A kognitív készségek rendszere és fejlődése

Additional file 7: Figure S1. The KEGG pathways separately enriched with hypermethylated (a) and hypomethylated (b) genes in at least 10% of the 539 TCGA lung adenocarcinoma samples

First-Principles Study of a Zirconium-Terminated Diamond (100) Surface with Promising Negative Electron Affinity and Surface Stability

Chemical modification of diamond surfaces generates a negative electron affinity (NEA), which shows great potential in realizing electron emission. In this study, zirconium (Zr) termination on clean and oxidized diamond (100) surfaces is theoretically proposed by using the structure prediction method, and electronic properties of these predicted surfaces are investigated by first-principles calculations. On the oxidized surfaces, the adsorption energy at 0.25 monolayer (ML) Zr coverage reaches a high value of −10.42 eV, further confirmed by the largest integrated crystal orbital Hamiltonian population value of 6.61 eV. For clean and oxidized diamond (100) surfaces, the largest NEA values at 0.25 ML Zr coverage are −3.75 eV and −3.45 eV, respectively. The dynamic stability of these surface structures is demonstrated by calculating phonon dispersion curves. Furthermore, ab initio molecular dynamics simulations confirm the high thermal stability of the oxidized diamond surface. Therefore, these results indicate that Zr-terminated diamond (100) surfaces possess good thermal stability and higher NEA, making them promising candidate materials for electron emission applications

The datasets of nine cancer types for analyzing batch effects.

<p>The datasets of nine cancer types for analyzing batch effects.</p

Batch effects on DM genes of six cancer types.

<p>For each cancer type denoted in the x-axis, a box plot in the y-axis represents the consistency score defined as the proportion of DM genes with consistent methylation states among all overlapping DM gene commonly detected in both of the two groups (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029686#s2" target="_blank">‘Methods’</a> section). The consistency score takes value ranging from 0 (no consistent states) to 1 (100% consistent states). Each box stretches from the lower hinge (defined as the 25th percentile) to the upper hinge (the 75th percentile) and the median is shown as a line across the box.</p

Batch effects on tumour samples for nine cancer types.

<p>(a) different batches and different laboratories; (b) the same laboratory but different batches; (c) the same batch but different laboratories; (d) Hierarchical clustering the tumour samples of ovarian serous cystadenocarcinoma in batch 9 and batch 12. For a cancer type denoted in the x-axis in graph a, b or c, a box plot in the y-axis represents the percentage of probes significantly susceptible to different batch conditions. The percentage takes value ranging from 0 (no susceptible probe) to 1 (100% susceptible probes). Each box stretches from the lower hinge (defined as the 25th percentile) to the upper hinge (the 75th percentile) and the median is shown as a line across the box.</p

Concordance between differential methylation and differential expression.

§<p>Gene number denotes the number of hypermethylated (or hypomethylated) genes which were determined to be differentially expressed in the expression data.</p

Keratin associated protein genes hypomethylated in five cancers.

<p>Keratin associated protein genes hypomethylated in five cancers.</p

The Methylation and Expression datasets of five cancer types for concordance analysis.

#<p>Each dataset is denoted by the following nomenclature: initial character of the cancer type followed by the total number of samples of the dataset; NA, not available.</p

Distinct Functional Patterns of Gene Promoter Hypomethylation and Hypermethylation in Cancer Genomes

<div><h3>Background</h3><p>Aberrant DNA methylation plays important roles in carcinogenesis. However, the functional significance of genome-wide hypermethylation and hypomethylation of gene promoters in carcinogenesis currently remain unclear.</p> <h3>Principal Findings</h3><p>Based on genome-wide methylation data for five cancer types, we showed that genes with promoter hypermethylation were highly consistent in function across different cancer types, and so were genes with promoter hypomethylation. Functions related to “developmental processes” and “regulation of biology processes” were significantly enriched with hypermethylated genes but were depleted of hypomethylated genes. In contrast, functions related to “cell killing” and “response to stimulus”, including immune and inflammatory response, were associated with an enrichment of hypomethylated genes and depletion of hypermethylated genes. We also observed that some families of cytokines secreted by immune cells, such as IL10 family cytokines and chemokines, tended to be hypomethylated in various cancer types. These results provide new hints for understanding the distinct functional roles of genome-wide hypermethylation and hypomethylation of gene promoters in carcinogenesis.</p> <h3>Conclusions</h3><p>Genes with promoter hypermethylation and hypomethylation are highly consistent in function across different cancer types, respectively, but these two groups of genes tend to be enriched in different functions associated with cancer. Especially, we speculate that hypomethylation of gene promoters may play roles in inducing immunity and inflammation disorders in precancerous conditions, which may provide hints for improving epigenetic therapy and immunotherapy of cancer.</p> </div

Genes Dysregulated to Different Extent or Oppositely in Estrogen Receptor-Positive and Estrogen Receptor-Negative Breast Cancers

<div><p>Background</p><p>Directly comparing gene expression profiles of estrogen receptor-positive (ER+) and estrogen receptor-negative (ER−) breast cancers cannot determine whether differentially expressed genes between these two subtypes result from dysregulated expression in ER+ cancer or ER− cancer versus normal controls, and thus would miss critical information for elucidating the transcriptomic difference between the two subtypes.</p><p>Principal Findings</p><p>Using microarray datasets from TCGA, we classified the genes dysregulated in both ER+ and ER− cancers versus normal controls into two classes: (i) genes dysregulated in the same direction but to a different extent, and (ii) genes dysregulated to opposite directions, and then validated the two classes in RNA-sequencing datasets of independent cohorts. We showed that the genes dysregulated to a larger extent in ER+ cancers than in ER− cancers enriched in glycerophospholipid and polysaccharide metabolic processes, while the genes dysregulated to a larger extent in ER− cancers than in ER+ cancers enriched in cell proliferation. Phosphorylase kinase and enzymes of glycosylphosphatidylinositol (GPI) anchor biosynthesis were upregulated to a larger extent in ER+ cancers than in ER− cancers, whereas glycogen synthase and phospholipase A2 were downregulated to a larger extent in ER+ cancers than in ER− cancers. We also found that the genes oppositely dysregulated in the two subtypes significantly enriched with known cancer genes and tended to closely collaborate with the cancer genes. Furthermore, we showed the possibility that these oppositely dysregulated genes could contribute to carcinogenesis of ER+ and ER− cancers through rewiring different subpathways.</p><p>Conclusions</p><p>GPI-anchor biosynthesis and glycogenolysis were elevated and hydrolysis of phospholipids was depleted to a larger extent in ER+ cancers than in ER− cancers. Our findings indicate that the genes oppositely dysregulated in the two subtypes are potential cancer genes which could contribute to carcinogenesis of both ER+ and ER− cancers through rewiring different subpathways.</p></div