gene expression microarray  (INFINIUM Inc)

Journal: Translational Psychiatry

Article Title: Exploring the association between serotonin transporter promoter region methylation levels and depressive symptoms: a systematic review and multi-level meta-analysis

doi: 10.1038/s41398-025-03356-w

Figure Lengend Snippet: Studies description table.

Article Snippet: Lei, 2015 , 99 , 48.3 , 100 , AFA , Depr. Sympt. , Mild average , - , CES-D , n/a , Healthy , Blood peri , Microarray Illumina 450K Human Methylation Beadchip , N not in this case , Average , 7 units , n/a Cg27569822, Cg10901968, Cg26741280, Cg25725890, Cg05016953, Cg14692377, Cg03363743 , 6139/090 + 6120/071 + 6101/50 + 6037/5 988 + 5796/47 + 5668/19 + 5457/08 , 1 , N.

Techniques: Methylation, Expressing, Software, Pyrosequencing Assay, Biomarker Discovery, Microarray, Sequencing

Journal: HemaSphere

Article Title: H1‐0 is a specific mediator of the repressive ETV6::RUNX1 transcriptional landscape in preleukemia and B cell acute lymphoblastic leukemia

doi: 10.1002/hem3.70116

Figure Lengend Snippet: H1‐0 is consistently upregulated in preleukemia and BCP‐ALL expressing ETV6::RUNX1 . (A) Principal component analysis (PCA) plot of ETV6::RUNX1 + (E::R) and wild‐type (WT) hiPSC transcriptome profiles based on all detected genes ( n = 16,328). (B) Hierarchical clustering analysis of differentially expressed genes (absolute fold change > 2 and p < 0.05) between ETV6::RUNX1 + and WT hiPSCs detected by RNA‐seq. (C) H1‐0 expression levels determined by RT‐qPCR in ETV6::RUNX1 + and WT hiPSCs subjected to RNA‐seq. Values were normalized to HW8 WT expression levels as well as to GAPDH expression. (D) Representative Western blot analysis of ETV6::RUNX1, H1‐0, ETV6, and β‐actin levels in ETV6::RUNX1 + and WT hiPSCs. (E) H1‐0 levels in HSCs (CD19‐CD34+CD45RA‐), IL7R+ (CD19‐CD34+CD45RA+IL7R+), and pro‐B (CD19+CD34+) cells differentiated from ETV6::RUNX1 + or reverted MIFF3 hiPSCs, and fetal liver cells. Data are derived from an RNA‐seq dataset by Böiers et al. (accession number E‐MTAB‐6382 ¹⁰ ). Data were analyzed for statistical significance using an ordinary one‐way ANOVA (* p < 0.05, ** p < 0.01). H1‐0 levels across two leukemia patient cohorts derived from the (F) PeCan St. Jude database ³⁰ , ³¹ and (G) an expression microarray dataset (accession number GSE87070 ³² ). The number of patients per leukemia entity and mean expression is indicated. Data were analyzed for statistical significance using an ordinary one‐way ANOVA (*** p < 0.001). (H) H1‐0 expression was quantified by RT‐qPCR in PDX samples ( n = 9). Mean expression ± standard deviation is shown. (I) RNA‐seq expression levels of H1‐0 in control and ETV6 shRNA‐transduced REH cells. Data are derived from E‐MTAB‐10308 ¹¹ and are normalized to control shRNA. Mean expression ± standard deviation is indicated. Statistical significance was determined by performing a one‐way ANOWA (*** p < 0.001). (J) Pearson correlation of H1‐0 and RUNX1 expression in healthy bone marrow cells ( n = 71) derived from the MILE study (R2 platform, accession number GSE13159 ³³ ).

Article Snippet: Hence, we analyzed previously published 450K Infinium microarray DNA methylation data comprising patient samples of T‐ALL and six B‐ALL subtypes ( n = 546).

Techniques: Expressing, RNA Sequencing, Quantitative RT-PCR, Western Blot, Derivative Assay, Microarray, Standard Deviation, Control, shRNA

Journal: HemaSphere

Article Title: H1‐0 is a specific mediator of the repressive ETV6::RUNX1 transcriptional landscape in preleukemia and B cell acute lymphoblastic leukemia

doi: 10.1002/hem3.70116

Figure Lengend Snippet: ETV6::RUNX1 induces H1‐0 promoter activation . (A) Schematic representation of the H1‐0 locus, including the 512‐bp region (nucleotides −351 to +161 from TSS) encompassing promoter‐like signature EH38E2163184 (ENCODE). The H1‐0 CpG island (CGI) shore and 450K Infinium array probes are indicated. (B) 293T cells were transfected with a vector encoding the H1‐0 promoter‐like signature indicated in (A) , together with the empty pcDNA3.1 vector or pcDNA3.1 expressing either ETV6::RUNX1 or RUNX1, and a vector expressing Renilla luciferase. Luciferase activities were normalized to Renilla luciferase activity and the empty vector control. Data represent mean values of three independent replicates ± standard deviation. Significance was calculated using an ordinary one‐way ANOVA (*** p < 0.001). Representative protein levels of ETV6::RUNX1, RUNX1, and β‐actin determined by Western blot are shown. (C) Pearson correlation of H1‐0 expression and mean DNA methylation of the H1‐0 CGI shore probes cg07141002 and cg01883777 in leukemia patients (accession number GSE49032 ⁴¹ ). Expression is shown for microarray probe 208886_at. Each dot represents a single patient. (D) H1‐0 DNA methylation in different leukemia entities is visualized as a heatmap with each column corresponding to a single patient (accession number GSE49032 ⁴¹ ). Within each entity, patients are sorted according to mean DNA methylation of CGI shore probes cg07141002 and cg01883777. The total number of patients per entity is indicated.

Article Snippet: Hence, we analyzed previously published 450K Infinium microarray DNA methylation data comprising patient samples of T‐ALL and six B‐ALL subtypes ( n = 546).

Techniques: Activation Assay, Transfection, Plasmid Preparation, Expressing, Luciferase, Activity Assay, Control, Standard Deviation, Western Blot, DNA Methylation Assay, Microarray

Journal: HemaSphere

Article Title: H1‐0 is a specific mediator of the repressive ETV6::RUNX1 transcriptional landscape in preleukemia and B cell acute lymphoblastic leukemia

doi: 10.1002/hem3.70116

Figure Lengend Snippet: H1‐0 expression decreases during hematopoiesis . (A) H1‐0 expression in ETV6::RUNX1 + BCP‐ALL ( n = 6) and healthy B cell precursor stages derived from a published RNA‐seq dataset (accession number GSE115656 ⁴⁵ ). B cell precursor fractions are HSCs (CD34+CD19‐IgM‐), pro‐B cells (CD34+CD19+IgM‐), pre‐B cells (CD34‐CD19+IgM‐) and immature B cells (CD34‐CD19+IgM+). (B) H1‐0 expression in healthy B cell precursor stages derived from a published expression microarray dataset (accession number GSE24759 ⁴⁶ ). B cell precursor fractions are HSCs (CD34+CD38‐), pro‐B cells (CD34+CD10+CD19+), pre‐B cells (CD34‐CD10+CD19+), naïve B cells (CD19+IgD+CD27‐), and mature B cells (CD19+IgD+CD27+). (B, C) Mean expression ± standard deviation is indicated and data was analyzed for statistical significance using an ordinary one‐way ANOVA (* p < 0.05, *** p < 0.001). (C) Min–max‐normalized mean expression per cell type derived from a fetal liver scRNA‐seq dataset (accession number E‐MTAB‐7407 ⁴⁷ ). (D) H1‐0 expression levels across normal B‐lymphoid differentiation distinguishing cell cycle status is depicted in a scRNA‐seq UMAP visualization of B cell precursor cells from bone marrow of eight healthy donors. ⁴⁸

Article Snippet: Hence, we analyzed previously published 450K Infinium microarray DNA methylation data comprising patient samples of T‐ALL and six B‐ALL subtypes ( n = 546).

Techniques: Expressing, Derivative Assay, RNA Sequencing, Microarray, Standard Deviation

Journal: Heliyon

Article Title: Blood-based biomarkers derived from tumor-informed DNA methylation analysis for lung adenocarcinoma

doi: 10.1016/j.heliyon.2025.e42581

Figure Lengend Snippet: Clinical characteristics of the methylome datasets.

Article Snippet: For the TCGA dataset, Illumina HumanMethylation450K BeadChip (450K) DNA methylation microarray data from 475 LUAD tumors and 32 associated normal tissues were obtained from the Genomic Data Commons data portal ( https://portal.gdc.cancer.gov/ ) using the TCGAbiolinks R package [ ].

Techniques: Biomarker Discovery

Journal: Heliyon

Article Title: Blood-based biomarkers derived from tumor-informed DNA methylation analysis for lung adenocarcinoma

doi: 10.1016/j.heliyon.2025.e42581

Figure Lengend Snippet: DNA methylation (DNAm) profiling of tissue samples of lung adenocarcinoma. A, B , Volcano plots of the differentially methylated positions (DMPs) from the combined GEO dataset (A) and the TCGA dataset (B). C, Venn diagram depicting DMPs overlapping between the combined GEO and TCGA datasets by methylation change. D, The distribution of DMPs by CpG density within the combined GEO dataset. E, Scatter plot of mean methylation difference versus log fold change of gene expression in the TCGA dataset (blue: hypomethylated and upregulated; red: hypermethylated and down-regulated).

Article Snippet: For the TCGA dataset, Illumina HumanMethylation450K BeadChip (450K) DNA methylation microarray data from 475 LUAD tumors and 32 associated normal tissues were obtained from the Genomic Data Commons data portal ( https://portal.gdc.cancer.gov/ ) using the TCGAbiolinks R package [ ].

Techniques: DNA Methylation Assay, Methylation, Gene Expression

Journal: Heliyon

Article Title: Blood-based biomarkers derived from tumor-informed DNA methylation analysis for lung adenocarcinoma

doi: 10.1016/j.heliyon.2025.e42581

Figure Lengend Snippet: Top 10 significant CpG sites by dataset.

Article Snippet: For the TCGA dataset, Illumina HumanMethylation450K BeadChip (450K) DNA methylation microarray data from 475 LUAD tumors and 32 associated normal tissues were obtained from the Genomic Data Commons data portal ( https://portal.gdc.cancer.gov/ ) using the TCGAbiolinks R package [ ].

Techniques:

Journal: Heliyon

Article Title: Blood-based biomarkers derived from tumor-informed DNA methylation analysis for lung adenocarcinoma

doi: 10.1016/j.heliyon.2025.e42581

Figure Lengend Snippet: Two DMP signatures for tissue and blood.

Article Snippet: For the TCGA dataset, Illumina HumanMethylation450K BeadChip (450K) DNA methylation microarray data from 475 LUAD tumors and 32 associated normal tissues were obtained from the Genomic Data Commons data portal ( https://portal.gdc.cancer.gov/ ) using the TCGAbiolinks R package [ ].

Techniques: Methylation

Journal: Heliyon

Article Title: Blood-based biomarkers derived from tumor-informed DNA methylation analysis for lung adenocarcinoma

doi: 10.1016/j.heliyon.2025.e42581

Figure Lengend Snippet: Performance of the two DMP signatures assessed by ROC curve analysis. A , Training datasets of tissue samples (combined GEO, and TCGA) and two blood datasets (Thai and Thai/Han Chinese). B, Testing datasets (GSE75008 and GSE56044).

Article Snippet: For the TCGA dataset, Illumina HumanMethylation450K BeadChip (450K) DNA methylation microarray data from 475 LUAD tumors and 32 associated normal tissues were obtained from the Genomic Data Commons data portal ( https://portal.gdc.cancer.gov/ ) using the TCGAbiolinks R package [ ].

Techniques:

Journal: Nutrients

Article Title: Epigenetic Effects of Resveratrol on Oncogenic Signaling in Breast Cancer

doi: 10.3390/nu16050699

Figure Lengend Snippet: Genome-wide DNA methylation profiling identifies hypermethylated genes that are linked to regulation of multiple oncogenic signaling pathways in response to resveratrol (RSV) in breast cancer cells. ( A ) A scheme depicting oncogenic signaling pathways with their upstream regulators that are hypermethylated upon RSV exposure. ( B ) Differences in DNA methylation between 15 µM RSV-treated and vehicle-treated MCF10CA1a breast cancer cells, as determined by Infinium HumanMethylation 450 K BeadChIP microarray. The difference is expressed as delta beta, which corresponds to the difference in bead intensities (beta values). The data were extracted from GSE80794 from our previous work .

Article Snippet: Alterations in DNA methylation patterns in regulatory regions of GLI2 and WNT4 following a 9-day treatment with 15 μM RSV were initially identified using the Illumina 450K DNA methylation microarray in highly invasive MCF10CA1a cells [ ] ( ).

Techniques: Genome Wide, DNA Methylation Assay, Protein-Protein interactions, Microarray

Journal: Nutrients

Article Title: Epigenetic Effects of Resveratrol on Oncogenic Signaling in Breast Cancer

doi: 10.3390/nu16050699

Figure Lengend Snippet: Hypermethylation and downregulation of GLI2 and WNT4 in response to resveratrol (RSV) in breast cancer cells. ( A , F ) A map of the GLI2 ( A ) and WNT4 ( F ) enhancer region, where the light blue shaded area represents the entire fragment tested by pyrosequencing and quantitative chromatin immunoprecipitation (qChIP). Transcription start site (TSS) is indicated by +1, transcription factors were predicted using Transfac, and pyrosequenced CpG sites are circled and numbered. CpG covered on Illumina microarray platform is indicated above the gene map along with CpG loci identifier number (so-called cluster CG#). ( B , C ) Average methylation status of CpG sites in GLI2, from the Hedgehog signaling pathway, in invasive MCF10CA1a ( B ) and non-invasive MCF-7 ( C ) breast cancer cells. ( D , E ) Expression of GLI2 upon 4-day and 9-day treatment of invasive MCF10CA1a ( D ) and non-invasive MCF-7 ( E ) breast cancer cells with 15 µM RSV, as determined by qRT-PCR. ( G , H ) Average methylation status of CpG sites in WNT4, from the Wnt pathway, in invasive MCF10CA1a ( G ) and non-invasive MCF-7 ( H ) breast cancer cells upon 9-day exposure to 15 µM RSV. ( I , J ) Expression of WNT4 upon 4-day and 9-day treatment of invasive MCF10CA1a ( I ) and non-invasive MCF-7 ( J ) breast cancer cells with 15 µM RSV. All results represent mean ± SD of three independent experiments; *** p < 0.001, ** p < 0.01, * p < 0.05.

Article Snippet: Alterations in DNA methylation patterns in regulatory regions of GLI2 and WNT4 following a 9-day treatment with 15 μM RSV were initially identified using the Illumina 450K DNA methylation microarray in highly invasive MCF10CA1a cells [ ] ( ).

Techniques: Chromatin Immunoprecipitation, Microarray, Methylation, Expressing, Quantitative RT-PCR