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Raslan MA, Raslan SA, Shehata EM, Mahmoud AS, Sabri NA. Advances in the Applications of Bioinformatics and Chemoinformatics. Pharmaceuticals (Basel) 2023; 16:1050. [PMID: 37513961 PMCID: PMC10384252 DOI: 10.3390/ph16071050] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Chemoinformatics involves integrating the principles of physical chemistry with computer-based and information science methodologies, commonly referred to as "in silico techniques", in order to address a wide range of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, the incorporation of machine learning has been considered of high importance in the field of drug design, enabling the extraction of chemical data from enormous compound databases to develop drugs endowed with significant biological features. The present review discusses the field of cheminformatics and proposes the use of virtual chemical libraries in virtual screening methods to increase the probability of discovering novel hit chemicals. The virtual libraries address the need to increase the quality of the compounds as well as discover promising ones. On the other hand, various applications of bioinformatics in disease classification, diagnosis, and identification of multidrug-resistant organisms were discussed. The use of ensemble models and brute-force feature selection methodology has resulted in high accuracy rates for heart disease and COVID-19 diagnosis, along with the role of special formulations for targeting meningitis and Alzheimer's disease. Additionally, the correlation between genomic variations and disease states such as obesity and chronic progressive external ophthalmoplegia, the investigation of the antibacterial activity of pyrazole and benzimidazole-based compounds against resistant microorganisms, and its applications in chemoinformatics for the prediction of drug properties and toxicity-all the previously mentioned-were presented in the current review.
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Affiliation(s)
| | | | | | - Amr S Mahmoud
- Department of Obstetrics and Gynecology, Faculty of Medicine, Ain Shams University, Cairo P.O. Box 11566, Egypt
| | - Nagwa A Sabri
- Department of Clinical Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo P.O. Box 11566, Egypt
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2
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Auslander N, Gussow AB, Koonin EV. Incorporating Machine Learning into Established Bioinformatics Frameworks. Int J Mol Sci 2021; 22:2903. [PMID: 33809353 PMCID: PMC8000113 DOI: 10.3390/ijms22062903] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/08/2021] [Accepted: 03/10/2021] [Indexed: 12/23/2022] Open
Abstract
The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.
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Affiliation(s)
| | | | - Eugene V. Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA;
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3
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Lyu J, Li JJ, Su J, Peng F, Chen YE, Ge X, Li W. DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features. SCIENCE ADVANCES 2020; 6:6/46/eaba6784. [PMID: 33177077 PMCID: PMC7673741 DOI: 10.1126/sciadv.aba6784] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 09/29/2020] [Indexed: 05/09/2023]
Abstract
Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.
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Affiliation(s)
- Jie Lyu
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Jianzhong Su
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Fanglue Peng
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yiling Elaine Chen
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Xinzhou Ge
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697, USA.
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4
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Guan X, Runger G, Liu L. Dynamic incorporation of prior knowledge from multiple domains in biomarker discovery. BMC Bioinformatics 2020; 21:77. [PMID: 32164534 PMCID: PMC7068914 DOI: 10.1186/s12859-020-3344-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Background In biomarker discovery, applying domain knowledge is an effective approach to eliminating false positive features, prioritizing functionally impactful markers and facilitating the interpretation of predictive signatures. Several computational methods have been developed that formulate the knowledge-based biomarker discovery as a feature selection problem guided by prior information. These methods often require that prior information is encoded as a single score and the algorithms are optimized for biological knowledge of a specific type. However, in practice, domain knowledge from diverse resources can provide complementary information. But no current methods can integrate heterogeneous prior information for biomarker discovery. To address this problem, we developed the Know-GRRF (know-guided regularized random forest) method that enables dynamic incorporation of domain knowledge from multiple disciplines to guide feature selection. Results Know-GRRF embeds domain knowledge in a regularized random forest framework. It combines prior information from multiple domains in a linear model to derive a composite score, which, together with other tuning parameters, controls the regularization of the random forests model. Know-GRRF concurrently optimizes the weight given to each type of domain knowledge and other tuning parameters to minimize the AIC of out-of-bag predictions. The objective is to select a compact feature subset that has a high discriminative power and strong functional relevance to the biological phenotype. Via rigorous simulations, we show that Know-GRRF guided by multiple-domain prior information outperforms feature selection methods guided by single-domain prior information or no prior information. We then applied Known-GRRF to a real-world study to identify prognostic biomarkers of prostate cancers. We evaluated the combination of cancer-related gene annotations, evolutionary conservation and pre-computed statistical scores as the prior knowledge to assemble a panel of biomarkers. We discovered a compact set of biomarkers with significant improvements on prediction accuracies. Conclusions Know-GRRF is a powerful novel method to incorporate knowledge from multiple domains for feature selection. It has a broad range of applications in biomarker discoveries. We implemented this method and released a KnowGRRF package in the R/CRAN archive.
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Affiliation(s)
- Xin Guan
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85004, USA.,Intel Corporation, Chandler, AZ, 85226, USA
| | - George Runger
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85004, USA
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85004, USA. .,Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA. .,Department of Neurology, Mayo Clinic, Scottsdale, AZ, 85259, USA.
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5
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Lin CY, Ruan P, Li R, Yang JM, See S, Song J, Akutsu T. Deep learning with evolutionary and genomic profiles for identifying cancer subtypes. J Bioinform Comput Biol 2019; 17:1940005. [DOI: 10.1142/s0219720019400055] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated to contain informative signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains largely unclear. Here, we identified the evolutionarily conserved genes (i.e. core genes) and observed that they are primarily involved in cellular pathways relevant to cell growth and metabolisms. By using these core genes, we developed two novel strategies, namely a feature-based strategy (FES) and an image-based strategy (IMS) by integrating their evolutionary and genomic profiles with the deep learning algorithm. In comparison with the FES using the random set and the strategy using the PAM50 classifier, the core gene set-based FES achieved a higher accuracy for identifying breast cancer subtypes. The IMS and FES using the core gene set yielded better performances than the other strategies, in terms of classifying both breast cancer subtypes and multiple cancer types. Moreover, the IMS is reproducible even using different gene expression data (i.e. RNA-seq and microarray). Comprehensive analysis of eight cancer types demonstrates that our evolutionary conservation-based models represent a valid and helpful approach for identifying cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.
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Affiliation(s)
- Chun-Yu Lin
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 6110011, Japan
| | - Peiying Ruan
- NVIDIA AI Technology Center, NVIDIA Corporation Japan, Tokyo 1070052, Japan
| | - Ruiming Li
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 6110011, Japan
| | - Jinn-Moon Yang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan
| | - Simon See
- NVIDIA AI Technology Center, NVIDIA Corporation Singapore, Singapore 138522, Singapore
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 6110011, Japan
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Zhou XH, Chu XY, Xue G, Xiong JH, Zhang HY. Identifying cancer prognostic modules by module network analysis. BMC Bioinformatics 2019; 20:85. [PMID: 30777030 PMCID: PMC6380061 DOI: 10.1186/s12859-019-2674-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 02/08/2019] [Indexed: 02/08/2023] Open
Abstract
Background The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer. Results Here, we proposed a new method that considers both the interactions among modules and the prognostic correlation of the modules to identify prognostic modules in cancers. First, dense sub-networks in the gene co-expression network of cancer patients were detected. Second, cross-talk between every two modules was identified by a permutation test, thus generating the module network. Third, the prognostic correlation of each module was evaluated by the resampling method. Then, the GeneRank algorithm, which takes the module network and the prognostic correlations of all the modules as input, was applied to prioritize the prognostic modules. Finally, the selected modules were validated by survival analysis in various data sets. Our method was applied in three kinds of cancers, and the results show that our method succeeded in identifying prognostic modules in all the three cancers. In addition, our method outperformed state-of-the-art methods. Furthermore, the selected modules were significantly enriched with known cancer-related genes and drug targets of cancer, which may indicate that the genes involved in the modules may be drug targets for therapy. Conclusions We proposed a useful method to identify key modules in cancer prognosis and our prognostic genes may be good candidates for drug targets. Electronic supplementary material The online version of this article (10.1186/s12859-019-2674-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiong-Hui Zhou
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Xin-Yi Chu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Gang Xue
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Jiang-Hui Xiong
- State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, People's Republic of China.,Lab of Epigenetics and Health Tracking Technology, Space Institute of Southern China, Shenzhen, People's Republic of China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
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7
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Chebouba L, Boughaci D, Guziolowski C. Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients' Classification. J Med Syst 2018; 42:129. [PMID: 29869179 DOI: 10.1007/s10916-018-0972-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Accepted: 05/18/2018] [Indexed: 01/02/2023]
Abstract
The use of data issued from high throughput technologies in drug target problems is widely widespread during the last decades. This study proposes a meta-heuristic framework using stochastic local search (SLS) combined with random forest (RF) where the aim is to specify the most important genes and proteins leading to the best classification of Acute Myeloid Leukemia (AML) patients. First we use a stochastic local search meta-heuristic as a feature selection technique to select the most significant proteins to be used in the classification task step. Then we apply RF to classify new patients into their corresponding classes. The evaluation technique is to run the RF classifier on the training data to get a model. Then, we apply this model on the test data to find the appropriate class. We use as metrics the balanced accuracy (BAC) and the area under the receiver operating characteristic curve (AUROC) to measure the performance of our model. The proposed method is evaluated on the dataset issued from DREAM 9 challenge. The comparison is done with a pure random forest (without feature selection), and with the two best ranked results of the DREAM 9 challenge. We used three types of data: only clinical data, only proteomics data, and finally clinical and proteomics data combined. The numerical results show that the highest scores are obtained when using clinical data alone, and the lowest is obtained when using proteomics data alone. Further, our method succeeds in finding promising results compared to the methods presented in the DREAM challenge.
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Affiliation(s)
- Lokmane Chebouba
- Department of Computer Science, LRIA Laboratory, Electrical Engineering and Computer Science Faculty, University of Science and Technology Houari Boumediene (USTHB), El-Alia BP 32, Bab-Ezzouar, Algiers, Algeria.
| | - Dalila Boughaci
- Department of Computer Science, LRIA Laboratory, Electrical Engineering and Computer Science Faculty, University of Science and Technology Houari Boumediene (USTHB), El-Alia BP 32, Bab-Ezzouar, Algiers, Algeria
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8
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Chebouba L, Miannay B, Boughaci D, Guziolowski C. Discriminate the response of Acute Myeloid Leukemia patients to treatment by using proteomics data and Answer Set Programming. BMC Bioinformatics 2018. [PMID: 29536824 PMCID: PMC5850944 DOI: 10.1186/s12859-018-2034-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background During the last years, several approaches were applied on biomedical data to detect disease specific proteins and genes in order to better target drugs. It was shown that statistical and machine learning based methods use mainly clinical data and improve later their results by adding omics data. This work proposes a new method to discriminate the response of Acute Myeloid Leukemia (AML) patients to treatment. The proposed approach uses proteomics data and prior regulatory knowledge in the form of networks to predict cancer treatment outcomes by finding out the different Boolean networks specific to each type of response to drugs. To show its effectiveness we evaluate our method on a dataset from the DREAM 9 challenge. Results The results are encouraging and demonstrate the benefit of our approach to distinguish patient groups with different response to treatment. In particular each treatment response group is characterized by a predictive model in the form of a signaling Boolean network. This model describes regulatory mechanisms which are specific to each response group. The proteins in this model were selected from the complete dataset by imposing optimization constraints that maximize the difference in the logical response of the Boolean network associated to each group of patients given the omic dataset. This mechanistic and predictive model also allow us to classify new patients data into the two different patient response groups. Conclusions We propose a new method to detect the most relevant proteins for understanding different patient responses upon treatments in order to better target drugs using a Prior Knowledge Network and proteomics data. The results are interesting and show the effectiveness of our method. Electronic supplementary material The online version of this article (10.1186/s12859-018-2034-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lokmane Chebouba
- Department of Computer Science, LRIA Laboratory, Electrical Engineering and Computer Science Faculty, University of Science and Technology Houari Boumediene (USTHB), El-Alia BP 32 Bab-Ezzouar, Algiers, 16111, Algeria.,LS2N, UMR 6004, École Centrale de Nantes, Nantes, France
| | | | - Dalila Boughaci
- Department of Computer Science, LRIA Laboratory, Electrical Engineering and Computer Science Faculty, University of Science and Technology Houari Boumediene (USTHB), El-Alia BP 32 Bab-Ezzouar, Algiers, 16111, Algeria
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Quan Y, Wang ZY, Chu XY, Zhang HY. Evolutionary and genetic features of drug targets. Med Res Rev 2018; 38:1536-1549. [PMID: 29341142 DOI: 10.1002/med.21487] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 11/26/2017] [Accepted: 12/28/2017] [Indexed: 01/07/2023]
Abstract
In the modern drug discovery pipeline, identification of novel drug targets is a critical step. Despite rapid progress in developing biomedical techniques, it is still a great challenge to find promising new targets from the ample space of human genes. This fact is partially responsible for the situation of "more investments, fewer drugs" in the pharmaceutical industry. A series of recent researches revealed that successfully targeted genes share some common evolutionary and genetic features, which means that the knowledge accumulated in modern evolutionary biology and genetics is very helpful to identify potential drug targets and to find new drugs as well. In this article, we comprehensively summarize the links between human drug targets and genetic diseases and their evolutionary origins, with an attempt to introduce these novel concepts and their medical implications to the biomedical community.
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Affiliation(s)
- Yuan Quan
- Huazhong Agricultural University, Wuhan, P. R. China
| | - Zhong-Yi Wang
- Huazhong Agricultural University, Wuhan, P. R. China.,University of Heidelberg (ZMBH), Heidelberg, Germany
| | - Xin-Yi Chu
- Huazhong Agricultural University, Wuhan, P. R. China
| | - Hong-Yu Zhang
- Huazhong Agricultural University, Wuhan, P. R. China
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10
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Cheng X, Jin VX. An Introduction to Integrative Genomics and Systems Medicine in Cancer. Genes (Basel) 2018; 9:genes9010037. [PMID: 29329216 PMCID: PMC5793188 DOI: 10.3390/genes9010037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 01/10/2018] [Accepted: 01/10/2018] [Indexed: 11/16/2022] Open
Abstract
In this Special Issue (SI), with a theme of "Integrative Genomics and Systems Medicine in Cancer", we have collected a total of 12 research and review articles from researchers in the field of genomics and systems medicine[...].
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Affiliation(s)
- Xiaolong Cheng
- Department of Molecular Medicine, The University of Texas Health San Antonio, San Antonio, TX 78229, USA.
| | - Victor X Jin
- Department of Molecular Medicine, The University of Texas Health San Antonio, San Antonio, TX 78229, USA.
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11
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Chu XY, Jiang LH, Zhou XH, Cui ZJ, Zhang HY. Evolutionary Origins of Cancer Driver Genes and Implications for Cancer Prognosis. Genes (Basel) 2017; 8:genes8070182. [PMID: 28708071 PMCID: PMC5541315 DOI: 10.3390/genes8070182] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 06/27/2017] [Accepted: 07/10/2017] [Indexed: 12/20/2022] Open
Abstract
The cancer atavistic theory suggests that carcinogenesis is a reverse evolution process. It is thus of great interest to explore the evolutionary origins of cancer driver genes and the relevant mechanisms underlying the carcinogenesis. Moreover, the evolutionary features of cancer driver genes could be helpful in selecting cancer biomarkers from high-throughput data. In this study, through analyzing the cancer endogenous molecular networks, we revealed that the subnetwork originating from eukaryota could control the unlimited proliferation of cancer cells, and the subnetwork originating from eumetazoa could recapitulate the other hallmarks of cancer. In addition, investigations based on multiple datasets revealed that cancer driver genes were enriched in genes originating from eukaryota, opisthokonta, and eumetazoa. These results have important implications for enhancing the robustness of cancer prognosis models through selecting the gene signatures by the gene age information.
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Affiliation(s)
- Xin-Yi Chu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Ling-Han Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Xiong-Hui Zhou
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Ze-Jia Cui
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
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