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Amrane K, Meur CL, Thuillier P, Berthou C, Uguen A, Deandreis D, Bourhis D, Bourbonne V, Abgral R. Review on radiomic analysis in 18F-fluorodeoxyglucose positron emission tomography for prediction of melanoma outcomes. Cancer Imaging 2024; 24:87. [PMID: 38970050 PMCID: PMC11225300 DOI: 10.1186/s40644-024-00732-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
Over the past decade, several strategies have revolutionized the clinical management of patients with cutaneous melanoma (CM), including immunotherapy and targeted tyrosine kinase inhibitor (TKI)-based therapies. Indeed, immune checkpoint inhibitors (ICIs), alone or in combination, represent the standard of care for patients with advanced disease without an actionable mutation. Notably BRAF combined with MEK inhibitors represent the therapeutic standard for disease disclosing BRAF mutation. At the same time, FDG PET/CT has become part of the routine staging and evaluation of patients with cutaneous melanoma. There is growing interest in using FDG PET/CT measurements to predict response to ICI therapy and/or target therapy. While semiquantitative values such as standardized uptake value (SUV) are limited for predicting outcome, new measures including tumor metabolic volume, total lesion glycolysis and radiomics seem promising as potential imaging biomarkers for nuclear medicine. The aim of this review, prepared by an interdisciplinary group of experts, is to take stock of the current literature on radiomics approaches that could improve outcomes in CM.
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Affiliation(s)
- Karim Amrane
- Department of Oncology, Regional Hospital of Morlaix, Morlaix, 29600, France.
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France.
| | - Coline Le Meur
- Department of Radiotherapy, University Hospital of Brest, Brest, France
| | - Philippe Thuillier
- Department of Endocrinology, University Hospital of Brest, Brest, France
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
| | - Christian Berthou
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France
- Department of Hematology, University Hospital of Brest, Brest, France
| | - Arnaud Uguen
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France
- Department of Pathology, University Hospital of Brest, Brest, France
| | - Désirée Deandreis
- Department of Nuclear Medicine, Gustave Roussy Institute, University of Paris Saclay, Paris, France
| | - David Bourhis
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
- Department of Nuclear Medicine, University Hospital of Brest, Brest, France
| | - Vincent Bourbonne
- Department of Radiotherapy, University Hospital of Brest, Brest, France
- Inserm, UMR1101, LaTIM, University of Western Brittany, Brest, France
| | - Ronan Abgral
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
- Department of Nuclear Medicine, University Hospital of Brest, Brest, France
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Yu SH, Chen SC, Wu PS, Kuo PI, Chen TA, Lee HY, Lin MH. Quantification Quality Control Emerges as a Crucial Factor to Enhance Single-Cell Proteomics Data Analysis. Mol Cell Proteomics 2024; 23:100768. [PMID: 38621647 PMCID: PMC11103571 DOI: 10.1016/j.mcpro.2024.100768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/12/2024] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
Mass spectrometry (MS)-based single-cell proteomics (SCP) provides us the opportunity to unbiasedly explore biological variability within cells without the limitation of antibody availability. This field is rapidly developed with the main focuses on instrument advancement, sample preparation refinement, and signal boosting methods; however, the optimal data processing and analysis are rarely investigated which holds an arduous challenge because of the high proportion of missing values and batch effect. Here, we introduced a quantification quality control to intensify the identification of differentially expressed proteins (DEPs) by considering both within and across SCP data. Combining quantification quality control with isobaric matching between runs (IMBR) and PSM-level normalization, an additional 12% and 19% of proteins and peptides, with more than 90% of proteins/peptides containing valid values, were quantified. Clearly, quantification quality control was able to reduce quantification variations and q-values with the more apparent cell type separations. In addition, we found that PSM-level normalization performed similar to other protein-level normalizations but kept the original data profiles without the additional requirement of data manipulation. In proof of concept of our refined pipeline, six uniquely identified DEPs exhibiting varied fold-changes and playing critical roles for melanoma and monocyte functionalities were selected for validation using immunoblotting. Five out of six validated DEPs showed an identical trend with the SCP dataset, emphasizing the feasibility of combining the IMBR, cell quality control, and PSM-level normalization in SCP analysis, which is beneficial for future SCP studies.
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Affiliation(s)
- Sung-Huan Yu
- Institute of Precision Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Shiau-Ching Chen
- Institute of Precision Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Pei-Shan Wu
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Pei-I Kuo
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Ting-An Chen
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Hsiang-Ying Lee
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Urology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Miao-Hsia Lin
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan.
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Wang B, Sun F, Luan Y. Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity. Sci Rep 2024; 14:7024. [PMID: 38528097 DOI: 10.1038/s41598-024-57670-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/20/2024] [Indexed: 03/27/2024] Open
Abstract
The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods have been proposed to mitigate these variations and enhance comparability. However, the performance of these methods in predicting binary phenotypes remains understudied. This study systematically evaluates different normalization methods in microbiome data analysis and their impact on disease prediction. Our findings highlight the strengths and limitations of scaling, compositional data analysis, transformation, and batch correction methods. Scaling methods like TMM show consistent performance, while compositional data analysis methods exhibit mixed results. Transformation methods, such as Blom and NPN, demonstrate promise in capturing complex associations. Batch correction methods, including BMC and Limma, consistently outperform other approaches. However, the influence of normalization methods is constrained by population effects, disease effects, and batch effects. These results provide insights for selecting appropriate normalization approaches in microbiome research, improving predictive models, and advancing personalized medicine. Future research should explore larger and more diverse datasets and develop tailored normalization strategies for microbiome data analysis.
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Affiliation(s)
- Beibei Wang
- Frontier Science Center for Nonlinear Expectations, Ministry of Education, Qingdao, 266237, China
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
- School of Mathematics, Shandong University, Jinan, 250100, China
| | - Fengzhu Sun
- Quantitative and Computational Biology Department, University of Southern California, Los Angeles, 90089, USA
| | - Yihui Luan
- Frontier Science Center for Nonlinear Expectations, Ministry of Education, Qingdao, 266237, China.
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China.
- School of Mathematics, Shandong University, Jinan, 250100, China.
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Liang S, Dou J, Iqbal R, Chen K. Label-aware distance mitigates temporal and spatial variability for clustering and visualization of single-cell gene expression data. Commun Biol 2024; 7:326. [PMID: 38486077 PMCID: PMC10940680 DOI: 10.1038/s42003-024-05988-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (LAD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate LAD on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). LAD provides better cell embedding than state-of-the-art batch correction methods on longitudinal datasets. It can be used in distance-based clustering and visualization methods to combine the power of multiple samples to help make biological findings.
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Affiliation(s)
- Shaoheng Liang
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA.
- Department of Computer Science, Rice University, Houston, TX, USA.
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Jinzhuang Dou
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ramiz Iqbal
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA.
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Kugler S, Hahnefeld L, Kloka JA, Ginzel S, Nürenberg-Goloub E, Zinn S, Vehreschild MJ, Zacharowski K, Lindau S, Ullrich E, Burmeister J, Kohlhammer J, Schwäble J, Gurke R, Dorochow E, Bennett A, Dauth S, Campe J, Knape T, Laux V, Kannt A, Köhm M, Geisslinger G, Resch E, Behrens F. Short-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units. Talanta 2024; 268:125295. [PMID: 37866305 DOI: 10.1016/j.talanta.2023.125295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/29/2023] [Accepted: 10/06/2023] [Indexed: 10/24/2023]
Abstract
BACKGROUND The COVID-19 pandemic challenged the management of technical and human resources in intensive care units (ICU) across the world. Several long-term predictors for COVID-19 disease progression have been discovered. However, predictors to support short-term planning of resources and medication that can be translated to future pandemics are still missing. A workflow was established to identify a predictor for short-term COVID-19 disease progression in the acute phase of intensive care patients to support clinical decision-making. METHODS Thirty-two patients with SARS-CoV-2 infection were recruited on admission to the ICU and clinical data collected. During their hospitalization, plasma samples were acquired from each patient on multiple occasions, excepting one patient for which only one time point was possible, and the proteome (Inflammation, Immune Response and Organ Damage panels from Olink® Target 96), metabolome and lipidome (flow injection analysis and liquid chromatography-mass spectrometry) analyzed for each sample. Patient visits were grouped according to changes in disease severity based on their respiratory and organ function, and evaluated using a combination of statistical analysis and machine learning. The resulting short-term predictor from this multi-omics approach was compared to the human assessment of disease progression. Furthermore, the potential markers were compared to the baseline levels of 50 healthy subjects with no known SARS-CoV-2 or other viral infections. RESULTS A total of 124 clinical parameters, 271 proteins and 782 unique metabolites and lipids were assessed. The dimensionality of the dataset was reduced, selecting 47 from the 1177 parameters available following down-selection, to build the machine learning model. Subsequently, two proteins (C-C motif chemokine 7 (CCL7) and carbonic anhydrase 14 (CA14)) and one lipid (hexosylceramide 18:2; O2/20:0) were linked to disease progression in the studied SARS-CoV-2 infections. Thus, a predictor delivering the prognosis of an upcoming worsening of the patient's condition up to five days in advance with a reasonable accuracy (79 % three days prior to event, 84 % four to five days prior to event) was found. Interestingly, the predictor's performance was complementary to the clinicians' capabilities to foresee a worsening of a patient. CONCLUSION This study presents a workflow to identify omics-based biomarkers to support clinical decision-making and resource management in the ICU. This was successfully applied to develop a short-term predictor for aggravation of COVID-19 symptoms. The applied methods can be adapted for future small cohort studies.
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Affiliation(s)
- Sabine Kugler
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven 1, St. Augustin, Germany
| | - Lisa Hahnefeld
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Goethe University Frankfurt, University Hospital, Institute of Clinical Pharmacology, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany.
| | - Jan Andreas Kloka
- Goethe University Frankfurt, University Hospital, Clinic for Anesthesiology, Intensive Care Medicine and Pain Therapy, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - Sebastian Ginzel
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven 1, St. Augustin, Germany
| | - Elina Nürenberg-Goloub
- Goethe University Frankfurt, University Hospital, Clinic for Anesthesiology, Intensive Care Medicine and Pain Therapy, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - Sebastian Zinn
- Goethe University Frankfurt, University Hospital, Clinic for Anesthesiology, Intensive Care Medicine and Pain Therapy, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany; Fraunhofer Leistungszentrum TheraNova, Theodor-Stern-Kai 6, 60596, Frankfurt am Main, Germany
| | - Maria Jgt Vehreschild
- Goethe University Frankfurt, University Hospital, Department of Internal Medicine, Infectious Diseases, 60590, Frankfurt am Main, Germany
| | - Kai Zacharowski
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Goethe University Frankfurt, University Hospital, Clinic for Anesthesiology, Intensive Care Medicine and Pain Therapy, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - Simone Lindau
- Goethe University Frankfurt, University Hospital, Clinic for Anesthesiology, Intensive Care Medicine and Pain Therapy, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - Evelyn Ullrich
- University Cancer Center Frankfurt (UCT), University Hospital, Goethe University Frankfurt, Frankfurt, Germany; Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany; Goethe University Frankfurt, Department of Pediatrics, Experimental Immunology and Cell Therapy, Frankfurt, Germany
| | - Jan Burmeister
- Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany
| | - Jörn Kohlhammer
- Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany
| | - Joachim Schwäble
- Goethe University Frankfurt, University Hospital, Institute of Transfusion Medicine and Immunohematology, German Red Cross Blood Transfusion Service Baden-Württemberg, Frankfurt, Germany
| | - Robert Gurke
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Goethe University Frankfurt, University Hospital, Institute of Clinical Pharmacology, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany
| | - Erika Dorochow
- Goethe University Frankfurt, University Hospital, Institute of Clinical Pharmacology, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - Alexandre Bennett
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany
| | - Stephanie Dauth
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany
| | - Julia Campe
- Goethe University Frankfurt, Department of Pediatrics, Experimental Immunology and Cell Therapy, Frankfurt, Germany; Goethe University Frankfurt, Biological Sciences, Frankfurt am Main, Germany
| | - Tilo Knape
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany
| | - Volker Laux
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany
| | - Aimo Kannt
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Fraunhofer Leistungszentrum TheraNova, Theodor-Stern-Kai 6, 60596, Frankfurt am Main, Germany
| | - Michaela Köhm
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Goethe University Frankfurt, University Hospital, Rheumatology, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany
| | - Gerd Geisslinger
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Goethe University Frankfurt, University Hospital, Institute of Clinical Pharmacology, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany
| | - Eduard Resch
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany
| | - Frank Behrens
- Fraunhofer Cluster of Excellence Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany; Goethe University Frankfurt, University Hospital, Rheumatology, Theodor-Stern-Kai 7, 60596, Frankfurt am Main, Germany
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Deng W, Xie Z, Chen L, Li W, Li M. Disulfidptosis status influences prognosis and therapeutic response in clear cell renal cell carcinoma. Aging (Albany NY) 2024; 16:1249-1275. [PMID: 38271056 PMCID: PMC10866437 DOI: 10.18632/aging.205405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 11/21/2023] [Indexed: 01/27/2024]
Abstract
Disulfidptosis is a recently identified type of programmed cell death. It is characterized by aberrant accumulation of intracellular disulfides. The clinical implications of disulfidptosis in clear cell renal cell carcinoma (ccRCC) remain unclear. A series of bioinformatics approaches were employed to analyze ten disulfidptosis-related molecules. Firstly, the expression patterns of the disulfidptosis-related molecules were different between normal and ccRCC tissues. A comprehensive cohort of patients with ccRCC was then assembled from three public databases and subjected to cluster analysis based on disulfidptosis-related molecules. Consensus cluster analysis revealed three distinct disulfidptosis clusters. We then conducted weighted gene co-expression network analysis (WGCNA) to identify highly correlated genes. 267 hub genes were screened out through WGCNA, and three gene clusters were then determined. Finally, we identified 87 genes with prognostic value and then used them to develop a disulfidptosis scoring (DSscore) system, which was proven to independently predict survival in ccRCC. Patients in the high-DSscore group exhibited a significant survival advantage and better immunotherapeutic responses compared with those in the low-DSscore group. However, the patients in the low-DSscore group exhibited a greater degree of chemotherapeutic response. In addition, the expression of disulfidptosis-related molecules was validated by qRT-PCR, and the potential of disulfidptosis-related molecules to indicate distinct cell subtypes were validated by single-cell RNA-sequencing. In conclusion, DSscore is a promising index for predicting the prognosis and efficacy of immunotherapy in patients with ccRCC and may provide a basis for novel strategies for future studies.
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Affiliation(s)
- Weiming Deng
- Department of Urology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Zhenwei Xie
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Libo Chen
- Department of Urology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Wenjin Li
- Department of Endocrinology, The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Mingyong Li
- Department of Urology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
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Chanchal S, Sharma S, Mohd S, Sultan A, Mishra A, Ashraf MZ. Unraveling Epigenetic Interplay between Inflammation, Thrombosis, and Immune-Related Disorders through a Network Meta-analysis. TH OPEN 2024; 8:e81-e92. [PMID: 38313596 PMCID: PMC10837039 DOI: 10.1055/a-2222-9126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/25/2023] [Indexed: 02/06/2024] Open
Abstract
Inflammation and thrombosis are two distinct yet interdependent physiological processes. The inflammation results in the activation of the coagulation system that directs the immune system and its activation, resulting in the initiation of the pathophysiology of thrombosis, a process termed immune-thrombosis. Still, the shared underlying molecular mechanism related to the immune system and coagulation has not yet been explored extensively. Inspired to answer this, we carried out a comprehensive gene expression meta-analysis using publicly available datasets of four diseases, including venous thrombosis, systemic lupus erythematosus, rheumatoid arthritis, and inflammatory bowel disease. A total of 609 differentially expressed genes (DEGs) shared by all four datasets were identified based on the combined effect size approach. The pathway enrichment analysis of the DEGs showed enrichment of various epigenetic pathways such as histone-modifying enzymes, posttranslational protein modification, chromatin organization, chromatin-modifying enzymes, HATs acetylate proteins. Network-based protein-protein interaction analysis showed epigenetic enzyme coding genes dominating among the top hub genes. The miRNA-interacting partner of the top 10 hub genes was determined. The predomination of epitranscriptomics regulation opens a layout for the meta-analysis of miRNA datasets of the same four diseases. We identified 30 DEmiRs shared by these diseases. There were 9 common DEmiRs selected from the list of miRNA-interacting partners of top 10 hub genes and shared significant DEmiRs from microRNAs dataset acquisition. These common DEmiRs were found to regulate genes involved in epigenetic modulation and indicate a promising epigenetic aspect that needs to be explored for future molecular studies in the context of immunothrombosis and inflammatory disease.
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Affiliation(s)
- Shankar Chanchal
- Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, India
| | - Swati Sharma
- Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, India
| | - Syed Mohd
- Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, India
| | - Armiya Sultan
- Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, India
| | - Aastha Mishra
- Cardio Respiratory Disease unit, CSIR- Institute of Genomics and Integrative Biology, Delhi, India
| | - Mohammad Zahid Ashraf
- Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, India
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Mizuno T, Kusuhara H. Investigation of normalization procedures for transcriptome profiles of compounds oriented toward practical study design. J Toxicol Sci 2024; 49:249-259. [PMID: 38825484 DOI: 10.2131/jts.49.249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The transcriptome profile is a representative phenotype-based descriptor of compounds, widely acknowledged for its ability to effectively capture compound effects. However, the presence of batch differences is inevitable. Despite the existence of sophisticated statistical methods, many of them presume a substantial sample size. How should we design a transcriptome analysis to obtain robust compound profiles, particularly in the context of small datasets frequently encountered in practical scenarios? This study addresses this question by investigating the normalization procedures for transcriptome profiles, focusing on the baseline distribution employed in deriving biological responses as profiles. Firstly, we investigated two large GeneChip datasets, comparing the impact of different normalization procedures. Through an evaluation of the similarity between response profiles of biological replicates within each dataset and the similarity between response profiles of the same compound across datasets, we revealed that the baseline distribution defined by all samples within each batch under batch-corrected condition is a good choice for large datasets. Subsequently, we conducted a simulation to explore the influence of the number of control samples on the robustness of response profiles across datasets. The results offer insights into determining the suitable quantity of control samples for diminutive datasets. It is crucial to acknowledge that these conclusions stem from constrained datasets. Nevertheless, we believe that this study enhances our understanding of how to effectively leverage transcriptome profiles of compounds and promotes the accumulation of essential knowledge for the practical application of such profiles.
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Affiliation(s)
- Tadahaya Mizuno
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo
| | - Hiroyuki Kusuhara
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo
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9
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You P, Liu S, Li Q, Xie D, Yao L, Guo C, Guo Z, Wang T, Qiu H, Guo Y, Li J, Zhou H. Radiation-sensitive genetic prognostic model identifies individuals at risk for radiation resistance in head and neck squamous cell carcinoma. J Cancer Res Clin Oncol 2023; 149:15623-15640. [PMID: 37656244 DOI: 10.1007/s00432-023-05304-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND The advantages of radiotherapy for head and neck squamous cell carcinoma (HNSCC) depend on the radiation sensitivity of the patient. Here, we established and verified radiological factor-related gene signature and built a prognostic risk model to predict whether radiotherapy would be beneficial. METHODS Data from The Cancer Genome Atlas, Gene Expression Omnibus, and RadAtlas databases were subjected to LASSO regression, univariate COX regression, and multivariate COX regression analyses to integrate genomic and clinical information from patients with HNSCC. HNSCC radiation-related prognostic genes were identified, and patients classified into high- and low-risk groups, based on risk scores. Variations in radiation sensitivity according to immunological microenvironment, functional pathways, and immunotherapy response were investigated. Finally, the expression of HNSCC radiation-related genes was verified by qRT-PCR. RESULTS We built a clinical risk prediction model comprising a 15-gene signature and used it to divide patients into two groups based on their susceptibility to radiation: radiation-sensitive and radiation-resistant. Overall survival was significantly greater in the radiation-sensitive than the radiation-resistant group. Further, our model was an independent predictor of radiotherapy response, outperforming other clinical parameters, and could be combined with tumor mutational burden, to identify the target population with good predictive value for prognosis at 1, 2, and 3 years. Additionally, the radiation-resistant group was more vulnerable to low levels of immune infiltration, which are significantly associated with DNA damage repair, hypoxia, and cell cycle regulation. Tumor Immune Dysfunction and Exclusion scores also suggested that the resistant group would respond less favorably to immunotherapy. CONCLUSIONS Our prognostic model based on a radiation-related gene signature has potential for application as a tool for risk stratification of radiation therapy for patients with HNSCC, helping to identify candidates for radiation therapy and overcome radiation resistance.
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Affiliation(s)
- Peimeng You
- Nanchang University, Nanchang, China
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Nanchang, China
| | - Shengbo Liu
- Second Clinical College of Medicine, Southern Medical University, Guangzhou, China
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Qiaxuan Li
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Daipeng Xie
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangzhou, China
| | - Lintong Yao
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Chenguang Guo
- Department of Radiation Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Zefeng Guo
- Department of Radiation Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Ting Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Hongrui Qiu
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Yangzhong Guo
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Nanchang, China
| | - Junyu Li
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Nanchang, China.
| | - Haiyu Zhou
- Nanchang University, Nanchang, China.
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Jiangxi Lung Cancer Institute, Nanchang, China.
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10
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Yu Y, Zhang N, Mai Y, Ren L, Chen Q, Cao Z, Chen Q, Liu Y, Hou W, Yang J, Hong H, Xu J, Tong W, Dong L, Shi L, Fang X, Zheng Y. Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method. Genome Biol 2023; 24:201. [PMID: 37674217 PMCID: PMC10483871 DOI: 10.1186/s13059-023-03047-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/18/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. RESULTS As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies. CONCLUSIONS Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.
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Affiliation(s)
- Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, Guangdong, China
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | | | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes, Shanghai, China.
| | - Xiang Fang
- National Institute of Metrology, Beijing, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
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11
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Zhang E, Zhou X, Fan X, Li S, Ding C, Hong H, Aikemu B, Yang G, Yesseyeva G, Yang X, Ma J, Zheng M. Exploring the relationship between lactate metabolism and immunological function in colorectal cancer through genes identification and analysis. Front Cell Dev Biol 2023; 11:1173803. [PMID: 37691826 PMCID: PMC10484590 DOI: 10.3389/fcell.2023.1173803] [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: 02/25/2023] [Accepted: 08/08/2023] [Indexed: 09/12/2023] Open
Abstract
Introduction: Metabolic dysregulation is a widely acknowledged contributor for the development and tumorigenesis of colorectal cancer (CRC), highlighting the need for reliable prognostic biomarkers in this malignancy. Methods: Herein, we identified key genes relevant to CRC metabolism through a comprehensive analysis of lactate metabolism-related genes from GSEA MsigDB, employing univariate Cox regression analysis and random forest algorithms. Clinical prognostic analysis was performed following identification of three key genes, and consistent clustering enabled the classification of public datasets into three patterns with significant prognostic differences. The molecular pathways and tumor microenvironment (TME) of these patterns were then investigated through correlation analyses. Quantitative PCR was employed to quantify the mRNA expression levels of the three pivotal genes in CRC tissue. Single-cell RNA sequencing data and fluorescent multiplex immunohistochemistry were utilized to analyze relevant T cells and validate the correlation between key genes and CD4+ T cells. Results: Our analysis revealed that MPC1, COQ2, and ADAMTS13 significantly stratify the cohort into three patterns with distinct prognoses. Additionally, the immune infiltration and molecular pathways were significantly different for each pattern. Among the key genes, MPC1 and COQ2 were positively associated with good prognosis, whereas ADAMTS13 was negatively associated with good prognosis. Single-cell RNA sequencing (scRNA-seq) data illustrated that the relationship between three key genes and T cells, which was further confirmed by the results of fluorescent multiplex immunohistochemistry demonstrating a positive correlation between MPC1 and COQ2 with CD4+ T cells and a negative correlation between ADAMTS13 and CD4+ T cells. Discussion: These findings suggest that the three key lactate metabolism genes, MPC1, COQ2, and ADAMTS13, may serve as effective prognostic biomarkers and support the link between lactate metabolism and the immune microenvironment in CRC.
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Affiliation(s)
- Enkui Zhang
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of General Surgery, Peking University First Hospital, Beijing, China
| | - Xueliang Zhou
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaodong Fan
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shuchun Li
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chengsheng Ding
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Hiju Hong
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Batuer Aikemu
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guang Yang
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Galiya Yesseyeva
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiao Yang
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of General Surgery and Carson International Cancer Research Center, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen, China
| | - Junjun Ma
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of General Surgery and Carson International Cancer Research Center, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen, China
| | - Minhua Zheng
- Department of General Surgery, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of General Surgery and Carson International Cancer Research Center, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen, China
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12
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Leach DT, Stratton KG, Irvahn J, Richardson R, Webb-Robertson BJM, Bramer LM. malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data. Anal Chem 2023; 95:12195-12199. [PMID: 37551970 DOI: 10.1021/acs.analchem.3c01289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Mass spectrometry is a powerful tool for identifying and analyzing biomolecules such as metabolites and lipids in complex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly involve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctuation, imprecise timing, column degradation, and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for omics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package, which consolidates 11 common batch effect correction methods for omics data into one place so users can easily implement and compare the following: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveICA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include the batch effect correction in a pmartR workflow without needing any additional data manipulation.
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Affiliation(s)
- Damon T Leach
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Jan Irvahn
- Artificial Intelligence and Data Analytics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Rachel Richardson
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Bobbie-Jo M Webb-Robertson
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
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13
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Forlin R, James A, Brodin P. Making human immune systems more interpretable through systems immunology. Trends Immunol 2023:S1471-4906(23)00113-8. [PMID: 37402600 DOI: 10.1016/j.it.2023.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/12/2023] [Accepted: 06/12/2023] [Indexed: 07/06/2023]
Abstract
The human immune system is a distributed system of specialized cell populations with unique functions that collectively give rise to immune responses to infections and during immune-mediated diseases. Cell composition, plasma proteins, and functional responses vary among individuals, making the system difficult to interpret, but this variation is nonrandom. With careful analyses using novel experimental and computational tools, human immune system composition and function carry interpretable information. Here, we propose that systems-level analyses offer an opportunity to make human immune responses more interpretable in the future, and we discuss herein important considerations and lessons learned to this end. Predictable human immunology holds implications for better diagnostic and curative precision in patients with infectious and immune-associated diseases.
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Affiliation(s)
- Rikard Forlin
- Unit for Clinical Pediatrics, Department of Women's and Children's Health, Karolinska Institutet, 17165, Solna, Sweden
| | - Anna James
- Unit for Clinical Pediatrics, Department of Women's and Children's Health, Karolinska Institutet, 17165, Solna, Sweden
| | - Petter Brodin
- Unit for Clinical Pediatrics, Department of Women's and Children's Health, Karolinska Institutet, 17165, Solna, Sweden; Department of Immunology and Inflammation, Imperial College London, London W12 0NN, UK; Medical Research Council London Institute of Medical Sciences (LMS), Imperial College Hammersmith Campus, London W12 0NN, UK.
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14
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Moon SJ, Jung SM, Baek IW, Park KS, Kim KJ. Molecular signature of neutrophil extracellular trap mediating disease module in idiopathic inflammatory myopathy. J Autoimmun 2023; 138:103063. [PMID: 37220716 DOI: 10.1016/j.jaut.2023.103063] [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: 03/20/2023] [Revised: 05/07/2023] [Accepted: 05/09/2023] [Indexed: 05/25/2023]
Abstract
The rarity and heterogeneity of idiopathic inflammatory myopathy (IIM) pose challenges for researching IIM in affected individuals. We analyzed integrated transcriptomic datasets obtained using muscle tissues from patients with five distinct IIM subtypes to investigate the shared and distinctive cellular and molecular characteristics. A transcriptomic dataset of muscle tissues from normal controls (n = 105) and patients with dermatomyositis (n = 89), polymyositis (n = 33), inclusion body myositis (n = 121), immune-mediated necrotizing myositis (n = 75), and anti-synthetase syndrome (n = 18) was used for differential gene-expression analysis, functional-enrichment analysis, gene set-enrichment analysis, disease-module identification, and kernel-based diffusion scoring. Damage-associated molecular pattern-associated pathways and neutrophil-mediated immunity were significantly enriched across different IIM subtypes, although their activities varied. Interferons-signaling pathways were differentially activated across all five IIM subtypes. In particular, neutrophil extracellular trap (NET) formation was significantly activated and correlated with Fcγ R-mediated signaling pathways. NET formation-associated genes were key for establishing disease modules, and FCGRs, C1QA, and SERPINE1 markedly perturbed the disease modules. Integrated transcriptomic analysis of muscle tissues identified NETs as key components of neutrophil-mediated immunity involved in the pathogenesis of IIM subtypes and, thus, has therapeutically targetable value.
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Affiliation(s)
- Su-Jin Moon
- Division of Rheumatology, Department of Internal Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seung Min Jung
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - In-Woon Baek
- Division of Rheumatology, Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | - Kyung-Su Park
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ki-Jo Kim
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
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15
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Fajarda O, Almeida JR, Duarte-Pereira S, Silva RM, Oliveira JL. Methodology to identify a gene expression signature by merging microarray datasets. Comput Biol Med 2023; 159:106867. [PMID: 37060770 DOI: 10.1016/j.compbiomed.2023.106867] [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: 10/19/2022] [Revised: 03/01/2023] [Accepted: 03/30/2023] [Indexed: 04/17/2023]
Abstract
A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.
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Affiliation(s)
- Olga Fajarda
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal.
| | - João Rafael Almeida
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Computation, University of A Coruña, A Coruña, Spain.
| | - Sara Duarte-Pereira
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Medical Sciences and iBiMED-Institute of Biomedicine, University of Aveiro, Aveiro, Portugal.
| | - Raquel M Silva
- Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal.
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16
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Salas-Perez F, Assmann TS, Ramos-Lopez O, Martínez JA, Riezu-Boj JI, Milagro FI. Crosstalk between Gut Microbiota and Epigenetic Markers in Obesity Development: Relationship between Ruminococcus, BMI, and MACROD2/ SEL1L2 Methylation. Nutrients 2023; 15:nu15071550. [PMID: 37049393 PMCID: PMC10097304 DOI: 10.3390/nu15071550] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/09/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023] Open
Abstract
Changes in gut microbiota composition and in epigenetic mechanisms have been proposed to play important roles in energy homeostasis, and the onset and development of obesity. However, the crosstalk between epigenetic markers and the gut microbiome in obesity remains unclear. The main objective of this study was to establish a link between the gut microbiota and DNA methylation patterns in subjects with obesity by identifying differentially methylated DNA regions (DMRs) that could be potentially regulated by the gut microbiota. DNA methylation and bacterial DNA sequencing analysis were performed on 342 subjects with a BMI between 18 and 40 kg/m2. DNA methylation analyses identified a total of 2648 DMRs associated with BMI, while ten bacterial genera were associated with BMI. Interestingly, only the abundance of Ruminococcus was associated with one BMI-related DMR, which is located between the MACROD2/SEL1L2 genes. The Ruminococcus abundance negatively correlated with BMI, while the hypermethylated DMR was associated with reduced MACROD2 protein levels in serum. Additionally, the mediation test showed that 19% of the effect of Ruminococcus abundance on BMI is mediated by the methylation of the MACROD2/SEL1L2 DMR. These findings support the hypothesis that a crosstalk between gut microbiota and epigenetic markers may be contributing to obesity development.
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Affiliation(s)
| | - Taís Silveira Assmann
- Graduate Program in Medical Sciences, Endocrinology, Department of Internal Medicine, Faculty of Medicine, Federal University of do Rio Grande do Sul, Porto Alegre 90035-003, Brazil
| | - Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Mexico
| | - J Alfredo Martínez
- Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain
- Department of Nutrition, Food Science and Physiology, University of Navarra, 31008 Pamplona, Spain
- Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y la Nutrición (CIBERobn), Carlos III Health Institute, 28029 Madrid, Spain
| | - Jose Ignacio Riezu-Boj
- Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain
| | - Fermín I Milagro
- Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain
- Department of Nutrition, Food Science and Physiology, University of Navarra, 31008 Pamplona, Spain
- Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y la Nutrición (CIBERobn), Carlos III Health Institute, 28029 Madrid, Spain
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain
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17
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Fallet M, Blanc M, Di Criscio M, Antczak P, Engwall M, Guerrero Bosagna C, Rüegg J, Keiter SH. Present and future challenges for the investigation of transgenerational epigenetic inheritance. ENVIRONMENT INTERNATIONAL 2023; 172:107776. [PMID: 36731188 DOI: 10.1016/j.envint.2023.107776] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 01/18/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Epigenetic pathways are essential in different biological processes and in phenotype-environment interactions in response to different stressors and they can induce phenotypic plasticity. They encompass several processes that are mitotically and, in some cases, meiotically heritable, so they can be transferred to subsequent generations via the germline. Transgenerational Epigenetic Inheritance (TEI) describes the phenomenon that phenotypic traits, such as changes in fertility, metabolic function, or behavior, induced by environmental factors (e.g., parental care, pathogens, pollutants, climate change), can be transferred to offspring generations via epigenetic mechanisms. Investigations on TEI contribute to deciphering the role of epigenetic mechanisms in adaptation, adversity, and evolution. However, molecular mechanisms underlying the transmission of epigenetic changes between generations, and the downstream chain of events leading to persistent phenotypic changes, remain unclear. Therefore, inter-, (transmission of information between parental and offspring generation via direct exposure) and transgenerational (transmission of information through several generations with disappearance of the triggering factor) consequences of epigenetic modifications remain major issues in the field of modern biology. In this article, we review and describe the major gaps and issues still encountered in the TEI field: the general challenges faced in epigenetic research; deciphering the key epigenetic mechanisms in inheritance processes; identifying the relevant drivers for TEI and implement a collaborative and multi-disciplinary approach to study TEI. Finally, we provide suggestions on how to overcome these challenges and ultimately be able to identify the specific contribution of epigenetics in transgenerational inheritance and use the correct tools for environmental science investigation and biomarkers identification.
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Affiliation(s)
- Manon Fallet
- Man-Technology-Environment Research Centre (MTM), School of Science and Technology, Örebro University, Fakultetsgatan 1, 70182 Örebro, Sweden; Department of Biochemistry, Dorothy Crowfoot Hodgkin Building, University of Oxford, South Parks Rd, Oxford OX1 3QU, United Kingdom.
| | - Mélanie Blanc
- MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, INRAE, Palavas, France
| | - Michela Di Criscio
- Department of Organismal Biology, Uppsala University, Norbyv. 18A, 75236 Uppsala, Sweden
| | - Philipp Antczak
- University of Cologne, Faculty of Medicine and Cologne University Hospital, Center for Molecular Medicine Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases, University of Cologne, Cologne, Germany
| | - Magnus Engwall
- Man-Technology-Environment Research Centre (MTM), School of Science and Technology, Örebro University, Fakultetsgatan 1, 70182 Örebro, Sweden
| | | | - Joëlle Rüegg
- Department of Organismal Biology, Uppsala University, Norbyv. 18A, 75236 Uppsala, Sweden
| | - Steffen H Keiter
- Man-Technology-Environment Research Centre (MTM), School of Science and Technology, Örebro University, Fakultetsgatan 1, 70182 Örebro, Sweden
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18
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Antibody Profiling and In Silico Functional Analysis of Differentially Reactive Antibody Signatures of Glioblastomas and Meningiomas. Int J Mol Sci 2023; 24:ijms24021411. [PMID: 36674927 PMCID: PMC9866115 DOI: 10.3390/ijms24021411] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/08/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
Studies on tumor-associated antigens in brain tumors are sparse. There is scope for enhancing our understanding of molecular pathology, in order to improve on existing forms, and discover new forms, of treatment, which could be particularly relevant to immuno-oncological strategies. To elucidate immunological differences, and to provide another level of biological information, we performed antibody profiling, based on a high-density protein array (containing 8173 human transcripts), using IgG isolated from the sera of n = 12 preoperative and n = 16 postoperative glioblastomas, n = 26 preoperative and n = 29 postoperative meningiomas, and n = 27 healthy, cancer-free controls. Differentially reactive antigens were compared to gene expression data from an alternate public GBM data set from OncoDB, and were analyzed using the Reactome pathway browser. Protein array analysis identified approximately 350-800 differentially reactive antigens, and revealed different antigen profiles in the glioblastomas and meningiomas, with approximately 20-30%-similar and 10-15%-similar antigens in preoperative and postoperative sera, respectively. Seroreactivity did not correlate with OncoDB-derived gene expression. Antigens in the preoperative glioblastoma sera were enriched for signaling pathways, such as signaling by Rho-GTPases, COPI-mediated anterograde transport and vesicle-mediated transport, while the infectious disease, SRP-dependent membrane targeting cotranslational proteins were enriched in the meningiomas. The pre-vs. postoperative seroreactivity in the glioblastomas was enriched for antigens, e.g., platelet degranulation and metabolism of lipid pathways; in the meningiomas, the antigens were enriched in infectious diseases, metabolism of amino acids and derivatives, and cell cycle. Antibody profiling in both tumor entities elucidated several hundred antigens and characteristic signaling pathways that may provide new insights into molecular pathology and may be of interest for the development of new treatment strategies.
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Yosef A, Shnaider E, Schneider M, Gurevich M. Normalization of Large-Scale Transcriptome Data Using Heuristic Methods. Bioinform Biol Insights 2023; 17:11779322231160397. [PMID: 37020503 PMCID: PMC10068970 DOI: 10.1177/11779322231160397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 02/09/2023] [Indexed: 04/03/2023] Open
Abstract
In this study, we introduce an artificial intelligent method for addressing the batch effect of a transcriptome data. The method has several clear advantages in comparison with the alternative methods presently in use. Batch effect refers to the discrepancy in gene expression data series, measured under different conditions. While the data from the same batch (measurements performed under the same conditions) are compatible, combining various batches into 1 data set is problematic because of incompatible measurements. Therefore, it is necessary to perform correction of the combined data (normalization), before performing biological analysis. There are numerous methods attempting to correct data set for batch effect. These methods rely on various assumptions regarding the distribution of the measurements. Forcing the data elements into pre-supposed distribution can severely distort biological signals, thus leading to incorrect results and conclusions. As the discrepancy between the assumptions regarding the data distribution and the actual distribution is wider, the biases introduced by such “correction methods” are greater. We introduce a heuristic method to reduce batch effect. The method does not rely on any assumptions regarding the distribution and the behavior of data elements. Hence, it does not introduce any new biases in the process of correcting the batch effect. It strictly maintains the integrity of measurements within the original batches.
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Hattaway ME, Black GP, Young TM. Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system. Anal Bioanal Chem 2023; 415:1321-1331. [PMID: 36627378 PMCID: PMC9928919 DOI: 10.1007/s00216-023-04511-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/08/2022] [Accepted: 01/02/2023] [Indexed: 01/12/2023]
Abstract
Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those monitoring complex matrices over long time periods, is a choice between analyzing samples in multiple batches as they are collected, or in one batch after all samples have been processed. While datasets acquired in multiple analytical batches can include the effects of instrumental variability over time, datasets acquired in a single batch risk compound degradation during sample storage. To assess the influence of batch effects on the analysis and interpretation of nontarget data, this study examined a set of 56 samples collected from a municipal wastewater system over 7 months. Each month's samples included 6 from sites within the collection system, one combined influent, and one treated effluent sample. Samples were analyzed using liquid chromatography high-resolution mass spectrometry in positive electrospray ionization mode in multiple batches as the samples were collected and in a single batch at the conclusion of the study. Data were aligned and normalized using internal standard scaling and ComBat, an empirical Bayes method developed for estimating and removing batch effects in microarrays. As judged by multiple lines of evidence, including comparing principal variance component analysis between single and multi-batch datasets and through patterns in principal components and hierarchical clustering analyses, ComBat appeared to significantly reduce the influence of batch effects. For this reason, we recommend the use of more, small batches with an appropriate batch correction step rather than acquisition in one large batch.
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Affiliation(s)
- Madison E. Hattaway
- grid.27860.3b0000 0004 1936 9684Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616 USA
| | - Gabrielle P. Black
- grid.27860.3b0000 0004 1936 9684Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616 USA
| | - Thomas M. Young
- grid.27860.3b0000 0004 1936 9684Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616 USA
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Campagna MP, Xavier A, Lea RA, Stankovich J, Maltby VE, Butzkueven H, Lechner-Scott J, Scott RJ, Jokubaitis VG. Whole-blood methylation signatures are associated with and accurately classify multiple sclerosis disease severity. Clin Epigenetics 2022; 14:194. [PMID: 36585691 PMCID: PMC9805090 DOI: 10.1186/s13148-022-01397-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/02/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The variation in multiple sclerosis (MS) disease severity is incompletely explained by genetics, suggesting genetic and environmental interactions are involved. Moreover, the lack of prognostic biomarkers makes it difficult for clinicians to optimise care. DNA methylation is one epigenetic mechanism by which gene-environment interactions can be assessed. Here, we aimed to identify DNA methylation patterns associated with mild and severe relapse-onset MS (RMS) and to test the utility of methylation as a predictive biomarker. METHODS We conducted an epigenome-wide association study between 235 females with mild (n = 119) or severe (n = 116) with RMS. Methylation was measured with the Illumina methylationEPIC array and analysed using logistic regression. To generate hypotheses about the functional consequence of differential methylation, we conducted gene set enrichment analysis using ToppGene. We compared the accuracy of three machine learning models in classifying disease severity: (1) clinical data available at baseline (age at onset and first symptoms) built using elastic net (EN) regression, (2) methylation data using EN regression and (3) a weighted methylation risk score of differentially methylated positions (DMPs) from the main analysis using logistic regression. We used a conservative 70:30 test:train split for classification modelling. A false discovery rate threshold of 0.05 was used to assess statistical significance. RESULTS Females with mild or severe RMS had 1472 DMPs in whole blood (839 hypermethylated, 633 hypomethylated in the severe group). Differential methylation was enriched in genes related to neuronal cellular compartments and processes, and B-cell receptor signalling. Whole-blood methylation levels at 1708 correlated CpG sites classified disease severity more accurately (machine learning model 2, AUC = 0.91) than clinical data (model 1, AUC = 0.74) or the wMRS (model 3, AUC = 0.77). Of the 1708 selected CpGs, 100 overlapped with DMPs from the main analysis at the gene level. These overlapping genes were enriched in neuron projection and dendrite extension, lending support to our finding that neuronal processes, rather than immune processes, are implicated in disease severity. CONCLUSION RMS disease severity is associated with whole-blood methylation at genes related to neuronal structure and function. Moreover, correlated whole-blood methylation patterns can assign disease severity in females with RMS more accurately than clinical data available at diagnosis.
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Affiliation(s)
- Maria Pia Campagna
- grid.1002.30000 0004 1936 7857Central Clinical School, Monash University, Melbourne, VIC Australia
| | - Alexandre Xavier
- grid.266842.c0000 0000 8831 109XHunter Medical Research Institute, University of Newcastle, Newcastle, NSW Australia
| | - Rodney A. Lea
- grid.1024.70000000089150953Queensland University of Technology, Brisbane, QLD Australia ,grid.1008.90000 0001 2179 088XUniversity of Melbourne, Melbourne, VIC Australia
| | - Jim Stankovich
- grid.1002.30000 0004 1936 7857Monash University, Melbourne, VIC Australia
| | - Vicki E. Maltby
- grid.266842.c0000 0000 8831 109XHunter Medical Research Institute, University of Newcastle, Newcastle, NSW Australia
| | - Helmut Butzkueven
- grid.1002.30000 0004 1936 7857Monash University, Melbourne, VIC Australia ,grid.1008.90000 0001 2179 088XUniversity of Melbourne, Melbourne, VIC Australia ,grid.416153.40000 0004 0624 1200Royal Melbourne Hospital, Melbourne, VIC Australia ,grid.414366.20000 0004 0379 3501Neurology Department, Eastern Health, Melbourne, VIC Australia ,grid.267362.40000 0004 0432 5259Neurology Department, Alfred Health, Melbourne, VIC Australia
| | - Jeannette Lechner-Scott
- grid.266842.c0000 0000 8831 109XHunter Medical Research Institute, University of Newcastle, Newcastle, NSW Australia ,grid.3006.50000 0004 0438 2042Neurology Department, John Hunter Hospital, Hunter New England Health, Newcastle, NSW Australia
| | - Rodney J. Scott
- grid.266842.c0000 0000 8831 109XSchool of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW Australia ,Division of Molecular Medicine, New South Wales Health Pathology North, Newcastle, NSW Australia
| | - Vilija G. Jokubaitis
- grid.1002.30000 0004 1936 7857Monash University, Melbourne, VIC Australia ,grid.1008.90000 0001 2179 088XUniversity of Melbourne, Melbourne, VIC Australia ,grid.416153.40000 0004 0624 1200Royal Melbourne Hospital, Melbourne, VIC Australia ,grid.267362.40000 0004 0432 5259Neurology Department, Alfred Health, Melbourne, VIC Australia
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Shi X, Ni H, Wu Y, Guo M, Wang B, Zhang Y, Zhang B, Xu Y. Diagnostic signature, subtype classification, and immune infiltration of key m6A regulators in osteomyelitis patients. Front Genet 2022; 13:1044264. [PMID: 36544487 PMCID: PMC9760713 DOI: 10.3389/fgene.2022.1044264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Background: As a recurrent inflammatory bone disease, the treatment of osteomyelitis is always a tricky problem in orthopaedics. N6-methyladenosine (m6A) regulators play significant roles in immune and inflammatory responses. Nevertheless, the function of m6A modification in osteomyelitis remains unclear. Methods: Based on the key m6A regulators selected by the GSE16129 dataset, a nomogram model was established to predict the incidence of osteomyelitis by using the random forest (RF) method. Through unsupervised clustering, osteomyelitis patients were divided into two m6A subtypes, and the immune infiltration of these subtypes was further evaluated. Validating the accuracy of the diagnostic model for osteomyelitis and the consistency of clustering based on the GSE30119 dataset. Results: 3 writers of Methyltransferase-like 3 (METTL3), RNA-binding motif protein 15B (RBM15B) and Casitas B-lineage proto-oncogene like 1 (CBLL1) and three readers of YT521-B homology domain-containing protein 1 (YTHDC1), YT521-B homology domain-containing family 3 (YTHDF2) and Leucine-rich PPR motif-containing protein (LRPPRC) were identified by difference analysis, and their Mean Decrease Gini (MDG) scores were all greater than 10. Based on these 6 significant m6A regulators, a nomogram model was developed to predict the incidence of osteomyelitis, and the fitting curve indicated a high degree of fit in both the test and validation groups. Two m6A subtypes (cluster A and cluster B) were identified by the unsupervised clustering method, and there were significant differences in m6A scores and the abundance of immune infiltration between the two m6A subtypes. Among them, two m6A regulators (METTL3 and LRPPRC) were closely related to immune infiltration in patients with osteomyelitis. Conclusion: m6A regulators play key roles in the molecular subtypes and immune response of osteomyelitis, which may provide assistance for personalized immunotherapy in patients with osteomyelitis.
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Affiliation(s)
- Xiangwen Shi
- School of Medicine, Kunming Medical University, Kunming, China
| | - Haonan Ni
- School of Medicine, Kunming Medical University, Kunming, China
| | - Yipeng Wu
- School of Medicine, Kunming Medical University, Kunming, China,Department of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force, Kunming, China,Laboratory of Clinical Medical Center, Yunnan Traumatology and Orthopedics, Kunming, China
| | - Minzheng Guo
- School of Medicine, Kunming Medical University, Kunming, China
| | - Bin Wang
- School of Medicine, Kunming Medical University, Kunming, China
| | - Yue Zhang
- School of Medicine, Kunming Medical University, Kunming, China
| | - Bihuan Zhang
- School of Medicine, Kunming Medical University, Kunming, China
| | - Yongqing Xu
- Department of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force, Kunming, China,Laboratory of Clinical Medical Center, Yunnan Traumatology and Orthopedics, Kunming, China,*Correspondence: Yongqing Xu,
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Jung SM, Baek IW, Park KS, Kim KJ. De novo molecular subtyping of salivary gland tissue in the context of Sjögren's syndrome heterogeneity. Clin Immunol 2022; 245:109171. [DOI: 10.1016/j.clim.2022.109171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 11/08/2022]
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Hao Y, Lu L, Liu A, Lin X, Xiao L, Kong X, Li K, Liang F, Xiong J, Qu L, Li Y, Li J. Integrating bioinformatic strategies in spatial life science research. Brief Bioinform 2022; 23:bbac415. [PMID: 36198665 PMCID: PMC9677476 DOI: 10.1093/bib/bbac415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/15/2022] [Accepted: 08/27/2022] [Indexed: 12/14/2022] Open
Abstract
As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools.
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Affiliation(s)
- Yangyang Hao
- Key Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Liang Lu
- The State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, No. 26 Beiqing Road, Haidian District, Beijing, 100094, China
| | - Anna Liu
- Key Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Xue Lin
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Li Xiao
- The State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, No. 26 Beiqing Road, Haidian District, Beijing, 100094, China
| | - Xiaoyue Kong
- Key Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Kai Li
- The State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, No. 26 Beiqing Road, Haidian District, Beijing, 100094, China
| | - Fengji Liang
- The State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, No. 26 Beiqing Road, Haidian District, Beijing, 100094, China
| | - Jianghui Xiong
- The State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, No. 26 Beiqing Road, Haidian District, Beijing, 100094, China
| | - Lina Qu
- The State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, No. 26 Beiqing Road, Haidian District, Beijing, 100094, China
| | - Yinghui Li
- The State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, No. 26 Beiqing Road, Haidian District, Beijing, 100094, China
| | - Jian Li
- Key Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China
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Zeng Z, Gu SS, Wong CJ, Yang L, Ouardaoui N, Li D, Zhang W, Brown M, Liu XS. Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response. SCIENCE ADVANCES 2022; 8:eabm8564. [PMID: 36240281 PMCID: PMC9565795 DOI: 10.1126/sciadv.abm8564] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 08/30/2022] [Indexed: 06/16/2023]
Abstract
Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in The Cancer Genome Atlas, which agreed well with existing clinical reports. Last, feature analysis implicated factors associated with ICB response. In summary, our computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.
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Affiliation(s)
- Zexian Zeng
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Shengqing Stan Gu
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Cheryl J. Wong
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115 USA
| | - Lin Yang
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Nofal Ouardaoui
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Dian Li
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Wubing Zhang
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
- School of Life Science and Technology, Tongji University, Shanghai 200060, China
| | - Myles Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - X. Shirley Liu
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
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Adamer MF, Brüningk SC, Tejada-Arranz A, Estermann F, Basler M, Borgwardt K. reComBat: batch-effect removal in large-scale multi-source gene-expression data integration. BIOINFORMATICS ADVANCES 2022; 2:vbac071. [PMID: 36699372 PMCID: PMC9710604 DOI: 10.1093/bioadv/vbac071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/01/2022] [Accepted: 09/26/2022] [Indexed: 01/28/2023]
Abstract
Motivation With the steadily increasing abundance of omics data produced all over the world under vastly different experimental conditions residing in public databases, a crucial step in many data-driven bioinformatics applications is that of data integration. The challenge of batch-effect removal for entire databases lies in the large number of batches and biological variation, which can result in design matrix singularity. This problem can currently not be solved satisfactorily by any common batch-correction algorithm. Results We present reComBat, a regularized version of the empirical Bayes method to overcome this limitation and benchmark it against popular approaches for the harmonization of public gene-expression data (both microarray and bulkRNAsq) of the human opportunistic pathogen Pseudomonas aeruginosa. Batch-effects are successfully mitigated while biologically meaningful gene-expression variation is retained. reComBat fills the gap in batch-correction approaches applicable to large-scale, public omics databases and opens up new avenues for data-driven analysis of complex biological processes beyond the scope of a single study. Availability and implementation The code is available at https://github.com/BorgwardtLab/reComBat, all data and evaluation code can be found at https://github.com/BorgwardtLab/batchCorrectionPublicData. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | | | | | | | - Marek Basler
- Biozentrum, University of Basel, Basel 4056, Switzerland
| | - Karsten Borgwardt
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland,Swiss Institute for Bioinformatics (SIB), Lausanne 1015, Switzerland
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Lanata CM, Nititham J, Taylor KE, Solomon O, Chung SA, Blazer A, Trupin L, Katz P, Dall'Era M, Yazdany J, Sirota M, Barcellos LF, Criswell LA. Dynamics of Methylation of CpG Sites Associated With Systemic Lupus Erythematosus Subtypes in a Longitudinal Cohort. Arthritis Rheumatol 2022; 74:1676-1686. [PMID: 35635730 PMCID: PMC9529797 DOI: 10.1002/art.42237] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 04/28/2022] [Accepted: 05/12/2022] [Indexed: 01/11/2023]
Abstract
OBJECTIVE Findings from cross-sectional studies have revealed associations between DNA methylation and systemic lupus erythematosus (SLE) outcomes. This study was undertaken to investigate the dynamics of DNA methylation by examining participants from an SLE longitudinal cohort using samples collected at 2 time points. METHODS A total of 101 participants from the California Lupus Epidemiology Study were included in our analysis. DNA was extracted from blood samples collected at the time of enrolment in the cohort and samples collected after 2 years and was analyzed using Illumina EPIC BeadChip kit. Paired t-tests were used to identify genome-wide changes which included 256 CpG sites previously found to be associated with SLE subtypes. Linear mixed models were developed to understand the relationship between DNA methylation and disease activity, medication use, and sample cell-type proportions, adjusted for age, sex, and genetic principal components. RESULTS The majority of CpGs that were previously determined to be associated with SLE subtypes remained stable over 2 years (185 CpGs [72.3%]; t-test false discovery rate >0.05). Compared to background genome-wide methylation, there was an enrichment of SLE subtype-associated CpGs that changed over time (27.7% versus 0.34%). Changes in cell-type proportions were associated with changes at 67 CpGs (P < 2.70 × 10-5 ), and 15 CpGs had at least 1 significant association with immunosuppressant use. CONCLUSION In this longitudinal SLE cohort, we identified a subset of SLE subtype-associated CpGs that remained stable over time and may be useful as biomarkers of disease subtypes. Another subset of SLE subtype-associated CpGs changed at a higher proportion compared to the genome-wide methylome. Additional studies are needed to understand the etiology and impact of these changes on methylation of SLE-associated CpGs.
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Affiliation(s)
| | - Joanne Nititham
- National Human Genome Research Institute, NIH, Bethesda, Maryland
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Arencibia A, Salazar LA. Microarray meta-analysis reveals IL6 and p38β/MAPK11 as potential targets of hsa-miR-124 in endothelial progenitor cells: Implications for stent re-endothelization in diabetic patients. Front Cardiovasc Med 2022; 9:964721. [PMID: 36176980 PMCID: PMC9513120 DOI: 10.3389/fcvm.2022.964721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/25/2022] [Indexed: 11/13/2022] Open
Abstract
Circulating endothelial progenitor cells (EPCs) play an important role in the repair processes of damaged vessels, favoring re-endothelization of stented vessels to minimize restenosis. EPCs number and function is diminished in patients with type 2 diabetes, a known risk factor for restenosis. Considering the impact of EPCs in vascular injury repair, we conducted a meta-analysis of microarray to assess the transcriptomic profile and determine target genes during the differentiation process of EPCs into mature ECs. Five microarray datasets, including 13 EPC and 12 EC samples were analyzed, using the online tool ExpressAnalyst. Differentially expressed genes (DEGs) analysis was done by Limma method, with an | log2FC| > 1 and FDR < 0.05. Combined p-value by Fisher exact method was computed for the intersection of datasets. There were 3,267 DEGs, 1,539 up-regulated and 1,728 down-regulated in EPCs, with 407 common DEGs in at least four datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed enrichment for terms related to “AGE-RAGE signaling pathway in diabetic complications.” Intersection of common DEGs, KEGG pathways genes and genes in protein-protein interaction network (PPI) identified four key genes, two up-regulated (IL1B and STAT5A) and two down-regulated (IL6 and MAPK11). MicroRNA enrichment analysis of common DEGs depicted five hub microRNA targeting 175 DEGs, including STAT5A, IL6 and MAPK11, with hsa-miR-124 as common regulator. This group of genes and microRNAs could serve as biomarkers of EPCs differentiation during coronary stenting as well as potential therapeutic targets to improve stent re-endothelization, especially in diabetic patients.
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Liu H, Xing K, Jiang Y, Liu Y, Wang C, Ding X. Using Machine Learning to Identify Biomarkers Affecting Fat Deposition in Pigs by Integrating Multisource Transcriptome Information. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:10359-10370. [PMID: 35953074 PMCID: PMC9413214 DOI: 10.1021/acs.jafc.2c03339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 06/15/2023]
Abstract
Fat deposition in pigs is not only closely related to pig production efficiency and pork quality but also an ideal model for human obesity. Transcriptome sequencing is widely used to study fat deposition. However, due to small sample sizes, high false positive rates, and poor consistency of results from different studies, new strategies are urgently needed. Machine learning, a new analysis method, can effectively fit complex data and accurately identify samples and genes. In this study, 36 samples of adipose tissue, muscle tissue, and liver tissue were collected from Songliao black pigs and Landrace pigs, and the mRNA of all the samples was sequenced. In addition, we collected transcriptome data for 64 samples in the GEO database from four different sources. After standardization and imputation of missing values in the data set comprising 100 samples, traditional differential expression analysis was carried out, and different numbers of expressed genes were selected as features for the training model of eight machine learning methods. In the 1000 replications of fourfold cross validation with 100 samples, AdaBoost performed best, with an average prediction accuracy greater than 93% and the highest mean area under the curve in predicting the high- and low-fat content groups among the eight ML methods. According to their performance-based ranks inferred by AdaBoost, 12 genes related to fat deposition were identified; among them, FASN and APOD were specifically expressed in adipose tissue, and APOA1 was specifically expressed in the liver, which could be important candidate biomarkers affecting fat deposition.
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30
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Mosquera Orgueira A, Peleteiro Raíndo A, Díaz Arias JÁ, Antelo Rodríguez B, López Riñón M, Cerchione C, de la Fuente Burguera A, González Pérez MS, Martinelli G, Montesinos Fernández P, Pérez Encinas MM. Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia. Front Oncol 2022; 12:968340. [PMID: 36059646 PMCID: PMC9428690 DOI: 10.3389/fonc.2022.968340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 07/28/2022] [Indexed: 11/17/2022] Open
Abstract
Risk stratification in acute myeloid leukemia (AML) has been extensively improved thanks to the incorporation of recurrent cytogenomic alterations into risk stratification guidelines. However, mortality rates among fit patients assigned to low or intermediate risk groups are still high. Therefore, significant room exists for the improvement of AML prognostication. In a previous work, we presented the Stellae-123 gene expression signature, which achieved a high accuracy in the prognostication of adult patients with AML. Stellae-123 was particularly accurate to restratify patients bearing high-risk mutations, such as ASXL1, RUNX1 and TP53. The intention of the present work was to evaluate the prognostic performance of Stellae-123 in external cohorts using RNAseq technology. For this, we evaluated the signature in 3 different AML cohorts (2 adult and 1 pediatric). Our results indicate that the prognostic performance of the Stellae-123 signature is reproducible in the 3 cohorts of patients. Additionally, we evidenced that the signature was superior to the European LeukemiaNet 2017 and the pediatric clinical risk scores in the prediction of survival at most of the evaluated time points. Furthermore, integration with age substantially enhanced the accuracy of the model. In conclusion, Stellae-123 is a reproducible machine learning algorithm based on a gene expression signature with promising utility in the field of AML.
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Affiliation(s)
- Adrián Mosquera Orgueira
- Department of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain
| | - Andrés Peleteiro Raíndo
- Department of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain
| | - José Ángel Díaz Arias
- Department of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain
| | - Beatriz Antelo Rodríguez
- Department of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain
| | | | - Claudio Cerchione
- Unit of Hematology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “DinoAmadori”, Meldola, Italy
| | | | | | - Giovanni Martinelli
- Unit of Hematology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “DinoAmadori”, Meldola, Italy
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31
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Wu HJ, Temko D, Maliga Z, Moreira AL, Sei E, Minussi DC, Dean J, Lee C, Xu Q, Hochart G, Jacobson CA, Yapp C, Schapiro D, Sorger PK, Seeley EH, Navin N, Downey RJ, Michor F. Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients. CELL GENOMICS 2022; 2:100165. [PMID: 36419822 PMCID: PMC9681138 DOI: 10.1016/j.xgen.2022.100165] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD.
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Affiliation(s)
- Hua-Jun Wu
- Center for Precision Medicine Multi-Omics Research, School of Basic Medical Sciences, Peking University Health Science Center and Peking University Cancer Hospital and Institute, Beijing, China,These authors contributed equally
| | - Daniel Temko
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA,These authors contributed equally
| | - Zoltan Maliga
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA,These authors contributed equally
| | - Andre L. Moreira
- Department of Pathology, New York University Langone Health, New York, NY 10016, USA
| | - Emi Sei
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Darlan Conterno Minussi
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jamie Dean
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Charlotte Lee
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA,Harvard-MIT Program in Health Sciences and Technology, Harvard Medical School, Boston, MA 02215, USA
| | - Qiong Xu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | | | - Connor A. Jacobson
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Clarence Yapp
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Denis Schapiro
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA,Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA,Ludwig Center at Harvard, Boston, MA 02215, USA
| | - Erin H. Seeley
- Department of Chemistry, University of Texas at Austin, Austin, TX, USA
| | - Nicholas Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Robert J. Downey
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA,Correspondence: (R.J.D.), (F.M.)
| | - Franziska Michor
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA,Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA,Ludwig Center at Harvard, Boston, MA 02215, USA,Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02215, USA,Lead contact,Correspondence: (R.J.D.), (F.M.)
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32
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Longitudinal phenotypic aging metrics in the Baltimore Longitudinal Study of Aging. NATURE AGING 2022; 2:635-643. [PMID: 36910594 PMCID: PMC9997119 DOI: 10.1038/s43587-022-00243-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
To define metrics of phenotypic aging, it is essential to identify biological and environmental factors that influence the pace of aging. Previous attempts to develop aging metrics were hampered by cross-sectional designs and/or focused on younger populations. In the Baltimore Longitudinal Study of Aging (BLSA), we collected longitudinally across the adult age range a comprehensive list of phenotypes within four domains (body composition, energetics, homeostatic mechanisms and neurodegeneration/neuroplasticity) and functional outcomes. We integrated individual deviations from population trajectories into a global longitudinal phenotypic metric of aging and demonstrate that accelerated longitudinal phenotypic aging is associated with faster physical and cognitive decline, faster accumulation of multimorbidity and shorter survival. These associations are more robust compared with the use of phenotypic and epigenetic measurements at a single time point. Estimation of these metrics required repeated measures of multiple phenotypes over time but may uniquely facilitate the identification of mechanisms driving phenotypic aging and subsequent age-related functional decline.
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HarmonizR enables data harmonization across independent proteomic datasets with appropriate handling of missing values. Nat Commun 2022; 13:3523. [PMID: 35725563 PMCID: PMC9209422 DOI: 10.1038/s41467-022-31007-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 05/25/2022] [Indexed: 01/01/2023] Open
Abstract
Dataset integration is common practice to overcome limitations in statistically underpowered omics datasets. Proteome datasets display high technical variability and frequent missing values. Sophisticated strategies for batch effect reduction are lacking or rely on error-prone data imputation. Here we introduce HarmonizR, a data harmonization tool with appropriate missing value handling. The method exploits the structure of available data and matrix dissection for minimal data loss, without data imputation. This strategy implements two common batch effect reduction methods—ComBat and limma (removeBatchEffect()). The HarmonizR strategy, evaluated on four exemplarily analyzed datasets with up to 23 batches, demonstrated successful data harmonization for different tissue preservation techniques, LC-MS/MS instrumentation setups, and quantification approaches. Compared to data imputation methods, HarmonizR was more efficient and performed superior regarding the detection of significant proteins. HarmonizR is an efficient tool for missing data tolerant experimental variance reduction and is easily adjustable for individual dataset properties and user preferences. Dataset integration is common practice to overcome limitations in statistically underpowered omics datasets. Here the authors present “HarmonizR”, a tool for missing data tolerant experimental variance reduction in large, integrated but independently generated datasets without data imputation, adjustable for individual dataset modalities, correction algorithm, and user preferences.
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34
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Nan Y, Ser JD, Walsh S, Schönlieb C, Roberts M, Selby I, Howard K, Owen J, Neville J, Guiot J, Ernst B, Pastor A, Alberich-Bayarri A, Menzel MI, Walsh S, Vos W, Flerin N, Charbonnier JP, van Rikxoort E, Chatterjee A, Woodruff H, Lambin P, Cerdá-Alberich L, Martí-Bonmatí L, Herrera F, Yang G. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2022; 82:99-122. [PMID: 35664012 PMCID: PMC8878813 DOI: 10.1016/j.inffus.2022.01.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/22/2021] [Accepted: 01/07/2022] [Indexed: 05/13/2023]
Abstract
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
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Affiliation(s)
- Yang Nan
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Javier Del Ser
- Department of Communications Engineering, University of the Basque Country UPV/EHU, Bilbao 48013, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), Derio 48160, Spain
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Carola Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
- Oncology R&D, AstraZeneca, Cambridge, Northern Ireland UK
| | - Ian Selby
- Department of Radiology, University of Cambridge, Cambridge, Northern Ireland UK
| | - Kit Howard
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - John Owen
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Jon Neville
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Julien Guiot
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | - Benoit Ernst
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | | | | | - Marion I. Menzel
- Technische Hochschule Ingolstadt, Ingolstadt, Germany
- GE Healthcare GmbH, Munich, Germany
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Nina Flerin
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Avishek Chatterjee
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Henry Woodruff
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Leonor Cerdá-Alberich
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Luis Martí-Bonmatí
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Francisco Herrera
- Department of Computer Sciences and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada, Granada, Spain
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, Northern Ireland UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, Northern Ireland UK
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35
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Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer. J Pers Med 2022; 12:jpm12040570. [PMID: 35455687 PMCID: PMC9028435 DOI: 10.3390/jpm12040570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 11/17/2022] Open
Abstract
Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.
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36
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Large-Scale Meta-Longitudinal Microbiome Data with a Known Batch Factor. Genes (Basel) 2022; 13:genes13030392. [PMID: 35327945 PMCID: PMC8953633 DOI: 10.3390/genes13030392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/05/2022] [Accepted: 02/18/2022] [Indexed: 12/04/2022] Open
Abstract
Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has been thoroughly investigated regarding the impact of the lurking variable of various batch sources, such as different days or different laboratories, in more complicated time series experimental designs, for instance, repeatedly measured longitudinal data and metadata. We highlight that the influence of batch factors is significant on subsequent downstream analyses, including longitudinal differential abundance tests, by performing a case study of microbiome time course data with two treatment groups and a simulation study of mimic microbiome longitudinal counts.
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37
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Tong J, Meng L, Bei C, Liu Q, Wang M, Yang T, Takiff HE, Zhang S, Gao Q, Wang C, Yan B. Modern Beijing sublineage of Mycobacterium tuberculosis shift macrophage into a hyperinflammatory status. Emerg Microbes Infect 2022; 11:715-724. [PMID: 35125072 PMCID: PMC8890550 DOI: 10.1080/22221751.2022.2037395] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The high prevalence of the modern Beijing sublineage of Mycobacterium tuberculosis may be related to increased virulence, although the responsible mechanisms remain poorly understood. We previously described enhanced triacylglycerol accumulation in modern Beijing strains. Here we show that modern Beijing strains grow faster in vitro and trigger a vigorous immune response and pronounced macrophage infiltration. Transcriptomic analysis of bone marrow derived macrophages infected with modern Beijing lineage strains revealed a significant enrichment of infection, cholesterol homeostasis and amino acid metabolic pathways. The upregulation of proinflammatory / bactericidal cytokines was confirmed by RT–PCR analysis, which is also in consistent with the reduced bacterial burden in modern strains infected macrophages. These results suggest that modern Beijing strains elicit a hyperinflammatory response which might indicate a stronger virulence and contribute to their extensive global prevalence.
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Affiliation(s)
- Jingfeng Tong
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Shanghai Medical College and Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Lu Meng
- The Center for Microbes, Development and Health, Key Laboratory of Molecular Virology & Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences/University of Chinese Academy of Sciences, Shanghai, China
| | - Cheng Bei
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Shanghai Medical College and Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Qingyun Liu
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Shanghai Medical College and Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Min Wang
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - TingTing Yang
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Shanghai Medical College and Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Howard E Takiff
- Department of Tuberculosis Control and Prevention, Shenzhen Nanshan Centre for Chronic Disease Control, Shenzhen, China.,Instituto Venezolano de Investigaciones Cientificas, Caracas, Venezuela
| | - Shuye Zhang
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Qian Gao
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Shanghai Medical College and Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Chuan Wang
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, Shanghai Medical College and Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Bo Yan
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
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38
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Mosquera Orgueira A, Cid López M, Peleteiro Raíndo A, Abuín Blanco A, Díaz Arias JÁ, González Pérez MS, Antelo Rodríguez B, Bao Pérez L, Ferreiro Ferro R, Aliste Santos C, Pérez Encinas MM, Fraga Rodríguez MF, Cerchione C, Mozas P, Bello López JL. Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models. Front Oncol 2022; 11:705010. [PMID: 35083135 PMCID: PMC8784530 DOI: 10.3389/fonc.2021.705010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 12/16/2021] [Indexed: 11/13/2022] Open
Abstract
Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.
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Affiliation(s)
- Adrián Mosquera Orgueira
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain.,University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Miguel Cid López
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain.,University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Andrés Peleteiro Raíndo
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain.,University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Aitor Abuín Blanco
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain
| | - Jose Ángel Díaz Arias
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain
| | - Marta Sonia González Pérez
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain
| | - Beatriz Antelo Rodríguez
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain.,University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Laura Bao Pérez
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain.,University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Roi Ferreiro Ferro
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain
| | - Carlos Aliste Santos
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain
| | - Manuel Mateo Pérez Encinas
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain.,University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Máximo Francisco Fraga Rodríguez
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain.,University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Claudio Cerchione
- Istituto Tumori della Romagna Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori" - Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Meldola, Italy
| | - Pablo Mozas
- Hospital Clinic de Barcelona, Barcelona, Spain
| | - José Luis Bello López
- University Hospital of Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain.,University of Santiago de Compostela, Santiago de Compostela, Spain
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39
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Xia Q, Thompson JA, Koestler DC. pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples. Stat Appl Genet Mol Biol 2022; 21:sagmb-2022-0003. [PMID: 36215429 PMCID: PMC9550194 DOI: 10.1515/sagmb-2022-0003] [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: 01/19/2022] [Accepted: 09/16/2022] [Indexed: 01/24/2023]
Abstract
Batch effect Reduction of mIcroarray data with Dependent samples usinG Empirical Bayes (BRIDGE) is a recently developed statistical method to address the issue of batch effect correction in batch-confounded microarray studies with dependent samples. The key component of the BRIDGE methodology is the use of samples run as technical replicates in two or more batches, "bridging samples", to inform batch effect correction/attenuation. While previously published results indicate a relationship between the number of bridging samples, M, and the statistical power of downstream statistical testing on the batch-corrected data, there is of yet no formal statistical framework or user-friendly software, for estimating M to achieve a specific statistical power for hypothesis tests conducted on the batch-corrected data. To fill this gap, we developed pwrBRIDGE, a simulation-based approach to estimate the bridging sample size, M, in batch-confounded longitudinal microarray studies. To illustrate the use of pwrBRIDGE, we consider a hypothetical, longitudinal batch-confounded study whose goal is to identify Alzheimer's disease (AD) progression-associated genes from amnestic mild cognitive impairment (aMCI) to AD in human blood after a 5-year follow-up. pwrBRIDGE helps researchers design and plan batch-confounded microarray studies with dependent samples to avoid over- or under-powered studies.
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Affiliation(s)
- Qing Xia
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, USA
| | - Jeffrey A. Thompson
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, USA
| | - Devin C. Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, USA
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40
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Xia Q, Thompson JA, Koestler DC. Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE). Stat Appl Genet Mol Biol 2021; 20:101-119. [PMID: 34905304 PMCID: PMC9617207 DOI: 10.1515/sagmb-2021-0020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 10/29/2021] [Indexed: 11/15/2022]
Abstract
Batch-effects present challenges in the analysis of high-throughput molecular data and are particularly problematic in longitudinal studies when interest lies in identifying genes/features whose expression changes over time, but time is confounded with batch. While many methods to correct for batch-effects exist, most assume independence across samples; an assumption that is unlikely to hold in longitudinal microarray studies. We propose Batch effect Reduction of mIcroarray data with Dependent samples usinGEmpirical Bayes (BRIDGE), a three-step parametric empirical Bayes approach that leverages technical replicate samples profiled at multiple timepoints/batches, so-called "bridge samples", to inform batch-effect reduction/attenuation in longitudinal microarray studies. Extensive simulation studies and an analysis of a real biological data set were conducted to benchmark the performance of BRIDGE against both ComBat and longitudinalComBat. Our results demonstrate that while all methods perform well in facilitating accurate estimates of time effects, BRIDGE outperforms both ComBat and longitudinal ComBat in the removal of batch-effects in data sets with bridging samples, and perhaps as a result, was observed to have improved statistical power for detecting genes with a time effect. BRIDGE demonstrated competitive performance in batch effect reduction of confounded longitudinal microarray studies, both in simulated and a real data sets, and may serve as a useful preprocessing method for researchers conducting longitudinal microarray studies that include bridging samples.
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Affiliation(s)
- Qing Xia
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160
| | - Jeffrey A. Thompson
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160
| | - Devin C. Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160
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Kim SK, Jung SM, Park KS, Kim KJ. Integrative analysis of lung molecular signatures reveals key drivers of idiopathic pulmonary fibrosis. BMC Pulm Med 2021; 21:404. [PMID: 34876074 PMCID: PMC8650281 DOI: 10.1186/s12890-021-01749-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 11/16/2021] [Indexed: 11/10/2022] Open
Abstract
Background Idiopathic pulmonary fibrosis (IPF) is a devastating disease with a high clinical burden. The molecular signatures of IPF were analyzed to distinguish molecular subgroups and identify key driver genes and therapeutic targets. Methods Thirteen datasets of lung tissue transcriptomics including 585 IPF patients and 362 normal controls were obtained from the databases and subjected to filtration of differentially expressed genes (DEGs). A functional enrichment analysis, agglomerative hierarchical clustering, network-based key driver analysis, and diffusion scoring were performed, and the association of enriched pathways and clinical parameters was evaluated. Results A total of 2,967 upregulated DEGs was filtered during the comparison of gene expression profiles of lung tissues between IPF patients and healthy controls. The core molecular network of IPF featured p53 signaling pathway and cellular senescence. IPF patients were classified into two molecular subgroups (C1, C2) via unsupervised clustering. C1 was more enriched in the p53 signaling pathway and ciliated cells and presented a worse prognostic score, while C2 was more enriched for cellular senescence, profibrosing pathways, and alveolar epithelial cells. The p53 signaling pathway was closely correlated with a decline in forced vital capacity and carbon monoxide diffusion capacity and with the activation of cellular senescence. CDK1/2, CKDNA1A, CSNK1A1, HDAC1/2, FN1, VCAM1, and ITGA4 were the key regulators as evidence by high diffusion scores in the disease module. Currently available and investigational drugs showed differential diffusion scores in terms of their target molecules. Conclusions An integrative molecular analysis of IPF lungs identified two molecular subgroups with distinct pathobiological characteristics and clinical prognostic scores. Inhibition against CDKs or HDACs showed great promise for controlling lung fibrosis. This approach provided molecular insights to support the prediction of clinical outcomes and the selection of therapeutic targets in IPF patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-021-01749-3.
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Affiliation(s)
- Sung Kyoung Kim
- Division of Pulmonology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seung Min Jung
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kyung-Su Park
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ki-Jo Kim
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. .,Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, The Catholic University of Korea, 93 Jungbu-daero, Paldal-gu, Suwon, Gyeonggi-do, 16247, Republic of Korea.
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Campagna MP, Xavier A, Lechner-Scott J, Maltby V, Scott RJ, Butzkueven H, Jokubaitis VG, Lea RA. Epigenome-wide association studies: current knowledge, strategies and recommendations. Clin Epigenetics 2021; 13:214. [PMID: 34863305 PMCID: PMC8645110 DOI: 10.1186/s13148-021-01200-8] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 11/19/2021] [Indexed: 02/06/2023] Open
Abstract
The aetiology and pathophysiology of complex diseases are driven by the interaction between genetic and environmental factors. The variability in risk and outcomes in these diseases are incompletely explained by genetics or environmental risk factors individually. Therefore, researchers are now exploring the epigenome, a biological interface at which genetics and the environment can interact. There is a growing body of evidence supporting the role of epigenetic mechanisms in complex disease pathophysiology. Epigenome-wide association studies (EWASes) investigate the association between a phenotype and epigenetic variants, most commonly DNA methylation. The decreasing cost of measuring epigenome-wide methylation and the increasing accessibility of bioinformatic pipelines have contributed to the rise in EWASes published in recent years. Here, we review the current literature on these EWASes and provide further recommendations and strategies for successfully conducting them. We have constrained our review to studies using methylation data as this is the most studied epigenetic mechanism; microarray-based data as whole-genome bisulphite sequencing remains prohibitively expensive for most laboratories; and blood-based studies due to the non-invasiveness of peripheral blood collection and availability of archived DNA, as well as the accessibility of publicly available blood-cell-based methylation data. Further, we address multiple novel areas of EWAS analysis that have not been covered in previous reviews: (1) longitudinal study designs, (2) the chip analysis methylation pipeline (ChAMP), (3) differentially methylated region (DMR) identification paradigms, (4) methylation quantitative trait loci (methQTL) analysis, (5) methylation age analysis and (6) identifying cell-specific differential methylation from mixed cell data using statistical deconvolution.
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Affiliation(s)
- Maria Pia Campagna
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Alexandre Xavier
- Centre for Information Based Medicine, Hunter Medical Research Institute, Newcastle, Australia
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, Australia
| | - Jeannette Lechner-Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, Australia
- Department of Neurology, Division of Medicine, John Hunter Hospital, Newcastle, Australia
| | - Vicky Maltby
- Centre for Information Based Medicine, Hunter Medical Research Institute, Newcastle, Australia
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, Australia
| | - Rodney J Scott
- Centre for Information Based Medicine, Hunter Medical Research Institute, Newcastle, Australia
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, Australia
- Division of Molecular Medicine, New South Wales Health Pathology North, Newcastle, Australia
| | - Helmut Butzkueven
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
- Department of Neurology, Alfred Health, Melbourne, Australia
| | - Vilija G Jokubaitis
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
- Department of Neurology, Alfred Health, Melbourne, Australia
| | - Rodney A Lea
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, Australia.
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia.
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Martins J, Czamara D, Sauer S, Rex-Haffner M, Dittrich K, Dörr P, de Punder K, Overfeld J, Knop A, Dammering F, Entringer S, Winter SM, Buss C, Heim C, Binder EB. Childhood adversity correlates with stable changes in DNA methylation trajectories in children and converges with epigenetic signatures of prenatal stress. Neurobiol Stress 2021; 15:100336. [PMID: 34095363 PMCID: PMC8163992 DOI: 10.1016/j.ynstr.2021.100336] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 04/01/2021] [Accepted: 05/01/2021] [Indexed: 12/12/2022] Open
Abstract
Childhood maltreatment (CM) is an established major risk factor for a number of negative health outcomes later in life. While epigenetic mechanisms, such as DNA methylation (DNAm), have been proposed as a means of embedding this environmental risk factor, little is known about its timing and trajectory, especially in very young children. It is also not clear whether additional environmental adversities, often experienced by these children, converge on similar DNAm changes. Here, we calculated a cumulative adversity score, which additionally to CM includes socioeconomic status (SES), other life events, parental psychopathology and epigenetic biomarkers of prenatal smoking and alcohol consumption. We investigated the effects of CM alone as well as the adversity score on longitudinal DNAm trajectories in the Berlin Longitudinal Child Study. This is a cohort of 173 children aged 3-5 years at baseline of whom 86 were exposed to CM. These children were followed-up for 2 years with extensive psychometric and biological assessments as well as saliva collection at 5 time points providing genome-wide DNAm levels. Overall, only a few DNAm patterns were stable over this timeframe, but less than 10 DNAm regions showed significant changes. At baseline, neither CM nor the adversity score associated with DNAm changes. However, in 6 differentially methylated regions (DMRs), CM and the adversity score significantly moderated DNAm trajectories over time. A number of these DMRs have previously been associated with adverse prenatal exposures. In our study, children exposed to CM also presented with epigenetic signatures indicative of increased prenatal exposure to tobacco and alcohol, as compared to non-CM exposed children. These epigenetic signatures of prenatal exposure strongly correlate with DNAm regions associated with CM and the adversity score. Finally, weighted correlation network analysis revealed a module of CpGs exclusively associated with CM. While our study identifies DNAm loci specifically associated with CM, especially within long non-coding RNAs, the majority of associations were found with the adversity score with convergent association with indicators of adverse prenatal exposures. This study highlights the importance of mapping not only of the epigenome but also the exposome and extending the observational timeframe to well before birth.
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Affiliation(s)
- Jade Martins
- Dept. of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Darina Czamara
- Dept. of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Susann Sauer
- Dept. of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Monika Rex-Haffner
- Dept. of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Katja Dittrich
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Campus Virchow, Department of Child and Adolescent Psychiatry, Augustenburger Platz 1, D-13353 Berlin, Germany
| | - Peggy Dörr
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Campus Virchow, Department of Child and Adolescent Psychiatry, Augustenburger Platz 1, D-13353 Berlin, Germany
| | - Karin de Punder
- Natura Foundation, Research and Development, Numansdrop, 3281, NC, Netherlands
| | - Judith Overfeld
- Charité − Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Institute of Medical Psychology, Campus Charité Mitte, Luisenstraße 57, 10117 Berlin, Germany
| | - Andrea Knop
- Charité − Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Institute of Medical Psychology, Campus Charité Mitte, Luisenstraße 57, 10117 Berlin, Germany
| | - Felix Dammering
- Charité − Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Institute of Medical Psychology, Campus Charité Mitte, Luisenstraße 57, 10117 Berlin, Germany
| | - Sonja Entringer
- Charité − Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Institute of Medical Psychology, Campus Charité Mitte, Luisenstraße 57, 10117 Berlin, Germany
- University of California, Irvine, Development, Health, and Disease Research Program, Orange, CA, USA
| | - Sibylle M. Winter
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Campus Virchow, Department of Child and Adolescent Psychiatry, Augustenburger Platz 1, D-13353 Berlin, Germany
| | - Claudia Buss
- Charité − Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Institute of Medical Psychology, Campus Charité Mitte, Luisenstraße 57, 10117 Berlin, Germany
- University of California, Irvine, Development, Health, and Disease Research Program, Orange, CA, USA
| | - Christine Heim
- Charité − Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Institute of Medical Psychology, Campus Charité Mitte, Luisenstraße 57, 10117 Berlin, Germany
- Dept. of Biobehavioral Health, College of Health & Human Development, The Pennsylvania State University, University Park, PA, USA
| | - Elisabeth B. Binder
- Dept. of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, 30329, USA
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Dammering F, Martins J, Dittrich K, Czamara D, Rex-Haffner M, Overfeld J, de Punder K, Buss C, Entringer S, Winter SM, Binder EB, Heim C. The pediatric buccal epigenetic clock identifies significant ageing acceleration in children with internalizing disorder and maltreatment exposure. Neurobiol Stress 2021; 15:100394. [PMID: 34621920 PMCID: PMC8482287 DOI: 10.1016/j.ynstr.2021.100394] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/26/2021] [Accepted: 09/09/2021] [Indexed: 01/15/2023] Open
Abstract
Background Studies reporting accelerated ageing in children with affective disorders or maltreatment exposure have relied on algorithms for estimating epigenetic age derived from adult samples. These algorithms have limited validity for epigenetic age estimation during early development. We here use a pediatric buccal epigenetic (PedBE) clock to predict DNA methylation-based ageing deviation in children with and without internalizing disorder and assess the moderating effect of maltreatment exposure. We further conduct a gene set enrichment analysis to assess the contribution of glucocorticoid signaling to PedBE clock-based results. Method DNA was isolated from saliva of 158 children [73 girls, 85 boys; mean age (SD) = 4.25 (0.8) years] including children with internalizing disorder and maltreatment exposure. Epigenetic age was estimated based on DNA methylation across 94 CpGs of the PedBE clock. Residuals of epigenetic age regressed against chronological age were contrasted between children with and without internalizing disorder. Maltreatment was coded in 3 severity levels and entered in a moderation model. Genome-wide dexamethasone-responsive CpGs were derived from an independent sample and enrichment of these CpGs within the PedBE clock was identified. Results Children with internalizing disorder exhibited significant acceleration of epigenetic ageing as compared to children without internalizing disorder (F1,147 = 6.67, p = .011). This association was significantly moderated by maltreatment severity (b = 0.49, 95% CI [0.073, 0.909], t = 2.322, p = .022). Children with internalizing disorder who had experienced maltreatment exhibited ageing acceleration relative to children with no internalizing disorder (1–2 categories: b = 0.50, 95% CI [0.170, 0.821], t = 3.008, p = .003; 3 or more categories: b = 0.99, 95% CI [0.380, 1.593], t = 3.215, p = .002). Children with internalizing disorder who were not exposed to maltreatment did not show epigenetic ageing acceleration. There was significant enrichment of dexamethasone-responsive CpGs within the PedBE clock (OR = 4.36, p = 1.65*10–6). Among the 94 CpGs of the PedBE clock, 18 (19%) were responsive to dexamethasone. Conclusion Using the novel PedBE clock, we show that internalizing disorder is associated with accelerated epigenetic ageing in early childhood. This association is moderated by maltreatment severity and may, in part, be driven by glucocorticoids. Identifying developmental drivers of accelerated epigenetic ageing after maltreatment will be critical to devise early targeted interventions.
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Affiliation(s)
- Felix Dammering
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Psychology, Berlin, Germany
| | - Jade Martins
- Dept. of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Katja Dittrich
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Dept. of Child & Adolescent Psychiatry, Psychotherapy, and Psychosomatics, Berlin, Germany
| | - Darina Czamara
- Dept. of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Monika Rex-Haffner
- Dept. of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Judith Overfeld
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Psychology, Berlin, Germany
| | - Karin de Punder
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Psychology, Berlin, Germany
| | - Claudia Buss
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Psychology, Berlin, Germany.,University of California, Irvine, Development, Health, and Disease Research Program, Orange, CA, USA
| | - Sonja Entringer
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Psychology, Berlin, Germany.,University of California, Irvine, Development, Health, and Disease Research Program, Orange, CA, USA
| | - Sibylle M Winter
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Dept. of Child & Adolescent Psychiatry, Psychotherapy, and Psychosomatics, Berlin, Germany
| | - Elisabeth B Binder
- Dept. of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Christine Heim
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Psychology, Berlin, Germany.,Dept. of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
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45
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Fiorito G, Caini S, Palli D, Bendinelli B, Saieva C, Ermini I, Valentini V, Assedi M, Rizzolo P, Ambrogetti D, Ottini L, Masala G. DNA methylation-based biomarkers of aging were slowed down in a two-year diet and physical activity intervention trial: the DAMA study. Aging Cell 2021; 20:e13439. [PMID: 34535961 PMCID: PMC8520727 DOI: 10.1111/acel.13439] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/09/2021] [Accepted: 07/08/2021] [Indexed: 12/13/2022] Open
Abstract
Several biomarkers of healthy aging have been proposed in recent years, including the epigenetic clocks, based on DNA methylation (DNAm) measures, which are getting increasingly accurate in predicting the individual biological age. The recently developed "next-generation clock" DNAmGrimAge outperforms "first-generation clocks" in predicting longevity and the onset of many age-related pathological conditions and diseases. Additionally, the total number of stochastic epigenetic mutations (SEMs), also known as the epigenetic mutation load (EML), has been proposed as a complementary DNAm-based biomarker of healthy aging. A fundamental biological property of epigenetic, and in particular DNAm modifications, is the potential reversibility of the effect, raising questions about the possible slowdown of epigenetic aging by modifying one's lifestyle. Here, we investigated whether improved dietary habits and increased physical activity have favorable effects on aging biomarkers in healthy postmenopausal women. The study sample consists of 219 women from the "Diet, Physical Activity, and Mammography" (DAMA) study: a 24-month randomized factorial intervention trial with DNAm measured twice, at baseline and the end of the trial. Women who participated in the dietary intervention had a significant slowing of the DNAmGrimAge clock, whereas increasing physical activity led to a significant reduction of SEMs in crucial cancer-related pathways. Our study provides strong evidence of a causal association between lifestyle modification and slowing down of DNAm aging biomarkers. This randomized trial elucidates the causal relationship between lifestyle and healthy aging-related epigenetic mechanisms.
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Affiliation(s)
- Giovanni Fiorito
- Laboratory of Biostatistics Department of Biomedical Sciences University of Sassari Sassari Italy
- MRC‐PHE Centre for Environment 43 and Health Imperial College London London UK
| | - Saverio Caini
- Institute for Cancer Research, Prevention and Clinical Network ‐ ISPRO Florence Italy
| | - Domenico Palli
- Institute for Cancer Research, Prevention and Clinical Network ‐ ISPRO Florence Italy
| | - Benedetta Bendinelli
- Institute for Cancer Research, Prevention and Clinical Network ‐ ISPRO Florence Italy
| | - Calogero Saieva
- Institute for Cancer Research, Prevention and Clinical Network ‐ ISPRO Florence Italy
| | - Ilaria Ermini
- Institute for Cancer Research, Prevention and Clinical Network ‐ ISPRO Florence Italy
| | | | - Melania Assedi
- Institute for Cancer Research, Prevention and Clinical Network ‐ ISPRO Florence Italy
| | - Piera Rizzolo
- Department of Molecular Medicine Sapienza University of Rome Rome Italy
| | - Daniela Ambrogetti
- Institute for Cancer Research, Prevention and Clinical Network ‐ ISPRO Florence Italy
| | - Laura Ottini
- Department of Molecular Medicine Sapienza University of Rome Rome Italy
| | - Giovanna Masala
- Institute for Cancer Research, Prevention and Clinical Network ‐ ISPRO Florence Italy
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46
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Zeng Z, Wong CJ, Yang L, Ouardaoui N, Li D, Zhang W, Gu S, Zhang Y, Liu Y, Wang X, Fu J, Zhou L, Zhang B, Kim S, Yates KB, Brown M, Freeman GJ, Uppaluri R, Manguso R, Liu XS. TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response. Nucleic Acids Res 2021; 50:D1391-D1397. [PMID: 34534350 PMCID: PMC8728303 DOI: 10.1093/nar/gkab804] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/28/2021] [Accepted: 09/16/2021] [Indexed: 01/11/2023] Open
Abstract
Syngeneic mouse models are tumors derived from murine cancer cells engrafted on genetically identical mouse strains. They are widely used tools for studying tumor immunity and immunotherapy response in the context of a fully functional murine immune system. Large volumes of syngeneic mouse tumor expression profiles under different immunotherapy treatments have been generated, although a lack of systematic collection and analysis makes data reuse challenging. We present Tumor Immune Syngeneic MOuse (TISMO), a database with an extensive collection of syngeneic mouse model profiles with interactive visualization features. TISMO contains 605 in vitro RNA-seq samples from 49 syngeneic cancer cell lines across 23 cancer types, of which 195 underwent cytokine treatment. TISMO also includes 1518 in vivo RNA-seq samples from 68 syngeneic mouse tumor models across 19 cancer types, of which 832 were from immune checkpoint blockade (ICB) studies. We manually annotated the sample metadata, such as cell line, mouse strain, transplantation site, treatment, and response status, and uniformly processed and quality-controlled the RNA-seq data. Besides data download, TISMO provides interactive web interfaces to investigate whether specific gene expression, pathway enrichment, or immune infiltration level is associated with differential immunotherapy response. TISMO is available at http://tismo.cistrome.org.
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Affiliation(s)
- Zexian Zeng
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Cheryl J Wong
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115 USA
| | - Lin Yang
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Nofal Ouardaoui
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Dian Li
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Wubing Zhang
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.,School of Life Science and Technology, Tongji University, Shanghai, 200060, China
| | - Shengqing Gu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Yi Zhang
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Yang Liu
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Xiaoqing Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Jingxin Fu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02129, USA
| | - Liye Zhou
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Boning Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Sarah Kim
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02129, USA
| | - Kathleen B Yates
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02129, USA.,Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA
| | - Myles Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Gordon J Freeman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Ravindra Uppaluri
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Department of Surgery, Brigham and Women's Hospital, Boston, MA 02215, USA
| | - Robert Manguso
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02129, USA.,Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA
| | - X Shirley Liu
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
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Malila Y, Uengwetwanit T, Thanatsang KV, Arayamethakorn S, Srimarut Y, Petracci M, Soglia F, Rungrassamee W, Visessanguan W. Insights Into Transcriptome Profiles Associated With Wooden Breast Myopathy in Broilers Slaughtered at the Age of 6 or 7 Weeks. Front Physiol 2021; 12:691194. [PMID: 34262480 PMCID: PMC8273767 DOI: 10.3389/fphys.2021.691194] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/07/2021] [Indexed: 11/18/2022] Open
Abstract
Transcriptomes associated with wooden breast (WB) were characterized in broilers at two different market ages. Breasts (Pectoralis major) were collected, 20-min postmortem, from male Ross 308 broilers slaughtered at 6 and 7 weeks of age. The breasts were classified as "non-WB" or "WB" based on palpation hardness scoring (non-WB = no abnormal hardness, WB = consistently hardened). Total RNA was isolated from 16 samples (n = 3 for 6 week non-WB, n = 3 for 6 week WB; n = 5 for 7 week non-WB, n = 5 for 7 week WB). Transcriptome was profiled using a chicken gene expression microarray with one-color hybridization technique, and compared between non-WB and WB samples of the same age. Among 6 week broilers, 910 transcripts were differentially expressed (DE) (false discovery rate, FDR < 0.05). Pathway analysis underlined metabolisms of glucose and lipids along with gap junctions, tight junction, and focal adhesion (FA) signaling as the top enriched pathways. For the 7 week broilers, 1,195 transcripts were identified (FDR < 0.05) with regulation of actin cytoskeleton, mitogen-activated protein kinase (MAPK) signaling, protein processing in endoplasmic reticulum and FA signaling highlighted as the enriched affected pathways. Absolute transcript levels of eight genes (actinin-1 - ACTN1, integrin-linked kinase - ILK, integrin subunit alpha 8 - ITGA8, integrin subunit beta 5 - ITGB5, protein tyrosine kinase 2 - PTK2, paxillin - PXN, talin 1 - TLN1, and vinculin - VCL) of FA signaling pathway were further elucidated using a droplet digital polymerase chain reaction. The results indicated that, in 6 week broilers, ITGA8 abundance in WB was greater than that of non-WB samples (p < 0.05). Concerning 7 week broilers, greater absolute levels of ACTN1, ILK, ITGA8, and TLN1, accompanied with a reduced ITGB5 were found in WB compared with non-WB (p < 0.05). Transcriptional modification of FA signaling underlined the potential of disrupted cell-cell communication that may incite aberrant molecular events in association with development of WB myopathy.
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Affiliation(s)
- Yuwares Malila
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Pathum Thani, Thailand
| | - Tanaporn Uengwetwanit
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Pathum Thani, Thailand
| | - Krittaporn V. Thanatsang
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Pathum Thani, Thailand
| | - Sopacha Arayamethakorn
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Pathum Thani, Thailand
| | - Yanee Srimarut
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Pathum Thani, Thailand
| | - Massimiliano Petracci
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Cesena, Italy
| | - Francesca Soglia
- Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna, Cesena, Italy
| | - Wanilada Rungrassamee
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Pathum Thani, Thailand
| | - Wonnop Visessanguan
- National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Pathum Thani, Thailand
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48
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Yan Q, Zheng W, Wang B, Ye B, Luo H, Yang X, Zhang P, Wang X. A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma. BioData Min 2021; 14:29. [PMID: 33962640 PMCID: PMC8106157 DOI: 10.1186/s13040-021-00261-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 04/20/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. METHODS Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. RESULTS A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. CONCLUSION Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.
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Affiliation(s)
- Qian Yan
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wenjiang Zheng
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Boqing Wang
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Baoqian Ye
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huiyan Luo
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xinqian Yang
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ping Zhang
- The First Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiongwen Wang
- Department of Oncology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
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49
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A Meta-Analysis of the Effects of High-LET Ionizing Radiations in Human Gene Expression. Life (Basel) 2021; 11:life11020115. [PMID: 33546472 PMCID: PMC7913660 DOI: 10.3390/life11020115] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/31/2021] [Accepted: 01/31/2021] [Indexed: 12/19/2022] Open
Abstract
The use of high linear energy transfer (LET) ionizing radiation (IR) is progressively being incorporated in radiation therapy due to its precise dose localization and high relative biological effectiveness. At the same time, these benefits of particle radiation become a high risk for astronauts in the case of inevitable cosmic radiation exposure. Nonetheless, DNA Damage Response (DDR) activated via complex DNA damage in healthy tissue, occurring from such types of radiation, may be instrumental in the induction of various chronic and late effects. An approach to elucidating the possible underlying mechanisms is studying alterations in gene expression. To this end, we identified differentially expressed genes (DEGs) in high Z and high energy (HZE) particle-, γ-ray- and X-ray-exposed healthy human tissues, utilizing microarray data available in public repositories. Differential gene expression analysis (DGEA) was conducted using the R programming language. Consequently, four separate meta-analyses were conducted, after DEG lists were grouped depending on radiation type, radiation dose and time of collection post-irradiation. To highlight the biological background of each meta-analysis group, functional enrichment analysis and biological network construction were conducted. For HZE particle exposure at 8–24 h post-irradiation, the most interesting finding is the variety of DNA repair mechanisms that were downregulated, a fact that is probably correlated with complex DNA damage formation. Simultaneously, after X-ray exposure during the same hours after irradiation, DNA repair mechanisms continue to take place. Finally, in a further comparison of low- and high-LET radiation effects, the most prominent result is that autophagy mechanisms seem to persist and that adaptive immune induction seems to be present. Such bioinformatics approaches may aid in obtaining an overview of the cellular response to high-LET particles. Understanding these response mechanisms can consequently aid in the development of countermeasures for future space missions and ameliorate heavy ion treatments.
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50
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Salas-Pérez F, Cuevas-Sierra A, Cuervo M, Goni L, Milagro FI, Martínez JA, Riezu-Boj JI. Differentially methylated regions (DMRs) in PON3 gene between responders and non-responders to a weight loss dietary intervention: a new tool for precision management of obesity. Epigenetics 2021; 17:81-92. [PMID: 33427034 DOI: 10.1080/15592294.2021.1873629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Differentially methylated regions (DMR) are genomic regions with different methylation status. The aim of this research was to identify DMRs in subjects with obesity that predict the response to a weight-loss dietary intervention and its association with metabolic variables. Based on the change in body mass index (BMI), 201 subjects with overweight and obesity were categorized in tertiles according to their response to a hypocaloric diet: Responders (R; n = 64) and Non-Responders (NR; n = 63). The R group lost 4.55 ± 0.91 BMI units (kg/m2) and the NR group lost 1.95 ± 0.73 kg/m2 (p < 0.001). DNA methylation was analysed in buffy coat through a methylation array at baseline. DMRs were analysed using a function of ChAMP (Chip Analysis Methylation Pipeline) in R software. Baseline DNA methylation analysis between R and NR exhibited a DMR located at paraoxonase 3 gene (PON3) consisting of 13 CpG sites, eleven of them significantly hypermethylated in R. To analyse the implication of these 11 CpGs on weight loss, a z-score was performed as a measure of DMR methylation. This analysis showed a correlation between PON3 DNA methylation and BMI loss. This z-score negatively correlated with PON3 protein serum levels. Total paraoxonase activity in serum was not different between groups, but PON enzymatic activity positively correlated with oxidized LDL levels. The present study identified a DMR within PON3 gene that is related to PON3 protein levels in serum, and that could be used as a potential biomarker to predict the response to weight-loss dietary interventions.
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Affiliation(s)
- Francisca Salas-Pérez
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain
| | - Amanda Cuevas-Sierra
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain
| | - Marta Cuervo
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Leticia Goni
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (Ciberobn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
| | - Fermín I Milagro
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (Ciberobn), Instituto de Salud Carlos III, Madrid, Spain
| | - J Alfredo Martínez
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (Ciberobn), Instituto de Salud Carlos III, Madrid, Spain
| | - José Ignacio Riezu-Boj
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (Ciberobn), Instituto de Salud Carlos III, Madrid, Spain
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