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Katta MR, Kalluru PKR, Bavishi DA, Hameed M, Valisekka SS. Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04625-1. [PMID: 36739356 DOI: 10.1007/s00432-023-04625-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 01/27/2023] [Indexed: 02/06/2023]
Abstract
PURPOSE This review aims to explore the role of AI in the application of pancreatic cancer management and make recommendations to minimize the impact of the limitations to provide further benefits from AI use in the future. METHODS A comprehensive review of the literature was conducted using a combination of MeSH keywords, including "Artificial intelligence", "Pancreatic cancer", "Diagnosis", and "Limitations". RESULTS The beneficial implications of AI in the detection of biomarkers, diagnosis, and prognosis of pancreatic cancer have been explored. In addition, current drawbacks of AI use have been divided into subcategories encompassing statistical, training, and knowledge limitations; data handling, ethical and medicolegal aspects; and clinical integration and implementation. CONCLUSION Artificial intelligence (AI) refers to computational machine systems that accomplish a set of given tasks by imitating human intelligence in an exponential learning pattern. AI in gastrointestinal oncology has continued to provide significant advancements in the clinical, molecular, and radiological diagnosis and intervention techniques required to improve the prognosis of many gastrointestinal cancer types, particularly pancreatic cancer.
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Affiliation(s)
| | | | | | - Maha Hameed
- Clinical Research Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
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2
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Newaz K, Milenkovic T. Inference of a Dynamic Aging-related Biological Subnetwork via Network Propagation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:974-988. [PMID: 32897864 DOI: 10.1109/tcbb.2020.3022767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Gene expression (GE)data capture valuable condition-specific information ("condition" can mean a biological process, disease stage, age, patient, etc.)However, GE analyses ignore physical interactions between gene products, i.e., proteins. Because proteins function by interacting with each other, and because biological networks (BNs)capture these interactions, BN analyses are promising. However, current BN data fail to capture condition-specific information. Recently, GE and BN data have been integrated using network propagation (NP)to infer condition-specific BNs. However, existing NP-based studies result in a static condition-specific subnetwork, even though cellular processes are dynamic. A dynamic process of our interest is human aging. We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related)subnetwork. Then, we study evolution of network structure with age - we identify proteins whose network positions significantly change with age and predict them as new aging-related candidates. We validate the predictions via e.g., functional enrichment analyses and literature search. Dynamic network inference via NP yields higher prediction quality than the only existing method for inferring a dynamic aging-related BN, which does not use NP. Our data and code are available at https://nd.edu/~cone/dynetinf.
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3
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Sferruzza G, Clarelli F, Mascia E, Ferrè L, Ottoboni L, Sorosina M, Santoro S, Filippi M, Provero P, Esposito F. Transcriptional effects of fingolimod treatment on peripheral T cells in relapsing remitting multiple sclerosis patients. Pharmacogenomics 2022; 23:161-171. [PMID: 35068175 DOI: 10.2217/pgs-2021-0118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: To investigate the transcriptional changes induced by Fingolimod (FTY) in T cells of relapsing remitting multiple sclerosis patients. Patients & methods: Transcriptomic changes after 6 months of FTY therapy were evaluated on T cells from 24 relapsing remitting multiple sclerosis patients through RNA-sequencing, followed by technical validation and pathway analysis. Results: Among differentially expressed genes, CX3CR1 and CCR7 resulted strongly up- and down-regulated, respectively. Two relevant genes were validated with quantitative PCR and we largely confirmed findings from two previous microarray-based studies with similar design. Pathway analysis pointed to an involvement of processes related to immune function and cell migration. Conclusion: Our data support the evidence that FTY induces major transcriptional changes in genes involved in immune response and cell trafficking in T lymphocytes.
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Affiliation(s)
- Giacomo Sferruzza
- Department of Neurology, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy.,Neuroimmunology Unit, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Ferdinando Clarelli
- Laboratory of Human Genetics of Neurological Disorders, CNS Inflammatory Unit & INSPE, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Elisabetta Mascia
- Laboratory of Human Genetics of Neurological Disorders, CNS Inflammatory Unit & INSPE, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Laura Ferrè
- Department of Neurology, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy.,Laboratory of Human Genetics of Neurological Disorders, CNS Inflammatory Unit & INSPE, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Linda Ottoboni
- Neuroimmunology Unit, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Melissa Sorosina
- Laboratory of Human Genetics of Neurological Disorders, CNS Inflammatory Unit & INSPE, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Silvia Santoro
- Laboratory of Human Genetics of Neurological Disorders, CNS Inflammatory Unit & INSPE, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Massimo Filippi
- Department of Neurology, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy.,Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan 20132, Italy
| | - Paolo Provero
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy.,Department of Neurosciences 'Rita Levi Montalcini,' University of Turin, Turin 10126, Italy
| | - Federica Esposito
- Department of Neurology, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy.,Laboratory of Human Genetics of Neurological Disorders, CNS Inflammatory Unit & INSPE, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
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4
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Nam S, Lee S, Park S, Lee J, Park A, Kim YH, Park T. PATHOME-Drug: a subpathway-based polypharmacology drug-repositioning method. Bioinformatics 2022; 38:444-452. [PMID: 34515762 DOI: 10.1093/bioinformatics/btab566] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 06/10/2021] [Accepted: 09/09/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. RESULTS Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. AVAILABILITY AND IMPLEMENTATION The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Seungyoon Nam
- Department of Genome Medicine and Science, College of Medicine, Gachon University, 21565 Incheon, Korea.,Department of Life Sciences, Gachon University, 13120 Seongnam, Korea.,Gachon Institute of Genomic Medicine and Science, Gachon University Gil Medical Center, 21565 Incheon, Korea.,Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, 21999 Incheon, Korea
| | - Sungyoung Lee
- Department of Genomic Medicine, Seoul National University Hospital, 03080 Seoul, Korea.,Center for Precision Medicine, Seoul National University Hospital, 03080 Seoul, Korea
| | - Sungjin Park
- Department of Genome Medicine and Science, College of Medicine, Gachon University, 21565 Incheon, Korea.,Gachon Institute of Genomic Medicine and Science, Gachon University Gil Medical Center, 21565 Incheon, Korea
| | - Jinhyuk Lee
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, 34141 Daejeon, Korea.,Department of Bioinformatics, University of Sciences and Technology, 34113 Daejeon, Korea
| | - Aron Park
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, 21999 Incheon, Korea
| | - Yon Hui Kim
- Department of Biomedical Science, Hanyang University, 04763 Seoul, Korea
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, 08826 Seoul, Korea.,Department of Statistics, Seoul National University, 08826 Seoul, Korea
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5
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Hayashi H, Uemura N, Matsumura K, Zhao L, Sato H, Shiraishi Y, Yamashita YI, Baba H. Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma. World J Gastroenterol 2021; 27:7480-7496. [PMID: 34887644 PMCID: PMC8613738 DOI: 10.3748/wjg.v27.i43.7480] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 08/02/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad availability of biomedical data has led to the advent of the "big data" era. To overcome this deadly disease, how to fully exploit big data is a new challenge in the era of precision medicine. Artificial intelligence (AI) is the ability of a machine to learn and display intelligence to solve problems. AI can help to transform big data into clinically actionable insights more efficiently, reduce inevitable errors to improve diagnostic accuracy, and make real-time predictions. AI-based omics analyses will become the next alterative approach to overcome this poor-prognostic disease by discovering biomarkers for early detection, providing molecular/genomic subtyping, offering treatment guidance, and predicting recurrence and survival. Advances in AI may therefore improve PDAC survival outcomes in the near future. The present review mainly focuses on recent advances of AI in PDAC for clinicians. We believe that breakthroughs will soon emerge to fight this deadly disease using AI-navigated precision medicine.
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Affiliation(s)
- Hiromitsu Hayashi
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Norio Uemura
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Kazuki Matsumura
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Liu Zhao
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Hiroki Sato
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Yuta Shiraishi
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Yo-ichi Yamashita
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Hideo Baba
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
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6
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Transcriptomic Analysis of Peripheral Monocytes upon Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients. Mol Neurobiol 2021; 58:4816-4827. [PMID: 34181235 DOI: 10.1007/s12035-021-02465-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 06/20/2021] [Indexed: 12/14/2022]
Abstract
Fingolimod (FTY), a second-line oral drug approved for relapsing remitting Multiple Sclerosis (RRMS) acts in preventing lymphocyte migration outside lymph nodes; moreover, several lines of evidence suggest that it also inhibits myeloid cell activation. In this study, we investigated the transcriptional changes induced by FTY in monocytes in order to better elucidate its mechanism of action. CD14+ monocytes were collected from 24 RRMS patients sampled at baseline and after 6 months of treatment and RNA profiles were obtained through next-generation sequencing. We conducted pathway and sub-paths analysis, followed by centrality analysis of cell-specific interactomes on differentially expressed genes (DEGs). We investigated also the predictive role of baseline monocyte transcription profile in influencing the response to FTY therapy. We observed a marked down-regulation effect (60 down-regulated vs. 0 up-regulated genes). Most of the down-regulated DEGs resulted related with monocyte activation and migration like IL7R, CCR7 and the Wnt signaling mediators LEF1 and TCF7. The involvement of Wnt signaling was also confirmed by subpaths analyses. Furthermore, pathway and network analyses showed an involvement of processes related to immune function and cell migration. Baseline transcriptional profile of the HLA class II gene HLA-DQA1 and HLA-DPA1 were associated with evidence of disease activity after 2 years of treatment. Our data support the evidence that FTY induces major transcriptional changes in monocytes, mainly regarding genes involved in cell trafficking and immune cell activation. The baseline transcriptional levels of genes associated with antigen presenting function were associated with disease activity after 2 years of FTY treatment.
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7
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Rian K, Hidalgo MR, Çubuk C, Falco MM, Loucera C, Esteban-Medina M, Alamo-Alvarez I, Peña-Chilet M, Dopazo J. Genome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data. Comput Struct Biotechnol J 2021; 19:2968-2978. [PMID: 34136096 PMCID: PMC8170118 DOI: 10.1016/j.csbj.2021.05.022] [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: 01/27/2021] [Revised: 04/21/2021] [Accepted: 05/11/2021] [Indexed: 12/13/2022] Open
Abstract
Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios.
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Affiliation(s)
- Kinza Rian
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Laboratory of Innovative Technologies (LTI), National School of Applied Sciences in Tangier, UAE, Morocco
| | - Marta R. Hidalgo
- Bioinformatics and Biostatistics Unit, Centro de Investigación Príncipe Felipe (CIPF), 46012 Valencia, Spain
| | - Cankut Çubuk
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
| | - Matias M. Falco
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Bioinformatics in RareDiseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Sevilla 41013, Spain
| | - Carlos Loucera
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Computational Systems Medicine. Institute of Biomedicine of Seville (IBiS), Sevilla 41013, Spain
| | - Marina Esteban-Medina
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Computational Systems Medicine. Institute of Biomedicine of Seville (IBiS), Sevilla 41013, Spain
| | - Inmaculada Alamo-Alvarez
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Computational Systems Medicine. Institute of Biomedicine of Seville (IBiS), Sevilla 41013, Spain
| | - María Peña-Chilet
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Bioinformatics in RareDiseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Sevilla 41013, Spain
- Computational Systems Medicine. Institute of Biomedicine of Seville (IBiS), Sevilla 41013, Spain
| | - Joaquín Dopazo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain
- Bioinformatics in RareDiseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Sevilla 41013, Spain
- Computational Systems Medicine. Institute of Biomedicine of Seville (IBiS), Sevilla 41013, Spain
- Functional Genomics Node (INB-ELIXIR-es), Sevilla, Spain
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8
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Koumakis L. Deep learning models in genomics; are we there yet? Comput Struct Biotechnol J 2020; 18:1466-1473. [PMID: 32637044 PMCID: PMC7327302 DOI: 10.1016/j.csbj.2020.06.017] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/07/2020] [Accepted: 06/08/2020] [Indexed: 12/23/2022] Open
Abstract
With the evolution of biotechnology and the introduction of the high throughput sequencing, researchers have the ability to produce and analyze vast amounts of genomics data. Since genomics produce big data, most of the bioinformatics algorithms are based on machine learning methodologies, and lately deep learning, to identify patterns, make predictions and model the progression or treatment of a disease. Advances in deep learning created an unprecedented momentum in biomedical informatics and have given rise to new bioinformatics and computational biology research areas. It is evident that deep learning models can provide higher accuracies in specific tasks of genomics than the state of the art methodologies. Given the growing trend on the application of deep learning architectures in genomics research, in this mini review we outline the most prominent models, we highlight possible pitfalls and discuss future directions. We foresee deep learning accelerating changes in the area of genomics, especially for multi-scale and multimodal data analysis for precision medicine.
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Affiliation(s)
- Lefteris Koumakis
- Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Heraklion, Crete, Greece
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9
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Lu J, Li Q, Wu Z, Zhong Z, Ji P, Li H, He C, Feng J, Zhang J. Two gene set variation indexes as potential diagnostic tool for sepsis. Am J Transl Res 2020; 12:2749-2759. [PMID: 32655806 PMCID: PMC7344106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 05/26/2020] [Indexed: 06/11/2023]
Abstract
Accurate diagnosis of sepsis remains challenging, new markers or combinations of markers are urgently needed. In the present study, we screened differentially expressed genes (DEGs) between sepsis and non-sepsis blood samples across three previously published gene expression data sets. Common upregulated and downregulated DEGs were ranked according to their average functional similarity. The ten genes (OLFM4, ORM1, CEP55, S100A12, S100P, LRG1, CEACAM8, MS4A4A, PLSCR1, and IL1R2) with the largest average functional similarity among the common upregulated genes and another ten genes (THEMIS, IL2RB, CD2, IL7R, CD3E, KLRB1, PVRIG, CCRR3, TGFBR3, and PLEKHA1) with the largest average functional similarity among the common downregulated genes were separately identified as the upregulated crucial gene set and the downregulated crucial gene set. Gene set variation analysis (GSVA) was used to obtain the GSVA index of each sample against the two crucial gene sets. Both the two crucial GSVA indexes may be robust markers for sepsis with high area under ROC curve. The diagnostic utility of the upregulated GSVA index was validated in another independent data set. Functional analyses revealed several sepsis-related pathways. In conclusion, we proposed two sepsis-related gene sets across multiple data sets and created two GSVA indexes with promising diagnostic value.
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Affiliation(s)
- Junyu Lu
- Intensive Care Unit, The Second Affiliated Hospital of Guangxi Medical UniversityNanning 530007, China
| | - Qian Li
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical UniversityNanning 530007, China
| | - Zimeng Wu
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical UniversityNanning 530007, China
| | - Zhimei Zhong
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical UniversityNanning 530007, China
| | - Pan Ji
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical UniversityNanning 530007, China
| | - Hongyuan Li
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical UniversityNanning 530007, China
| | - Cuiying He
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical UniversityNanning 530007, China
| | - Jihua Feng
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical UniversityNanning 530007, China
| | - Jianfeng Zhang
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical UniversityNanning 530007, China
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10
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Kondylakis H, Bucur A, Crico C, Dong F, Graf N, Hoffman S, Koumakis L, Manenti A, Marias K, Mazzocco K, Pravettoni G, Renzi C, Schera F, Triberti S, Tsiknakis M, Kiefer S. Patient empowerment for cancer patients through a novel ICT infrastructure. J Biomed Inform 2019; 101:103342. [PMID: 31816400 DOI: 10.1016/j.jbi.2019.103342] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 11/15/2019] [Accepted: 11/21/2019] [Indexed: 12/17/2022]
Abstract
As a result of recent advances in cancer research and "precision medicine" approaches, i.e. the idea of treating each patient with the right drug at the right time, more and more cancer patients are being cured, or might have to cope with a life with cancer. For many people, cancer survival today means living with a complex and chronic condition. Surviving and living with or beyond cancer requires the long-term management of the disease, leading to a significant need for active rehabilitation of the patients. In this paper, we present a novel methodology employed in the iManageCancer project for cancer patient empowerment in which personal health systems, serious games, psychoemotional monitoring and other novel decision-support tools are combined into an integrated patient empowerment platform. We present in detail the ICT infrastructure developed and our evaluation with the involvement of cancer patients on two sites, a large-scale pilot for adults and a small-scale test for children. The evaluation showed mixed evidences on the improvement of patient empowerment, while ability to cope with cancer, including improvement in mood and resilience to cancer, increased for the participants of the adults' pilot.
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Affiliation(s)
| | - Anca Bucur
- PHILIPS Research Europe, Eindhoven, The Netherlands
| | | | - Feng Dong
- Department of Computer Science and Technology, University of Bedfordshire, Luton, UK
| | - Norbert Graf
- Saarland University, Pediatric Oncology and Hematology, Homburg, Germany
| | | | | | | | - Kostas Marias
- Computational BioMedicine Laboratory, FORTH-ICS, Heraklion, Greece
| | | | | | | | - Fatima Schera
- Fraunhofer Institute for Biomedical Engineering, Germany
| | | | | | - Stephan Kiefer
- Fraunhofer Institute for Biomedical Engineering, Germany
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11
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Amadoz A, Hidalgo MR, Çubuk C, Carbonell-Caballero J, Dopazo J. A comparison of mechanistic signaling pathway activity analysis methods. Brief Bioinform 2019; 20:1655-1668. [PMID: 29868818 PMCID: PMC6917216 DOI: 10.1093/bib/bby040] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 03/31/2018] [Indexed: 12/11/2022] Open
Abstract
Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.
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Affiliation(s)
- Alicia Amadoz
- Department of Bioinformatics, Igenomix S.L., 46980 Valencia, Spain
| | - Marta R Hidalgo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain
| | - Cankut Çubuk
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain
| | - José Carbonell-Caballero
- Chromatin and Gene expression Lab, Gene Regulation, Stem Cells and Cancer Program, Centre de Regulació Genòmica (CRG), The Barcelona Institute of Science and Technology, PRBB, Barcelona 08003, Spain
| | - Joaquín Dopazo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain
- Chromatin and Gene expression Lab, Gene Regulation, Stem Cells and Cancer Program, Centre de Regulació Genòmica (CRG), The Barcelona Institute of Science and Technology, PRBB, Barcelona 08003, Spain
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla 41013, Spain, Functional Genomics Node (INB), FPS, Hospital Virgen del Rocío, Sevilla 41013, Spain and Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, Sevilla 41013, Spain
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12
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Liu S, Zheng B, Sheng Y, Kong Q, Jiang Y, Yang Y, Han X, Cheng L, Zhang Y, Han J. Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data. Front Genet 2019; 10:441. [PMID: 31156704 PMCID: PMC6529853 DOI: 10.3389/fgene.2019.00441] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 04/29/2019] [Indexed: 12/29/2022] Open
Abstract
A subpathway is defined as the local region of a biological pathway with specific biological functions. With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression (defined as ICDS_M, ICDS_CNV, or ICDS_G, respectively). With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool (https://cran.r-project.org/web/packages/ICDS).
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Affiliation(s)
- Siyao Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Baotong Zheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yuqi Sheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qingfei Kong
- College of Basic Medical Science, Harbin Medical University, Harbin, China
| | - Ying Jiang
- College of Basic Medical Science, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Yang Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xudong Han
- College of Basic Medical Science, Harbin Medical University, Harbin, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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Shao F, Wang Y, Zhao Y, Yang S. Identifying and exploiting gene-pathway interactions from RNA-seq data for binary phenotype. BMC Genet 2019; 20:36. [PMID: 30890140 PMCID: PMC6423879 DOI: 10.1186/s12863-019-0739-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 03/12/2019] [Indexed: 11/29/2022] Open
Abstract
Background RNA sequencing (RNA-seq) technology has identified multiple differentially expressed (DE) genes associated to complex disease, however, these genes only explain a modest part of variance. Omnigenic model assumes that disease may be driven by genes with indirect relevance to disease and be propagated by functional pathways. Here, we focus on identifying the interactions between the external genes and functional pathways, referring to gene-pathway interactions (GPIs). Specifically, relying on the relationship between the garrote kernel machine (GKM) and variance component test and permutations for the empirical distributions of score statistics, we propose an efficient analysis procedure as Permutation based gEne-pAthway interaction identification in binary phenotype (PEA). Results Various simulations show that PEA has well-calibrated type I error rates and higher power than the traditional likelihood ratio test (LRT). In addition, we perform the gene set enrichment algorithms and PEA to identifying the GPIs from a pan-cancer data (GES68086). These GPIs and genes possibly further illustrate the potential etiology of cancers, most of which are identified and some external genes and significant pathways are consistent with previous studies. Conclusions PEA is an efficient tool for identifying the GPIs from RNA-seq data. It can be further extended to identify the interactions between one variable and one functional set of other omics data for binary phenotypes. Electronic supplementary material The online version of this article (10.1186/s12863-019-0739-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Fang Shao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu, People's Republic of China
| | - Yaqi Wang
- Department of Pharmacy Informatics, School of Science, China Pharmaceutical University, 24 Tongjia Xiang, Nanjing , Jiangsu, People's Republic of China
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu, People's Republic of China
| | - Sheng Yang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu, People's Republic of China.
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Koumakis L, Roussos P, Potamias G. minepath.org: a free interactive pathway analysis web server. Nucleic Acids Res 2017; 45:W116-W121. [PMID: 28431175 PMCID: PMC5570234 DOI: 10.1093/nar/gkx278] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Revised: 03/24/2017] [Accepted: 04/06/2017] [Indexed: 01/02/2023] Open
Abstract
Minepath ( www.minepath.org ) is a web-based platform that elaborates on, and radically extends the identification of differentially expressed sub-paths in molecular pathways. Besides the network topology, the underlying MinePath algorithmic processes exploit exact gene-gene molecular relationships (e.g. activation, inhibition) and are able to identify differentially expressed pathway parts. Each pathway is decomposed into all its constituent sub-paths, which in turn are matched with corresponding gene expression profiles. The highly ranked, and phenotype inclined sub-paths are kept. Apart from the pathway analysis algorithm, the fundamental innovation of the MinePath web-server concerns its advanced visualization and interactive capabilities. To our knowledge, this is the first pathway analysis server that introduces and offers visualization of the underlying and active pathway regulatory mechanisms instead of genes. Other features include live interaction, immediate visualization of functional sub-paths per phenotype and dynamic linked annotations for the engaged genes and molecular relations. The user can download not only the results but also the corresponding web viewer framework of the performed analysis. This feature provides the flexibility to immediately publish results without publishing source/expression data, and get all the functionality of a web based pathway analysis viewer.
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Affiliation(s)
- Lefteris Koumakis
- Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), Heraklion, Crete 70013, Greece
| | - Panos Roussos
- Department of Psychiatry; Department of Genetics and Genomic Sciences and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - George Potamias
- Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), Heraklion, Crete 70013, Greece
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Lee S, Park Y, Kim S. MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data. Methods 2017; 124:13-24. [PMID: 28579402 DOI: 10.1016/j.ymeth.2017.05.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Accepted: 05/30/2017] [Indexed: 11/18/2022] Open
Abstract
Pathway based analysis of high throughput transcriptome data is a widely used approach to investigate biological mechanisms. Since a pathway consists of multiple functions, the recent approach is to determine condition specific sub-pathways or subpaths. However, there are several challenges. First, few existing methods utilize explicit gene expression information from RNA-seq. More importantly, subpath activity is usually an average of statistical scores, e.g., correlations, of edges in a candidate subpath, which fails to reflect gene expression quantity information. In addition, none of existing methods can handle multiple phenotypes. To address these technical problems, we designed and implemented an algorithm, MIDAS, that determines condition specific subpaths, each of which has different activities across multiple phenotypes. MIDAS utilizes gene expression quantity information fully and the network centrality information to determine condition specific subpaths. To test performance of our tool, we used TCGA breast cancer RNA-seq gene expression profiles with five molecular subtypes. 36 differentially activate subpaths were determined. The utility of our method, MIDAS, was demonstrated in four ways. All 36 subpaths are well supported by the literature information. Subsequently, we showed that these subpaths had a good discriminant power for five cancer subtype classification and also had a prognostic power in terms of survival analysis. Finally, in a performance comparison of MIDAS to a recent subpath prediction method, PATHOME, our method identified more subpaths and much more genes that are well supported by the literature information. AVAILABILITY http://biohealth.snu.ac.kr/software/MIDAS/.
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Affiliation(s)
- Sangseon Lee
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Youngjune Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea; Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea.
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