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Wang SS, Wang CC, Wang CL, Lin YC, Tung CW. Incorporating Tissue-Specific Gene Expression Data to Improve Chemical-Disease Inference of in Silico Toxicogenomics Methods. J Xenobiot 2024; 14:1023-1035. [PMID: 39189172 PMCID: PMC11348041 DOI: 10.3390/jox14030057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 07/08/2024] [Accepted: 07/30/2024] [Indexed: 08/28/2024] Open
Abstract
In silico toxicogenomics methods are resource- and time-efficient approaches for inferring chemical-protein-disease associations with potential mechanism information for exploring toxicological effects. However, current in silico toxicogenomics systems make inferences based on only chemical-protein interactions without considering tissue-specific gene/protein expressions. As a result, inferred diseases could be overpredicted with false positives. In this work, six tissue-specific expression datasets of genes and proteins were collected from the Expression Atlas. Genes were then categorized into high, medium, and low expression levels in a tissue- and dataset-specific manner. Subsequently, the tissue-specific expression datasets were incorporated into the chemical-protein-disease inference process of our ChemDIS system by filtering out relatively low-expressed genes. By incorporating tissue-specific gene/protein expression data, the enrichment rate for chemical-disease inference was largely improved with up to 62.26% improvement. A case study of melamine showed the ability of the proposed method to identify more specific disease terms that are consistent with the literature. A user-friendly user interface was implemented in the ChemDIS system. The methodology is expected to be useful for chemical-disease inference and can be implemented for other in silico toxicogenomics tools.
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Affiliation(s)
- Shan-Shan Wang
- Ph.D. Program in Environmental and Occupational Medicine, College of Medicine, Kaohsiung Medical University and National Health Research Institutes, Kaohsiung 80708, Taiwan;
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 10675, Taiwan;
| | - Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei 10617, Taiwan;
| | - Chien-Lun Wang
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 10675, Taiwan;
| | - Ying-Chi Lin
- Master and Doctoral Degree Program in Toxicology, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 80756, Taiwan;
- School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 80756, Taiwan
| | - Chun-Wei Tung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 10675, Taiwan;
- Master and Doctoral Degree Program in Toxicology, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 80756, Taiwan;
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Big Data, Decision Models, and Public Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148543. [PMID: 35886394 PMCID: PMC9324609 DOI: 10.3390/ijerph19148543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022]
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Lin RH, Wang CC, Tung CW. A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19084839. [PMID: 35457705 PMCID: PMC9025386 DOI: 10.3390/ijerph19084839] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 12/14/2022]
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder with an insidious onset and irreversible condition. Patients with mild cognitive impairment (MCI) are at high risk of converting to AD. Early diagnosis of unstable MCI patients is therefore vital for slowing the progression to AD. However, current diagnostic methods are either highly invasive or expensive, preventing their wide applications. Developing low-invasive and cost-efficient screening methods is desirable as the first-tier approach for identifying unstable MCI patients or excluding stable MCI patients. This study developed feature selection and machine learning algorithms to identify blood-sample gene biomarkers for predicting stable MCI patients. Two datasets obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were utilized to conclude 29 genes biomarkers (31 probes) for predicting stable MCI patients. A random forest-based classifier performed well with area under the receiver operating characteristic curve (AUC) values of 0.841 and 0.775 for cross-validation and test datasets, respectively. For patients with a prediction score greater than 0.9, an excellent concordance of 97% was obtained, showing the usefulness of the proposed method for identifying stable MCI patients. In the context of precision medicine, the proposed prediction model is expected to be useful for identifying stable MCI patients and providing medical doctors and patients with new first-tier diagnosis options.
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Affiliation(s)
- Run-Hsin Lin
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan;
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 10675, Taiwan
| | - Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei 10617, Taiwan;
| | - Chun-Wei Tung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan;
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 10675, Taiwan
- Correspondence: ; Tel.: +88-6-3724-6166 (ext. 35771); Fax: +88-6-3758-6456
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Desaulniers D, Vasseur P, Jacobs A, Aguila MC, Ertych N, Jacobs MN. Integration of Epigenetic Mechanisms into Non-Genotoxic Carcinogenicity Hazard Assessment: Focus on DNA Methylation and Histone Modifications. Int J Mol Sci 2021; 22:10969. [PMID: 34681626 PMCID: PMC8535778 DOI: 10.3390/ijms222010969] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/30/2021] [Accepted: 10/04/2021] [Indexed: 12/15/2022] Open
Abstract
Epigenetics involves a series of mechanisms that entail histone and DNA covalent modifications and non-coding RNAs, and that collectively contribute to programing cell functions and differentiation. Epigenetic anomalies and DNA mutations are co-drivers of cellular dysfunctions, including carcinogenesis. Alterations of the epigenetic system occur in cancers whether the initial carcinogenic events are from genotoxic (GTxC) or non-genotoxic (NGTxC) carcinogens. NGTxC are not inherently DNA reactive, they do not have a unifying mode of action and as yet there are no regulatory test guidelines addressing mechanisms of NGTxC. To fil this gap, the Test Guideline Programme of the Organisation for Economic Cooperation and Development is developing a framework for an integrated approach for the testing and assessment (IATA) of NGTxC and is considering assays that address key events of cancer hallmarks. Here, with the intent of better understanding the applicability of epigenetic assays in chemical carcinogenicity assessment, we focus on DNA methylation and histone modifications and review: (1) epigenetic mechanisms contributing to carcinogenesis, (2) epigenetic mechanisms altered following exposure to arsenic, nickel, or phenobarbital in order to identify common carcinogen-specific mechanisms, (3) characteristics of a series of epigenetic assay types, and (4) epigenetic assay validation needs in the context of chemical hazard assessment. As a key component of numerous NGTxC mechanisms of action, epigenetic assays included in IATA assay combinations can contribute to improved chemical carcinogen identification for the better protection of public health.
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Affiliation(s)
- Daniel Desaulniers
- Environmental Health Sciences and Research Bureau, Hazard Identification Division, Health Canada, AL:2203B, Ottawa, ON K1A 0K9, Canada
| | - Paule Vasseur
- CNRS, LIEC, Université de Lorraine, 57070 Metz, France;
| | - Abigail Jacobs
- Independent at the Time of Publication, Previously US Food and Drug Administration, Rockville, MD 20852, USA;
| | - M. Cecilia Aguila
- Toxicology Team, Division of Human Food Safety, Center for Veterinary Medicine, US Food and Drug Administration, Department of Health and Human Services, Rockville, MD 20852, USA;
| | - Norman Ertych
- German Centre for the Protection of Laboratory Animals (Bf3R), German Federal Institute for Risk Assessment, Diedersdorfer Weg 1, 12277 Berlin, Germany;
| | - Miriam N. Jacobs
- Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Chilton OX11 0RQ, UK;
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Su M, Qiao KY, Xie XL, Zhu XY, Gao FL, Li CJ, Zhao DQ. Development of a Prognostic Signature Based on Single-Cell RNA Sequencing Data of Immune Cells in Intrahepatic Cholangiocarcinoma. Front Genet 2021; 11:615680. [PMID: 33613623 PMCID: PMC7890365 DOI: 10.3389/fgene.2020.615680] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/30/2020] [Indexed: 12/19/2022] Open
Abstract
Analysis of single-cell RNA sequencing (scRNA-seq) data of immune cells from the tumor microenvironment (TME) may identify tumor progression biomarkers. This study was designed to investigate the prognostic value of differentially expressed genes (DEGs) in intrahepatic cholangiocarcinoma (ICC) using scRNA-seq. We downloaded the scRNA-seq data of 33,991 cell samples, including 17,090 ICC cell samples and 16,901 ICC adjacent tissue cell samples regarded as normal cells. scRNA-seq data were processed and classified into 20 clusters. The immune cell clusters were extracted and processed again in the same way, and each type of immune cells was divided into several subclusters. In total, 337 marker genes of macrophages and 427 marker genes of B cells were identified by comparing ICC subclusters with normal subclusters. Finally, 659 DEGs were obtained by merging B cell and macrophage marker genes. ICC sample clinical information and gene expression data were downloaded. A nine-prognosis-related-gene (PRG) signature was established by analyzing the correlation between DEGs and overall survival in ICC. The robustness and validity of the signature were verified. Functional enrichment analysis revealed that the nine PRGs were mainly involved in tumor immune mechanisms. In conclusion, we established a PRG signature based on scRNA-seq data from immune cells of patients with ICC. This PRG signature not only reflects the TME immune status but also provides new biomarkers for ICC prognosis.
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Affiliation(s)
- Miao Su
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, China.,Department of Gastroenterology, Hengshui People's Hospital, Hengshui, China
| | - Kuang-Yuan Qiao
- Basic Medical College, Hebei Medical University, Shijiazhuang, China
| | - Xiao-Li Xie
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xin-Ying Zhu
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, China.,Department of Gastroenterology, The Third Hospital of Hebei Medical University, Shijiazhuang, China
| | - Fu-Lai Gao
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Chang-Juan Li
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Dong-Qiang Zhao
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
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Chan CL, Chang CC. Big Data, Decision Models, and Public Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17186723. [PMID: 32942728 PMCID: PMC7558933 DOI: 10.3390/ijerph17186723] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 09/14/2020] [Indexed: 12/23/2022]
Abstract
Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.
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Affiliation(s)
- Chien-Lung Chan
- Department of Information Management, Yuan Ze University, Taoyuan 320, Taiwan;
- Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 320, Taiwan
| | - Chi-Chang Chang
- School of Medical Informatics, Chung Shan Medical University & IT Office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Correspondence: ; Tel.: +886-4-24730022
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