions of known segmental duplication in human. Together, our results implicate chromosomal structural variation brought about by segmental duplication with the emergence of novel transcripts and translated protein products.
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Affiliation(s)
- Ann M McCartney
- Bioinformatics and Molecular Evolution Group, School of Biotechnology, Dublin City University, Ireland.,Computational and Molecular Evolutionary Biology Group, School of Biology, Faculty of Biological Sciences, The University of Leeds, United Kingdom
| | - Edel M Hyland
- Bioinformatics and Molecular Evolution Group, School of Biotechnology, Dublin City University, Ireland.,Institute for Global Food Security, Queens University Belfast, United Kingdom
| | - Paul Cormican
- Teagasc Animal and Bioscience Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Grange, Dunsany, County Meath, Ireland
| | - Raymond J Moran
- Bioinformatics and Molecular Evolution Group, School of Biotechnology, Dublin City University, Ireland.,Computational and Molecular Evolutionary Biology Group, School of Biology, Faculty of Biological Sciences, The University of Leeds, United Kingdom
| | - Andrew E Webb
- Bioinformatics and Molecular Evolution Group, School of Biotechnology, Dublin City University, Ireland
| | - Kate D Lee
- Bioinformatics and Molecular Evolution Group, School of Biotechnology, Dublin City University, Ireland.,School of Biological Sciences, University of Auckland, New Zealand.,School of Fundamental Sciences, Massey University, New Zealand
| | | | - Javier Prado-Martinez
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain.,Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Christopher J Creevey
- Institute for Global Food Security, Queens University Belfast, United Kingdom.,Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, United Kingdom
| | - Julie L Aspden
- School of Molecular and Cellular Biology, Faculty of Biological Sciences, The University of Leeds, United Kingdom
| | - James O McInerney
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, M13 9PL, United Kingdom.,School of Life Sciences, Faculty of Medicine and Health Sciences, The University of Nottingham, NG7 2RD, United Kingdom
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain.,Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain.,NAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain.,Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193 Cerdanyola del Vallés, Barcelona, Spain
| | - Mary J O'Connell
- Bioinformatics and Molecular Evolution Group, School of Biotechnology, Dublin City University, Ireland.,Computational and Molecular Evolutionary Biology Group, School of Biology, Faculty of Biological Sciences, The University of Leeds, United Kingdom.,School of Life Sciences, Faculty of Medicine and Health Sciences, The University of Nottingham, NG7 2RD, United Kingdom
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4
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Systems biology and bioinformatics approach to identify gene signatures, pathways and therapeutic targets of Alzheimer's disease. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100439] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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5
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Arnatkeviciute A, Fulcher BD, Fornito A. A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage 2019; 189:353-367. [PMID: 30648605 DOI: 10.1016/j.neuroimage.2019.01.011] [Citation(s) in RCA: 307] [Impact Index Per Article: 61.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 01/03/2019] [Accepted: 01/05/2019] [Indexed: 12/19/2022] Open
Abstract
The recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using the AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.
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Affiliation(s)
- Aurina Arnatkeviciute
- Brain and Mental Health Research Hub, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, 770 Blackburn Rd, Clayton, 3168, VIC, Australia.
| | - Ben D Fulcher
- Brain and Mental Health Research Hub, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, 770 Blackburn Rd, Clayton, 3168, VIC, Australia; School of Physics, Sydney University, Sydney, 2006, NSW, Australia
| | - Alex Fornito
- Brain and Mental Health Research Hub, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, 770 Blackburn Rd, Clayton, 3168, VIC, Australia
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6
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Chen L, Zhang S, Pan X, Hu X, Zhang YH, Yuan F, Huang T, Cai YD. HIV infection alters the human epigenetic landscape. Gene Ther 2018; 26:29-39. [PMID: 30443044 DOI: 10.1038/s41434-018-0051-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 10/30/2018] [Accepted: 10/31/2018] [Indexed: 02/07/2023]
Abstract
Many complex diseases or traits are the results of both genetic and environmental factors. The environmental factors affect the human body by modifying its epigenetics, which controls the activity of genomes without mutating it. Viral infection is one of the common environmental factors for complex diseases. For example, the human immunodeficiency virus (HIV) infection can cause acquired immune deficiency syndrome (AIDS), HBV, and HCV infections are associated with hepatocellular carcinoma, and human papillomavirus infection is a causal factor in cervical carcinoma. In this study, to investigate how HIV infection affects DNA methylation, we analyzed the blood DNA methylation data of 485 512 sites in 44 HIV- and 142 HIV + patients. Several advanced computational methods were applied to identify the core distinctive features that were different between the HIV patients and the healthy controls. These methods can be used for differentiating HIV-infected patients from uninfected ones. These core distinctive DNA methylation features were confirmed to be functionally connected to premature aging and abnormal immune regulation, two typical pathological symptoms of HIV infection, revealing the potential regulatory mechanisms of HIV infection on the DNA methylation status of the host cells and provided novel insights on the pathogenesis of HIV infection and AIDS.
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Affiliation(s)
- Lei Chen
- School of Life Sciences, Shanghai University, Shanghai, 200444, China.,Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai, 200241, China.,College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China
| | - Shiqi Zhang
- Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Xiaoyong Pan
- Department of Medical Informatics, Erasmus MC, Rotterdam, Netherlands
| | - XiaoHua Hu
- Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Yu-Hang Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Fei Yuan
- Department of Science & Technology, Binzhou Medical University Hospital, Binzhou, 256603, Shandong, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, 200444, China.
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7
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Identification of the Gene Expression Rules That Define the Subtypes in Glioma. J Clin Med 2018; 7:jcm7100350. [PMID: 30322114 PMCID: PMC6210469 DOI: 10.3390/jcm7100350] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 10/09/2018] [Accepted: 10/11/2018] [Indexed: 11/16/2022] Open
Abstract
As a common brain cancer derived from glial cells, gliomas have three subtypes: glioblastoma, diffuse astrocytoma, and anaplastic astrocytoma. The subtypes have distinctive clinical features but are closely related to each other. A glioblastoma can be derived from the early stage of diffuse astrocytoma, which can be transformed into anaplastic astrocytoma. Due to the complexity of these dynamic processes, single-cell gene expression profiles are extremely helpful to understand what defines these subtypes. We analyzed the single-cell gene expression profiles of 5057 cells of anaplastic astrocytoma tissues, 261 cells of diffuse astrocytoma tissues, and 1023 cells of glioblastoma tissues with advanced machine learning methods. In detail, a powerful feature selection method, Monte Carlo feature selection (MCFS) method, was adopted to analyze the gene expression profiles of cells, resulting in a feature list. Then, the incremental feature selection (IFS) method was applied to the obtained feature list, with the help of support vector machine (SVM), to extract key features (genes) and construct an optimal SVM classifier. Several key biomarker genes, such as IGFBP2, IGF2BP3, PRDX1, NOV, NEFL, HOXA10, GNG12, SPRY4, and BCL11A, were identified. In addition, the underlying rules of classifying the three subtypes were produced by Johnson reducer algorithm. We found that in diffuse astrocytoma, PRDX1 is highly expressed, and in glioblastoma, the expression level of PRDX1 is low. These rules revealed the difference among the three subtypes, and how they are formed and transformed. These genes are not only biomarkers for glioma subtypes, but also drug targets that may switch the clinical features or even reverse the tumor progression.
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Chen L, Zhang YH, Huang G, Pan X, Wang S, Huang T, Cai YD. Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection. Mol Genet Genomics 2017; 293:137-149. [DOI: 10.1007/s00438-017-1372-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2017] [Accepted: 09/07/2017] [Indexed: 12/15/2022]
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9
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Chen L, Zhang YH, Wang S, Zhang Y, Huang T, Cai YD. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways. PLoS One 2017; 12:e0184129. [PMID: 28873455 PMCID: PMC5584762 DOI: 10.1371/journal.pone.0184129] [Citation(s) in RCA: 163] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 08/18/2017] [Indexed: 12/20/2022] Open
Abstract
Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.
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Affiliation(s)
- Lei Chen
- School of Life Sciences, Shanghai University, Shanghai, People’s Republic of China
- College of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of China
| | - Yu-Hang Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - ShaoPeng Wang
- School of Life Sciences, Shanghai University, Shanghai, People’s Republic of China
| | - YunHua Zhang
- Anhui province key lab of farmland ecological conversation and pollution prevention, School of Resources and Environment, Anhui Agricultural University, Hefei, People’s Republic of China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, People’s Republic of China
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10
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Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer. BIOMED RESEARCH INTERNATIONAL 2016; 2016:7861274. [PMID: 27957501 PMCID: PMC5120232 DOI: 10.1155/2016/7861274] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 07/18/2016] [Accepted: 09/07/2016] [Indexed: 12/16/2022]
Abstract
Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.
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