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Suszynska M, Machowska M, Fraszczyk E, Michalczyk M, Philips A, Galka-Marciniak P, Kozlowski P. CMC: Cancer miRNA Census - a list of cancer-related miRNA genes. Nucleic Acids Res 2024; 52:1628-1644. [PMID: 38261968 PMCID: PMC10899758 DOI: 10.1093/nar/gkae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 01/03/2024] [Indexed: 01/25/2024] Open
Abstract
A growing body of evidence indicates an important role of miRNAs in cancer; however, there is no definitive, convenient-to-use list of cancer-related miRNAs or miRNA genes that may serve as a reference for analyses of miRNAs in cancer. To this end, we created a list of 165 cancer-related miRNA genes called the Cancer miRNA Census (CMC). The list is based on a score, built on various types of functional and genetic evidence for the role of particular miRNAs in cancer, e.g. miRNA-cancer associations reported in databases, associations of miRNAs with cancer hallmarks, or signals of positive selection of genetic alterations in cancer. The presence of well-recognized cancer-related miRNA genes, such as MIR21, MIR155, MIR15A, MIR17 or MIRLET7s, at the top of the CMC ranking directly confirms the accuracy and robustness of the list. Additionally, to verify and indicate the reliability of CMC, we performed a validation of criteria used to build CMC, comparison of CMC with various cancer data (publications and databases), and enrichment analyses of biological pathways and processes such as Gene Ontology or DisGeNET. All validation steps showed a strong association of CMC with cancer/cancer-related processes confirming its usefulness as a reference list of miRNA genes associated with cancer.
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Affiliation(s)
- Malwina Suszynska
- Department of Molecular Genetics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 61-704, Poland
| | - Magdalena Machowska
- Department of Molecular Genetics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 61-704, Poland
| | - Eliza Fraszczyk
- Department of Molecular Genetics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 61-704, Poland
| | - Maciej Michalczyk
- Laboratory of Bioinformatics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Anna Philips
- Laboratory of Bioinformatics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Paulina Galka-Marciniak
- Department of Molecular Genetics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 61-704, Poland
| | - Piotr Kozlowski
- Department of Molecular Genetics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 61-704, Poland
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2
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Nourbakhsh M, Saksager A, Tom N, Chen XS, Colaprico A, Olsen C, Tiberti M, Papaleo E. A workflow to study mechanistic indicators for driver gene prediction with Moonlight. Brief Bioinform 2023; 24:bbad274. [PMID: 37551622 PMCID: PMC10516357 DOI: 10.1093/bib/bbad274] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/28/2023] [Accepted: 07/10/2023] [Indexed: 08/09/2023] Open
Abstract
Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.
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Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark
| | - Nikola Tom
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark
| | - Xi Steven Chen
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Antonio Colaprico
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Catharina Olsen
- Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Clinical Sciences, Reproduction and Genetics
- Brussels Interuniversity Genomics High Throughput core (BRIGHTcore), VUB-ULB, Brussels 1090, Belgium
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels 1050, Belgium
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, Copenhagen, Denmark
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3
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Tang YJ, Shuldiner EG, Karmakar S, Winslow MM. High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In Vivo. Cold Spring Harb Perspect Med 2023; 13:a041382. [PMID: 37277208 PMCID: PMC10317066 DOI: 10.1101/cshperspect.a041382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The vast number of genomic and molecular alterations in cancer pose a substantial challenge to uncovering the mechanisms of tumorigenesis and identifying therapeutic targets. High-throughput functional genomic methods in genetically engineered mouse models allow for rapid and systematic investigation of cancer driver genes. In this review, we discuss the basic concepts and tools for multiplexed investigation of functionally important cancer genes in vivo using autochthonous cancer models. Furthermore, we highlight emerging technical advances in the field, potential opportunities for future investigation, and outline a vision for integrating multiplexed genetic perturbations with detailed molecular analyses to advance our understanding of the genetic and molecular basis of cancer.
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Affiliation(s)
- Yuning J Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Emily G Shuldiner
- Department of Biology, Stanford University, Stanford, California 94305, USA
| | - Saswati Karmakar
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Monte M Winslow
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
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4
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Cifuentes-Bernal AM, Pham VVH, Li X, Liu L, Li J, Duy Le T. Dynamic cancer drivers: a causal approach for cancer driver discovery based on bio-pathological trajectories. Brief Funct Genomics 2022; 21:455-465. [PMID: 36124841 PMCID: PMC10467634 DOI: 10.1093/bfgp/elac030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/08/2022] [Accepted: 08/23/2022] [Indexed: 12/14/2022] Open
Abstract
The traditional way for discovering genes which drive cancer (namely cancer drivers) neglects the dynamic information of cancer development, even though it is well known that cancer progresses dynamically. To enhance cancer driver discovery, we expand cancer driver concept to dynamic cancer driver as a gene driving one or more bio-pathological transitions during cancer progression. Our method refers to the fact that cancer should not be considered as a single process but a compendium of altered biological processes causing the disease to develop over time. Reciprocally, different drivers of cancer can potentially be discovered by analysing different bio-pathological pathways. We propose a novel approach for causal inference of genes driving one or more core processes during cancer development (i.e. dynamic cancer driver). We use the concept of pseudotime for inferring the latent progression of samples along a biological transition during cancer and identifying a critical event when such a process is significantly deviated from normal to carcinogenic. We infer driver genes by assessing the causal effect they have on the process after such a critical event. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method outperforms well-recognized cancer driver inference methods. These results suggest that including information of the underlying dynamics of cancer improves the inference process (in comparison with using static data), and allows us to discover different sets of driver genes from different processes in cancer. R scripts and datasets can be found at https://github.com/AndresMCB/DynamicCancerDriver.
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Affiliation(s)
- Andres M Cifuentes-Bernal
- UniSA STEM Unit, University of South Australia,
Mawson Lakes Blvd, 5095, South Australia , Australia
| | - Vu V H Pham
- UniSA STEM Unit, University of South Australia,
Mawson Lakes Blvd, 5095, South Australia , Australia
| | - Xiaomei Li
- UniSA STEM Unit, University of South Australia,
Mawson Lakes Blvd, 5095, South Australia , Australia
| | - Lin Liu
- UniSA STEM Unit, University of South Australia,
Mawson Lakes Blvd, 5095, South Australia , Australia
| | - Jiuyong Li
- UniSA STEM Unit, University of South Australia,
Mawson Lakes Blvd, 5095, South Australia , Australia
| | - Thuc Duy Le
- UniSA STEM Unit, University of South Australia,
Mawson Lakes Blvd, 5095, South Australia , Australia
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5
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Nussinov R, Tsai CJ, Jang H. A New View of Activating Mutations in Cancer. Cancer Res 2022; 82:4114-4123. [PMID: 36069825 PMCID: PMC9664134 DOI: 10.1158/0008-5472.can-22-2125] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/16/2022] [Accepted: 09/01/2022] [Indexed: 12/14/2022]
Abstract
A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
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6
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Zhu Y, Zhang H, Yang Y, Zhang C, Ou-Yang L, Bai L, Deng M, Yi M, Liu S, Wang C. Discovery of pan-cancer related genes via integrative network analysis. Brief Funct Genomics 2022; 21:325-338. [PMID: 35760070 DOI: 10.1093/bfgp/elac012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/14/2022] [Accepted: 05/25/2022] [Indexed: 01/02/2023] Open
Abstract
Identification of cancer-related genes is helpful for understanding the pathogenesis of cancer, developing targeted drugs and creating new diagnostic and therapeutic methods. Considering the complexity of the biological laboratory methods, many network-based methods have been proposed to identify cancer-related genes at the global perspective with the increasing availability of high-throughput data. Some studies have focused on the tissue-specific cancer networks. However, cancers from different tissues may share common features, and those methods may ignore the differences and similarities across cancers during the establishment of modeling. In this work, in order to make full use of global information of the network, we first establish the pan-cancer network via differential network algorithm, which not only contains heterogeneous data across multiple cancer types but also contains heterogeneous data between tumor samples and normal samples. Second, the node representation vectors are learned by network embedding. In contrast to ranking analysis-based methods, with the help of integrative network analysis, we transform the cancer-related gene identification problem into a binary classification problem. The final results are obtained via ensemble classification. We further applied these methods to the most commonly used gene expression data involving six tissue-specific cancer types. As a result, an integrative pan-cancer network and several biologically meaningful results were obtained. As examples, nine genes were ultimately identified as potential pan-cancer-related genes. Most of these genes have been reported in published studies, thus showing our method's potential for application in identifying driver gene candidates for further biological experimental verification.
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Affiliation(s)
- Yuan Zhu
- School of Automation, China University of Geosciences, Lumo Road, 430074, Wuhan, China.,Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Lumo Road, 430074, Wuhan, China.,Engineering Research Center of Intelligent Technology for Geo-Exploration, Lumo Road, 430074, Wuhan, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University), Ministry of Education, Handan Road, 200433, Shanghai, China
| | - Houwang Zhang
- Electrical Engineering, City University of HongKong, Kowloon, 999077, HongKong, China
| | - Yuanhang Yang
- School of Mathematics and Physics, China University of Geosciences, Lumo Road, 430074, Wuhan, China
| | - Chaoyang Zhang
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, USA
| | - Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Nanhai Avenue, 518060, Shenzhen, China
| | - Litai Bai
- School of Automation, China University of Geosciences, Lumo Road, 430074, Wuhan, China.,Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Lumo Road, 430074, Wuhan, China.,Engineering Research Center of Intelligent Technology for Geo-Exploration, Lumo Road, 430074, Wuhan, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, No.5 Yiheyuan Road, 100871, Beijing, China
| | - Ming Yi
- School of Mathematics and Physics, China University of Geosciences, Lumo Road, 430074, Wuhan, China
| | - Song Liu
- School of Automation, China University of Geosciences, Lumo Road, 430074, Wuhan, China.,Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Lumo Road, 430074, Wuhan, China.,Engineering Research Center of Intelligent Technology for Geo-Exploration, Lumo Road, 430074, Wuhan, China
| | - Chao Wang
- Hepatic Surgery Center, Institute of Hepato-Pancreato-Biliary Surgery, Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, 430030, Wuhan, China
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7
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Erten C, Houdjedj A, Kazan H, Taleb Bahmed AA. PersonaDrive: A Method for the Identification and Prioritization of Personalized Cancer Drivers. Bioinformatics 2022; 38:3407-3414. [PMID: 35579340 DOI: 10.1093/bioinformatics/btac329] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 05/06/2022] [Accepted: 05/11/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION A major challenge in cancer genomics is to distinguish the driver mutations that are causally linked to cancer from passenger mutations that do not contribute to cancer development. The majority of existing methods provide a single driver gene list for the entire cohort of patients. However, since mutation profiles of patients from the same cancer type show a high degree of heterogeneity, a more ideal approach is to identify patient-specific drivers. RESULTS We propose a novel method that integrates genomic data, biological pathways, and protein connectivity information for personalized identification of driver genes. The method is formulated on a personalized bipartite graph for each patient. Our approach provides a personalized ranking of the mutated genes of a patient based on the sum of weighted 'pairwise pathway coverage' scores across all the samples, where appropriate pairwise patient similarity scores are used as weights to normalize these coverage scores. We compare our method against three state-of-the-art patient-specific cancer gene prioritization methods. The comparisons are with respect to a novel evaluation method that takes into account the personalized nature of the problem. We show that our approach outperforms the existing alternatives for both the TCGA and the cell line data. Additionally, we show that the KEGG/Reactome pathways enriched in our ranked genes and those that are enriched in cell lines' reference sets overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods. Our findings can provide valuable information towards the development of personalized treatments and therapies. AVAILABILITY All the code and data are available at https://github.com/abu-compbio/PersonaDrive (archived at https://doi.org/10.5281/zenodo.6520187). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cesim Erten
- Department of Computer Engineering, Antalya Bilim University, Antalya, 07190, Turkey
| | - Aissa Houdjedj
- Department of Computer Engineering, Antalya Bilim University, Antalya, 07190, Turkey.,Department of Computer Engineering, Akdeniz University, Antalya, 07070, Turkey
| | - Hilal Kazan
- Department of Computer Engineering, Antalya Bilim University, Antalya, 07190, Turkey
| | - Ahmed Amine Taleb Bahmed
- Electrical and Computer Engineering Graduate Program, Antalya Bilim University, Antalya, 07190, Turkey
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8
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Andrades R, Recamonde-Mendoza M. Machine learning methods for prediction of cancer driver genes: a survey paper. Brief Bioinform 2022; 23:6551145. [PMID: 35323900 DOI: 10.1093/bib/bbac062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/06/2022] [Accepted: 02/08/2022] [Indexed: 12/21/2022] Open
Abstract
Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.
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Affiliation(s)
- Renan Andrades
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
| | - Mariana Recamonde-Mendoza
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
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9
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Kan Y, Jiang L, Tang J, Guo Y, Guo F. A systematic view of computational methods for identifying driver genes based on somatic mutation data. Brief Funct Genomics 2021; 20:333-343. [PMID: 34312663 DOI: 10.1093/bfgp/elab032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 06/16/2021] [Accepted: 06/22/2021] [Indexed: 11/13/2022] Open
Abstract
Abnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile-Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data.
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Affiliation(s)
- Yingxin Kan
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Limin Jiang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S
| | - Yan Guo
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, U.S
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China
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10
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Pham VVH, Liu L, Bracken CP, Nguyen T, Goodall GJ, Li J, Le TD. pDriver : A novel method for unravelling personalised coding and miRNA cancer drivers. Bioinformatics 2021; 37:3285-3292. [PMID: 33904576 DOI: 10.1093/bioinformatics/btab262] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 03/19/2021] [Accepted: 04/22/2021] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalised methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level. RESULTS We propose the novel method, pDriver, to discover personalised cancer drivers. pDriver includes two stages: (1) Constructing gene networks for each cancer patient and (2) Discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyse the predicted personalised drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer. AVAILABILITY AND IMPLEMENTATION pDriver is available at https://github.com/pvvhoang/pDriver. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vu V H Pham
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Lin Liu
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Cameron P Bracken
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA 5000, Australia.,Department of Medicine, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Thin Nguyen
- Applied Artificial Intelligence Institute, Deakin University, Australia
| | - Gregory J Goodall
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA 5000, Australia.,Department of Medicine, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Jiuyong Li
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Thuc D Le
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
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