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We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein-protein interaction) are pre-loaded and can be used for functional annotations.
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PMID:ExAtlas: An interactive online tool for meta-analysis of gene expression data. 2622 99

Coronary artery disease caused about 1 of every 7 deaths in the United States and early prevention was potential to decrease the incidence and mortality. We aimed to figure the genes involving in the coronary artery disease using meta-anlaysis. Five datasets of coronary heart disease from GEO series were retrieved and data preprocessing and quality control were carried out. Moderated t-test was used to decide the differentially expressed genes for a single dataset. And the combined p-value using systematic-analysis methods were conducted using MetaDE. The pathway enrichment was carried out using Reactome database. Protein-protein interactions of the identified differentially expressed genes were also analyzed using STRING v10.0 online tool. After removing unidentified or intermediate samples and a total of 238 cases and 189 matched or partially matched control from five microarray datasets were retrieved from GEO. Six different quality control measures were calculated and PCA biplots were plotted in order to visualize the quantitative measure. The first two PCs captured 91% of the variance and we decided to include all of the datasets for systematic analysis. Using the FDR cut-off as 0.1, nine genes, including LFNG, ID3, PLA2G7, FOLR3, PADI4, ARG1, IL1R2, NFIL3 and MGAM, were differentially expressed according to maxP. Their protein-protein interactions showed that they were closely connected and 24 Reactome pathways were related to coronary artery disease. We concluded that pathways related to immune responses, especially neutrophil degranulation, were associated with coronary heart disease.
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PMID:Systematic analysis of coronary artery disease datasets revealed the potential biomarker and treatment target. 2890 66

Chaperone-mediated autophagy (CMA) is a lysosomal degradation pathway of select soluble proteins. Nearly one-third of the soluble proteins are predicted to be recognized by this pathway, yet only a minor fraction of this proteome has been identified as CMA substrates in cancer cells. Here, we undertook a quantitative multiplex mass spectrometry approach to study the proteome of isolated lysosomes in cancer cells during CMA-activated conditions. By integrating bioinformatics analyses, we identified and categorized proteins of multiple cellular pathways that were specifically targeted by CMA. Beyond verifying metabolic pathways, we show that multiple components involved in select biological processes, including cellular translation, was specifically targeted for degradation by CMA. In particular, several proteins of the translation initiation complex were identified as bona fide CMA substrates in multiple cancer cell lines of distinct origin and we show that CMA suppresses cellular translation. We further show that the identified CMA substrates display high expression in multiple primary cancers compared to their normal counterparts. Combined, these findings uncover cellular processes affected by CMA and reveal a new role for CMA in the control of translation in cancer cells. Abbreviations: 6-AN: 6-aminonicotinamide; ACTB: actin beta; AR7: atypical retinoid 7; CHX: cycloheximide; CMA: chaperone-mediated autophagy; CQ: chloroquine; CTS: cathepsins; DDX3X: DEAD-box helicase 3 X-linked; EEF2: eukaryotic translation elongation factor 2; EIF4A1: eukaryotic translation initiation factor 4A1; EIF4H: eukaryotic translation initiation factor 4H; GEO: Gene Expression Omnibus; GO: Gene Ontology; GSEA: gene set enrichment analysis; HK2: hexokinase 2; HSPA8/HSC70: heat shock protein family A (Hsp70) member 8; LAMP: lysosomal-associated membrane protein; LDHA: lactate dehydrogenase A; NES: normalized enrichment score; NFKBIA: NFKB inhibitor alpha; PCA: principle component analysis; PQ: paraquat; S.D.: standard deviation; SUnSET: surface sensing of translation; TMT: tandem mass tags; TOMM40/TOM40: translocase of outer mitochondrial membrane 40.
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PMID:Targetome analysis of chaperone-mediated autophagy in cancer cells. 3082 13

Here we report a bio-statistical/informatics tool, ABioTrans, developed in R for gene expression analysis. The tool allows the user to directly read RNA-Seq data files deposited in the Gene Expression Omnibus or GEO database. Operated using any web browser application, ABioTrans provides easy options for multiple statistical distribution fitting, Pearson and Spearman rank correlations, PCA, k-means and hierarchical clustering, differential expression (DE) analysis, Shannon entropy and noise (square of coefficient of variation) analyses, as well as Gene ontology classifications.
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PMID:ABioTrans: A Biostatistical Tool for Transcriptomics Analysis. 3121 45