Journal: Computational and Structural Biotechnology Journal

Article Title: i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability

doi: 10.1016/j.csbj.2022.06.058

Figure Lengend Snippet: Ranking scheme of multi-omics signatures. (A) Copy number variation signatures ranking. (B) Gene expression signatures ranking. (C) DNA methylation signatures ranking. (D) miRNA expression signatures ranking. (E) Protein expression signatures ranking. (F) Somatic mutation signatures ranking.

Article Snippet: We obtained multi-omics glioma datasets, including RNA sequencing data (TPM normalized gene expression quantification), protein expression data (Reverse Phase Protein Array RPPA), miRNA-seq expression data (reads per million for miRNA mapping to miRbase 20), DNA methylation data (Infinium HumanMethylation450 BeadChip), copy number variation data (Affymetrix SNP Array 6.0) and somatic mutation data (DNA sequencing).

Techniques: Biomarker Discovery, Gene Expression, DNA Methylation Assay, Expressing, Mutagenesis

Journal: Cancer discovery

Article Title: Succinate Dehydrogenase Mutation Underlies Global Epigenomic Divergence in Gastrointestinal Stromal Tumor

doi: 10.1158/2159-8290.CD-13-0092

Figure Lengend Snippet: Comparison of isocitrate dehydrogenase (IDH)-mutant glioma with SDH-mutant GIST. A, histomorphology of high-grade glioma, low-grade glioma, and comparison reference glial tissue. (Reference neuronal tissue is previously shown in Fig. 1A.) B, left, PCA segregates glial neoplasms according to oncogenotype, and reveals greater divergence from baseline glia for IDH mutants. The plot includes 7 IDH1-mutant glial tumors, 20 IDH-wt glial tumors, and 12 glia and 13 neuronal reference tissues. The PCA plot data are 386 autosomal targets filtered for methylation β variance > 0.5 among the 46 samples (GoldenGate methylation data). Right: Unsupervised 2-D hierarchical clustering of the same data. C, left, hypomethylated DMT (group delta β > 0.1 and P < 0.05, n = 140 targets) identified in IDH-mutant glioma relative to reference glial tissue. Right, hypermethylated DMT (group delta β > 0.1 and P < 0.05, n = 388 targets) identified in IDH-mutant glioma relative to reference glial tissue (GoldenGate methylation data). D, unsupervised hierarchical clustering of all tumors in the study. Top colorbar: tumor type; bottom colorbar: oncogenotype. The y-axis data are 575 autosomal targets filtered for methylation β variance > 0.5 among the 113 samples. Oncogenotype drives higher level segregation. Also evident is the marked hypermethylation of SDH/IDH-mutant tumors of different lineage and anatomic sites. E, unsupervised PCA plot of 186 study samples annotated as normal tissue, SDH/IDH-mutant tumor, or SDH/IDH-wt/kinase-mutant tumor (var 0.5, 649 targets). F, quantities of significant hyper- and hypomethylated DMT in different tumor lineages as a function of mutant versus wt SDH/IDH status. mut, mutant; wt, wild-type.

Article Snippet: Right, hypermethylated DMT (group delta β > 0.1 and P < 0.05, n = 388 targets) identified in IDH -mutant glioma relative to reference glial tissue (GoldenGate methylation data).

Techniques: Comparison, Mutagenesis, Methylation

Journal: GeroScience

Article Title: Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity

doi: 10.1007/s11357-023-00731-7

Figure Lengend Snippet: Cross-validation analysis of three epigenetic clocks for centenarians. Age estimation 20-fold cross-validation (LOFO20) of the ENCen40 + , ENCen100 + , and NNCen40 + clocks in blood, saliva, and buccals cells, for different age ranges (columns). The panels relate chronological age ( x -axis) to DNAm age estimates ( y -axis) from the ENCen40 + ( A , B , C ) and NNCen40 + ( D , E , F ), and ENCen100 + ( G , H , I ), respectively. Each column corresponds to a different age range. DNA methylation data from age 40 to 115 ( A , D , G ), 100 to 115 ( B , E , H ), and 80 to 115 ( C , F , I ). Each panel reports the sample size (N), the median absolute error (MAE), Pearson correlation coefficient ( r ), the p value ( p ), and each point is color coded by sex (blue = male)

Article Snippet: Please contact Steve Horvath (shorvath@mednet.ucla.edu) regarding access to the methylation data.

Techniques: Biomarker Discovery, DNA Methylation Assay

Journal: GeroScience

Article Title: Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity

doi: 10.1007/s11357-023-00731-7

Figure Lengend Snippet: Individual CpGs and mean CpG in chromatin states. Chronological age ( x -axis) versus A ELOVL2 methylation ( y -axis) or mean methylation in B chromatin state BivProm2, C target sites of polycomb repressive complex 2, D chromatin state EnhA1, E chromatin state TxEx4, and F chromatin state PromF2. Each panel reports the sample size ( N ), Pearson correlation coefficient ( r ), and the p value ( p ), and red line is the LOWESS regression smooth curve

Article Snippet: Please contact Steve Horvath (shorvath@mednet.ucla.edu) regarding access to the methylation data.

Techniques: Methylation

Journal: iScience

Article Title: DNA methylation dynamics associated with long-term isolation of simulated space travel

doi: 10.1016/j.isci.2022.104493

Figure Lengend Snippet:

Article Snippet: The normalized 450k methylation data were downloaded from GeneLab database (GLDS-140), where detailed data normalization flow could be viewed ( ).

Techniques: DNA Methylation Assay, Expressing, Software

Journal: PLoS ONE

Article Title: DNA Methylation in Multiple Myeloma Is Weakly Associated with Gene Transcription

doi: 10.1371/journal.pone.0052626

Figure Lengend Snippet: (A) Pearson correlation was used to measure linear relationships between DNA methylation and gene expression levels for 1505 CpG probes represented on the GoldenGate Methylation BeadArray. The panels represent examples of a gene with high (left) and low (right) Pearson correlation coefficients when analyzing DNA methylation levels (x axis) against gene expression levels (y axis). (B) A discretization approach was used to classify samples into methylated (M) or unmethylated (U) groups based on the mean ( μ ) methylation value and standard deviation ( σ ) of a given probe. Statistically significant gene expression differences between M and U groups indicated a methylation-expression correlation for the gene in question.

Article Snippet: For these approaches we used DNA methylation data obtained with the GoldenGate BeadArray technology along with corresponding array-based gene expression data from 193 human MM samples.

Techniques: DNA Methylation Assay, Expressing, Methylation, Standard Deviation

Journal: PLoS ONE

Article Title: DNA Methylation in Multiple Myeloma Is Weakly Associated with Gene Transcription

doi: 10.1371/journal.pone.0052626

Figure Lengend Snippet: Box plots represent gene expression levels generated by either microarray or qRT-PCR. Data are shown for samples classified as U or M based on the methylation status of p16 (A), DLC1 (B), IGF1R (C), or IL17RB (D). For microarray data, probe intensities are plotted on the y-axis. Relative fold-change differences are plotted for data generated by qRT-PCR. The number of samples in each group is displayed above each plot. The GoldenGate BeadArray probe names are indicated above each pair of box plots.

Article Snippet: For these approaches we used DNA methylation data obtained with the GoldenGate BeadArray technology along with corresponding array-based gene expression data from 193 human MM samples.

Techniques: Expressing, Generated, Microarray, Quantitative RT-PCR, Methylation