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Sahoo K, Sundararajan V. Methods in DNA methylation array dataset analysis: A review. Comput Struct Biotechnol J 2024; 23:2304-2325. [PMID: 38845821 PMCID: PMC11153885 DOI: 10.1016/j.csbj.2024.05.015] [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: 12/18/2023] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.
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Affiliation(s)
| | - Vino Sundararajan
- Correspondence to: Department of Bio Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India.
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Li Y, Mao X, Li M, Li L, Tong X, Huang L. The predictive value of BTG1 for the response of newly diagnosed acute myeloid leukemia to decitabine. Clin Epigenetics 2024; 16:16. [PMID: 38254153 PMCID: PMC10802042 DOI: 10.1186/s13148-024-01627-9] [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: 11/02/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Decitabine has been widely used to treat acute myeloid leukemia (AML); however as AML is a heterogeneous disease, not all patients benefit from decitabine. This study aimed to identify markers for predicting the response to decitabine. METHODS An intersection of in vitro experiments and bioinformatics was performed using a combination of epigenetic and transcriptomic analysis. A tumor-suppressor gene associated with methylation and the response to decitabine was screened. Then the sensitivity and specificity of this marker in predicting the response to decitabine was confirmed in 54 samples from newly diagnosed AML patients treated with decitabine plus IA regimen in a clinical trial (ChiCTR2000037928). RESULTS In vitro experiments showed that decitabine caused hypomethylation and upregulation of BTG1, while downregulation of BTG1 attenuated the inhibitory effect of decitabine. In newly diagnosed AML patients who received decitabine plus IA regimen, the predictive value of BTG1 to predict complete remission (CR) was assigned with a sensitivity of 86.7% and a specificity of 100.0% when BTG1 expression was < 0.292 (determined using real-time quantitative PCR), with area under the curve (AUC) = 0.933, P = 0.021. The predictive value of BTG1 to predict measurable residual disease (MRD) negativity was assigned with a sensitivity of 100.0% and a specificity of 80.0% when BTG1 expression was < 0.292 (AUC = 0.892, P = 0.012). Patients were divided into low and high BTG1 expression groups according to a cutoff of 0.292, and the CR rate of the low-expression group was significantly higher than that of the high-expression group (97.5% vs. 50%, P < 0.001). CONCLUSIONS Low expression of BTG1 was associated with CR and MRD negativity in newly diagnosed AML patients treated with a decitabine-containing regimen, suggesting that BTG1 is a potential marker for predicting the response to decitabine in newly diagnosed AML. CLINICAL TRIAL REGISTRATION ChiCTR2000037928.
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Affiliation(s)
- Yi Li
- Renmin Hospital of Wuhan University, Wuhan, China
| | - Xia Mao
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jie-fang Avenue, Wuhan, 430030, Hubei, China
| | - Mengyuan Li
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jie-fang Avenue, Wuhan, 430030, Hubei, China
| | - Li Li
- Xinqiao Hospital of Army Medical University, Chongqing, China
| | - Xiwen Tong
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jie-fang Avenue, Wuhan, 430030, Hubei, China
| | - Lifang Huang
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jie-fang Avenue, Wuhan, 430030, Hubei, China.
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Carrizosa-Molina T, Casillas-Díaz N, Pérez-Nadador I, Vales-Villamarín C, López-Martínez MÁ, Riveiro-Álvarez R, Wilhelm L, Cervera-Juanes R, Garcés C, Lomniczi A, Soriano-Guillén L. Methylation analysis by targeted bisulfite sequencing in large for gestational age (LGA) newborns: the LARGAN cohort. Clin Epigenetics 2023; 15:191. [PMID: 38093359 PMCID: PMC10717641 DOI: 10.1186/s13148-023-01612-8] [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: 06/23/2023] [Accepted: 12/02/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND In 1990, David Barker proposed that prenatal nutrition is directly linked to adult cardiovascular disease. Since then, the relationship between adult cardiovascular risk, metabolic syndrome and birth weight has been widely documented. Here, we used the TruSeq Methyl Capture EPIC platform to compare the methylation patterns in cord blood from large for gestational age (LGA) vs adequate for gestational age (AGA) newborns from the LARGAN cohort. RESULTS We found 1672 differentially methylated CpGs (DMCs) with a nominal p < 0.05 and 48 differentially methylated regions (DMRs) with a corrected p < 0.05 between the LGA and AGA groups. A systems biology approach identified several biological processes significantly enriched with genes in association with DMCs with FDR < 0.05, including regulation of transcription, regulation of epinephrine secretion, norepinephrine biosynthesis, receptor transactivation, forebrain regionalization and several terms related to kidney and cardiovascular development. Gene ontology analysis of the genes in association with the 48 DMRs identified several significantly enriched biological processes related to kidney development, including mesonephric duct development and nephron tubule development. Furthermore, our dataset identified several DNA methylation markers enriched in gene networks involved in biological pathways and rare diseases of the cardiovascular system, kidneys, and metabolism. CONCLUSIONS Our study identified several DMCs/DMRs in association with fetal overgrowth. The use of cord blood as a material for the identification of DNA methylation biomarkers gives us the possibility to perform follow-up studies on the same patients as they grow. These studies will not only help us understand how the methylome responds to continuum postnatal growth but also link early alterations of the DNA methylome with later clinical markers of growth and metabolic fitness.
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Affiliation(s)
- Tamara Carrizosa-Molina
- Department of Pediatrics, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid, Avda. Reyes Católicos, 2, 28040, Madrid, Spain
| | - Natalia Casillas-Díaz
- Department of Pediatrics, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid, Avda. Reyes Católicos, 2, 28040, Madrid, Spain
| | | | | | - Miguel Ángel López-Martínez
- Department of Genetics and Genomics, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid, Madrid, Spain
| | - Rosa Riveiro-Álvarez
- Department of Genetics and Genomics, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid, Madrid, Spain
| | - Larry Wilhelm
- Department of Physiology and Pharmacology, Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Rita Cervera-Juanes
- Department of Physiology and Pharmacology, Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Carmen Garcés
- Lipid Research Laboratory, IIS-Fundación Jiménez Díaz, Madrid, Spain
| | - Alejandro Lomniczi
- Department of Physiology and Biophysics, Dalhousie University School of Medicine, 5850 College Street, Halifax, NS, B3H 4R2, Canada.
| | - Leandro Soriano-Guillén
- Department of Pediatrics, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid, Avda. Reyes Católicos, 2, 28040, Madrid, Spain.
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Meyer OS, Andersen MM, Børsting C, Morling N, Wulf HC, Philipsen PA, Lerche CM, Dyrberg Andersen J. Comparison of global DNA methylation analysis by whole genome bisulfite sequencing and the Infinium Mouse Methylation BeadChip using fresh and fresh-frozen mouse epidermis. Epigenetics 2023; 18:2144574. [PMID: 36373380 PMCID: PMC9980693 DOI: 10.1080/15592294.2022.2144574] [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] [Indexed: 11/16/2022] Open
Abstract
Until recently, studying the murine methylome was restricted to sequencing-based methods. In this study we compared the global DNA methylation levels of hairless mouse epidermis using the recently released Infinium Mouse Methylation BeadChip from Illumina and whole genome bisulphite sequencing (WGBS). We also studied the effect of sample storage conditions by using fresh and fresh-frozen epidermis. The DNA methylation levels of 123,851 CpG sites covered by both the BeadChip and WGBS were compared. DNA methylation levels obtained with WGBS and the BeadChip were strongly correlated (Pearson correlation r = 0.984). We applied a threshold of 15 reads for the WGBS methylation analysis. Even at a threshold of 10 reads, we observed no substantial difference in DNA methylation levels compared with that obtained with the BeadChip. The DNA methylation levels from the fresh and the fresh-frozen samples were strongly correlated when analysed with both the BeadChip (r = 0.999) and WGBS (r = 0.994). We conclude that the two methods of analysis generally work equally well for studies of DNA methylation of mouse epidermis and find that fresh and fresh-frozen epidermis can generally be used equally well. The choice of method will depend on the specific study's aims and the available resources in the laboratory.
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Affiliation(s)
- Olivia Strunge Meyer
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100Copenhagen, Denmark,CONTACT Olivia Strunge Meyer Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Heafth and Medical Sciences, University of Copenhagen. Frederik V's vej 11, 2100 Copenhagen, Denmark
| | - Mikkel Meyer Andersen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100Copenhagen, Denmark,Department of Mathematical Sciences, Aalborg University, 9220Aalborg, Denmark
| | - Claus Børsting
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100Copenhagen, Denmark
| | - Niels Morling
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100Copenhagen, Denmark,Department of Mathematical Sciences, Aalborg University, 9220Aalborg, Denmark
| | - Hans Christian Wulf
- Department of Dermatology, Copenhagen University Hospital - Bispebjerg and Frederiksberg, 2400Copenhagen, Denmark
| | - Peter Alshede Philipsen
- Department of Dermatology, Copenhagen University Hospital - Bispebjerg and Frederiksberg, 2400Copenhagen, Denmark
| | - Catharina Margrethe Lerche
- Department of Dermatology, Copenhagen University Hospital - Bispebjerg and Frederiksberg, 2400Copenhagen, Denmark,Department of Pharmacy, University of Copenhagen, 2100Copenhagen, Denmark
| | - Jeppe Dyrberg Andersen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100Copenhagen, Denmark
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Differentially methylated and expressed genes in familial type 1 diabetes. Sci Rep 2022; 12:11045. [PMID: 35773317 PMCID: PMC9247163 DOI: 10.1038/s41598-022-15304-5] [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: 03/15/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
There has recently been a growing interest in examining the role of epigenetic modifications, such as DNA methylation, in the etiology of type 1 diabetes (T1D). This study aimed to delineate differences in methylation patterns between T1D-affected and healthy individuals by examining the genome-wide methylation of individuals from three Arab families from Kuwait with T1D-affected mono-/dizygotic twins and non-twinned siblings. Bisulfite sequencing of DNA from the peripheral blood of the affected and healthy individuals from each of the three families was performed. Methylation profiles of the affected individuals were compared to those of the healthy individuals Principal component analysis on the observed methylation profiling based on base-pair resolution clustered the T1D-affected twins together family-wide. The sites/regions that were differentially methylated between the T1D and healthy samples harbored 84 genes, of which 18 were known to be differentially methylated in T1D individuals compared to healthy individuals in publicly available gene expression data resources. We further validated two of the 18 genes—namely ICA1 and DRAM1 that were hypermethylated in T1D samples compared to healthy samples—for upregulation in T1D samples from an extended study cohort of familial T1D. The study confirmed that the ICA1 and DRAM1 genes are differentially expressed in T1D samples compared to healthy samples.
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Yousefi PD, Suderman M, Langdon R, Whitehurst O, Davey Smith G, Relton CL. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet 2022; 23:369-383. [PMID: 35304597 DOI: 10.1038/s41576-022-00465-w] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2022] [Indexed: 12/12/2022]
Abstract
DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.
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Affiliation(s)
- Paul D Yousefi
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Matthew Suderman
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Ryan Langdon
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Oliver Whitehurst
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
| | - Caroline L Relton
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.
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Yu C, Dugué PA, Dowty JG, Hammet F, Joo JE, Wong EM, Hosseinpour M, Giles GG, Hopper JL, Nguyen-Dumont T, MacInnis RJ, Southey MC. Repeatability of methylation measures using a QIAseq targeted methyl panel and comparison with the Illumina HumanMethylation450 assay. BMC Res Notes 2021; 14:394. [PMID: 34689793 PMCID: PMC8543877 DOI: 10.1186/s13104-021-05809-z] [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: 08/31/2021] [Accepted: 10/11/2021] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE In previous studies using Illumina Infinium methylation arrays, we have identified DNA methylation marks associated with cancer predisposition and progression. In the present study, we have sought to find appropriate technology to both technically validate our data and expand our understanding of DNA methylation in these genomic regions. Here, we aimed to assess the repeatability of methylation measures made using QIAseq targeted methyl panel and to compare them with those obtained from the Illumina HumanMethylation450 (HM450K) assay. We included in the analysis high molecular weight DNA extracted from whole blood (WB) and DNA extracted from formalin-fixed paraffin-embedded tissues (FFPE). RESULTS The repeatability of QIAseq-methylation measures was assessed at 40 CpGs, using the Intraclass Correlation Coefficient (ICC). The mean ICCs and 95% confidence intervals (CI) were 0.72 (0.62-0.81), 0.59 (0.47-0.71) and 0.80 (0.73-0.88) for WB, FFPE and both sample types combined, respectively. For technical replicates measured using QIAseq and HM450K, the mean ICCs (95% CI) were 0.53 (0.39-0.68), 0.43 (0.31-0.56) and 0.70 (0.59-0.80), respectively. Bland-Altman plots indicated good agreement between QIAseq and HM450K measurements. These results demonstrate that the QIAseq targeted methyl panel produces reliable and reproducible methylation measurements across the 40 CpGs that were examined.
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Affiliation(s)
- Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia
| | - Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia
- Cancer Council Victoria, Cancer Epidemiology Division, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Fleur Hammet
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia
| | - JiHoon E Joo
- Department of Clinical Pathology, The Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia
| | - Mahnaz Hosseinpour
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia
- Cancer Council Victoria, Cancer Epidemiology Division, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia
- Department of Clinical Pathology, The Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
| | - Robert J MacInnis
- Cancer Council Victoria, Cancer Epidemiology Division, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia.
- Cancer Council Victoria, Cancer Epidemiology Division, Melbourne, Australia.
- Department of Clinical Pathology, The Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia.
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Wang SC, Liao LM, Ansar M, Lin SY, Hsu WW, Su CM, Chung YM, Liu CC, Hung CS, Lin RK. Automatic Detection of the Circulating Cell-Free Methylated DNA Pattern of GCM2, ITPRIPL1 and CCDC181 for Detection of Early Breast Cancer and Surgical Treatment Response. Cancers (Basel) 2021; 13:cancers13061375. [PMID: 33803633 PMCID: PMC8002961 DOI: 10.3390/cancers13061375] [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: 01/27/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 12/31/2022] Open
Abstract
The early detection of cancer can reduce cancer-related mortality. There is no clinically useful noninvasive biomarker for early detection of breast cancer. The aim of this study was to develop accurate and precise early detection biomarkers and a dynamic monitoring system following treatment. We analyzed a genome-wide methylation array in Taiwanese and The Cancer Genome Atlas (TCGA) breast cancer (BC) patients. Most breast cancer-specific circulating methylated CCDC181, GCM2 and ITPRIPL1 biomarkers were found in the plasma. An automatic analysis process of methylated ccfDNA was established. A combined analysis of CCDC181, GCM2 and ITPRIPL1 (CGIm) was performed in R using Recursive Partitioning and Regression Trees to establish a new prediction model. Combined analysis of CCDC181, GCM2 and ITPRIPL1 (CGIm) was found to have a sensitivity level of 97% and an area under the curve (AUC) of 0.955 in the training set, and a sensitivity level of 100% and an AUC of 0.961 in the test set. The circulating methylated CCDC181, GCM2 and ITPRIPL1 was also significantly decreased after surgery (all p < 0.001). The aberrant methylation patterns of the CCDC181, GCM2 and ITPRIPL1 genes means that they are potential biomarkers for the detection of early BC and can be combined with breast imaging data to achieve higher accuracy, sensitivity and specificity, facilitating breast cancer detection. They may also be applied to monitor the surgical treatment response.
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Affiliation(s)
- Sheng-Chao Wang
- Ph.D. Program in Drug Discovery and Development Industry, College of Pharmacy, Taipei Medical University, No. 250, Wuxing Street, Taipei 110, Taiwan;
| | - Li-Min Liao
- Division of General Surgery, Department of Surgery, Taipei Medical University Shuang Ho Hospital, No.291, Zhongzheng Rd., Zhonghe District, New Taipei City 23561, Taiwan; (L.-M.L.); (C.-M.S.)
| | - Muhamad Ansar
- Ph.D. Program in the Clinical Drug Development of Herbal Medicine, Taipei Medical University, 250 Wu-Hsing Street, Taipei 110, Taiwan;
| | - Shih-Yun Lin
- Graduate Institute of Pharmacognosy, Taipei Medical University, 250 Wu-Hsing Street, Taipei 110, Taiwan;
| | - Wei-Wen Hsu
- Department of Statistics, College of Arts and Sciences, Kansas State University, 101 Dickens Hall, 1116 Mid-Campus Drive N, Manhattan, KS 66506-0802, USA;
| | - Chih-Ming Su
- Division of General Surgery, Department of Surgery, Taipei Medical University Shuang Ho Hospital, No.291, Zhongzheng Rd., Zhonghe District, New Taipei City 23561, Taiwan; (L.-M.L.); (C.-M.S.)
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, No. 250, Wuxing Street, Taipei 110, Taiwan
| | - Yu-Mei Chung
- Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, Taipei Medical University, 250 Wu-Hsing Street, Taipei 110, Taiwan;
| | - Cai-Cing Liu
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Hsing Street, Taipei 110, Taiwan;
| | - Chin-Sheng Hung
- Division of General Surgery, Department of Surgery, Taipei Medical University Shuang Ho Hospital, No.291, Zhongzheng Rd., Zhonghe District, New Taipei City 23561, Taiwan; (L.-M.L.); (C.-M.S.)
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, No. 250, Wuxing Street, Taipei 110, Taiwan
- Correspondence: (C.-S.H.); (R.-K.L.); Tel.: +886-970-405-127 (C.-S.H.); +886-2-2736-1661 (ext. 6162) (R.-K.L.)
| | - Ruo-Kai Lin
- Ph.D. Program in Drug Discovery and Development Industry, College of Pharmacy, Taipei Medical University, No. 250, Wuxing Street, Taipei 110, Taiwan;
- Ph.D. Program in the Clinical Drug Development of Herbal Medicine, Taipei Medical University, 250 Wu-Hsing Street, Taipei 110, Taiwan;
- Graduate Institute of Pharmacognosy, Taipei Medical University, 250 Wu-Hsing Street, Taipei 110, Taiwan;
- Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, Taipei Medical University, 250 Wu-Hsing Street, Taipei 110, Taiwan;
- Clinical trial center, Taipei Medical University Hospital, 252 Wu-Hsing Street, Taipei 110, Taiwan
- Correspondence: (C.-S.H.); (R.-K.L.); Tel.: +886-970-405-127 (C.-S.H.); +886-2-2736-1661 (ext. 6162) (R.-K.L.)
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