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Schcolnik-Cabrera A, Juárez-López D, Duenas-Gonzalez A. Perspectives on Drug Repurposing. Curr Med Chem 2021; 28:2085-2099. [PMID: 32867630 DOI: 10.2174/0929867327666200831141337] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 05/01/2020] [Accepted: 05/22/2020] [Indexed: 11/22/2022]
Abstract
Complex common diseases are a significant burden for our societies and demand not only preventive measures but also more effective, safer, and more affordable treatments. The whole process of the current model of drug discovery and development implies a high investment by the pharmaceutical industry, which ultimately impact in high drug prices. In this sense, drug repurposing would help meet the needs of patients to access useful and novel treatments. Unlike the traditional approach, drug repurposing enters both the preclinical evaluation and clinical trials of the compound of interest faster, budgeting research and development costs, and limiting potential biosafety risks. The participation of government, society, and private investors is needed to secure the funds for experimental design and clinical development of repurposing candidates to have affordable, effective, and safe repurposed drugs. Moreover, extensive advertising of repurposing as a concept in the health community, could reduce prescribing bias when enough clinical evidence exists, which will support the employment of cheaper and more accessible repurposed compounds for common conditions.
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Affiliation(s)
- Alejandro Schcolnik-Cabrera
- Departement de Biochimie et Medecine Moleculaire, Universite de Montreal, C.P. 6128, Succursale Centre- Ville, Montreal, QC, Canada
| | - Daniel Juárez-López
- Posgrado en Ciencias Biologicas, Universidad Nacional Autonoma de Mexico; Av. Ciudad Universitaria 3000, C.P. 04510, Coyoacan, Ciudad de Mexico, Mexico
| | - Alfonso Duenas-Gonzalez
- Division de Investigacion Basica, Instituto Nacional de Cancerologia, Ciudad de Mexico 14080, Mexico
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Differential metabolic network construction for personalized medicine: Study of type 2 diabetes mellitus patients' response to gliclazide-modified-release-treated. J Biomed Inform 2021; 118:103796. [PMID: 33932596 DOI: 10.1016/j.jbi.2021.103796] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 02/26/2021] [Accepted: 04/26/2021] [Indexed: 11/21/2022]
Abstract
Individual variation in genetic and environmental factors can cause the differences in metabolic phenotypes, which may have an effect on drug responses of patients. Deep exploration of patients' responses to therapeutic agents is a crucial and urgent event in the personalized treatment study. Using machine learning methods for the discovery of suitability evaluation biomarkers can provide deep insight into the mechanism of disease therapy and facilitate the development of personalized medicine. To find important metabolic network signals for the prediction of patients' drug responses, a novel method referred to as differential metabolic network construction (DMNC) was proposed. In DMNC, concentration changes in metabolite ratios between different pathological states are measured to construct differential metabolic networks, which can be used to advance clinical decision-making. In this study, DMNC was applied to characterize type 2 diabetes mellitus (T2DM) patients' responses against gliclazide modified-release (MR) therapy. Two T2DM metabolomics datasets from different batches of subjects treated by gliclazide MR were analyzed in depth. A network biomarker was defined to assess the patients' suitability for gliclazide MR. It can be effective in the prediction of significant responders from nonsignificant responders, achieving area under the curve values of 0.893 and 1.000 for the discovery and validation sets, respectively. Compared with the metabolites selected by the other methods, the network biomarker selected by DMNC was more stable and precise to reflect the metabolic responses in patients to gliclazide MR therapy, thereby contributing for the personalized medicine of T2DM patients. The better performance of DMNC validated its potential for the identification of network biomarkers to characterize the responses against therapeutic treatments and provide valuable information for personalized medicine.
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RASMA: a reverse search algorithm for mining maximal frequent subgraphs. BioData Min 2021; 14:19. [PMID: 33726790 PMCID: PMC7962222 DOI: 10.1186/s13040-021-00250-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 02/21/2021] [Indexed: 11/23/2022] Open
Abstract
Background Given a collection of coexpression networks over a set of genes, identifying subnetworks that appear frequently is an important research problem known as mining frequent subgraphs. Maximal frequent subgraphs are a representative set of frequent subgraphs; A frequent subgraph is maximal if it does not have a super-graph that is frequent. In the bioinformatics discipline, methodologies for mining frequent and/or maximal frequent subgraphs can be used to discover interesting network motifs that elucidate complex interactions among genes, reflected through the edges of the frequent subnetworks. Further study of frequent coexpression subnetworks enhances the discovery of biological modules and biological signatures for gene expression and disease classification. Results We propose a reverse search algorithm, called RASMA, for mining frequent and maximal frequent subgraphs in a given collection of graphs. A key innovation in RASMA is a connected subgraph enumerator that uses a reverse-search strategy to enumerate connected subgraphs of an undirected graph. Using this enumeration strategy, RASMA obtains all maximal frequent subgraphs very efficiently. To overcome the computationally prohibitive task of enumerating all frequent subgraphs while mining for the maximal frequent subgraphs, RASMA employs several pruning strategies that substantially improve its overall runtime performance. Experimental results show that on large gene coexpression networks, the proposed algorithm efficiently mines biologically relevant maximal frequent subgraphs. Conclusion Extracting recurrent gene coexpression subnetworks from multiple gene expression experiments enables the discovery of functional modules and subnetwork biomarkers. We have proposed a reverse search algorithm for mining maximal frequent subnetworks. Enrichment analysis of the extracted maximal frequent subnetworks reveals that subnetworks that are frequent are highly enriched with known biological ontologies.
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Pognan F, Steger-Hartmann T, Díaz C, Blomberg N, Bringezu F, Briggs K, Callegaro G, Capella-Gutierrez S, Centeno E, Corvi J, Drew P, Drewe WC, Fernández JM, Furlong LI, Guney E, Kors JA, Mayer MA, Pastor M, Piñero J, Ramírez-Anguita JM, Ronzano F, Rowell P, Saüch-Pitarch J, Valencia A, van de Water B, van der Lei J, van Mulligen E, Sanz F. The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives. Pharmaceuticals (Basel) 2021; 14:ph14030237. [PMID: 33800393 PMCID: PMC7999019 DOI: 10.3390/ph14030237] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/25/2021] [Accepted: 02/27/2021] [Indexed: 12/19/2022] Open
Abstract
eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.
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Affiliation(s)
- François Pognan
- Preclinical Safety/Translational Medicine, Novartis, 4057 Basel, Switzerland;
| | | | - Carlos Díaz
- Synapse Research Managers SL, 28006 Madrid, Spain;
| | | | - Frank Bringezu
- Chemical & Preclinical Safety, Merck Healthcare KGaA, 64293 Darmstadt, Germany;
| | | | - Giulia Callegaro
- Leiden Academic Centre for Drug Research (LACDR), Leiden University, 2300 RA Leiden, The Netherlands; (G.C.); (B.v.d.W.)
| | | | - Emilio Centeno
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Javier Corvi
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; (S.C.-G.); (J.C.); (J.M.F.); (A.V.)
| | | | | | - José M. Fernández
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; (S.C.-G.); (J.C.); (J.M.F.); (A.V.)
| | - Laura I. Furlong
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
- MedBioinformatics Solutions SL, 08018 Barcelona, Spain
| | - Emre Guney
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Jan A. Kors
- Department of Medical Informatics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (J.A.K.); (J.v.d.L.); (E.v.M.)
| | - Miguel Angel Mayer
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Manuel Pastor
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Janet Piñero
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Juan Manuel Ramírez-Anguita
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Francesco Ronzano
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Philip Rowell
- Lhasa Limited, Leeds LS11 5PS, UK; (K.B.); (W.C.D.); (P.R.)
| | - Josep Saüch-Pitarch
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; (S.C.-G.); (J.C.); (J.M.F.); (A.V.)
- Catalan Institution for Research and Advanced Studies (ICREA), 08010 Barcelona, Spain
| | - Bob van de Water
- Leiden Academic Centre for Drug Research (LACDR), Leiden University, 2300 RA Leiden, The Netherlands; (G.C.); (B.v.d.W.)
| | - Johan van der Lei
- Department of Medical Informatics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (J.A.K.); (J.v.d.L.); (E.v.M.)
| | - Erik van Mulligen
- Department of Medical Informatics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (J.A.K.); (J.v.d.L.); (E.v.M.)
| | - Ferran Sanz
- GRIB, Hospital del Mar Institute of Medical Research (IMIM), DCEXS, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (E.C.); (L.I.F.); (E.G.); (M.A.M.); (M.P.); (J.P.); (J.M.R.-A.); (F.R.); (J.S.-P.)
- Correspondence:
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Shojaie A. Differential Network Analysis: A Statistical Perspective. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL STATISTICS 2021; 13:e1508. [PMID: 37050915 PMCID: PMC10088462 DOI: 10.1002/wics.1508] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 03/03/2020] [Indexed: 11/06/2022]
Abstract
Networks effectively capture interactions among components of complex systems, and have thus become a mainstay in many scientific disciplines. Growing evidence, especially from biology, suggest that networks undergo changes over time, and in response to external stimuli. In biology and medicine, these changes have been found to be predictive of complex diseases. They have also been used to gain insight into mechanisms of disease initiation and progression. Primarily motivated by biological applications, this article provides a review of recent statistical machine learning methods for inferring networks and identifying changes in their structures.
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Affiliation(s)
- Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle WA
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56
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Mahmood TB, Chowdhury AS, Hossain MU, Hasan M, Mizan S, Aakil MMUI, Hossan MI. Evaluation of the susceptibility and fatality of lung cancer patients towards the COVID-19 infection: A systemic approach through analyzing the ACE2, CXCL10 and their co-expressed genes. CURRENT RESEARCH IN MICROBIAL SCIENCES 2021; 2:100022. [PMID: 33585826 PMCID: PMC7871107 DOI: 10.1016/j.crmicr.2021.100022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 01/30/2021] [Accepted: 02/03/2021] [Indexed: 12/24/2022] Open
Abstract
The expression of ACE2 and CXCL10 is upregulated in lung cancer. 64 and 6 mutations were identified in ACE2 and CXCL10 protein sequences, respectively. ACE2 and CXCL10 are found as the hub proteins in the PPI network of COVID-19 development. 803 co-expressed genes of ACE2 are found to be involved in binding activity. 68 co-expressed genes of CXCL10 are identified involving in the immune response.
Coronavirus disease-2019 (COVID-19) is a recent world pandemic disease that is caused by a newly discovered strain of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS- CoV-2). Patients with comorbidities are most vulnerable to this disease. Therefore, cancer patients are reported to be more susceptible to COVID-19 infection, particularly lung cancer patients. To evaluate the probable reasons behind the excessive susceptibility and fatality of lung cancer patients to COVID-19 infection, we targeted the two most crucial agents, Angiotensin-converting enzyme 2 (ACE2) and C-X-C motif 10 (CXCL10). ACE2 is a receptor protein that plays a vital role in the entry of SARS-CoV-2 into the host cell and CXCL10 is a cytokine mainly responsible for the lung cell damage involving in a cytokine storm. By using the UALCAN and GEPIA2 databases, we observed that ACE2 and CXCL10 are mostly overexpressed in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). We then identified the functional significance of ACE2 and CXCL10 in lung cancer development by determining the genetic alteration frequency in their amino acid sequences using the cBioPortal web portal. Lastly, we did the pathological assessment of targeted genes using the PANTHER database. Here, we found that ACE2 and CXCL10 along with their commonly co-expressed genes are involved respectively in the binding activity and immune responses in case of lung cancer and COVID-19 infection. Finally, based on this systemic analysis, we concluded that ACE2 and CXCL10 are two possible biomarkers responsible for the higher susceptibility and fatality of lung cancer patients towards the COVID-19.
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Affiliation(s)
- Tousif Bin Mahmood
- Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Afrin Sultana Chowdhury
- Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | | | - Mehedee Hasan
- Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Shagufta Mizan
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chittagong 4331, Bangladesh
| | - Md Mezbah-Ul-Islam Aakil
- Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Mohammad Imran Hossan
- Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
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57
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Integrating Patient-Specific Information into Logic Models of Complex Diseases: Application to Acute Myeloid Leukemia. J Pers Med 2021; 11:jpm11020117. [PMID: 33578936 PMCID: PMC7916657 DOI: 10.3390/jpm11020117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/05/2021] [Accepted: 02/05/2021] [Indexed: 12/12/2022] Open
Abstract
High throughput technologies such as deep sequencing and proteomics are increasingly becoming mainstream in clinical practice and support diagnosis and patient stratification. Developing computational models that recapitulate cell physiology and its perturbations in disease is a required step to help with the interpretation of results of high content experiments and to devise personalized treatments. As complete cell-models are difficult to achieve, given limited experimental information and insurmountable computational problems, approximate approaches should be considered. We present here a general approach to modeling complex diseases by embedding patient-specific genomics data into actionable logic models that take into account prior knowledge. We apply the strategy to acute myeloid leukemia (AML) and assemble a network of logical relationships linking most of the genes that are found frequently mutated in AML patients. We derive Boolean models from this network and we show that by priming the model with genomic data we can infer relevant patient-specific clinical features. Here we propose that the integration of literature-derived causal networks with patient-specific data should be explored to help bedside decisions.
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58
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Wu L, Han L, Li Q, Wang G, Zhang H, Li L. Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding. MOLECULAR PLANT 2021; 14:77-94. [PMID: 33340690 DOI: 10.1016/j.molp.2020.12.012] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 12/11/2020] [Accepted: 12/14/2020] [Indexed: 05/27/2023]
Abstract
The functional genes underlying phenotypic variation and their interactions represent "genetic mysteries". Understanding and utilizing these genetic mysteries are key solutions for mitigating the current threats to agriculture posed by population growth and individual food preferences. Due to advances in high-throughput multi-omics technologies, we are stepping into an Interactome Big Data era that is certain to revolutionize genetic research. In this article, we provide a brief overview of current strategies to explore genetic mysteries. We then introduce the methods for constructing and analyzing the Interactome Big Data and summarize currently available interactome resources. Next, we discuss how Interactome Big Data can be used as a versatile tool to dissect genetic mysteries. We propose an integrated strategy that could revolutionize genetic research by combining Interactome Big Data with machine learning, which involves mining information hidden in Big Data to identify the genetic models or networks that control various traits, and also provide a detailed procedure for systematic dissection of genetic mysteries,. Finally, we discuss three promising future breeding strategies utilizing the Interactome Big Data to improve crop yields and quality.
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Affiliation(s)
- Leiming Wu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Qing Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Guoying Wang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hongwei Zhang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
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59
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Cheung FKM, Qin J. The Methods and Tools for Molecular Network Construction. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11464-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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60
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Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis. Biosci Rep 2020; 40:226923. [PMID: 33169793 PMCID: PMC7724614 DOI: 10.1042/bsr20201087] [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: 05/01/2020] [Revised: 11/01/2020] [Accepted: 11/09/2020] [Indexed: 12/20/2022] Open
Abstract
Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.
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61
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Ata SK, Wu M, Fang Y, Ou-Yang L, Kwoh CK, Li XL. Recent advances in network-based methods for disease gene prediction. Brief Bioinform 2020; 22:6023077. [PMID: 33276376 DOI: 10.1093/bib/bbaa303] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/29/2020] [Accepted: 10/10/2020] [Indexed: 01/28/2023] Open
Abstract
Disease-gene association through genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms that correlate with specific diseases needs statistical analysis of associations. Considering the huge number of possible mutations, in addition to its high cost, another important drawback of GWAS analysis is the large number of false positives. Thus, researchers search for more evidence to cross-check their results through different sources. To provide the researchers with alternative and complementary low-cost disease-gene association evidence, computational approaches come into play. Since molecular networks are able to capture complex interplay among molecules in diseases, they become one of the most extensively used data for disease-gene association prediction. In this survey, we aim to provide a comprehensive and up-to-date review of network-based methods for disease gene prediction. We also conduct an empirical analysis on 14 state-of-the-art methods. To summarize, we first elucidate the task definition for disease gene prediction. Secondly, we categorize existing network-based efforts into network diffusion methods, traditional machine learning methods with handcrafted graph features and graph representation learning methods. Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases. We also provide distinguishing findings about the discussed methods based on our empirical analysis. Finally, we highlight potential research directions for future studies on disease gene prediction.
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Affiliation(s)
- Sezin Kircali Ata
- School of Computer Science and Engineering Nanyang Technological University (NTU)
| | - Min Wu
- Institute for Infocomm Research (I2R), A*STAR, Singapore
| | - Yuan Fang
- School of Information Systems, Singapore Management University, Singapore
| | - Le Ou-Yang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen China
| | | | - Xiao-Li Li
- Department head and principal scientist at I2R, A*STAR, Singapore
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Zanin M, Santos BFR, Antony PMA, Berenguer-Escuder C, Larsen SB, Hanss Z, Barbuti PA, Baumuratov AS, Grossmann D, Capelle CM, Weber J, Balling R, Ollert M, Krüger R, Diederich NJ, He FQ. Mitochondria interaction networks show altered topological patterns in Parkinson's disease. NPJ Syst Biol Appl 2020; 6:38. [PMID: 33173039 PMCID: PMC7655803 DOI: 10.1038/s41540-020-00156-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 10/02/2020] [Indexed: 02/07/2023] Open
Abstract
Mitochondrial dysfunction is linked to pathogenesis of Parkinson's disease (PD). However, individual mitochondria-based analyses do not show a uniform feature in PD patients. Since mitochondria interact with each other, we hypothesize that PD-related features might exist in topological patterns of mitochondria interaction networks (MINs). Here we show that MINs formed nonclassical scale-free supernetworks in colonic ganglia both from healthy controls and PD patients; however, altered network topological patterns were observed in PD patients. These patterns were highly correlated with PD clinical scores and a machine-learning approach based on the MIN features alone accurately distinguished between patients and controls with an area-under-curve value of 0.989. The MINs of midbrain dopaminergic neurons (mDANs) derived from several genetic PD patients also displayed specific changes. CRISPR/CAS9-based genome correction of alpha-synuclein point mutations reversed the changes in MINs of mDANs. Our organelle-interaction network analysis opens another critical dimension for a deeper characterization of various complex diseases with mitochondrial dysregulation.
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Affiliation(s)
- Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), E-07122, Palma de Mallorca, Spain
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Campus of Montegancedo, E-28223, Pozuelo de Alarcón, Madrid, Spain
| | - Bruno F R Santos
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), 1A-B, rue Thomas Edison, L-1445, Strassen, Luxembourg
- Disease Modeling and Screening Platform (DMSP), Luxembourg Institute of Systems Biomedicine, University of Luxembourg & Luxembourg Institute of Health, 6 avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Paul M A Antony
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg
- Disease Modeling and Screening Platform (DMSP), Luxembourg Institute of Systems Biomedicine, University of Luxembourg & Luxembourg Institute of Health, 6 avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Clara Berenguer-Escuder
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Simone B Larsen
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Zoé Hanss
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Peter A Barbuti
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), 1A-B, rue Thomas Edison, L-1445, Strassen, Luxembourg
| | - Aidos S Baumuratov
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Dajana Grossmann
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Christophe M Capelle
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, L-4354, Esch-sur-Alzette, Luxembourg
| | - Joseph Weber
- Centre Hospitalier de Luxembourg (CHL) 4, Rue Nicolas Ernest Barblé, L-1210, Luxembourg, Luxembourg
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Markus Ollert
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, L-4354, Esch-sur-Alzette, Luxembourg
- Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis (ORCA), University of Southern Denmark, 5000C, Odense, Denmark
| | - Rejko Krüger
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), 1A-B, rue Thomas Edison, L-1445, Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL) 4, Rue Nicolas Ernest Barblé, L-1210, Luxembourg, Luxembourg
| | - Nico J Diederich
- Centre Hospitalier de Luxembourg (CHL) 4, Rue Nicolas Ernest Barblé, L-1210, Luxembourg, Luxembourg
| | - Feng Q He
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, L-4367, Belvaux, Luxembourg.
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, L-4354, Esch-sur-Alzette, Luxembourg.
- Institute of Medical Microbiology, University Hospital Essen, University Duisburg-Essen, D-45122, Essen, Germany.
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Rahem SM, Epsi NJ, Coffman FD, Mitrofanova A. Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer. EBioMedicine 2020; 61:103047. [PMID: 33099086 PMCID: PMC7585053 DOI: 10.1016/j.ebiom.2020.103047] [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: 03/06/2020] [Revised: 09/02/2020] [Accepted: 09/18/2020] [Indexed: 01/10/2023] Open
Abstract
Background Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes. Methods In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer. Findings We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients. Interpretation We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens. Funding This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.
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Affiliation(s)
- Sarra M Rahem
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA
| | - Nusrat J Epsi
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA
| | - Frederick D Coffman
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA; Department of Physician Assistant Studies and Practice, USA; Department of Pathology & Laboratory Medicine, New Jersey Medical School, Newark, New Jersey 07107, USA
| | - Antonina Mitrofanova
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, USA.
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64
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Lucchetta M, Pellegrini M. Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method. Sci Rep 2020; 10:17628. [PMID: 33077837 PMCID: PMC7573595 DOI: 10.1038/s41598-020-74705-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 09/30/2020] [Indexed: 12/21/2022] Open
Abstract
Genes are organized in functional modules (or pathways), thus their action and their dysregulation in diseases may be better understood by the identification of the modules most affected by the disease (aka disease modules, or active subnetworks). We describe how an algorithm based on the Core&Peel method is used to detect disease modules in co-expression networks of genes. We first validate Core&Peel for the general task of functional module detection by comparison with 42 methods participating in the Disease Module Identification DREAM challenge. Next, we use four specific disease test cases (colorectal cancer, prostate cancer, asthma, and rheumatoid arthritis), four state-of-the-art algorithms (ModuleDiscoverer, Degas, KeyPathwayMiner, and ClustEx), and several pathway databases to validate the proposed algorithm. Core&Peel is the only method able to find significant associations of the predicted disease module with known validated relevant pathways for all four diseases. Moreover, for the two cancer datasets, Core&Peel detects further eight relevant pathways not discovered by the other methods used in the comparative analysis. Finally, we apply Core&Peel and other methods to explore the transcriptional response of human cells to SARS-CoV-2 infection, finding supporting evidence for drug repositioning efforts at a pre-clinical level.
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Affiliation(s)
- Marta Lucchetta
- Institute of Informatics and Telematics (IIT), CNR, Pisa, 56124, Italy
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, 53100, Italy
| | - Marco Pellegrini
- Institute of Informatics and Telematics (IIT), CNR, Pisa, 56124, Italy.
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65
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Li J, Chen F, Zhang Q, Meng X, Yao X, Risacher SL, Yan J, Saykin AJ, Liang H, Shen L. Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease. Curr Alzheimer Res 2020; 16:1163-1174. [PMID: 31755389 DOI: 10.2174/1567205016666191121142558] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 11/19/2019] [Accepted: 11/21/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. OBJECTIVE The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. METHODS First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. RESULTS We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. CONCLUSION The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.
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Affiliation(s)
- Jin Li
- College of Automation, Harbin Engineering University, Harbin, China
| | - Feng Chen
- College of Automation, Harbin Engineering University, Harbin, China
| | - Qiushi Zhang
- College of Information Engineering, Northeast Dianli University, Jilin, China
| | - Xianglian Meng
- College of Automation, Harbin Engineering University, Harbin, China
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Hong Liang
- College of Automation, Harbin Engineering University, Harbin, China
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
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Nam JH, Couch D, da Silveira WA, Yu Z, Chung D. PALMER: improving pathway annotation based on the biomedical literature mining with a constrained latent block model. BMC Bioinformatics 2020; 21:432. [PMID: 33008309 PMCID: PMC7532116 DOI: 10.1186/s12859-020-03756-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 09/16/2020] [Indexed: 11/23/2022] Open
Abstract
Background In systems biology, it is of great interest to identify previously unreported associations between genes. Recently, biomedical literature has been considered as a valuable resource for this purpose. While classical clustering algorithms have popularly been used to investigate associations among genes, they are not tuned for the literature mining data and are also based on strong assumptions, which are often violated in this type of data. For example, these approaches often assume homogeneity and independence among observations. However, these assumptions are often violated due to both redundancies in functional descriptions and biological functions shared among genes. Latent block models can be alternatives in this case but they also often show suboptimal performances, especially when signals are weak. In addition, they do not allow to utilize valuable prior biological knowledge, such as those available in existing databases. Results In order to address these limitations, here we propose PALMER, a constrained latent block model that allows to identify indirect relationships among genes based on the biomedical literature mining data. By automatically associating relevant Gene Ontology terms, PALMER facilitates biological interpretation of novel findings without laborious downstream analyses. PALMER also allows researchers to utilize prior biological knowledge about known gene-pathway relationships to guide identification of gene–gene associations. We evaluated PALMER with simulation studies and applications to studies of pathway-modulating genes relevant to cancer signaling pathways, while utilizing biological pathway annotations available in the KEGG database as prior knowledge. Conclusions We showed that PALMER outperforms traditional latent block models and it provides reliable identification of novel gene–gene associations by utilizing prior biological knowledge, especially when signals are weak in the biomedical literature mining dataset. We believe that PALMER and its relevant user-friendly software will be powerful tools that can be used to improve existing pathway annotations and identify novel pathway-modulating genes.
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Affiliation(s)
- Jin Hyun Nam
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.,School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea
| | - Daniel Couch
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | | | - Zhenning Yu
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
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Adnan N, Lei C, Ruan J. Robust edge-based biomarker discovery improves prediction of breast cancer metastasis. BMC Bioinformatics 2020; 21:359. [PMID: 32998692 PMCID: PMC7526355 DOI: 10.1186/s12859-020-03692-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background The abundance of molecular profiling of breast cancer tissues entailed active research on molecular marker-based early diagnosis of metastasis. Recently there is a surging interest in combining gene expression with gene networks such as protein-protein interaction (PPI) network, gene co-expression (CE) network and pathway information to identify robust and accurate biomarkers for metastasis prediction, reflecting the common belief that cancer is a systems biology disease. However, controversy exists in the literature regarding whether network markers are indeed better features than genes alone for predicting as well as understanding metastasis. We believe much of the existing results may have been biased by the overly complicated prediction algorithms, unfair evaluation, and lack of rigorous statistics. In this study, we propose a simple approach to use network edges as features, based on two types of networks respectively, and compared their prediction power using three classification algorithms and rigorous statistical procedure on one of the largest datasets available. To detect biomarkers that are significant for the prediction and to compare the robustness of different feature types, we propose an unbiased and novel procedure to measure feature importance that eliminates the potential bias from factors such as different sample size, number of features, as well as class distribution. Results Experimental results reveal that edge-based feature types consistently outperformed gene-based feature type in random forest and logistic regression models under all performance evaluation metrics, while the prediction accuracy of edge-based support vector machine (SVM) model was poorer, due to the larger number of edge features compared to gene features and the lack of feature selection in SVM model. Experimental results also show that edge features are much more robust than gene features and the top biomarkers from edge feature types are statistically more significantly enriched in the biological processes that are well known to be related to breast cancer metastasis. Conclusions Overall, this study validates the utility of edge features as biomarkers but also highlights the importance of carefully designed experimental procedures in order to achieve statistically reliable comparison results.
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Affiliation(s)
- Nahim Adnan
- Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, 78249, TX, USA
| | - Chengwei Lei
- Department of Computer & Electrical Engineering/Computer Science, California State University, Bakersfield, 9001 Stockdale Highway, Bakersfield, 93311, CA, USA
| | - Jianhua Ruan
- Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, 78249, TX, USA.
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Kim YA, Sarto Basso R, Wojtowicz D, Liu AS, Hochbaum DS, Vandin F, Przytycka TM. Identifying Drug Sensitivity Subnetworks with NETPHIX. iScience 2020; 23:101619. [PMID: 33089107 PMCID: PMC7566085 DOI: 10.1016/j.isci.2020.101619] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 09/08/2020] [Accepted: 09/24/2020] [Indexed: 12/29/2022] Open
Abstract
Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.
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Affiliation(s)
- Yoo-Ah Kim
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA
| | - Rebecca Sarto Basso
- Department of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA 94709, USA
| | - Damian Wojtowicz
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA
| | - Amanda S Liu
- Montgomery Blair High School, Silver Spring, MD 20901, USA
| | - Dorit S Hochbaum
- Department of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA 94709, USA
| | - Fabio Vandin
- Department of Information Engineering, University of Padova, Padova 35131, Italy
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA
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Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, Chimusa ER. Computational/in silico methods in drug target and lead prediction. Brief Bioinform 2020; 21:1663-1675. [PMID: 31711157 PMCID: PMC7673338 DOI: 10.1093/bib/bbz103] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 07/17/2019] [Accepted: 07/18/2019] [Indexed: 01/10/2023] Open
Abstract
Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.
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Affiliation(s)
- Francis E Agamah
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- African Institute for Mathematical Sciences, Muizenberg, Cape Town 7945, South Africa
| | - Radia Hassan
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
| | - Christian D Bope
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo
| | - Nicholas E Thomford
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- School of Medical Sciences, University of Cape Coast, PMB, Cape Coast, Ghana
| | - Anita Ghansah
- Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, PO Box LG 581, Legon, Ghana
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
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Momenzadeh M, Sehhati M, Rabbani H. Using hidden Markov model to predict recurrence of breast cancer based on sequential patterns in gene expression profiles. J Biomed Inform 2020; 111:103570. [PMID: 32961308 DOI: 10.1016/j.jbi.2020.103570] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 09/06/2020] [Accepted: 09/10/2020] [Indexed: 12/16/2022]
Abstract
A new approach is presented to predict breast cancer recurrence through gene expression profiles using hidden Markov models (HMM). In this regard, 322 genes were selected from 44 published gene lists related to breast cancer prognosis. Afterwards, using gene set enrichment analysis, 922 gene sets were found from subsets of genes with the same biological meaning. In order to extract the sequential patterns from gene expression data, we ranked the gene sets using appropriate criteria and used HMM in which the ranked gene sets considered as observation sequences and hidden states represented priority of gene sets for discriminating between expression profiles. In this experiment, seven publicly available microarray datasets, including 1271 breast tumor samples, were used to classify cancer patients into two groups according to risk of recurrence. Our experiments indicated the greater performance and more robustness of the proposed model compared with other widely used classification methods.
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Affiliation(s)
- Mohammadreza Momenzadeh
- Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammadreza Sehhati
- Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; Department of Bioinformatics, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Hossein Rabbani
- Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
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Perscheid C. Integrative biomarker detection on high-dimensional gene expression data sets: a survey on prior knowledge approaches. Brief Bioinform 2020; 22:5881664. [PMID: 32761115 DOI: 10.1093/bib/bbaa151] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 06/15/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.
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Affiliation(s)
- Cindy Perscheid
- Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany
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Abstract
Breast cancer is one of the most common cancers worldwide, which makes it a very impactful malignancy in the society. Breast cancers can be classified through different systems based on the main tumor features and gene, protein, and cell receptors expression, which will determine the most advisable therapeutic course and expected outcomes. Multiple therapeutic options have already been proposed and implemented for breast cancer treatment. Nonetheless, their use and efficacy still greatly depend on the tumor classification, and treatments are commonly associated with invasiveness, pain, discomfort, severe side effects, and poor specificity. This has demanded an investment in the research of the mechanisms behind the disease progression, evolution, and associated risk factors, and on novel diagnostic and therapeutic techniques. However, advances in the understanding and assessment of breast cancer are dependent on the ability to mimic the properties and microenvironment of tumors in vivo, which can be achieved through experimentation on animal models. This review covers an overview of the main animal models used in breast cancer research, namely in vitro models, in vivo models, in silico models, and other models. For each model, the main characteristics, advantages, and challenges associated to their use are highlighted.
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Comte B, Baumbach J, Benis A, Basílio J, Debeljak N, Flobak Å, Franken C, Harel N, He F, Kuiper M, Méndez Pérez JA, Pujos-Guillot E, Režen T, Rozman D, Schmid JA, Scerri J, Tieri P, Van Steen K, Vasudevan S, Watterson S, Schmidt HH. Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine. NETWORK AND SYSTEMS MEDICINE 2020; 3:67-90. [PMID: 32954378 PMCID: PMC7500076 DOI: 10.1089/nsm.2020.0004] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2020] [Indexed: 12/14/2022] Open
Abstract
Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.
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Affiliation(s)
- Blandine Comte
- Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Université Clermont Auvergne, INRAE, UNH, Clermont-Ferrand, France
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan (WZW), Technical University of Munich (TUM), Freising-Weihenstephan, Germany
| | | | - José Basílio
- Institute of Vascular Biology and Thrombosis Research, Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Nataša Debeljak
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- The Cancer Clinic, St. Olav's University Hospital, Trondheim, Norway
| | - Christian Franken
- Digital Health Systems, Einsingen, Germany
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| | | | - Feng He
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg
- Institute of Medical Microbiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Martin Kuiper
- Department of Biology, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Juan Albino Méndez Pérez
- Department of Computer Science and Systems Engineering, Universidad de La Laguna, Tenerife, Spain
| | - Estelle Pujos-Guillot
- Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Université Clermont Auvergne, INRAE, UNH, Clermont-Ferrand, France
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Johannes A. Schmid
- Institute of Vascular Biology and Thrombosis Research, Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Jeanesse Scerri
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Sona Vasudevan
- Georgetown University Medical Centre, Washington, District of Columbia, USA
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, MeHNS, Maastricht University, The Netherlands
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Ma X, Sun P, Gong M. An integrative framework of heterogeneous genomic data for cancer dynamic modules based on matrix decomposition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 19:305-316. [PMID: 32750874 DOI: 10.1109/tcbb.2020.3004808] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Cancer progression is dynamic, and tracking dynamic modules is promising for cancer diagnosis and therapy. Accumulated genomic data provide us an opportunity to investigate the underlying mechanisms of cancers. However, as far as we know, no algorithm has been designed for dynamic modules by integrating heterogeneous omics data. To address this issue, we propose an integrative framework for dynamic module detection based on regularized nonnegative matrix factorization method (DrNMF) by integrating the gene expression and protein interaction network. To remove the heterogeneity of genomic data, we divide the samples of expression profiles into groups to construct gene co-expression networks. To characterize the dynamics of modules, the temporal smoothness framework is adopted, in which the gene co-expression network at the previous stage and protein interaction network are incorporated into the objective function of DrNMF via regularization. The experimental results demonstrate that DrNMF is superior to state-of-the-art methods in terms of accuracy. For breast cancer data, the obtained dynamic modules are more enriched by the known pathways, and can be used to predict the stages of cancers and survival time of patients. The proposed model and algorithm provide an effective integrative analysis of heterogeneous genomic data for cancer progression.
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Jubair S, Alkhateeb A, Tabl AA, Rueda L, Ngom A. A novel approach to identify subtype-specific network biomarkers of breast cancer survivability. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/s13721-020-00249-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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76
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Kim YA, Wojtowicz D, Sarto Basso R, Sason I, Robinson W, Hochbaum DS, Leiserson MDM, Sharan R, Vadin F, Przytycka TM. Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer. Genome Med 2020; 12:52. [PMID: 32471470 PMCID: PMC7260830 DOI: 10.1186/s13073-020-00745-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 05/07/2020] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Studies of cancer mutations have typically focused on identifying cancer driving mutations that confer growth advantage to cancer cells. However, cancer genomes accumulate a large number of passenger somatic mutations resulting from various endogenous and exogenous causes, including normal DNA damage and repair processes or cancer-related aberrations of DNA maintenance machinery as well as mutations triggered by carcinogenic exposures. Different mutagenic processes often produce characteristic mutational patterns called mutational signatures. Identifying mutagenic processes underlying mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis. METHODS To investigate the genetic aberrations associated with mutational signatures, we took a network-based approach considering mutational signatures as cancer phenotypes. Specifically, our analysis aims to answer the following two complementary questions: (i) what are functional pathways whose gene expression activities correlate with the strengths of mutational signatures, and (ii) are there pathways whose genetic alterations might have led to specific mutational signatures? To identify mutated pathways, we adopted a recently developed optimization method based on integer linear programming. RESULTS Analyzing a breast cancer dataset, we identified pathways associated with mutational signatures on both expression and mutation levels. Our analysis captured important differences in the etiology of the APOBEC-related signatures and the two clock-like signatures. In particular, it revealed that clustered and dispersed APOBEC mutations may be caused by different mutagenic processes. In addition, our analysis elucidated differences between two age-related signatures-one of the signatures is correlated with the expression of cell cycle genes while the other has no such correlation but shows patterns consistent with the exposure to environmental/external processes. CONCLUSIONS This work investigated, for the first time, a network-level association of mutational signatures and dysregulated pathways. The identified pathways and subnetworks provide novel insights into mutagenic processes that the cancer genomes might have undergone and important clues for developing personalized drug therapies.
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Affiliation(s)
- Yoo-Ah Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894 USA
| | - Damian Wojtowicz
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894 USA
| | - Rebecca Sarto Basso
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894 USA
- Department of Industrial Engineering and Operations Research, University of California, Berkeley, 94720 CA USA
| | - Itay Sason
- School of Computer Science, Tel Aviv University, Tel Aviv, 69978 Israel
| | - Welles Robinson
- Center for Bioinformatics and Computational Biology, University of Maryland, 8314 Paint Branch Dr, College Park, 20742 USA
| | - Dorit S. Hochbaum
- Department of Industrial Engineering and Operations Research, University of California, Berkeley, 94720 CA USA
| | - Mark D. M. Leiserson
- Center for Bioinformatics and Computational Biology, University of Maryland, 8314 Paint Branch Dr, College Park, 20742 USA
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv, 69978 Israel
| | - Fabio Vadin
- Department of Information Engineering, University of Padova, Via Gradenigo 6/A, Padua, I-35131 Italy
| | - Teresa M. Przytycka
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894 USA
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77
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Farooq QUA, Shaukat Z, Zhou T, Aiman S, Gong W, Li C. Inferring Virus-Host relationship between HPV and its host Homo sapiens using protein interaction network. Sci Rep 2020; 10:8719. [PMID: 32457456 PMCID: PMC7251128 DOI: 10.1038/s41598-020-65837-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 05/11/2020] [Indexed: 12/14/2022] Open
Abstract
Human papilloma virus (HPV) is a serious threat to human life globally with over 100 genotypes including cancer causing high risk HPVs. Study on protein interaction maps of pathogens with their host is a recent trend in ‘omics’ era and has been practiced by researchers to find novel drug targets. In current study, we construct an integrated protein interaction map of HPV with its host human in Cytoscape and analyze it further by using various bioinformatics tools. We found out 2988 interactions between 12 HPV and 2061 human proteins among which we identified MYLK, CDK7, CDK1, CDK2, JAK1 and 6 other human proteins associated with multiple viral oncoproteins. The functional enrichment analysis of these top-notch key genes is performed using KEGG pathway and Gene Ontology analysis, which reveals that the gene set is enriched in cell cycle a crucial cellular process, and the second most important pathway in which the gene set is involved is viral carcinogenesis. Among the viral proteins, E7 has the highest number of associations in the network followed by E6, E2 and E5. We found out a group of genes which is not targeted by the existing drugs available for HPV infections. It can be concluded that the molecules found in this study could be potential targets and could be used by scientists in their drug design studies.
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Affiliation(s)
- Qurat Ul Ain Farooq
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Zeeshan Shaukat
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Tong Zhou
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Sara Aiman
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Weikang Gong
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Chunhua Li
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China.
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78
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Yuan L, Guo F, Wang L, Zou Q. Prediction of tumor metastasis from sequencing data in the era of genome sequencing. Brief Funct Genomics 2020; 18:412-418. [PMID: 31204784 DOI: 10.1093/bfgp/elz010] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 02/22/2019] [Accepted: 04/26/2019] [Indexed: 02/01/2023] Open
Abstract
Tumor metastasis is the key reason for the high mortality rate of tumor. Growing number of scholars have begun to pay attention to the research on tumor metastasis and have achieved satisfactory results in this field. The advent of the era of sequencing has enabled us to study cancer metastasis at the molecular level, which is essential for understanding the molecular mechanism of metastasis, identifying diagnostic markers and therapeutic targets and guiding clinical decision-making. We reviewed the metastasis-related studies using sequencing data, covering detection of metastasis origin sites, determination of metastasis potential and identification of distal metastasis sites. These findings include the discovery of relevant markers and the presentation of prediction tools. Finally, we discussed the challenge of studying metastasis considering the difficulty of obtaining metastatic cancer data, the complexity of tumor heterogeneity and the uncertainty of sample labels.
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Affiliation(s)
- Linlin Yuan
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Lei Wang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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79
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Wang S, Wu YY, Wang X, Shen P, Jia Q, Yu S, Wang Y, Li X, Chen W, Wang A, Lu Y. Lycopene prevents carcinogen-induced cutaneous tumor by enhancing activation of the Nrf2 pathway through p62-triggered autophagic Keap1 degradation. Aging (Albany NY) 2020; 12:8167-8190. [PMID: 32365333 PMCID: PMC7244072 DOI: 10.18632/aging.103132] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 03/30/2020] [Indexed: 12/17/2022]
Abstract
Biologically active natural products have been used for the chemoprevention of cutaneous tumors. Lycopene is the main active phytochemical in tomatoes. We herein aimed to assess the cancer preventive effects of lycopene and to find potential molecular targets. In chemically-induced cutaneous tumor mice and cell models, lycopene attenuated cutaneous tumor incidence and multiplicity as well as the tumorigenesis of normal cutaneous cells in phase-selectivity (only in the promotion phase) manners. By utilizing a comprehensive approach combining bioinformatics with network pharmacology, we predicted that intracellular autophagy and redox status were associated with lycopene’s preventive effect on cutaneous tumors. Lycopene stimulated the activation of antioxidant enzymes and the translocation of the transcription factor Nrf2 (nuclear factor erythroid 2-related factor 2) that predominantly maintained intracellular redox equilibrium. The cancer chemopreventive effects were mediated by Nrf2. Further, lycopene enhanced the expression of autophagy protein p62. Therefore this led to the degradation of Keap1(Kelch ECH associating protein 1), the main protein locking Nrf2 in cytoplasm. In conclusion, our study provides preclinical evidence of the chemopreventive effects of lycopene on cutaneous tumors and reveals the mechanistic link between lycopene’s stimulation of Nrf2 signaling pathway and p62-mediated degradation of Keap1 via the autophagy-lysosomal pathway.
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Affiliation(s)
- Siliang Wang
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China.,Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, P.R. China
| | - Yuan-Yuan Wu
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China
| | - Xu Wang
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China
| | - Peiliang Shen
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China
| | - Qi Jia
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China
| | - Suyun Yu
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China
| | - Yuan Wang
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China
| | - Xiaoman Li
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China
| | - Wenxing Chen
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China
| | - Aiyun Wang
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China
| | - Yin Lu
- Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China.,Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine (TCM) Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing 210023, P.R. China
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80
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Single-cell transcriptional networks in differentiating preadipocytes suggest drivers associated with tissue heterogeneity. Nat Commun 2020; 11:2117. [PMID: 32355218 PMCID: PMC7192917 DOI: 10.1038/s41467-020-16019-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 04/03/2020] [Indexed: 12/14/2022] Open
Abstract
White adipose tissue plays an important role in physiological homeostasis and metabolic disease. Different fat depots have distinct metabolic and inflammatory profiles and are differentially associated with disease risk. It is unclear whether these differences are intrinsic to the pre-differentiated stage. Using single-cell RNA sequencing, a unique network methodology and a data integration technique, we predict metabolic phenotypes in differentiating cells. Single-cell RNA-seq profiles of human preadipocytes during adipogenesis in vitro identifies at least two distinct classes of subcutaneous white adipocytes. These differences in gene expression are separate from the process of browning and beiging. Using a systems biology approach, we identify a new network of zinc-finger proteins that are expressed in one class of preadipocytes and is potentially involved in regulating adipogenesis. Our findings gain a deeper understanding of both the heterogeneity of white adipocytes and their link to normal metabolism and disease. The origin of the heterogeneity of metabolic and inflammatory profiles exhibited by white adipocytes is little understood. Here, using scRNA-seq and computational methods, the authors show that differentiating preadipocytes exhibit gene expression differences and suggest underlying regulators.
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81
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Al-Harazi O, El Allali A, Colak D. Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2020; 23:138-151. [PMID: 30883301 DOI: 10.1089/omi.2018.0205] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.
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Affiliation(s)
- Olfat Al-Harazi
- 1 Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.,2 Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Achraf El Allali
- 2 Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Dilek Colak
- 1 Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
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82
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Adnan N, Liu Z, Huang THM, Ruan J. Comparative evaluation of network features for the prediction of breast cancer metastasis. BMC Med Genomics 2020; 13:40. [PMID: 32241278 PMCID: PMC7119280 DOI: 10.1186/s12920-020-0676-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Background Discovering a highly accurate and robust gene signature for the prediction of breast cancer metastasis from gene expression profiling of primary tumors is one of the most challenging tasks to reduce the number of deaths in women. Due to the limited success of gene-based features in achieving satisfactory prediction accuracy, many methodologies have been proposed in recent years to develop network-based features by integrating network information with gene expression. However, evaluation results are inconsistent to confirm the effectiveness of network-based features, because of many confounding factors involved in classification model learning process, such as data normalization, dimension reduction, and feature selection. An unbiased comparative evaluation is essential for uncovering the strength of network-based features. Methods In this study, we compared several types of network-based features obtained using different mathematical operators (Mean, Maximum, Minimum, Median, Variance) on geneset (i.e., a gene and its’ neighbors in the network) in protein-protein interaction network and gene co-expression network for their ability in predicting breast cancer metastasis using gene expression data from more than 10 patient cohorts. Results While network-based features are usually statistically more significant than gene-based feature, a consistent improvement of prediction performance using network-based features requires a substantial number of patients in the dataset. In contrary to many previous reports, no evidence was found to support the robustness of network-based features and we argue some of the robustness may be due to the inherent bias associated with node degree in the network. In addition, different types of network features seem to cover different pathways and are complementary to each other. Consequently, an ensemble classifier combining different network features was proposed and was found to significantly outperform classifiers based on gene-based feature or any single type of network-based features. Conclusions Network-based features and their combination show promise for improving the prediction of breast cancer metastasis but may require a large amount of training data. Robustness claim of network-based features needs to be re-examined with network node degree and other confounding factors in consideration.
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Affiliation(s)
- Nahim Adnan
- Department of Computer Science, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA
| | - Zhijie Liu
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78230, USA
| | - Tim H M Huang
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78230, USA
| | - Jianhua Ruan
- Department of Computer Science, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA. .,Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78230, USA.
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83
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Lin Y, Qian F, Shen L, Chen F, Chen J, Shen B. Computer-aided biomarker discovery for precision medicine: data resources, models and applications. Brief Bioinform 2020; 20:952-975. [PMID: 29194464 DOI: 10.1093/bib/bbx158] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2017] [Revised: 10/17/2017] [Indexed: 12/21/2022] Open
Abstract
Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.
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Affiliation(s)
- Yuxin Lin
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Fuliang Qian
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Li Shen
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Feifei Chen
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Jiajia Chen
- School of Chemistry, Biology and Material Engineering, Suzhou University of Science and Technology, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
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84
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Liu W, Gan C, Wang W, Liao L, Li C, Xu L, Li E. Identification of lncRNA-associated differential subnetworks in oesophageal squamous cell carcinoma by differential co-expression analysis. J Cell Mol Med 2020; 24:4804-4818. [PMID: 32164040 PMCID: PMC7176870 DOI: 10.1111/jcmm.15159] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 02/21/2020] [Accepted: 02/25/2020] [Indexed: 02/06/2023] Open
Abstract
Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co-expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co-expression of lncRNAs and protein-coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA-PCG differential co-expression network (DCN). DCN was characterized as a scale-free, small-world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA-PCG subnetworks were identified from the DCN by integrating both differential expression and differential co-expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers.
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Affiliation(s)
- Wei Liu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan AreaShantou University Medical CollegeShantouChina
- Department of Biochemistry and Molecular BiologyShantou University Medical CollegeShantouChina
- Department of MathematicsHeilongjiang Institute of TechnologyHarbinChina
| | - Cai‐Yan Gan
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan AreaShantou University Medical CollegeShantouChina
- Department of Biochemistry and Molecular BiologyShantou University Medical CollegeShantouChina
| | - Wei Wang
- Department of MathematicsHeilongjiang Institute of TechnologyHarbinChina
| | - Lian‐Di Liao
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan AreaShantou University Medical CollegeShantouChina
- Institute of Oncologic PathologyShantou University Medical CollegeShantouChina
| | - Chun‐Quan Li
- Department of Medical InformaticsHarbin Medical University‐DaqingDaqingChina
| | - Li‐Yan Xu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan AreaShantou University Medical CollegeShantouChina
- Institute of Oncologic PathologyShantou University Medical CollegeShantouChina
| | - En‐Min Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan AreaShantou University Medical CollegeShantouChina
- Department of Biochemistry and Molecular BiologyShantou University Medical CollegeShantouChina
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85
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Mallik S, Zhao Z. Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data. Brief Bioinform 2020; 21:368-394. [PMID: 30649169 PMCID: PMC7373185 DOI: 10.1093/bib/bby120] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 10/26/2018] [Accepted: 11/21/2018] [Indexed: 12/20/2022] Open
Abstract
Cancer is well recognized as a complex disease with dysregulated molecular networks or modules. Graph- and rule-based analytics have been applied extensively for cancer classification as well as prognosis using large genomic and other data over the past decade. This article provides a comprehensive review of various graph- and rule-based machine learning algorithms that have been applied to numerous genomics data to determine the cancer-specific gene modules, identify gene signature-based classifiers and carry out other related objectives of potential therapeutic value. This review focuses mainly on the methodological design and features of these algorithms to facilitate the application of these graph- and rule-based analytical approaches for cancer classification and prognosis. Based on the type of data integration, we divided all the algorithms into three categories: model-based integration, pre-processing integration and post-processing integration. Each category is further divided into four sub-categories (supervised, unsupervised, semi-supervised and survival-driven learning analyses) based on learning style. Therefore, a total of 11 categories of methods are summarized with their inputs, objectives and description, advantages and potential limitations. Next, we briefly demonstrate well-known and most recently developed algorithms for each sub-category along with salient information, such as data profiles, statistical or feature selection methods and outputs. Finally, we summarize the appropriate use and efficiency of all categories of graph- and rule mining-based learning methods when input data and specific objective are given. This review aims to help readers to select and use the appropriate algorithms for cancer classification and prognosis study.
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Affiliation(s)
- Saurav Mallik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston
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Molecular classification of breast cancer using the mRNA expression profiles of immune-related genes. Sci Rep 2020; 10:4800. [PMID: 32179831 PMCID: PMC7075995 DOI: 10.1038/s41598-020-61710-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 03/02/2020] [Indexed: 01/03/2023] Open
Abstract
Breast cancer is the most lethal cancer in women and displaying a broad range of heterogeneity in terms of clinical, molecular behavior and response to therapy. Increasing evidence demonstrated that immune-related genes were an important source of prognostic information for several types of tumors. In this study, the k-mean clustering was applied to gene expression data from the immune-related genes, two molecular clusters were identified for 1980 breast cancer patients. The prognostic significance of the immune-related genes based classification was confirmed in the log-rank test. These clusters were also associated with immune checkpoints, immune-related features and tumor infiltrating levels. In addition, we used the shrunken centroid algorithm to predict the cluster of a given breast cancer sample, and good predictive results were obtained by this algorithm. These results indicated that the proposed classification method is a promising method, and we hope that this method may improve the treatment stratification of breast cancer in the future.
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87
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Kataka E, Zaucha J, Frishman G, Ruepp A, Frishman D. Edgetic perturbation signatures represent known and novel cancer biomarkers. Sci Rep 2020; 10:4350. [PMID: 32152446 PMCID: PMC7062722 DOI: 10.1038/s41598-020-61422-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 02/20/2020] [Indexed: 02/07/2023] Open
Abstract
Isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RNA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer sub-type specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at EdgeExplorer: “http://webclu.bio.wzw.tum.de/EdgeExplorer”) are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.
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Affiliation(s)
- Evans Kataka
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Maximus-von-Imhof-Forum 3, 85354, Freising, Germany
| | - Jan Zaucha
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Maximus-von-Imhof-Forum 3, 85354, Freising, Germany
| | - Goar Frishman
- Institute of Experimental Genetics (IEG), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany
| | - Andreas Ruepp
- Institute of Experimental Genetics (IEG), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Maximus-von-Imhof-Forum 3, 85354, Freising, Germany. .,Laboratory of Bioinformatics, RASA Research Center, St Petersburg State Polytechnic University, St Petersburg, 195251, Russia.
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88
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Chekalin EV, Zolotarenko AD, Bruskin SA. IQGAP Genes in Psoriasis. RUSS J GENET+ 2020. [DOI: 10.1134/s1022795420030047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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89
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Yan J, Wu L, Jia C, Yu S, Lu Z, Sun Y, Chen J. Development of a four-gene prognostic model for pancreatic cancer based on transcriptome dysregulation. Aging (Albany NY) 2020; 12:3747-3770. [PMID: 32081836 PMCID: PMC7066910 DOI: 10.18632/aging.102844] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 02/04/2020] [Indexed: 12/14/2022]
Abstract
We systematically developed a prognostic model for pancreatic cancer that was compatible across different transcriptomic platforms and patient cohorts. After performing quality control measures, we used seven microarray datasets and two RNA sequencing datasets to identify consistently dysregulated genes in pancreatic cancer patients. Weighted gene co-expression network analysis was performed to explore the associations between gene expression patterns and clinical features. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to construct a prognostic model. We tested the predictive power of the model by determining the area under the curve of the risk score for time-dependent survival. Most of the differentially expressed genes in pancreatic cancer were enriched in functions pertaining to the tumor immune microenvironment. The transcriptome profiles were found to be associated with overall survival, and four genes were identified as independent prognostic factors. A prognostic risk score was then proposed, which displayed moderate accuracy in the training and self-validation cohorts. Furthermore, patients in two independent microarray cohorts were successfully stratified into high- and low-risk prognostic groups. Thus, we constructed a reliable prognostic model for pancreatic cancer, which should be beneficial for clinical therapeutic decision-making.
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Affiliation(s)
- Jie Yan
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Liangcai Wu
- Department of Obstetrics and Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, China
| | - Congwei Jia
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Shuangni Yu
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Zhaohui Lu
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Yueping Sun
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100020, China
| | - Jie Chen
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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90
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Abstract
The abundance of high-throughput data and technical refinements in graph theories have allowed network analysis to become an effective approach for various medical fields. This chapter introduces co-expression, Bayesian, and regression-based network construction methods, which are the basis of network analysis. Various methods in network topology analysis are explained, along with their unique features and applications in biomedicine. Furthermore, we explain the role of network embedding in reducing the dimensionality of networks and outline several popular algorithms used by researchers today. Current literature has implemented different combinations of topology analysis and network embedding techniques, and we outline several studies in the fields of genetic-based disease prediction, drug-target identification, and multi-level omics integration.
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91
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Krzhizhanovskaya VV, Závodszky G, Lees MH, Dongarra JJ, Sloot PMA, Brissos S, Teixeira J. Biological Network Visualization for Targeted Proteomics Based on Mean First-Passage Time in Semi-Lazy Random Walks. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7304027 DOI: 10.1007/978-3-030-50420-5_40] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Experimental data from protein microarrays or other targeted assays are often analyzed using network-based visualization and modeling approaches. Reference networks, such as a graph of known protein-protein interactions, can be used to place experimental data in the context of biological pathways, making the results more interpretable. The first step in network-based visualization and modeling involves mapping the measured experimental endpoints to network nodes, but in targeted assays many network nodes have no corresponding measured endpoints. This leads to a novel problem – given full network structure and a subset of vertices that correspond to measured protein endpoints, infer connectivity between those vertices. We solve the problem by defining a semi-lazy random walk in directed graphs, and quantifying the mean first-passage time for graph nodes. Using simulated and real networks and data, we show that the graph connectivity structure inferred by the proposed method has higher agreement with underlying biology than two alternative strategies.
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92
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Gong L, Tang H, Luo Z, Sun X, Tan X, Xie L, Lei Y, Cai M, He C, Ma J, Han S. Tamoxifen induces fatty liver disease in breast cancer through the MAPK8/FoxO pathway. Clin Transl Med 2020; 10:137-150. [PMID: 32508033 PMCID: PMC7240857 DOI: 10.1002/ctm2.5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 02/29/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Prevention of metabolic complications of long-term adjuvant endocrine therapy in breast cancers remained a challenge. We aimed to investigate the molecular mechanism in the development of tamoxifen (TAM)-induced fatty liver in both estrogen receptor (ER)-positive and ER-negative breast cancer. METHODS AND RESULTS First, the direct protein targets (DPTs) of TAM were identified using DrugBank5.1.7. We found that mitogen-activated protein kinase 8 (MAPK8) was one DPT of TAM. We identified significant genes in breast cancer and fatty liver disease (FLD) using the MalaCards human disease database. Next, we analyzed the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of those significant genes in breast cancer and FLD using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). We found that overlapping KEGG pathways in these two diseases were MAPK signaling pathway, Forkhead box O (FoxO) signaling pathway, HIF-1 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and PI3K-Akt signaling pathway. Furthermore, the KEGG Mapper showed that the MAPK signaling pathway was related to the FoxO signaling pathway. Finally, the functional relevance of breast cancer and TAM-induced FLD was validated by Western blot analysis. We verified that TAM may induce fatty liver in breast cancer through the MAPK8/FoxO signaling pathway. CONCLUSION Bioinformatics analysis combined with conventional experiments may improve our understanding of the molecular mechanisms underlying side effects of cancer drugs, thereby making this method a new paradigm for guiding future studies on this issue.
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Affiliation(s)
- Liuyun Gong
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
| | - Hanmin Tang
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
| | - Zhenzhen Luo
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
| | - Xiao Sun
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
| | - Xinyue Tan
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
| | - Lina Xie
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
| | - Yutiantian Lei
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
| | - Mengjiao Cai
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
| | - Chenchen He
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
| | - Jinlu Ma
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
| | - Suxia Han
- Department of OncologyThe First Affiliated HospitalXi'an Jiaotong UniversityXi'anPR China
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93
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Wallace ZS, Rosenthal SB, Fisch KM, Ideker T, Sasik R. On entropy and information in gene interaction networks. Bioinformatics 2019; 35:815-822. [PMID: 30102349 DOI: 10.1093/bioinformatics/bty691] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 06/14/2018] [Accepted: 08/08/2018] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Modern biological experiments often produce candidate lists of genes presumably related to the studied phenotype. One can ask if the gene list as a whole makes sense in the context of existing knowledge: Are the genes in the list reasonably related to each other or do they look like a random assembly? There are also situations when one wants to know if two or more gene sets are closely related. Gene enrichment tests based on counting the number of genes two sets have in common are adequate if we presume that two genes are related only when they are in fact identical. If by related we mean well connected in the interaction network space, we need a new measure of relatedness for gene sets. RESULTS We derive entropy, interaction information and mutual information for gene sets on interaction networks, starting from a simple phenomenological model of a living cell. Formally, the model describes a set of interacting linear harmonic oscillators in thermal equilibrium. Because the energy function is a quadratic form of the degrees of freedom, entropy and all other derived information quantities can be calculated exactly. We apply these concepts to estimate the probability that genes from several independent genome-wide association studies are not mutually informative; to estimate the probability that two disjoint canonical metabolic pathways are not mutually informative; and to infer relationships among human diseases based on their gene signatures. We show that the present approach is able to predict observationally validated relationships not detectable by gene enrichment methods. The converse is also true; the two methods are therefore complementary. AVAILABILITY AND IMPLEMENTATION The functions defined in this paper are available in an R package, gsia, available for download at https://github.com/ucsd-ccbb/gsia.
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Affiliation(s)
- Z S Wallace
- Department of Mathematics, Tufts University School of Arts and Sciences, Medford, MA, USA
| | - S B Rosenthal
- Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA, USA
| | - K M Fisch
- Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA, USA
| | - T Ideker
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - R Sasik
- Department of Medicine, Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA, USA
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94
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Gonzalez-Fierro A, Dueñas-González A. Drug repurposing for cancer therapy, easier said than done. Semin Cancer Biol 2019; 68:123-131. [PMID: 31877340 DOI: 10.1016/j.semcancer.2019.12.012] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 11/26/2019] [Accepted: 12/15/2019] [Indexed: 12/24/2022]
Abstract
Drug repurposing for cancer therapy is currently a hot topic of research. Theoretically, in contrast to the known hurdles of developing new molecular entities, the approach of repurposing has several advantages. Mostly, it is said that it is faster, safer, easier, and cheaper. In the real world, however, there are only three repurposed drugs so far, that are listed in widely recognized cancer guidelines, but a large number of them are being studied. Among the many barriers to repurposing cancer drugs, economical-driven are the most important that difficult the clinical development of them. In this review, we provide an overview of the current status of drug repurposing for cancer therapy and the barriers that need to be overcome to realize the benefit of this approach. It means to have repositioned drugs for cancer therapy accepted as standard therapy for cancer indications at low cost.
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Affiliation(s)
| | - Alfonso Dueñas-González
- Division of Basic Researach, Instituto Nacional de Cancerología, Mexico City, Mexico; Unit of Biomedical Research in Cancer, Instituto de Investigaciones Biomédicas, Universidad Nacional Autonoma de Mexico NAM/ Instituto Nacional de Cancerología, Mexico City, Mexico.
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95
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Martini P, Chiogna M, Calura E, Romualdi C. MOSClip: multi-omic and survival pathway analysis for the identification of survival associated gene and modules. Nucleic Acids Res 2019; 47:e80. [PMID: 31049575 PMCID: PMC6698707 DOI: 10.1093/nar/gkz324] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 03/29/2019] [Accepted: 04/29/2019] [Indexed: 01/09/2023] Open
Abstract
Survival analyses of gene expression data has been a useful and widely used approach in clinical applications. But, in complex diseases, such as cancer, the identification of survival-associated cell processes - rather than single genes - provides more informative results because the efficacy of survival prediction increases when multiple prognostic features are combined to enlarge the possibility of having druggable targets. Moreover, genome-wide screening in molecular medicine has rapidly grown, providing not only gene expression but also multi-omic measurements such as DNA mutations, methylation, expression, and copy number data. In cancer, virtually all these aberrations can contribute in synergy to pathological processes, and their measurements can improve a patient’s outcome and help in diagnosis and treatment decisions. Here, we present MOSClip, an R package implementing a new topological pathway analysis tool able to integrate multi-omic data and look for survival-associated gene modules. MOSClip tests the survival association of dimensionality-reduced multi-omic data using multivariate models, providing graphical devices for management, browsing and interpretation of results. Using simulated data we evaluated MOSClip performance in terms of false positives and false negatives in different settings, while the TCGA ovarian cancer dataset is used as a case study to highlight MOSClip’s potential.
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Affiliation(s)
- Paolo Martini
- Department of Biology, University of Padova, Via U.Bassi 58B, 35121 Padova, Italy
| | - Monica Chiogna
- Department of Statistical Sciences 'Paolo Fortunati', University of Bologna, via delle Belle Arti 41, 40126 Bologna, Italy
| | - Enrica Calura
- Department of Biology, University of Padova, Via U.Bassi 58B, 35121 Padova, Italy
| | - Chiara Romualdi
- Department of Biology, University of Padova, Via U.Bassi 58B, 35121 Padova, Italy
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96
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Afiqah-Aleng N, Altaf-Ul-Amin M, Kanaya S, Mohamed-Hussein ZA. Graph cluster approach in identifying novel proteins and significant pathways involved in polycystic ovary syndrome. Reprod Biomed Online 2019; 40:319-330. [PMID: 32001161 DOI: 10.1016/j.rbmo.2019.11.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 11/07/2019] [Accepted: 11/25/2019] [Indexed: 12/18/2022]
Abstract
RESEARCH QUESTION Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with diverse clinical implications, such as infertility, metabolic disorders, cardiovascular diseases and psychological problems among others. The heterogeneity of conditions found in PCOS contribute to its various phenotypes, leading to difficulties in identifying proteins involved in this abnormality. Several studies, however, have shown the feasibility in identifying molecular evidence underlying other diseases using graph cluster analysis. Therefore, is it possible to identify proteins and pathways related to PCOS using the same approach? METHODS Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways. RESULTS The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity. CONCLUSIONS These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.
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Affiliation(s)
- Nor Afiqah-Aleng
- Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia; Institute of Marine Biotechnology, Universiti Malaysia Terengganu (UMT), 21030 Kuala Nerus, Terengganu, Malaysia
| | - M Altaf-Ul-Amin
- Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara 630-0192, Japan
| | - Shigehiko Kanaya
- Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara 630-0192, Japan
| | - Zeti-Azura Mohamed-Hussein
- Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia; Centre for Frontier Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
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97
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Tshabalala T, Ncube B, Madala NE, Nyakudya TT, Moyo HP, Sibanda M, Ndhlala AR. Scribbling the Cat: A Case of the "Miracle" Plant, Moringa oleifera. PLANTS (BASEL, SWITZERLAND) 2019; 8:E510. [PMID: 31731759 PMCID: PMC6918402 DOI: 10.3390/plants8110510] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 10/27/2019] [Accepted: 10/31/2019] [Indexed: 12/21/2022]
Abstract
This paper reviews the properties of the most cultivated species of the Moringaceae family, Moringa oleifera Lam. The paper takes a critical look at the positive and the associated negative properties of the plant, with particular emphasis on its chemistry, selected medicinal and nutritional properties, as well as some ecological implications of the plant. The review highlights the importance of glucosinolates (GS) compounds which are relatively unique to the Moringa species family, with glucomoriginin and its acylated derivative being the most abundant. We highlight some new research findings revealing that not all M. oleifera cultivars contain an important flavonoid, rutin. The review also focuses on phenolic acids, tannin, minerals and vitamins, which are in high amounts when compared to most vegetables and fruits. Although there are numerous benefits of using M. oleifera for medicinal purposes, there are reports of contraindications. Nonetheless, we note that there are no major harmful effects of M. oleifera that have been reported by the scientific community. M. oleifera is suspected to be potentially invasive and moderately invasive in some regions of the world because of its ability to grow in a wide range of environmental conditions. However, the plant is currently classified as a low potential invasive species and thus there is a need to constantly monitor the species. Despite the numerous benefits associated with the plant, there is still a paucity of data on clinical trials proving both the positive and negative effects of the plant. We recommend further clinical trials to ascertain the properties associated with the plant, especially regarding long term use.
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Affiliation(s)
- Thulani Tshabalala
- Agricultural Research Council (ARC), Vegetable and Ornamental Plants (VOP), Private Bag X923, Pretoria 0001, South Africa; (T.T.); (B.N.)
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal Pietermaritzburg, Private Bag X01, Scottsville 3209, South Africa;
| | - Bhekumthetho Ncube
- Agricultural Research Council (ARC), Vegetable and Ornamental Plants (VOP), Private Bag X923, Pretoria 0001, South Africa; (T.T.); (B.N.)
| | - Ntakadzeni Edwin Madala
- Department of Biochemistry, School of Mathematical and Natural Sciences, University of Venda, Private Bag X5050, Thohoyandou, 0950, South Africa;
| | - Trevor Tapiwa Nyakudya
- Department of Physiology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa;
- Department of Human Anatomy and Physiology, Faculty of Health Sciences, University of Johannesburg, Doornfontein, Johannesburg 2002, South Africa
| | | | - Mbulisi Sibanda
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal Pietermaritzburg, Private Bag X01, Scottsville 3209, South Africa;
| | - Ashwell Rungano Ndhlala
- Agricultural Research Council (ARC), Vegetable and Ornamental Plants (VOP), Private Bag X923, Pretoria 0001, South Africa; (T.T.); (B.N.)
- Department of Life and Consumer Sciences, College of Agriculture and Environmental Sciences, University of South Africa, Private Bag X6, Florida 1710, South Africa
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98
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Saberi Ansar E, Eslahchii C, Rahimi M, Geranpayeh L, Ebrahimi M, Aghdam R, Kerdivel G. Significant random signatures reveals new biomarker for breast cancer. BMC Med Genomics 2019; 12:160. [PMID: 31703592 PMCID: PMC6842262 DOI: 10.1186/s12920-019-0609-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 10/24/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND In 2012, Venet et al. proposed that at least in the case of breast cancer, most published signatures are not significantly more associated with outcome than randomly generated signatures. They suggested that nominal p-value is not a good estimator to show the significance of a signature. Therefore, one can reasonably postulate that some information might be present in such significant random signatures. METHODS In this research, first we show that, using an empirical p-value, these published signatures are more significant than their nominal p-values. In other words, the proposed empirical p-value can be considered as a complimentary criterion for nominal p-value to distinguish random signatures from significant ones. Secondly, we develop a novel computational method to extract information that are embedded within significant random signatures. In our method, a score is assigned to each gene based on the number of times it appears in significant random signatures. Then, these scores are diffused through a protein-protein interaction network and a permutation procedure is used to determine the genes with significant scores. The genes with significant scores are considered as the set of significant genes. RESULTS First, we applied our method on the breast cancer dataset NKI to achieve a set of significant genes in breast cancer considering significant random signatures. Secondly, prognostic performance of the computed set of significant genes is evaluated using DMFS and RFS datasets. We have observed that the top ranked genes from this set can successfully separate patients with poor prognosis from those with good prognosis. Finally, we investigated the expression pattern of TAT, the first gene reported in our set, in malignant breast cancer vs. adjacent normal tissue and mammospheres. CONCLUSION Applying the method, we found a set of significant genes in breast cancer, including TAT, a gene that has never been reported as an important gene in breast cancer. Our results show that the expression of TAT is repressed in tumors suggesting that this gene could act as a tumor suppressor in breast cancer and could be used as a new biomarker.
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Affiliation(s)
- Elnaz Saberi Ansar
- Curie Institute, INSERM U830, Translational Research Department, PSL Research University, Paris, 75005 France
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Changiz Eslahchii
- Department of Computer Sciences, Faculty of Mathematical Sciences, Shahid-Beheshti University, GC, Tehran, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mahsa Rahimi
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Lobat Geranpayeh
- Department of Surgery, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Marzieh Ebrahimi
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Rosa Aghdam
- Department of Computer Sciences, Faculty of Mathematical Sciences, Shahid-Beheshti University, GC, Tehran, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Gwenneg Kerdivel
- Institut Cochin, Department Development, Reproduction, Inserm U1016, CNRS, UMR 8104, Université Paris Descartes UMR-S1016, Paris, 75014 France
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99
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Do KT, Rasp DJNP, Kastenmüller G, Suhre K, Krumsiek J. MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions. Bioinformatics 2019; 35:532-534. [PMID: 30032270 PMCID: PMC6361241 DOI: 10.1093/bioinformatics/bty650] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 07/18/2018] [Indexed: 11/13/2022] Open
Abstract
Summary Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data. Availability and implementation https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kieu Trinh Do
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - David J N-P Rasp
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Gabi Kastenmüller
- German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum, Neuherberg, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College-Qatar Education City, Doha, Qatar
| | - Jan Krumsiek
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
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Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer. Sci Rep 2019; 9:15918. [PMID: 31685861 PMCID: PMC6828742 DOI: 10.1038/s41598-019-52093-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 10/07/2019] [Indexed: 12/15/2022] Open
Abstract
We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data. Our devised method consists of a biased tree ensemble that is built according to a probabilistic bias weight distribution. The bias weight distribution is obtained from the assignment of high weights to the drug targets and propagating the assigned weights over a protein-protein interaction network such as STRING. The propagation of weights, defines neighborhoods of influence around the drug targets and as such simulates the spread of perturbations within the cell, following drug administration. Using a synthetic dataset, we showcase how application of biased tree ensembles (BiTE) results in significant accuracy gains at a much lower computational cost compared to the unbiased random forests (RF) algorithm. We then apply NetBiTE to the Genomics of Drug Sensitivity in Cancer (GDSC) dataset and demonstrate that NetBiTE outperforms RF in predicting IC50 drug sensitivity, only for drugs that target membrane receptor pathways (MRPs): RTK, EGFR and IGFR signaling pathways. We propose based on the NetBiTE results, that for drugs that inhibit MRPs, the expression of target genes prior to drug administration is a biomarker for IC50 drug sensitivity following drug administration. We further verify and reinforce this proposition through control studies on, PI3K/MTOR signaling pathway inhibitors, a drug category that does not target MRPs, and through assignment of dummy targets to MRP inhibiting drugs and investigating the variation in NetBiTE accuracy.
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