Humanmethylation27

Manufactured by Illumina

The HumanMethylation27 is a lab equipment product developed by Illumina. It is a DNA methylation profiling platform that can be used to analyze DNA methylation patterns across the human genome.

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Lab products found in correlation

450k beadchip, by Illumina (2 mentions) Humanmethylation450 beadchip, by Illumina (1 mentions) Snp 6, by Thermo Fisher Scientific (1 mentions) Human methylation 450, by Illumina (1 mentions) 27k platform, by Illumina (1 mentions)

Close spelling variants of this product

HumanMethylation27 BeadChip (64 citations) Infinium HumanMethylation27 BeadChip (44 citations) Infinium HumanMethylation27 (15 citations) Infinium HumanMethylation27 BeadChip array (8 citations) Infinium HumanMethylation27 Bead Array (6 citations) HumanMethylation27 BeadChip Array (4 citations) Infinium HumanMethylation27 assay (3 citations) HumanMethylation27 array (2 citations) Infinium HumanMethylation27 BeadChip assay (2 citations) Infinium HumanMethylation27 (27K) BeadChip array (2 citations)

11 protocols using humanmethylation27

Genome-wide DNA Methylation in Cervical Cancer

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The genome-wide DNA methylation array studies were extracted from Gene Expression Omnibus (GEO) databases by using following items “Cervical cancer”, “Methylation” and “Homo sapiens”. Eligible criteria were as follows: (1) the quantitative methylation levels of datasets were detected by the Illumina HumanMethylation 27 or 450 k Beadchip; (2) datasets using cohort or case-control designs. Exclusion criteria were as follows: (1) datasets regarding in vitro or ex vivo experiments of cell lines or animals; (2) datasets without CpG number.
The following information of each eligible datasets were collected: submission and last update date, ethnicity, country, sample size, methylation detection methods, source of controls, involved diseases (LSIL, HSIL, CC).

Huang J., Luo J.Y, & Tan H.Z. (2019). Associations of MGMT promoter hypermethylation with squamous intraepithelial lesion and cervical carcinoma: A meta-analysis. PLoS ONE, 14(10), e0222772.

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Genomic Profiling of Glioblastoma Patients

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A total of 106 GBM patients were analyzed in our study. MR images, including presurgical axial contrast-enhanced T1-weighted images (CE-T1WI) and T2-weighted fluid-attenuated inversion recovery (FLAIR) images, were collected from The Cancer Imaging Archive (http://www.cancerimagingarchive.net). The images were originated from four centers (Henry Ford Hospital, University of California San Francisco, Anderson Cancer Center, and Emory University). Clinical and molecular data were also obtained from the open-access data tier of the TCGA website.
Genomic data were from the TCGA data portal. MGMT methylation status analysis was performed on Illumina HumanMethylation27 and HumanMethylation450 BeadChip platforms. A median cutoff using the level 3 beta-value present in the TCGA was utilized for categorizing methylation status. Illumina Human Methylation probes (cg12434587 and cg12981137) were selected in this study [27 (link)].
Of 106 GBM cases, 87 cases were with CE-T1W images, and 66 cases with FLAIR images. We randomly split the cases into training and testing sets with the ratio of 8 : 2 and applied 10-fold cross-validation to the training set with scikit-learn library (https://scikit-learn.org/stable/). The dataset distribution is listed in Table 1.

Chen X., Zeng M., Tong Y., Zhang T., Fu Y., Li H., Zhang Z., Cheng Z., Xu X., Yang R., Liu Z., Wei X, & Jiang X. (2020). Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis. BioMed Research International, 2020, 9258649.

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DNA Methylation Profiles Predict Survival in ccRCC

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The DNA methylation information of ccRCC (platform: Illumina Human Methylation 27; downloaded in March 27, 2019; depending on GRCh38, the genomic coordinates of the CpGs were gained) and relevant clinical data were obtained from the TCGA database (https://cancergenome.nih.gov/). No further approval from the Ethics Committee was requested due to the data being obtained from the TCGA dataset. We studied how DNA methylation levels impacted survival in ccRCC using dates reported in survival records of the patients. β values were used to express DNA methylation levels, and M/(M+U) was computed. In M/(M+U), M is defined as the signal from methylated beads, while U is defined as the signal from unmethylated beads at targeted CpG sites. Ultimately, 215 samples comprised 96 ccRCCs of grade 2, 82 of grade 3, and 28 of grade 421 (link) and 113 of stage I, 27 of stage II, 49 of stage III, and 25 of stage IV.22 (link) In total, 23171 DNA methylation sites were selected in this work, and samples with relevant clinical records were obtained from the TCGA dataset. According to the TCGA series number, these 215 samples were assigned into a training dataset (60%) and a validation dataset (40%): one was used for discriminating and designing prognostic markers, while the other was applied to prove the precision of the biomarkers in survival prediction.

Shi S., Ye S., Wu X., Xu M., Zhuo R., Liao Q, & Xi Y. (2019). A Two-DNA Methylation Signature to Improve Prognosis Prediction of Clear Cell Renal Cell Carcinoma. Yonsei Medical Journal, 60(11), 1013-1020.

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Age-associated DNA Methylation Analysis

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Using NCBI Human Genome (build 36.1) and the Illumina annotation files for the HumanMethylation27 platform, CpG loci were mapped to a chromosome. P-value significances of the numbers of hypermethylated or hypomethylated loci with age were assessed using hypergeometric tests for each chromosome.

Kim J., Kim K., Kim H., Yoon G, & Lee K. (2014). Characterization of age signatures of DNA methylation in normal and cancer tissues from multiple studies. BMC Genomics, 15(1), 997.

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Integrative Analysis of UCEC, UCS, and OV

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We downloaded copy number data (masked copy number segment data from Affymetrix SNP 6.0), expression data (RNA-seq data normalized by Fragments Per Kilobase of exon per Million mapped fragments [FPKM]), and DNA methylome data (methylation beta-value data from Illumina Human Methylation 450 or Human Methylation 27) for 373 uterine corpus endometrial cancer (UCEC), 57 uterine carcinosarcoma (UCS) and 273 high-grade serous ovarian cancer (OV) samples generated by TCGA from Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov) for the analysis described in Fig. 4. For the expression analysis, the FPKMs were transformed by log10 after adding a pseudo value of 1 to avoid an infinite value. Information of somatic SNV/indels per sample in TCGA UCEC, TCGA UCS, and TCGA OV was obtained through cBioPortal (http://cbioportal.org) and used for further analyses including genomic aberration and driver mutation subtyping as described above.

Gotoh O., Sugiyama Y., Takazawa Y., Kato K., Tanaka N., Omatsu K., Takeshima N., Nomura H., Hasegawa K., Fujiwara K., Taki M., Matsumura N., Noda T, & Mori S. (2019). Clinically relevant molecular subtypes and genomic alteration-independent differentiation in gynecologic carcinosarcoma. Nature Communications, 10, 4965.

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Harmonizing Illumina Methylation Arrays

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Two generations of Illumina infinium DNA methylation beadarrays, including HumanMethylation27 (HM27) and HumanMethylation450 (HM450), were used to assay 1,406 pan-squamous tumor samples and 156 normal samples in total. Data from HM27 and HM450 were combined and further normalized by using a probe-by-probe proportional rescaling method to yield a common set of 22,601 probes with comparative methylation levels between two platforms, as described in detail in Syn7073804 on Synapse. Briefly, we rescaled the HM27 data based on between-platform difference measured by technical replicates.

Campbell J.D., Yau C., Bowlby R., Liu Y., Brennan K., Fan H., Taylor A.M., Wang C., Walter V., Akbani R., Byers L.A., Creighton C.J., Coarfa C., Shih J., Cherniack A.D., Gevaert O., Prunello M., Shen H., Anur P., Chen J., Cheng H., Hayes D.N., Bullman S., Pedamallu C.S., Ojesina A.I., Sadeghi S., Mungall K.L., Robertson A.G., Benz C., Schultz A., Kanchi R.S., Gay C.M., Hegde A., Diao L., Wang J., Ma W., Sumazin P., Chiu H.S., Chen T.W., Gunaratne P., Donehower L., Rader J.S., Zuna R., Al-Ahmadie H., Lazar A.J., Flores E.R., Tsai K.Y., Zhou J.H., Rustgi A.K., Drill E., Shen R., Wong C.K., Stuart J.M., Laird P.W., Hoadley K.A., Weinstein J.N., Peto M., Pickering C.R., Chen Z, & Van Waes C. (2018). Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas. Cell reports, 23(1), 194-212.e6.

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Comprehensive CRC Methylation and Expression Profiling

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DNA methylation data of CRC was from Level 3 Cancer Genome Altas (TCGA) [1 (link)], with Illumina Human Methylation 27 as the data chip platform, including 37 cases of normal CRC data and 166 cases of CRC data. The 166 cases of CRC data included 32 cases of stage I data, 64 of stage II data, 45 of stage III data, 24 of stage IV data, and 1 case with unclear stage. This study mainly analyzed the methylation data of the 165 CRC cases.
CRC gene expression profile data were taken from TCGA, with Agilent G4502A as the data chip platform, including 174 cases of expression profile data (19 cases of normal CRC and 155 cases of CRC) (Table 1).

Qi L, & Ding Y. (2017). Screening of Tumor Suppressor Genes in Metastatic Colorectal Cancer. BioMed Research International, 2017, 2769140.

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Fibulin-5, MMP-7, and c-Myc Expression in Lung Cancer

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Fibulin-5, MMP-7 and c-Myc expression and fibulin-5 DNA methylation in The Cancer Genome Atlas (TCGA) databases were analyzed by using The UCSC Cancer Genomics Browser (https://genome-cancer.soe.ucsc.edu/proj/site/hgHeatmap/) [25 (link)]. The TCGA lung cancer (LUNG) RNAseq (IlluminaHiSeq; N = 1081) and DNA methylation (HumanMethylation27; Illumina 27K platform; N = 311) datasets were used to compare fibulin-5, MMP-7 and c-Myc expression and fibulin-5 DNA methylation, because these datasets include results from control normal tissues. Heatmap mode was used to display the results. Kaplan-Meier curves were generated using the Kaplan-Meier plotter program (http://kmplot.com/analysis/) as described [26 (link)].

Chen X., Song X., Yue W., Chen D., Yu J., Yao Z, & Zhang L. (2015). Fibulin-5 inhibits Wnt/β-catenin signaling in lung cancer. Oncotarget, 6(17), 15022-15034.

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Validating Lung Cancer Methylation Biomarkers

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In the validation phase, four candidate CpGs were validated using two TCGA cohorts. LUAD methylation status was profiled by two platforms, namely, the HumanMethylation27 and HumanMethylation450 systems. Since both platforms contain all four selected probes, both the above two datasets were used for model validation. LUAD methylation datasets profiled by Illumina Infinium HumanMethylation27 platform had a total of 151 samples, with 127 from LUADs and 24 from non-malignant samples, and the data from the HumanMethylation450 platform consisted of 492 samples, which contained 458 samples from tumors and only 34 from normal tissues. Owing to the batch effect mentioned above, K-means clustering was recalculated for the four selected CpG probes in the TCGA HumanMethylation27 and HumanMethylation450 datasets, respectively, to reassign the binary methylation levels for each probe in all samples. For both datasets, 80% samples were randomly extracted as training set, while the remaining 20% samples were set aside for performance testing separately. Because of the heavy imbalance of the datasets, in the training phase, random oversampling examples (ROSEs) were applied to make non-malignant samples accounting for 50% of the training set to improve the model performance.

Shen N., Du J., Zhou H., Chen N., Pan Y., Hoheisel J.D., Jiang Z., Xiao L., Tao Y, & Mo X. (2019). A Diagnostic Panel of DNA Methylation Biomarkers for Lung Adenocarcinoma. Frontiers in Oncology, 9, 1281.

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Genome-wide Methylation Profiling of Cervical Cancer

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Genome-wide methylation profiles of the TCGA CESC project were obtained. Quantitative methylation datasets were initially searched from the GEO database (https://www.ncbi.nlm.nih.gov/gds/) by using the following keywords: “cervical cancer” and “methylation” and “Homo sapiens.” Methylation signals of the datasets above were detected by Illumina HumanMethylation 27 or 450 k Beadchip. The level of methylation at each CpG island site is expressed as a beta value, which is the ratio of quantile-normalized methylation intensity to total locus intensity (methylated and unmethylated).

Huang J., Gao H, & Tan H.Z. (2020). SOX1 Promoter Hypermethylation as a Potential Biomarker for High-Grade Squamous Intraepithelial Neoplasia Lesion and Cervical Carcinoma: A Meta-Analysis With Trial Sequential Analysis. Frontiers in Genetics, 11, 633.

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