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Lundin JI, Peters U, Hu Y, Ammous F, Avery CL, Benjamin EJ, Bis JC, Brody JA, Carlson C, Cushman M, Gignoux C, Guo X, Haessler J, Haiman C, Joehanes R, Kasela S, Kenny E, Lapalainien T, Levy D, Liu C, Liu Y, Loos RJ, Lu A, Matise T, North KE, Park SL, Ratliff SM, Reiner A, Rich SS, Rotter JI, Smith JA, Sotoodehnia N, Tracy R, Van den Berg D, Xu H, Ye T, Zhao W, Raffield LM, Kooperberg C. Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations. Epigenetics 2024; 19:2333668. [PMID: 38571307 PMCID: PMC10996836 DOI: 10.1080/15592294.2024.2333668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/17/2024] [Indexed: 04/05/2024] Open
Abstract
Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p < 1.2E-7). The downstream pathways affected by DNA methylation included the identification of IFI16 and IRF7 CpG-gene transcript pairs which contributed to the innate immune response gene enrichment pathway along with NLRC5, NOD2, and AIM2. Gene enrichment analysis also identified the nuclear factor-kappaB transcription pathway. Using two-sample Mendelian randomization (MR) we inferred methylation at three CpG sites as causal for CRP levels using both White and Black or African American MR instrument variables. Overall, we identified novel CpG sites and gene transcripts that could be valuable in understanding the specific cellular processes and pathogenic mechanisms involved in inflammation.
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Affiliation(s)
- Jessica I. Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yao Hu
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Farah Ammous
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Christy L. Avery
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Emelia J. Benjamin
- Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston University School of Public Health, Boston, MA, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Chris Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Mary Cushman
- Department of Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT, USA
| | - Chris Gignoux
- Interdisciplinary Quantitative Biology, University of Colorado, Boulder, CO, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jeff Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Chris Haiman
- Department of Environmental Medicine and Public Health, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Roby Joehanes
- Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, Bethesda, MD, USA
| | | | - Eimear Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, Bethesda, MD, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Yongmei Liu
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Ruth J.F. Loos
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ake Lu
- Department of Human Genetics, University of California LA, Los Angeles, CA, USA
| | - Tara Matise
- Department of Genetics, Rutgers University, New Brunswick, NJ, USA
| | - Kari E. North
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Sungshim L. Park
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Scott M. Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Alex Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, and Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Harborview Medical Center, Seattle, WA, USA
| | - Russell Tracy
- Department of Biochemistry, University of Vermont, Burlington, VT, USA
| | - David Van den Berg
- Department of Environmental Medicine and Public Health, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Huichun Xu
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ting Ye
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Wei Zhao
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - On Behalf of the PAGE Study
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston University School of Public Health, Boston, MA, USA
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT, USA
- Interdisciplinary Quantitative Biology, University of Colorado, Boulder, CO, USA
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Environmental Medicine and Public Health, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, Bethesda, MD, USA
- New York Genome Center, New York, NY
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
- Department of Human Genetics, University of California LA, Los Angeles, CA, USA
- Department of Genetics, Rutgers University, New Brunswick, NJ, USA
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Epidemiology, School of Public Health, and Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- Cardiovascular Health Research Unit, Harborview Medical Center, Seattle, WA, USA
- Department of Biochemistry, University of Vermont, Burlington, VT, USA
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
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Peng C, Huang J, Li M, Liu G, Liu L, Lin J, Sun W, Liu H, Huang Y, Chen X. Uncovering periodontitis-associated markers through the aggregation of transcriptomics information from diverse sources. Front Genet 2024; 15:1398582. [PMID: 38919957 PMCID: PMC11196414 DOI: 10.3389/fgene.2024.1398582] [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: 03/19/2024] [Accepted: 05/10/2024] [Indexed: 06/27/2024] Open
Abstract
Introduction Periodontitis, a common chronic inflammatory disease, significantly impacted oral health. To provide novel biological indicators for the diagnosis and treatment of periodontitis, we analyzed public microarray datasets to identify biomarkers associated with periodontitis. Method The Gene Expression Omnibus (GEO) datasets GSE16134 and GSE106090 were downloaded. We performed differential analysis and robust rank aggregation (RRA) to obtain a list of differential genes. To obtain the core modules and core genes related to periodontitis, we evaluated differential genes through enrichment analysis, correlation analysis, protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network analysis. Potential biomarkers for periodontitis were identified through comparative analysis of dual networks (PPI network and ceRNA network). PPI network analysis was performed in STRING. The ceRNA network consisted of RRA differentially expressed messenger RNAs (RRA_DEmRNAs) and RRA differentially expressed long non-coding RNAs (RRA_DElncRNAs), which regulated each other's expression by sharing microRNA (miRNA) target sites. Results RRA_DEmRNAs were significantly enriched in inflammation-related biological processes, osteoblast differentiation, inflammatory response pathways and immunomodulatory pathways. Comparing the core ceRNA module and the core PPI module, C1QA, CENPK, CENPU and BST2 were found to be the common genes of the two core modules, and C1QA was highly correlated with inflammatory functionality. C1QA and BST2 were significantly enriched in immune-regulatory pathways. Meanwhile, LINC01133 played a significant role in regulating the expression of the core genes during the pathogenesis of periodontitis. Conclusion The identified biomarkers C1QA, CENPK, CENPU, BST2 and LINC01133 provided valuable insight into periodontitis pathology.
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Affiliation(s)
- Chujun Peng
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, China
| | - Jinhang Huang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, China
| | - Mingyue Li
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Guanru Liu
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Lingxian Liu
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Jiechun Lin
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Weijun Sun
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Hongtao Liu
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Yonghui Huang
- School of Automation, Guangdong University of Technology, Guangzhou, China
| | - Xin Chen
- School of Automation, Guangdong University of Technology, Guangzhou, China
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Lin W, Yang H, Lin J, Yang X, Liao Z, Zheng Y, Luo P, Liu C. OralExplorer: a web server for exploring the mechanisms of oral inflammatory diseases. J Transl Med 2024; 22:282. [PMID: 38491529 PMCID: PMC10943789 DOI: 10.1186/s12967-024-05019-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/22/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Oral inflammatory diseases are localized infectious diseases primarily caused by oral pathogens with the potential for serious systemic complications. However, publicly available datasets for these diseases are underutilized. To address this issue, a web tool called OralExplorer was developed. This tool integrates the available data and provides comprehensive online bioinformatic analysis. METHODS Human oral inflammatory disease-related datasets were obtained from the GEO database and normalized using a standardized process. Transcriptome data were then subjected to differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis, and visualization. The single-cell sequencing data was visualized as cluster plot, feature plot, and heatmaps. The web platform was primarily built using Shiny. The biomarkers identified in OralExplorer were validated using local clinical samples through qPCR and IHC. RESULTS A total of 35 human oral inflammatory disease-related datasets, covering 6 main disease types and 901 samples, were included in the study to identify potential molecular signatures of the mechanisms of oral diseases. OralExplorer consists of 5 main analysis modules (differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis and single-cell analysis), with multiple visualization options. The platform offers a simple and intuitive interface, high-quality images for visualization, and detailed analysis results tables for easy access by users. Six markers (IL1β, SRGN, CXCR1, FGR, ARHGEF2, and PTAFR) were identified by OralExplorer. qPCR- and IHC-based experimental validation showed significantly higher levels of these genes in the periodontitis group. CONCLUSIONS OralExplorer is a comprehensive analytical platform for oral inflammatory diseases. It allows users to interactively explore the molecular mechanisms underlying the action and regression of these diseases. It also aids dental researchers in unlocking the potential value of transcriptomics data related to oral diseases. OralExplorer can be accessed at https://smuonco.shinyapps.io/OralExplorer/ (Alternate URL: http://robinl-lab.com/OralExplorer ).
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Affiliation(s)
- Weiyin Lin
- Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China
| | - Hong Yang
- The Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jiayu Lin
- Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China
| | - Xia Yang
- Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China
| | - Zhihao Liao
- Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China
| | - Yifan Zheng
- Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China
| | - Peng Luo
- The Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
| | - Chufeng Liu
- Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China.
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Zheng Q, Wang T, Jiang G, Li M, Zhang Z, Chen Y, Tian X. Immunoglobulin superfamily 6 is a molecule involved in the anti-tumor activity of macrophages in lung adenocarcinoma. BMC Cancer 2023; 23:1170. [PMID: 38037023 PMCID: PMC10688083 DOI: 10.1186/s12885-023-11681-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 11/27/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Immunoglobulin superfamily 6 (IGSF6) is a novel member of the immunoglobulin superfamily and has been implicated in various diseases. However, the specific role of IGSF6 in the anti-tumor immunity within lung adenocarcinoma (LUAD) remains unclear. METHODS We analyzed the IGSF6 expression in LUAD using data from TCGA, and we performed qRT-PCR and western blotting to validate these findings using tissue samples obtained from LUAD patients. Images of IHC staining were obtained from HPA. To assess the clinical relevance of IGSF6 expression, we utilized UALCAN and SPSS to analyze its association with major clinical features of LUAD. Additionally, we employed ROC curves and survival analysis to evaluate the potential diagnostic and prognostic value of IGSF6 in LUAD. To gain insights into the functional implications of IGSF6, we performed enrichment analysis using the R software clusterProfiler package. Moreover, we utilized TIMER2.0 and TISIDB to investigate the relationship between IGSF6 and immune infiltrates in LUAD. The proportion of tumor-infiltrating immune cells in LUAD was assessed using FCM, and their correlation with IGSF6 expression in tumor tissues was analyzed. The localization of IGSF6 protein on macrophages was confirmed using the HPA and FCM. To determine the regulatory role of IGSF6 on macrophage activity in LUAD, we employed ELISA, FCM, and tumor-bearing models. RESULTS We discovered that both IGSF6 mRNA and protein levels were significantly decreased in LUAD. Additionally, we observed a negative correlation between IGSF6 expression and TNM stages as well as pathologic stages in LUAD. Notably, IGSF6 exhibited high sensitivity and specificity in diagnosing LUAD, and was positively associated with the survival rate of LUAD patients. Furthermore, IGSF6 expression was closely linked to gene sets involved in immune response. IGSF6 expression showed a positive correlation with immune infiltrates exhibiting anti-tumor activity, particularly M1 macrophages. We confirmed the predominant localization of the IGSF6 protein on the membrane of M1 macrophages. Importantly, the knockdown of IGSF6 resulted in a reduction in the anti-tumor activity of M1 macrophages, thereby promoting tumor progression. CONCLUSION IGSF6 is a molecule that is essential for the anti-tumor activity of macrophages in LUAD.
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Affiliation(s)
- Qisi Zheng
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China
| | - Ting Wang
- Department of Laboratory Medicine, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Gechen Jiang
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China
| | - Miao Li
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China
| | - Zhi Zhang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
| | - Yuxin Chen
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.
| | - Xinyu Tian
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China.
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