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Xu J, Pang B, Lan Y, Dou R, Wang S, Kang S, Zhang W, Liu Y, Zhang Y, Ping Y. Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals. Mol Oncol 2023; 17:2472-2490. [PMID: 37491836 PMCID: PMC10620122 DOI: 10.1002/1878-0261.13499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 06/21/2023] [Accepted: 07/24/2023] [Indexed: 07/27/2023] Open
Abstract
High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of MCM4 could significantly inhibit proliferation, invasion, migration, and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration, and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels and could be applied to more cancer types.
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Affiliation(s)
- Jinyuan Xu
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Bo Pang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yujia Lan
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Renjie Dou
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Shuai Wang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Shaobo Kang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Wanmei Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yuanyuan Liu
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yijing Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
| | - Yanyan Ping
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityChina
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2
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Qiu L, Jing Q, Li Y, Han J. RNA modification: mechanisms and therapeutic targets. MOLECULAR BIOMEDICINE 2023; 4:25. [PMID: 37612540 PMCID: PMC10447785 DOI: 10.1186/s43556-023-00139-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 07/28/2023] [Indexed: 08/25/2023] Open
Abstract
RNA modifications are dynamic and reversible chemical modifications on substrate RNA that are regulated by specific modifying enzymes. They play important roles in the regulation of many biological processes in various diseases, such as the development of cancer and other diseases. With the help of advanced sequencing technologies, the role of RNA modifications has caught increasing attention in human diseases in scientific research. In this review, we briefly summarized the basic mechanisms of several common RNA modifications, including m6A, m5C, m1A, m7G, Ψ, A-to-I editing and ac4C. Importantly, we discussed their potential functions in human diseases, including cancer, neurological disorders, cardiovascular diseases, metabolic diseases, genetic and developmental diseases, as well as immune disorders. Through the "writing-erasing-reading" mechanisms, RNA modifications regulate the stability, translation, and localization of pivotal disease-related mRNAs to manipulate disease development. Moreover, we also highlighted in this review all currently available RNA-modifier-targeting small molecular inhibitors or activators, most of which are designed against m6A-related enzymes, such as METTL3, FTO and ALKBH5. This review provides clues for potential clinical therapy as well as future study directions in the RNA modification field. More in-depth studies on RNA modifications, their roles in human diseases and further development of their inhibitors or activators are needed for a thorough understanding of epitranscriptomics as well as diagnosis, treatment, and prognosis of human diseases.
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Affiliation(s)
- Lei Qiu
- State Key Laboratory of Biotherapy and Cancer Center, Research Laboratory of Tumor Epigenetics and Genomics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, P.R. China
| | - Qian Jing
- State Key Laboratory of Biotherapy and Cancer Center, Research Laboratory of Tumor Epigenetics and Genomics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, P.R. China
| | - Yanbo Li
- State Key Laboratory of Biotherapy and Cancer Center, Research Laboratory of Tumor Epigenetics and Genomics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, P.R. China
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Junhong Han
- State Key Laboratory of Biotherapy and Cancer Center, Research Laboratory of Tumor Epigenetics and Genomics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, P.R. China.
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3
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Algorithmic reconstruction of glioblastoma network complexity. iScience 2022; 25:104179. [PMID: 35479408 PMCID: PMC9036113 DOI: 10.1016/j.isci.2022.104179] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/16/2022] [Accepted: 03/24/2022] [Indexed: 12/16/2022] Open
Abstract
Glioblastoma is a complex disease that is difficult to treat. Network and data science offer alternative approaches to classical bioinformatics pipelines to study gene expression patterns from single-cell datasets, helping to distinguish genes associated with the control of differentiation and aggression. To identify the key molecular regulators of the networks driving glioblastoma/GSC and predict their cell fate dynamics, we applied a host of data theoretic techniques to gene expression patterns from pediatric and adult glioblastoma, and adult glioma-derived stem cells (GSCs). We identified eight transcription factors (OLIG1/2, TAZ, GATA2, FOXG1, SOX6, SATB2, and YY1) and four signaling genes (ATL3, MTSS1, EMP1, and TPT1) as coordinators of cell state transitions and, thus, clinically targetable putative factors differentiating pediatric and adult glioblastomas from adult GSCs. Our study provides strong evidence of complex systems approaches for inferring complex dynamics from reverse-engineering gene networks, bolstering the search for new clinically relevant targets in glioblastoma. Complex cell fate attractors capture glioblastoma differentiation dynamics Graph theoretic approaches decode master regulators of GBM glioblastoma cell fate decisions Network dynamics of pediatric glioblastoma resemble adult GSCs Transcriptional networks may help reprogram glioblastoma behavioral patterns
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Puzanov GA, Senchenko VN. SCP Phosphatases and Oncogenesis. Mol Biol 2021. [DOI: 10.1134/s0026893321030092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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5
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Liu N, Wu Y, Cheng W, Wu Y, Wang L, Zhuang L. Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer. BMC Cancer 2021; 21:460. [PMID: 33902514 PMCID: PMC8073914 DOI: 10.1186/s12885-021-08210-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/13/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Gastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics. METHODS We integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation. RESULTS We comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration of WASHC5 (also known as KIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value. CONCLUSIONS These findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC.
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Affiliation(s)
- Nannan Liu
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Yun Wu
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Weipeng Cheng
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Yuxuan Wu
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Liguo Wang
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China.
| | - Liwei Zhuang
- The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150001, Heilongjiang, China.
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A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning. Biomolecules 2021; 11:biom11040565. [PMID: 33921457 PMCID: PMC8070530 DOI: 10.3390/biom11040565] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 02/06/2023] Open
Abstract
Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient’s quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount of information. To compensate, researchers have tried to increase the amount of information available from a single test using high-throughput technologies. This approach, referred to as single-omics analysis, has only been partially successful as one type of data may not be able to appropriately describe all the characteristics of a tumor. It is presently unclear what type of data can describe a particular clinical situation. One way to solve this problem is to use multi-omics data. When using many types of data, a selected data type or a combination of them may effectively resolve a clinical question. Hence, we conducted a comprehensive survey of papers in the field of neuro-oncology that used multi-omics data for analysis and found that most of the papers utilized machine learning techniques. This fact shows that it is useful to utilize machine learning techniques in multi-omics analysis. In this review, we discuss the current status of multi-omics analysis in the field of neuro-oncology and the importance of using machine learning techniques.
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7
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Abstract
Post-synthesis modification of biomolecules is an efficient way of regulating and optimizing their functions. The human epitranscriptome includes a variety of more than 100 modifications known to exist in all RNA subtypes. Modifications of non-coding RNAs are particularly interesting since they can directly affect their structure, stability, interaction and function. Indeed, non-coding RNAs such as tRNA and rRNA are the most modified RNA species in eukaryotic cells. In the last 20 years, new functions of non-coding RNAs have been discovered and their involvement in human disease, including cancer, became clear. In this review, we will present the evidence connecting modifications of different non-coding RNA subtypes and their role in cancer.
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Affiliation(s)
| | | | - Luca Pandolfini
- Corresponding authors: Isaia Barbieri, University of Cambridge, Department of pathology, Division of cellular and molecular pathology, Addenbrooke's hospital, Lab block, level 3 Box 231, CB2 0QQ, Cambridge, UK. Tel.: +44 (0)1223 333917; E-mail: , Luca Pandolfini, Istituto Italiano di Tecnologia, via Enrico Melen 83, Building B, 16152 Genova, Italy. Tel.: +39 010 2897623; E-mail:
| | - Isaia Barbieri
- Corresponding authors: Isaia Barbieri, University of Cambridge, Department of pathology, Division of cellular and molecular pathology, Addenbrooke's hospital, Lab block, level 3 Box 231, CB2 0QQ, Cambridge, UK. Tel.: +44 (0)1223 333917; E-mail: , Luca Pandolfini, Istituto Italiano di Tecnologia, via Enrico Melen 83, Building B, 16152 Genova, Italy. Tel.: +39 010 2897623; E-mail:
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8
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Mehrjardi NZ, Molcanyi M, Hatay FF, Timmer M, Shahbazi E, Ackermann JP, Herms S, Heilmann-Heimbach S, Wunderlich TF, Prochnow N, Haghikia A, Lampert A, Hescheler J, Neugebauer EAM, Baharvand H, Šarić T. Acquisition of chromosome 1q duplication in parental and genome-edited human-induced pluripotent stem cell-derived neural stem cells results in their higher proliferation rate in vitro and in vivo. Cell Prolif 2020; 53:e12892. [PMID: 32918782 PMCID: PMC7574866 DOI: 10.1111/cpr.12892] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 07/16/2020] [Accepted: 07/18/2020] [Indexed: 02/06/2023] Open
Abstract
Objectives Genetic engineering of human‐induced pluripotent stem cell‐derived neural stem cells (hiPSC‐NSC) may increase the risk of genomic aberrations. Therefore, we asked whether genetic modification of hiPSC‐NSCs exacerbates chromosomal abnormalities that may occur during passaging and whether they may cause any functional perturbations in NSCs in vitro and in vivo. Materials and Methods The transgenic cassette was inserted into the AAVS1 locus, and the genetic integrity of zinc‐finger nuclease (ZFN)‐modified hiPSC‐NSCs was assessed by the SNP‐based karyotyping. The hiPSC‐NSC proliferation was assessed in vitro by the EdU incorporation assay and in vivo by staining of brain slices with Ki‐67 antibody at 2 and 8 weeks after transplantation of ZFN‐NSCs with and without chromosomal aberration into the striatum of immunodeficient rats. Results During early passages, no chromosomal abnormalities were detected in unmodified or ZFN‐modified hiPSC‐NSCs. However, at higher passages both cell populations acquired duplication of the entire long arm of chromosome 1, dup(1)q. ZNF‐NSCs carrying dup(1)q exhibited higher proliferation rate than karyotypically intact cells, which was partly mediated by increased expression of AKT3 located on Chr1q. Compared to karyotypically normal ZNF‐NSCs, cells with dup(1)q also exhibited increased proliferation in vivo 2 weeks, but not 2 months, after transplantation. Conclusions These results demonstrate that, independently of ZFN‐editing, hiPSC‐NSCs have a propensity for acquiring dup(1)q and this aberration results in increased proliferation which might compromise downstream hiPSC‐NSC applications.
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Affiliation(s)
- Narges Zare Mehrjardi
- Center for Physiology and Pathophysiology, Institute for Neurophysiology, Medical Faculty, University of Cologne, Cologne, Germany.,Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Marek Molcanyi
- Center for Physiology and Pathophysiology, Institute for Neurophysiology, Medical Faculty, University of Cologne, Cologne, Germany
| | - Firuze Fulya Hatay
- Center for Physiology and Pathophysiology, Institute for Neurophysiology, Medical Faculty, University of Cologne, Cologne, Germany
| | - Marco Timmer
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Ebrahim Shahbazi
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Justus P Ackermann
- Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany
| | - Stefan Herms
- Department of Genomics, Life & Brain Center, Institute for Human Genetics, University of Bonn, Bonn, Germany.,Department of Biomedicine, Medical Genetics, Research Group Genomics, University Hospital Basel, Basel, Switzerland
| | - Stefanie Heilmann-Heimbach
- Department of Genomics, Life & Brain Center, Institute for Human Genetics, University of Bonn, Bonn, Germany
| | - Thomas F Wunderlich
- Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany.,Max Planck Institute for Metabolism Research and Institute for Genetics, University of Cologne, Cologne, Germany.,Cologne Cluster of Excellence in Cellular Stress Responses in Aging-Associated Diseases (CECAD), Cologne, Germany
| | - Nora Prochnow
- Clinic for Neurology, St. Josef-Hospital, Clinic of the Ruhr-University Bochum, Bochum, Germany
| | - Aiden Haghikia
- Clinic for Neurology, St. Josef-Hospital, Clinic of the Ruhr-University Bochum, Bochum, Germany
| | - Angelika Lampert
- Institute of Physiology, Uniklinik, RWTH Aachen University, Aachen, Germany
| | - Jürgen Hescheler
- Center for Physiology and Pathophysiology, Institute for Neurophysiology, Medical Faculty, University of Cologne, Cologne, Germany
| | - Edmund A M Neugebauer
- Medizinische Hochschule Brandenburg Theodor Fontane, Campus Neuruppin, Neuruppin, Germany
| | - Hossein Baharvand
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.,Department of Developmental Biology, University of Science and Culture, Tehran, Iran
| | - Tomo Šarić
- Center for Physiology and Pathophysiology, Institute for Neurophysiology, Medical Faculty, University of Cologne, Cologne, Germany
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Ping Y, Zhou Y, Hu J, Pang L, Xu C, Xiao Y. Dissecting the Functional Mechanisms of Somatic Copy-Number Alterations Based on Dysregulated ceRNA Networks across Cancers. MOLECULAR THERAPY-NUCLEIC ACIDS 2020; 21:464-479. [PMID: 32668393 PMCID: PMC7358224 DOI: 10.1016/j.omtn.2020.06.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/04/2020] [Accepted: 06/15/2020] [Indexed: 01/14/2023]
Abstract
Somatic copy-number alterations (SCNAs) drive tumor growth and evolution. However, the functional roles of SCNAs across the genome are still poorly understood. We provide an integrative strategy to characterize the functional roles of driver SCNAs in cancers based on dysregulated competing endogenous RNA (ceRNA) networks. We identified 44 driver SCNAs in lower-grade glioma (LGG). The dysregulated patterns losing all correlation relationships dominated dysregulated ceRNA networks. Homozygous deletion of six genes in 9p21.3 characterized an LGG subtype with poor prognosis and contributed to the dysfunction of cancer-associated pathways in a complementary way. The pan-cancer analysis showed that different cancer types harbored different driver SCNAs through dysregulating the crosstalk with common ceRNAs. The same SCNAs destroyed their ceRNA networks through different miRNA-mediated ceRNA regulations in different cancers. Additionally, some SCNAs performed different functional mechanisms in different cancers, which added another layer of complexity to cancer heterogeneity. Compared with previous methods, our strategy could directly dissect functional roles of SCNAs from the view of ceRNA networks, which not only complemented the functions of protein-coding genes but also provided a new avenue to characterize the functions of noncoding RNAs. Also, our strategy could be applied to more types of cancers to identify pathogenic mechanism driven by the SCNAs.
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Affiliation(s)
- Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Yao Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Jing Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Lin Pang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China.
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China; Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Harbin, Heilongjiang 150086, China.
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10
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Abstract
Specific chemical modifications of biological molecules are an efficient way of regulating molecular function, and a plethora of downstream signalling pathways are influenced by the modification of DNA and proteins. Many of the enzymes responsible for regulating protein and DNA modifications are targets of current cancer therapies. RNA epitranscriptomics, the study of RNA modifications, is the new frontier of this arena. Despite being known since the 1970s, eukaryotic RNA modifications were mostly identified on transfer RNA and ribosomal RNA until the last decade, when they have been identified and characterized on mRNA and various non-coding RNAs. Increasing evidence suggests that RNA modification pathways are also misregulated in human cancers and may be ideal targets of cancer therapy. In this Review we highlight the RNA epitranscriptomic pathways implicated in cancer, describing their biological functions and their connections to the disease.
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Affiliation(s)
- Isaia Barbieri
- The Gurdon Institute, University of Cambridge, Cambridge, UK
- Department of Pathology, University of Cambridge, Cambridge, UK
- Division of Cellular and Molecular Pathology, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
| | - Tony Kouzarides
- The Gurdon Institute, University of Cambridge, Cambridge, UK.
- Department of Pathology, University of Cambridge, Cambridge, UK.
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11
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Li P, Guo M, Sun B. Integration of multi-omics data to mine cancer-related gene modules. J Bioinform Comput Biol 2020; 17:1950038. [PMID: 32019413 DOI: 10.1142/s0219720019500380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related genes based on network models. First, using random forest-based feature selection method multi-omics data are integrated to identify key regulatory factors that affect gene expression, and then genome-wide regulatory networks are constructed. Next, by comparing the regulatory networks of key candidate genes in variant samples and non-variant samples, a differential expression regulatory network is generated. The differential network contains a collection of abnormal regulatory genes of key candidate genes. Then, by introducing the functional similarity as a distance metric for gene sets, a density-based clustering method is used to mine gene modules related to cancer. We applied this method to LUSC (lung squamous cell carcinoma) and mined cancer-related gene modules composed of 20 genes. GO function and KEGG pathway analyses indicated that the modules were closely related to cancer. A survival analysis was used to verify that the excavated gene modules can effectively distinguish between high- and low-risk groups. Overall, these results suggest that the proposed method can be used to identify cancer-related gene modules, providing a basis for the development of biomarkers for diagnosis and treatment.
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Affiliation(s)
- Peng Li
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, P. R. China.,School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, P. R. China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, P. R. China
| | - Bo Sun
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, P. R. China
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12
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Zhang J, Deng Y, Zuo Y, Wang J, Zhao Y. Analysis of Colorectal Cancer-Associated Alternative Splicing Based on Transcriptome. DNA Cell Biol 2020; 39:16-24. [PMID: 31808724 DOI: 10.1089/dna.2019.5111] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Affiliation(s)
- Jiting Zhang
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, P.R. China
| | - Yulan Deng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, P.R. China
| | - Yuanli Zuo
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, P.R. China
| | - Jin Wang
- State/National Key Laboratory of Biotherapy, Sichuan University, Chengdu, P.R. China
| | - Yun Zhao
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, P.R. China
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13
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Che H, Liu Y, Zhang M, Meng J, Feng X, Zhou J, Liang C. Integrated Analysis Revealed Prognostic Factors for Prostate Cancer Patients. Med Sci Monit 2019; 25:9991-10007. [PMID: 31876269 PMCID: PMC6944160 DOI: 10.12659/msm.918045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Prostate cancer (PCa) is one of the major causes of cancer-induced death among males. Here, we applied integrated bioinformatics analysis to identify key prognostic factors for PCa patients. MATERIAL AND METHODS The gene expression data were obtained from the UCSC Xena website. We calculated the differentially expressed genes between PCa tissues and normal controls. Pathway enrichment analyses found cell cycle-related pathways might play crucial roles during PCa tumorigenesis. The genes were assigned into 22 modules established via weighted gene co-expression network analysis (WGCNA). RESULTS The results indicated that the purple and red modules were obviously linked to the Gleason score, pathological N, pathological T, recurrence, and recurrence-free survival (RFS). In addition, Kaplan-Meier curve analysis found 8 modules were markedly correlated with RFS, serving as prognostic markers for PCa patients. Then, the hub genes in the most 2 critical modules (purple and red) were visualized by Cytoscape software. Pathway enrichment analyses confirmed the above findings that cell cycle-related pathways might play vital roles during PCa initiation and progression. Lastly, we randomly chose the PILRß (also termed PILRB) in the red module for clinical validation. The immunohistochemistry (IHC) results showed that PILRß was significantly increased in the high-risk PCa population compared with low-/middle-risk patients. CONCLUSIONS We used integrated bioinformatics approaches to identify hub genes that can serve as prognosis markers and potential treatment targets for PCa patients.
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Affiliation(s)
- Hong Che
- Department of Cardiac Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China (mainland)
| | - Yi Liu
- Department of Urology, The First Affiliated Hospital of Anhui Medical University and Institute of Urology and Anhui Province Key Laboratory of Genitourinary Diseases, Hefei, Anhui, China (mainland)
| | - Meng Zhang
- Department of Urology, The First Affiliated Hospital of Anhui Medical University and Institute of Urology and Anhui Province Key Laboratory of Genitourinary Diseases, Hefei, Anhui, China (mainland).,Urology Institute of Shenzhen University, The Third Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, Guangdong, China (mainland)
| | - Jialin Meng
- Department of Urology, The First Affiliated Hospital of Anhui Medical University and Institute of Urology and Anhui Province Key Laboratory of Genitourinary Diseases, Hefei, Anhui, China (mainland)
| | - Xingliang Feng
- Department of Urology, The First Affiliated Hospital of Anhui Medical University and Institute of Urology and Anhui Province Key Laboratory of Genitourinary Diseases, Hefei, Anhui, China (mainland)
| | - Jun Zhou
- Department of Urology, The First Affiliated Hospital of Anhui Medical University and Institute of Urology and Anhui Province Key Laboratory of Genitourinary Diseases, Hefei, Anhui, China (mainland)
| | - Chaozhao Liang
- Department of Urology, The First Affiliated Hospital of Anhui Medical University and Institute of Urology and Anhui Province Key Laboratory of Genitourinary Diseases, Hefei, Anhui, China (mainland)
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14
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Deng Y, Luo S, Deng C, Luo T, Yin W, Zhang H, Zhang Y, Zhang X, Lan Y, Ping Y, Xiao Y, Li X. Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability. Brief Bioinform 2019; 20:254-266. [PMID: 28968730 DOI: 10.1093/bib/bbx109] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Indexed: 02/06/2023] Open
Abstract
Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combinatorial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable evidence showing that, mutual exclusivity reflects alternative functions in tumor initiation and progression, or suggests adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to study mutual exclusivity using genomic profiles alone, or by integrating networks and phenotypes. Some of them have been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the identification of significant mutually exclusive patterns, compare the performance of representative algorithms from simulated data sets and discuss their common confounders.
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Affiliation(s)
- Yulan Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Shangyi Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Chunyu Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Tao Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Wenkang Yin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongyi Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yujia Lan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
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15
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Yang H, Baladandayuthapani V, Rao AUK, Morris JS. Quantile Function on Scalar Regression Analysis for Distributional Data. J Am Stat Assoc 2019; 115:90-106. [PMID: 32981991 PMCID: PMC7517594 DOI: 10.1080/01621459.2019.1609969] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 03/08/2019] [Accepted: 04/07/2019] [Indexed: 02/05/2023]
Abstract
Radiomics involves the study of tumor images to identify quantitative markers explaining cancer heterogeneity. The predominant approach is to extract hundreds to thousands of image features, including histogram features comprised of summaries of the marginal distribution of pixel intensities, which leads to multiple testing problems and can miss out on insights not contained in the selected features. In this paper, we present methods to model the entire marginal distribution of pixel intensities via the quantile function as functional data, regressed on a set of demographic, clinical, and genetic predictors to investigate their effects of imaging-based cancer heterogeneity. We call this approach quantile functional regression, regressing subject-specific marginal distributions across repeated measurements on a set of covariates, allowing us to assess which covariates are associated with the distribution in a global sense, as well as to identify distributional features characterizing these differences, including mean, variance, skewness, heavy-tailedness, and various upper and lower quantiles. To account for smoothness in the quantile functions, account for intrafunctional correlation, and gain statistical power, we introduce custom basis functions we call quantlets that are sparse, regularized, near-lossless, and empirically defined, adapting to the features of a given data set and containing a Gaussian subspace so non-Gaussianness can be assessed. We fit this model using a Bayesian framework that uses nonlinear shrinkage of quantlet coefficients to regularize the functional regression coefficients and provides fully Bayesian inference after fitting a Markov chain Monte Carlo. We demonstrate the benefit of the basis space modeling through simulation studies, and apply the method to Magnetic resonance imaging (MRI) based radiomic dataset from Glioblastoma Multiforme to relate imaging-based quantile functions to various demographic, clinical, and genetic predictors, finding specific differences in tumor pixel intensity distribution between males and females and between tumors with and without DDIT3 mutations.
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Affiliation(s)
- Hojin Yang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | | | - Arvind U K Rao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Jeffrey S Morris
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
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16
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Zhang Y, Liao G, Bai J, Zhang X, Xu L, Deng C, Yan M, Xie A, Luo T, Long Z, Xiao Y, Li X. Identifying Cancer Driver lncRNAs Bridged by Functional Effectors through Integrating Multi-omics Data in Human Cancers. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 17:362-373. [PMID: 31302496 PMCID: PMC6626872 DOI: 10.1016/j.omtn.2019.05.030] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 04/23/2019] [Accepted: 05/15/2019] [Indexed: 01/18/2023]
Abstract
The accumulation of somatic driver mutations in the human genome enables cells to gradually acquire a growth advantage and contributes to tumor development. Great efforts on protein-coding cancer drivers have yielded fruitful discoveries and clinical applications. However, investigations on cancer drivers in non-coding regions, especially long non-coding RNAs (lncRNAs), are extremely scarce due to the limitation of functional understanding. Thus, to identify driver lncRNAs integrating multi-omics data in human cancers, we proposed a computational framework, DriverLncNet, which dissected the functional impact of somatic copy number alteration (CNA) of lncRNAs on regulatory networks and captured key functional effectors in dys-regulatory networks. Applying it to 5 cancer types from The Cancer Genome Atlas (TCGA), we portrayed the landscape of 117 driver lncRNAs and revealed their associated cancer hallmarks through their functional effectors. Moreover, lncRNA RP11-571M6.8 was detected to be highly associated with immunotherapeutic targets (PD-1, PD-L1, and CTLA-4) and regulatory T cell infiltration level and their markers (IL2RA and FCGR2B) in glioblastoma multiforme, highlighting its immunosuppressive function. Meanwhile, a high expression of RP11-1020A11.1 in bladder carcinoma was predictive of poor survival independent of clinical characteristics, and CTD-2256P15.2 in lung adenocarcinoma responded to the sensitivity of methyl ethyl ketone (MEK) inhibitors. In summary, this study provided a framework to decipher the mechanisms of tumorigenesis from driver lncRNA level, established a new landscape of driver lncRNAs in human cancers, and offered potential clinical implications for precision oncology.
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Affiliation(s)
- Yong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Gaoming Liao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Jing Bai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Liwen Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Chunyu Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Min Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Aimin Xie
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Tao Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Zhilin Long
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China; Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, Heilongjiang 150086, China.
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China; Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, Heilongjiang 150086, China.
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17
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Jenkinson G, Abante J, Koldobskiy MA, Feinberg AP, Goutsias J. Ranking genomic features using an information-theoretic measure of epigenetic discordance. BMC Bioinformatics 2019; 20:175. [PMID: 30961526 PMCID: PMC6454630 DOI: 10.1186/s12859-019-2777-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 03/25/2019] [Indexed: 02/07/2023] Open
Abstract
Background Establishment and maintenance of DNA methylation throughout the genome is an important epigenetic mechanism that regulates gene expression whose disruption has been implicated in human diseases like cancer. It is therefore crucial to know which genes, or other genomic features of interest, exhibit significant discordance in DNA methylation between two phenotypes. We have previously proposed an approach for ranking genes based on methylation discordance within their promoter regions, determined by centering a window of fixed size at their transcription start sites. However, we cannot use this method to identify statistically significant genomic features and handle features of variable length and with missing data. Results We present a new approach for computing the statistical significance of methylation discordance within genomic features of interest in single and multiple test/reference studies. We base the proposed method on a well-articulated hypothesis testing problem that produces p- and q-values for each genomic feature, which we then use to identify and rank features based on the statistical significance of their epigenetic dysregulation. We employ the information-theoretic concept of mutual information to derive a novel test statistic, which we can evaluate by computing Jensen-Shannon distances between the probability distributions of methylation in a test and a reference sample. We design the proposed methodology to simultaneously handle biological, statistical, and technical variability in the data, as well as variable feature lengths and missing data, thus enabling its wide-spread use on any list of genomic features. This is accomplished by estimating, from reference data, the null distribution of the test statistic as a function of feature length using generalized additive regression models. Differential assessment, using normal/cancer data from healthy fetal tissue and pediatric high-grade glioma patients, illustrates the potential of our approach to greatly facilitate the exploratory phases of clinically and biologically relevant methylation studies. Conclusions The proposed approach provides the first computational tool for statistically testing and ranking genomic features of interest based on observed DNA methylation discordance in comparative studies that accounts, in a rigorous manner, for biological, statistical, and technical variability in methylation data, as well as for variability in feature length and for missing data. Electronic supplementary material The online version of this article (10.1186/s12859-019-2777-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Garrett Jenkinson
- Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, USA.,Center for Epigenetics, Johns Hopkins School of Medicine, Baltimore, MD, USA.,Currently with Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jordi Abante
- Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Michael A Koldobskiy
- Center for Epigenetics, Johns Hopkins School of Medicine, Baltimore, MD, USA.,Pediatric Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins School of Medicine, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - John Goutsias
- Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, USA.
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18
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A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLoS One 2018; 13:e0191105. [PMID: 29462216 PMCID: PMC5819766 DOI: 10.1371/journal.pone.0191105] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 12/28/2017] [Indexed: 01/08/2023] Open
Abstract
Changes in gene expression can help reveal the mechanisms of disease processes and the mode of action for toxicities and adverse effects on cellular responses induced by exposures to chemicals, drugs and environment agents. The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. Our approach is modular, applicable to any species, and facilitates a robust, quantitative evaluation of performance. In particular, we were able to perform gene selection such that the resulting set of “sentinel genes” adequately represents all known canonical pathways from Molecular Signature Database (MSigDB v4.0) and can be used to infer expression changes for the remainder of the transcriptome. The resulting computational model allowed us to choose a purely data-driven subset of 1500 sentinel genes, referred to as the S1500 set, which was then augmented using a knowledge-driven selection of additional genes to create the final S1500+ gene set. Our results indicate that the sentinel genes selected can be used to accurately predict pathway perturbations and biological relationships for samples under study.
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19
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Dopazo J, Erten C. Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes. BMC SYSTEMS BIOLOGY 2017; 11:110. [PMID: 29166896 PMCID: PMC5700672 DOI: 10.1186/s12918-017-0495-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 11/13/2017] [Indexed: 12/18/2022]
Abstract
BACKGROUND Identification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally. RESULTS We provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. The network measures under consideration are based on appropriate formulations of various centrality measures: betweenness, clustering coefficients, degree centrality, random walk distances, graph-theoretical distances, and Jaccard index centrality. CONCLUSIONS Among all the studied centrality-based graph-theoretical properties, we show that a betweenness-based measure differentiates BRCA genes across all normal versus tumor network pairs, than the rest of the popular centrality-based measures. The AUROC and AUPR values of the gene lists ordered with respect to the measures under study as compared to NCBI BioSystems pathway and the COSMIC database of cancer genes are the largest with the betweenness-based differentiation, followed by the measure based on degree centrality. In order to test the robustness of the suggested measures in prioritizing cancer genes, we further tested the two most promising measures, those based on betweenness and degree centralities, on randomly rewired networks. We show that both measures are quite resilient to noise in the input interaction network. We also compared the same measures against a state-of-the-art alternative disease gene prioritization method, MUFFFINN. We show that both our graph-theoretical measures outperform MUFFINN prioritizations in terms of ROC and precions/recall analysis. Finally, we filter the ordered list of the best measure, the betweenness-based differentiation, via a maximum-weight independent set formulation and investigate the top 50 genes in regards to literature verification. We show that almost all genes in the list are verified by the breast cancer literature and three genes are presented as novel genes that may potentialy be BRCA-related but missing in literature.
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Affiliation(s)
- Joaquin Dopazo
- Clinical Bioinformatics Research Area, Fundación Progreso y Salud, Hospital Virgen del Rocío, Sevilla, Spain
| | - Cesim Erten
- Computer Engineering, Antalya Bilim University, Antalya, Turkey.
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20
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Delavan B, Roberts R, Huang R, Bao W, Tong W, Liu Z. Computational drug repositioning for rare diseases in the era of precision medicine. Drug Discov Today 2017; 23:382-394. [PMID: 29055182 DOI: 10.1016/j.drudis.2017.10.009] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/19/2017] [Accepted: 10/11/2017] [Indexed: 12/12/2022]
Abstract
There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.
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Affiliation(s)
- Brian Delavan
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; University of Arkansas at Little Rock, Little Rock, AR 72204, USA
| | - Ruth Roberts
- ApconiX, BioHub at Alderley Park, Alderley Edge SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health Rockville, MD 20850, USA
| | | | - Weida Tong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Zhichao Liu
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
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21
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Ren W, Li W, Wang D, Hu S, Suo J, Ying X. Combining multi-dimensional data to identify key genes and pathways in gastric cancer. PeerJ 2017; 5:e3385. [PMID: 28603669 PMCID: PMC5463969 DOI: 10.7717/peerj.3385] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2017] [Accepted: 05/06/2017] [Indexed: 12/22/2022] Open
Abstract
Gastric cancer is an aggressive cancer that is often diagnosed late. Early detection and treatment require a better understanding of the molecular pathology of the disease. The present study combined data on gene expression and regulatory levels (microRNA, methylation, copy number) with the aim of identifying key genes and pathways for gastric cancer. Data used in this study was retrieved from The Cancer Genomic Atlas. Differential analyses between gastric cancer and normal tissues were carried out using Limma. Copy number alterations were identified for tumor samples. Bimodal filtering of differentially expressed genes (DEGs) based on regulatory changes was performed to identify candidate genes. Protein–protein interaction networks for candidate genes were generated by Cytoscape software. Gene ontology and pathway analyses were performed, and disease-associated network was constructed using the Agilent literature search plugin on Cytoscape. In total, we identified 3602 DEGs, 251 differentially expressed microRNAs, 604 differential methylation-sites, and 52 copy number altered regions. Three groups of candidate genes controlled by different regulatory mechanisms were screened out. Interaction networks for candidate genes were constructed consisting of 415, 228, and 233 genes, respectively, all of which were enriched in cell cycle, P53 signaling, DNA replication, viral carcinogenesis, HTLV-1 infection, and progesterone mediated oocyte maturation pathways. Nine hub genes (SRC, KAT2B, NR3C1, CDK6, MCM2, PRKDC, BLM, CCNE1, PARK2) were identified that were presumed to be key regulators of the networks; seven of these were shown to be implicated in gastric cancer through disease-associated network construction. The genes and pathways identified in our study may play pivotal roles in gastric carcinogenesis and have clinical significance.
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Affiliation(s)
- Wu Ren
- Department of Gastrointestinal Surgery, The First Hospital of Jilin University, Changchun, China.,Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Wei Li
- Department of Gastrointestinal Surgery, The First Hospital of Jilin University, Changchun, China
| | - Daguang Wang
- Department of Gastrointestinal Surgery, The First Hospital of Jilin University, Changchun, China
| | - Shuofeng Hu
- Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Jian Suo
- Department of Gastrointestinal Surgery, The First Hospital of Jilin University, Changchun, China
| | - Xiaomin Ying
- Beijing Institute of Basic Medical Sciences, Beijing, China
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22
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Zhang H, Deng Y, Zhang Y, Ping Y, Zhao H, Pang L, Zhang X, Wang L, Xu C, Xiao Y, Li X. Cooperative genomic alteration network reveals molecular classification across 12 major cancer types. Nucleic Acids Res 2016; 45:567-582. [PMID: 27899621 PMCID: PMC5314758 DOI: 10.1093/nar/gkw1087] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 10/18/2016] [Accepted: 10/27/2016] [Indexed: 11/22/2022] Open
Abstract
The accumulation of somatic genomic alterations that enables cells to gradually acquire growth advantage contributes to tumor development. This has the important implication of the widespread existence of cooperative genomic alterations in the accumulation process. Here, we proposed a computational method HCOC that simultaneously consider genetic context and downstream functional effects on cancer hallmarks to uncover somatic cooperative events in human cancers. Applying our method to 12 TCGA cancer types, we totally identified 1199 cooperative events with high heterogeneity across human cancers, and then constructed a pan-cancer cooperative alteration network. These cooperative events are associated with genomic alterations of some high-confident cancer drivers, and can trigger the dysfunction of hallmark associated pathways in a co-defect way rather than single alterations. We found that these cooperative events can be used to produce a prognostic classification that can provide complementary information with tissue-of-origin. In a further case study of glioblastoma, using 23 cooperative events identified, we stratified patients into molecularly relevant subtypes with a prognostic significance independent of the Glioma-CpG Island Methylator Phenotype (GCIMP). In summary, our method can be effectively used to discover cancer-driving cooperative events that can be valuable clinical markers for patient stratification.
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Affiliation(s)
- Hongyi Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yulan Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Hongying Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Lin Pang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Li Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
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23
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Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis. Genome Med 2016; 8:38. [PMID: 27137215 PMCID: PMC4853852 DOI: 10.1186/s13073-016-0282-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 02/19/2016] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Pancreatic cancer is an aggressive cancer with dismal prognosis, urgently necessitating better biomarkers to improve therapeutic options and early diagnosis. Traditional approaches of biomarker detection that consider only one aspect of the biological continuum like gene expression alone are limited in their scope and lack robustness in identifying the key regulators of the disease. We have adopted a multidimensional approach involving the cross-talk between the omics spaces to identify key regulators of disease progression. METHODS Multidimensional domain-specific disease signatures were obtained using rank-based meta-analysis of individual omics profiles (mRNA, miRNA, DNA methylation) related to pancreatic ductal adenocarcinoma (PDAC). These domain-specific PDAC signatures were integrated to identify genes that were affected across multiple dimensions of omics space in PDAC (genes under multiple regulatory controls, GMCs). To further pin down the regulators of PDAC pathophysiology, a systems-level network was generated from knowledge-based interaction information applied to the above identified GMCs. Key regulators were identified from the GMC network based on network statistics and their functional importance was validated using gene set enrichment analysis and survival analysis. RESULTS Rank-based meta-analysis identified 5391 genes, 109 miRNAs and 2081 methylation-sites significantly differentially expressed in PDAC (false discovery rate ≤ 0.05). Bimodal integration of meta-analysis signatures revealed 1150 and 715 genes regulated by miRNAs and methylation, respectively. Further analysis identified 189 altered genes that are commonly regulated by miRNA and methylation, hence considered GMCs. Systems-level analysis of the scale-free GMCs network identified eight potential key regulator hubs, namely E2F3, HMGA2, RASA1, IRS1, NUAK1, ACTN1, SKI and DLL1, associated with important pathways driving cancer progression. Survival analysis on individual key regulators revealed that higher expression of IRS1 and DLL1 and lower expression of HMGA2, ACTN1 and SKI were associated with better survival probabilities. CONCLUSIONS It is evident from the results that our hierarchical systems-level multidimensional analysis approach has been successful in isolating the converging regulatory modules and associated key regulatory molecules that are potential biomarkers for pancreatic cancer progression.
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Xu X, Zhou Y, Miao R, Chen W, Qu K, Pang Q, Liu C. Transcriptional modules related to hepatocellular carcinoma survival: coexpression network analysis. Front Med 2016; 10:183-90. [DOI: 10.1007/s11684-016-0440-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 02/06/2016] [Indexed: 12/21/2022]
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25
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Thingholm LB, Andersen L, Makalic E, Southey MC, Thomassen M, Hansen LL. Strategies for Integrated Analysis of Genetic, Epigenetic, and Gene Expression Variation in Cancer: Addressing the Challenges. Front Genet 2016; 7:2. [PMID: 26870081 PMCID: PMC4740898 DOI: 10.3389/fgene.2016.00002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2015] [Accepted: 01/11/2016] [Indexed: 12/15/2022] Open
Abstract
The development and progression of cancer, a collection of diseases with complex genetic architectures, is facilitated by the interplay of multiple etiological factors. This complexity challenges the traditional single-platform study design and calls for an integrated approach to data analysis. However, integration of heterogeneous measurements of biological variation is a non-trivial exercise due to the diversity of the human genome and the variety of output data formats and genome coverage obtained from the commonly used molecular platforms. This review article will provide an introduction to integration strategies used for analyzing genetic risk factors for cancer. We critically examine the ability of these strategies to handle the complexity of the human genome and also accommodate information about the biological and functional interactions between the elements that have been measured-making the assessment of disease risk against a composite genomic factor possible. The focus of this review is to provide an overview and introduction to the main strategies and to discuss where there is a need for further development.
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Affiliation(s)
- Louise B Thingholm
- Department of Pathology, The University of MelbourneMelbourne, VIC, Australia; Department of Biomedicine, The University of AarhusAarhus, Denmark
| | - Lars Andersen
- Department of Clinical Genetics, Odense University Hospital Odense, Denmark
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, The University of Melbourne Melbourne, VIC, Australia
| | - Melissa C Southey
- Department of Pathology, The University of Melbourne Melbourne, VIC, Australia
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital Odense, Denmark
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Shi K, Gao L, Wang B. Discovering potential cancer driver genes by an integrated network-based approach. MOLECULAR BIOSYSTEMS 2016; 12:2921-31. [DOI: 10.1039/c6mb00274a] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
An integrated network-based approach is proposed to nominate driver genes. It is composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between the gene mutations and gene expression, the relationship between the mutated genes and the heterogeneous characteristic of the patient mutation.
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Affiliation(s)
- Kai Shi
- School of Computer Science and Technology
- Xidian University
- Xi'an
- China
- College of Science
| | - Lin Gao
- School of Computer Science and Technology
- Xidian University
- Xi'an
- China
| | - Bingbo Wang
- School of Computer Science and Technology
- Xidian University
- Xi'an
- China
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Chandrasekaran G, Tátrai P, Gergely F. Hitting the brakes: targeting microtubule motors in cancer. Br J Cancer 2015; 113:693-8. [PMID: 26180922 PMCID: PMC4559828 DOI: 10.1038/bjc.2015.264] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Revised: 06/08/2015] [Accepted: 06/16/2015] [Indexed: 01/17/2023] Open
Abstract
Despite the growing number of therapies that target cancer-specific pathways, cytotoxic treatments remain important clinical tools. The rationale for targeting cell proliferation by chemotherapeutic agents stems from the assumption that tumours harbour a greater fraction of actively dividing cells than normal tissues. One such group of cytotoxic drugs impair microtubule polymers, which are cytoskeletal components of cells essential for many processes including mitosis. However, in addition to their antimitotic action, these agents cause debilitating and dose-limiting neurotoxicity because of the essential functions of microtubules in neurons. To overcome this limitation, drugs against mitosis-specific targets have been developed over the past decade, albeit with variable clinical success. Here we review the key lessons learnt from antimitotic therapies with a focus on inhibitors of microtubule motor proteins. Furthermore, based on the cancer genome data, we describe a number of motor proteins with tumour type-specific alterations, which warrant further investigation in the quest for cytotoxic targets with increased cancer specificity.
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Affiliation(s)
- Gayathri Chandrasekaran
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK
| | - Péter Tátrai
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK
| | - Fanni Gergely
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK
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