Combining multi-dimensional data to identify key genes and pathways in gastric cancer

Gastric cancer is an aggressive cancer that is often diagnosed late. Early detection and treatment require a better understanding of the molecular pathology of the disease. The present study combined data on gene expression and regulatory levels (microRNA, methylation, copy number) with the aim of identifying key genes and pathways for gastric cancer. Data used in this study was retrieved from The Cancer Genomic Atlas. Differential analyses between gastric cancer and normal tissues were carried out using Limma. Copy number alterations were identified for tumor samples. Bimodal filtering of differentially expressed genes (DEGs) based on regulatory changes was performed to identify candidate genes. Protein–protein interaction networks for candidate genes were generated by Cytoscape software. Gene ontology and pathway analyses were performed, and disease-associated network was constructed using the Agilent literature search plugin on Cytoscape. In total, we identified 3602 DEGs, 251 differentially expressed microRNAs, 604 differential methylation-sites, and 52 copy number altered regions. Three groups of candidate genes controlled by different regulatory mechanisms were screened out. Interaction networks for candidate genes were constructed consisting of 415, 228, and 233 genes, respectively, all of which were enriched in cell cycle, P53 signaling, DNA replication, viral carcinogenesis, HTLV-1 infection, and progesterone mediated oocyte maturation pathways. Nine hub genes (SRC, KAT2B, NR3C1, CDK6, MCM2, PRKDC, BLM, CCNE1, PARK2) were identified that were presumed to be key regulators of the networks; seven of these were shown to be implicated in gastric cancer through disease-associated network construction. The genes and pathways identified in our study may play pivotal roles in gastric carcinogenesis and have clinical significance

Hetero-Element-Doped Molybdenum Oxide Materials for Energy Storage Systems

In order to meet the growing demand for the electronics market, many new materials have been studied to replace traditional electrode materials for energy storage systems. Molybdenum oxide materials are electrode materials with higher theoretical capacity than graphene, which was originally used as anode electrodes for lithium-ion batteries. In subsequent studies, they have a wider application in the field of energy storage, such as being used as cathodes or anodes for other ion batteries (sodium-ion batteries, potassium-ion batteries, etc.), and electrode materials for supercapacitors. However, molybdenum oxide materials have serious volume expansion concerns and irreversible capacity dropping during the cycles. To solve these problems, doping with different elements has become a suitable option, being an effective method that can change the crystal structure of the materials and improve the performances. Therefore, there are many research studies on metal element doping or non-metal doping molybdenum oxides. This paper summarizes the recent research on the application of hetero-element-doped molybdenum oxides in the field of energy storage, and it also provides some brief analysis and insights

Additional file 3: of Gut metagenomes of type 2 diabetic patients have characteristic single-nucleotide polymorphism distribution in Bacteroides coprocola

Phylogenetic trees of the 63 genes, except the top two, of the 65 genes with the most differentiated SNP distribution. Each tree is in a separate JPG file named by the GenBank accession of the corresponding gene. (RAR 13817 kb

Coherent random lasing controlled by Brownian motion of the active scatterer

The stability of the scattering loop is fundamental for coherent random lasing in a dynamic scattering system. In this work, fluorescence of DPP (N, N-di [3-(isobutyl polyhedral oligomeric silsesquioxanes) propyl] perylene diimide) is scattered to produce RL and we realize the transition from incoherent RL to coherent RL by controlling the Brownian motion of the scatterers (dimer aggregates of DPP) and the stability of scattering loop. To produce coherent random lasers, the loop needs to maintain a stable state within the loop-stable time, which can be determined through controlled Brownian motion of scatterers in the scattering system. The result shows that the loop-stable time is within 5.83 × 10−5 s to 1.61 × 10−4 s based on the transition from coherent to incoherent random lasing. The time range could be tuned by finely controlling the viscosity of the solution. This work not only develops a method to predict the loop-stable time, but also develops the study between Brownian motion and random lasers, which opens the road to a variety of novel interdisciplinary investigations involving modern statistical mechanics and disordered photonics

Additional file 3: of Gut metagenomes of type 2 diabetic patients have characteristic single-nucleotide polymorphism distribution in Bacteroides coprocola

Phylogenetic trees of the 63 genes, except the top two, of the 65 genes with the most differentiated SNP distribution. Each tree is in a separate JPG file named by the GenBank accession of the corresponding gene. (RAR 13817 kb

Additional file 2: of Gut metagenomes of type 2 diabetic patients have characteristic single-nucleotide polymorphism distribution in Bacteroides coprocola

Figure S1. Validation of relative abundance distribution in the T2D and HC groups. Figure S2. Comparisons of SNP densities in 20 gut bacterial species between the T2D and normal groups. Figure S3. Brief flowchart for bioinformatics analysis of metagenomics NGS data at strain level. Figure S4. Distributions of MuAFs at variant sites of B. coprocola in T2D (A), HC (B) and all (C) samples with sufficient NGS reads. Figure S5. Phylogenetic tree of B. coprocola strains based on variant sites with >0.8 MuAFs. Figure S6. Results of AP clustering and hierarchical clustering based on MuAFs of variant sites in B. coprocola. Figure S7. Distributions of MuAFs at variant sites in EDU99824.1 (A) and EDUV02303.1 (B). Figure S8. Results of AP clustering and hierarchical clustering based on MuAFs of variant sites. (PDF 761 kb

Additional file 1: Table S1. of Gut metagenomes of type 2 diabetic patients have characteristic single-nucleotide polymorphism distribution in Bacteroides coprocola

The taxonomic lineages of the 356 bacterial species and accessions of their reference genome assemblies. Table S2. Species with significantly different abundance profiles between the T2D and HC groups. Table S3. Detailed descriptive information for 20 bacterial species with sufficiently supporting NGS reads in a sufficient number of samples. Table S4. Summary of the 1300 genes found to have significantly different SNP densities between the T2D and HC groups. The top 65 genes with the most differentiated SNP distributions between the groups are highlighted in light green. Table S5. Intra-tree distances and enrichment analysis based on phylogenetic trees, hierarchical clustering trees, and affinity propagation clustering for the selected 65 genes. Table S6. SNP densities, intra-tree distances, and numbers of the most biased genes under different mutated allele frequency (MuAF) thresholds (>0.2, >0.5, and >0.8). (XLS 766 kb

Additional file 4: of Gut metagenomes of type 2 diabetic patients have characteristic single-nucleotide polymorphism distribution in Bacteroides coprocola

Results of AP clustering, MuAF distributions and hierarchical clustering for 63 genes in the most differentiated SNP distribution except the top two genes. For a given gene, the clusters of AP clustering, MuAF distribution, and hierarchical tree are presented in separated files named by its GenBank accession, within the folders of APclusterTrees, MuAFhistogram, and HlustTrees, respectively. For gene EDV02481.1, samples were failed to be clustered into two clusters, so it has no AP clustering result. (RAR 14532 kb