1
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Zhang CL, Shen Q, Liu FD, Yang F, Gao MQ, Jiang XC, Li Y, Zhang XY, En GE, Pan X, Pang B. SDC1 and ITGA2 as novel prognostic biomarkers for PDAC related to IPMN. Sci Rep 2023; 13:18727. [PMID: 37907515 PMCID: PMC10618477 DOI: 10.1038/s41598-023-44646-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: 04/25/2023] [Accepted: 10/11/2023] [Indexed: 11/02/2023] Open
Abstract
The existing biomarkers are insufficient for predicting the prognosis of pancreatic ductal adenocarcinoma (PDAC). Intraductal papillary mucinous neoplasm (IPMN) is a precursor to PDAC; therefore, identifying biomarkers from differentially expressed genes (DEGs) of PDAC and IPMN is a new and reliable strategy for predicting the prognosis of PDAC. In this study, four datasets were downloaded from the Gene Expression Omnibus database and standardized using the R package 'limma.' A total of 51 IPMN and 81 PDAC samples were analyzed, and 341 DEGs in PDAC and IPMN were identified; DEGs were involved in the extracellular matrix and tumor microenvironment. An acceptable survival prognosis was demonstrated by SDC1 and ITGA2, which were highly expressed during in vitro PDAC cell proliferation, apoptosis, and migration. SDC1high was enriched in interferon alpha (IFN-α) response and ITGA2high was primarily detected in epithelial-mesenchymal transition (EMT), which was verified using western blotting. We concluded that SDC1 and ITGA2 are potential prognostic biomarkers for PDAC associated with IPMN. Downregulation of SDC1 and ITGA2 expression in PDAC occurs via a mechanism involving possible regulation of IFN-α response, EMT, and immunity, which may act as new targets for PDAC therapy.
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Affiliation(s)
- Chuan-Long Zhang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Qian Shen
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Fu-Dong Liu
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Fan Yang
- Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Meng-Qi Gao
- Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China
| | - Xiao-Chen Jiang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Yi Li
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Xi-Yuan Zhang
- Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Ge-Er En
- Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Xue Pan
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
- Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, 100029, China.
| | - Bo Pang
- International Medical Department of Guang'anmen Hospital China Academy of Chinese Medical Sciences, Beijing, 100053, China.
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2
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Linares-Pineda T, Peña-Montero N, Fragoso-Bargas N, Gutiérrez-Repiso C, Lima-Rubio F, Suarez-Arana M, Sánchez-Pozo A, Tinahones FJ, Molina-Vega M, Picón-César MJ, Sommer C, Morcillo S. Epigenetic marks associated with gestational diabetes mellitus across two time points during pregnancy. Clin Epigenetics 2023; 15:110. [PMID: 37415231 PMCID: PMC10324212 DOI: 10.1186/s13148-023-01523-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/23/2023] [Indexed: 07/08/2023] Open
Abstract
An adverse intrauterine or periconceptional environment, such as hyperglycemia during pregnancy, can affect the DNA methylation pattern both in mothers and their offspring. In this study, we explored the epigenetic profile in maternal peripheral blood samples through pregnancy to find potential epigenetic biomarkers for gestational diabetes mellitus (GDM), as well as candidate genes involved in GDM development. We performed an epigenome-wide association study in maternal peripheral blood samples in 32 pregnant women (16 with GDM and 16 non-GDM) at pregnancy week 24-28 and 36-38. Biochemical, anthropometric, and obstetrical variables were collected from all the participants. The main results were validated in an independent cohort with different ethnic origin (European = 307; South Asians = 165). Two hundred and seventy-two CpGs sites remained significantly different between GDM and non-GDM pregnant women across two time points during pregnancy. The significant CpG sites were related to pathways associated with type I diabetes mellitus, insulin resistance and secretion. Cg01459453 (SELP gene) was the most differentiated in the GDM group versus non-GDM (73.6 vs. 60.9, p = 1.06E-11; FDR = 7.87E-06). Three CpG sites (cg01459453, cg15329406, and cg04095097) were able to discriminate between GDM cases and controls (AUC = 1; p = 1.26E-09). Three differentially methylated positions (DMPs) were replicated in an independent cohort. To conclude, epigenetic marks during pregnancy differed between GDM cases and controls suggesting a role for these genes in GDM development. Three CpGs were able to discriminate GDM and non-GDM groups with high specificity and sensitivity, which may be biomarker candidates for diagnosis or prediction of GDM.
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Affiliation(s)
- Teresa Linares-Pineda
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain
- Departamento de Bioquímica y Biología Molecular 2, Universidad de Granada, Granada, Spain
| | - Nerea Peña-Montero
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain
| | - Nicolás Fragoso-Bargas
- Department of Endocrinology Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Carolina Gutiérrez-Repiso
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, 29029, Madrid, Spain
| | - Fuensanta Lima-Rubio
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain
| | - María Suarez-Arana
- Departamento de Obstetricia y Ginecología, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Regional Universitario de Málaga, 29009, Málaga, Spain
| | - Antonio Sánchez-Pozo
- Departamento de Bioquímica y Biología Molecular 2, Universidad de Granada, Granada, Spain
| | - Francisco J Tinahones
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain
- Department of Endocrinology Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
- Departamento de Medicina y Dermatología, Universidad de Málaga, 29010, Málaga, Spain
| | - María Molina-Vega
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain
| | - María José Picón-César
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain.
| | - Christine Sommer
- Department of Endocrinology Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sonsoles Morcillo
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga-IBIMA_Plataforma Bionand, Hospital Universitario Virgen de la Victoria, 29010, Málaga, Spain.
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, 29029, Madrid, Spain.
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3
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Donovan SM, Aghaeepour N, Andres A, Azad MB, Becker M, Carlson SE, Järvinen KM, Lin W, Lönnerdal B, Slupsky CM, Steiber AL, Raiten DJ. Evidence for human milk as a biological system and recommendations for study design-a report from "Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)" Working Group 4. Am J Clin Nutr 2023; 117 Suppl 1:S61-S86. [PMID: 37173061 PMCID: PMC10356565 DOI: 10.1016/j.ajcnut.2022.12.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 05/15/2023] Open
Abstract
Human milk contains all of the essential nutrients required by the infant within a complex matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is a source of bioactive components, living cells and microbes that facilitate the transition to life outside the womb. Our ability to fully appreciate the importance of this matrix relies on the recognition of short- and long-term health benefits and, as highlighted in previous sections of this supplement, its ecology (i.e., interactions among the lactating parent and breastfed infant as well as within the context of the human milk matrix itself). Designing and interpreting studies to address this complexity depends on the availability of new tools and technologies that account for such complexity. Past efforts have often compared human milk to infant formula, which has provided some insight into the bioactivity of human milk, as a whole, or of individual milk components supplemented with formula. However, this experimental approach cannot capture the contributions of the individual components to the human milk ecology, the interaction between these components within the human milk matrix, or the significance of the matrix itself to enhance human milk bioactivity on outcomes of interest. This paper presents approaches to explore human milk as a biological system and the functional implications of that system and its components. Specifically, we discuss study design and data collection considerations and how emerging analytical technologies, bioinformatics, and systems biology approaches could be applied to advance our understanding of this critical aspect of human biology.
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Affiliation(s)
- Sharon M Donovan
- Department of Food Science and Human Nutrition, University of Illinois, Urbana-Champaign, IL, USA.
| | - Nima Aghaeepour
- Department of Anesthesiology, Pain, and Perioperative Medicine, Department of Pediatrics, and Department of Biomedical Data Sciences, School of Medicine, Stanford University, Stanford, CA, USA
| | - Aline Andres
- Arkansas Children's Nutrition Center and Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Meghan B Azad
- Manitoba Interdisciplinary Lactation Centre (MILC), Children's Hospital Research Institute of Manitoba, Department of Pediatrics and Child Health and Department of Immunology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Martin Becker
- Department of Anesthesiology, Pain, and Perioperative Medicine, Department of Pediatrics, and Department of Biomedical Data Sciences, School of Medicine, Stanford University, Stanford, CA, USA
| | - Susan E Carlson
- Department of Dietetics and Nutrition, University of Kansas Medical Center, Kansas City, KS, USA
| | - Kirsi M Järvinen
- Department of Pediatrics, Division of Allergy and Immunology and Center for Food Allergy, University of Rochester Medical Center, New York, NY, USA
| | - Weili Lin
- Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bo Lönnerdal
- Department of Nutrition, University of California, Davis, CA, USA
| | - Carolyn M Slupsky
- Department of Nutrition, University of California, Davis, CA, USA; Department of Food Science and Technology, University of California, Davis, CA, USA
| | | | - Daniel J Raiten
- Pediatric Growth and Nutrition Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
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4
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Bhandari N, Walambe R, Kotecha K, Khare SP. A comprehensive survey on computational learning methods for analysis of gene expression data. Front Mol Biosci 2022; 9:907150. [PMID: 36458095 PMCID: PMC9706412 DOI: 10.3389/fmolb.2022.907150] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 09/28/2022] [Indexed: 09/19/2023] Open
Abstract
Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.
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Affiliation(s)
- Nikita Bhandari
- Computer Science Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
| | - Rahee Walambe
- Electronics and Telecommunication Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
- Symbiosis Center for Applied AI (SCAAI), Symbiosis International (Deemed University), Pune, India
| | - Ketan Kotecha
- Computer Science Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
- Symbiosis Center for Applied AI (SCAAI), Symbiosis International (Deemed University), Pune, India
| | - Satyajeet P. Khare
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
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5
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UHPLC-MS/MS-Based Metabolomics and Clinical Phenotypes Analysis Reveal Broad-Scale Perturbations in Early Pregnancy Related to Gestational Diabetes Mellitus. DISEASE MARKERS 2022; 2022:4231031. [PMID: 36061360 PMCID: PMC9433254 DOI: 10.1155/2022/4231031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/20/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
Abstract
Gestational diabetes mellitus (GDM) is the most common metabolic disturbance during pregnancy, with adverse effects on both mother and fetus. The establishment of early diagnosis and risk assessment model is of great significance for preventing and reducing adverse outcomes of GDM. In this study, the broad-scale perturbations related to GDM were explored through the integration analysis of metabolic and clinical phenotypes. Maternal serum samples from the first trimester were collected for targeted metabolomics analysis by using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Statistical analysis was conducted based on the levels of the 184 metabolites and 76 clinical indicators from GDM women (
=60) and matched healthy controls (
=90). Metabolomics analysis revealed the down-regulation of fatty acid oxidation in the first trimester of GDM women, which was supposed to be related to the low serum level of dehydroepiandrosterone.While the significantly altered clinical phenotypes were mainly related to the increased risk of cardiovascular disease, abnormal iron metabolism, and inflammation. A phenotype panel established from the significantly changed serum indicators can be used for the early prediction of GDM, with the area under the receiver-operating characteristic curve (ROC) 0.83. High serum uric acid and C-reaction protein levels were risk factors for GDM independent of body mass indexes, with ORs 4.76 (95% CI: 2.08-10.90) and 3.10 (95% CI: 1.38-6.96), respectively. Predictive phenotype panel of GDM, together with the risk factors of GDM, will provide novel perspectives for the early clinical warning and diagnosis of GDM.
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6
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Deng B, Chen X, Xu L, Zheng L, Zhu X, Shi J, Yang L, Wang D, Jiang D. Chordin-like 1 is a novel prognostic biomarker and correlative with immune cell infiltration in lung adenocarcinoma. Aging (Albany NY) 2022; 14:389-409. [PMID: 35021154 PMCID: PMC8791215 DOI: 10.18632/aging.203814] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 12/29/2021] [Indexed: 11/25/2022]
Abstract
Chordin-like 1 (CHRDL1), an inhibitor of bone morphogenetic proteins(BMPs), has been recently reported to participate in the progression of numerous tumors, however, its role in lung adenocarcinoma (LUAD) remains unclear. Our study aimed to demonstrate relationship between CHRDL1 and LUAD based on data from The Cancer Genome Atlas (TCGA). Among them, CHRDL1 expression revealed promising power for distinguishing LUAD tissues form normal sample. Low CHRDL1 was correlated with poor clinicopathologic features, including high T stage (OR=0.45, P<0.001), high N stage (OR=0.57, P<0.003), bad treatment effect (OR=0.64, P=0.047), positive tumor status (OR=0.63, P=0.018), and TP53 mutation (OR=0.49, P<0.001). The survival curve illustrated that low CHRDL1 was significantly correlative with a poor overall survival (HR=0.60, P<0.001). At multivariate Cox regression analysis, CHRDL1 remained independently correlative with overall survival. GSEA identified that the CHRDL1 expression was related to cell cycle and immunoregulation. Immune infiltration analysis suggested that CHRDL1 was significantly correlative with 7 kinds of immune cells. Immunohistochemical validation showed that CHRDL1 was abnormally elevated and negatively correlated with Th2 cells in LUAD tissues. In conclusion, CHRDL1 might become a novel prognostic biomarker and therapy target in LUAD. Moreover, CHRDL1 may improve the effectiveness of immunotherapy by regulating immune infiltration.
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Affiliation(s)
- Bing Deng
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaorui Chen
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lingfang Xu
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Zheng
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoqian Zhu
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junwei Shi
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lei Yang
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dian Wang
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Depeng Jiang
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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7
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Harville EW, Mishra PP, Kähönen M, Raitoharju E, Marttila S, Raitakari O, Lehtimäki T. Reproductive history and blood cell DNA methylation later in life: the Young Finns Study. Clin Epigenetics 2021; 13:227. [PMID: 34930449 PMCID: PMC8690999 DOI: 10.1186/s13148-021-01215-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 12/09/2021] [Indexed: 01/16/2023] Open
Abstract
Background Women with a history of complications of pregnancy, including hypertensive disorders, gestational diabetes or an infant fetal growth restriction or preterm birth, are at higher risk for cardiovascular disease later in life. We aimed to examine differences in maternal DNA methylation following pregnancy complications. Methods Data on women participating in the Young Finns study (n = 836) were linked to the national birth registry. DNA methylation in whole blood was assessed using the Infinium Methylation EPIC BeadChip. Epigenome-wide analysis was conducted on differential CpG methylation at 850 K sites. Reproductive history was also modeled as a predictor of four epigenetic age indices. Results Fourteen significant differentially methylated sites were found associated with both history of pre-eclampsia and overall hypertensive disorders of pregnancy. No associations were found between reproductive history and any epigenetic age acceleration measure. Conclusions Differences in epigenetic methylation profiles could represent pre-existing risk factors, or changes that occurred as a result of experiencing these complications. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01215-1.
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Affiliation(s)
- Emily W Harville
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA. .,Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, 33520, Tampere, Finland.
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, 33520, Tampere, Finland.,Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, 33521, Tampere, Finland.,Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, 33521, Tampere, Finland
| | - Emma Raitoharju
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, 33520, Tampere, Finland.,Department of Molecular Epidemiology, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Saara Marttila
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, 33520, Tampere, Finland.,Department of Molecular Epidemiology, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,Gerontology Research Center, Tampere University, Tampere, Finland
| | - Olli Raitakari
- Research Center of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, 33520, Tampere, Finland.,Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, 33521, Tampere, Finland.,Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
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8
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Wang X, Huang J, Zheng Y, Long S, Lin H, Zhang N, Tian M, Wu X, An R, Ma S, Tan H. Study on the relationship between DNA methylation of target CpG sites in peripheral blood and gestational diabetes during early pregnancy. Sci Rep 2021; 11:20455. [PMID: 34650136 PMCID: PMC8516930 DOI: 10.1038/s41598-021-99836-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 09/28/2021] [Indexed: 11/15/2022] Open
Abstract
Genome-wide DNA methylation profiling have been used to find maternal CpG sites related to the occurrence of gestational diabetes mellitus (GDM). However, none of these differential sites found has been verified in a larger sample. Here, our aim was to evaluate whether first trimester changes in target CpG sites in the peripheral blood of pregnancy women predict subsequent development of GDM. This nested case–control study was based upon an early pregnancy follow-up cohort (ChiCTR1900020652). Target CpG sites were extracted from related published literature and bioinformatics analysis. The DNA methylation levels at 337 CpG sites of 80 GDM cases and 80 matched healthy controls during the early pregnancy (10–15 weeks) were assessed using MethylTarget sequencing. The best cut-off level for methylation of CpG site was determined using the generated ROC curve. The independent effect of CpG site methylation status on GDM was analyzed using conditional logistic regression. Methylation levels at 6 CpG sites were significantly higher in the GDM group than in controls, whereas those at another 6 CpG sites were significantly lower (FDR < 0.05). The area under the ROC curve at each methylation level of the significant CpG sites ranged between 0.593 and 0.650 for the occurrence of GDM. After adjusting for possible confounders, the hypermethylation status of CpG site 68167324 (OR = 3.168, 1.038–9.666) and 24837915 (OR = 5.232, 1.659–16.506) was identified as more strongly associated with GDM; meanwhile, the hypermethylation of CpG site 157130156 (OR = 0.361, 0.135–0.966) and 89438648 (OR = 0.206, 0.065–0.655) might indicate lower risk of GDM. The methylation status of target CpG sites in the peripheral blood of pregnant women during the first trimester may be associated with GDM pathogenesis, and has potential as a predictor of GDM.
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Affiliation(s)
- Xiaolei Wang
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Xiangya Road, Kaifu District, Changsha City, Hunan Province, 410078, China.,Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha City, Hunan Province, 410078, China
| | - Jin Huang
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Xiangya Road, Kaifu District, Changsha City, Hunan Province, 410078, China.,Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha City, Hunan Province, 410078, China
| | - Yixiang Zheng
- Department of Infectious Diseases, Key Laboratory of Viral Hepatitis of Hunan, Xiangya Hospital, Central South University, Changsha City, Hunan Province, 410078, China
| | - Sisi Long
- Hospital Infection Control Center, The Second Xiangya Hospital, Central South University, Changsha City, Hunan Province, 410078, China
| | - Huijun Lin
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Xiangya Road, Kaifu District, Changsha City, Hunan Province, 410078, China.,Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha City, Hunan Province, 410078, China
| | - Na Zhang
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Xiangya Road, Kaifu District, Changsha City, Hunan Province, 410078, China.,Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha City, Hunan Province, 410078, China
| | - Mengyuan Tian
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Xiangya Road, Kaifu District, Changsha City, Hunan Province, 410078, China.,Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha City, Hunan Province, 410078, China
| | - Xinrui Wu
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Xiangya Road, Kaifu District, Changsha City, Hunan Province, 410078, China.,Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha City, Hunan Province, 410078, China
| | - Rongjing An
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Xiangya Road, Kaifu District, Changsha City, Hunan Province, 410078, China.,Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha City, Hunan Province, 410078, China
| | - Shujuan Ma
- Reproductive and Genetic Hospital of CITIC-Xiangya, Clinical Research Center for Reproduction and Genetics in Hunan Province, Changsha City, Hunan Province, 410008, China.
| | - Hongzhuan Tan
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Xiangya Road, Kaifu District, Changsha City, Hunan Province, 410078, China. .,Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha City, Hunan Province, 410078, China.
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9
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Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning. BIOMED RESEARCH INTERNATIONAL 2021; 2021:1984690. [PMID: 34104645 PMCID: PMC8162250 DOI: 10.1155/2021/1984690] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 04/01/2021] [Accepted: 05/06/2021] [Indexed: 12/13/2022]
Abstract
Background Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis. Methods DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable. Results We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667. Conclusion We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.
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Espinosa C, Becker M, Marić I, Wong RJ, Shaw GM, Gaudilliere B, Aghaeepour N, Stevenson DK. Data-Driven Modeling of Pregnancy-Related Complications. Trends Mol Med 2021; 27:762-776. [PMID: 33573911 DOI: 10.1016/j.molmed.2021.01.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/01/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022]
Abstract
A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.
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Affiliation(s)
- Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Ivana Marić
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald J Wong
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary M Shaw
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA; Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
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Gao Z, S A, Li XM, Li XL, Sui LN. Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis. Front Endocrinol (Lausanne) 2021; 12:721202. [PMID: 34557161 PMCID: PMC8453249 DOI: 10.3389/fendo.2021.721202] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 08/18/2021] [Indexed: 12/24/2022] Open
Abstract
Globally, nearly 40 percent of all diabetic patients develop serious diabetic kidney disease (DKD). The identification of the potential early-stage biomarkers and elucidation of their underlying molecular mechanisms in DKD are required. In this study, we performed integrated bioinformatics analysis on the expression profiles GSE111154, GSE30528 and GSE30529 associated with early diabetic nephropathy (EDN), glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. A total of 1,241, 318 and 280 differentially expressed genes (DEGs) were identified for GSE30258, GSE30529, and GSE111154 respectively. Subsequently, 280 upregulated and 27 downregulated DEGs shared between the three GSE datasets were identified. Further analysis of the gene expression levels conducted on the hub genes revealed SPARC (Secreted Protein Acidic And Cysteine Rich), POSTN (periostin), LUM (Lumican), KNG1 (Kininogen 1), FN1 (Fibronectin 1), VCAN (Versican) and PTPRO (Protein Tyrosine Phosphatase Receptor Type O) having potential roles in DKD progression. FN1, LUM and VCAN were identified as upregulated genes for GDKD whereas the downregulation of PTPRO was associated with all three diseases. Both POSTN and SPARC were identified as the overexpressed putative biomarkers whereas KNG1 was found as downregulated in TDKD. Additionally, we also identified two drugs, namely pidorubicine, a topoisomerase inhibitor (LINCS ID- BRD-K04548931) and Polo-like kinase inhibitor (LINCS ID- BRD-K41652870) having the validated role in reversing the differential gene expression patterns observed in the three GSE datasets used. Collectively, this study aids in the understanding of the molecular drivers, critical genes and pathways that underlie DKD initiation and progression.
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Affiliation(s)
- Zhuo Gao
- Department of Nephrology, Air Force Medical Center, Beijing, China
- *Correspondence: Zhuo Gao,
| | - Aishwarya S
- Department of Bioinformatics, Stella Maris College (Autonomous), Chennai, India
| | - Xiao-mei Li
- Department of Nephrology, Air Force Medical Center, Beijing, China
| | - Xin-lun Li
- Department of Nephrology, Air Force Medical Center, Beijing, China
| | - Li-na Sui
- Department of Nephrology, Air Force Medical Center, Beijing, China
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