We analyzed CAS/CSE1L gene expression profiles using three public datasets from the Gene Expression Omnibus (GEO) (16). Two platforms were considered: the Affymetrix HG-U133A GeneChip and the Illumina Human V2 array. Using GeneAnnot (17), we found three Affymetrix HG-U133A probesets to be of highest quality, which were used for our analysis: 201111_at, 201112_s_at, and 210766_s_at. The three datasets consisted of 1) GSE1420 (8 paired normal esophageal epithelium, BE, and esophageal adenocarcinoma) (18) run on the Affymetrix HG-U133A array; 2) GSE26886 (19 esophageal squamous epithelium from healthy patients, 20 BE, 21 esophageal adenocarcinomas, and 9 esophageal squamous cell carcinoma (19) run on the HG-U133A array; and 3) GSE13898 (64 primary esophageal adenocarcinomas, 15 BE, and 28 surrounding normal fresh frozen tissues) (20) run on the Illumina Human V2 array. Affymetrix GeneChip data were processed using RMA (21) for normalization. GSE13898 was downloaded directly from GEO. Data were visualized using boxplots, and differences among groups of samples were identified using the Kruskal-Wallis test.
Hg u133a array
Manufactured by Thermo Fisher Scientific
Sourced in United States
The HG-U133A array is a high-density oligonucleotide microarray manufactured by Thermo Fisher Scientific. It is designed to analyze the expression of over 22,000 human genes and transcripts. The array utilizes probe sets to interrogate specific regions of the human genome, enabling comprehensive gene expression profiling.
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Lab products found in correlation
Hg u133 plus 2.0 array, by Thermo Fisher Scientific (3 mentions)
Human ht 12 expression beadchips, by Illumina (2 mentions)
Trizol reagent, by Thermo Fisher Scientific (1 mentions)
Rneasy spin column, by Qiagen (1 mentions)
T7 polymerase, by New England Biolabs (1 mentions)
Fluidics station 400, by Thermo Fisher Scientific (1 mentions)
Gene array scanner 2500, by Agilent Technologies (1 mentions)
Microarray suite 5.0 mas5, by Thermo Fisher Scientific (1 mentions)
Hiseq rna seqv2 rsem, by Illumina (1 mentions)
Transcriptome analysis console, by Thermo Fisher Scientific (1 mentions)
16 protocols using hg u133a array
1
Esophageal Cancer Gene Expression Profiles
2
Global Gene Expression Analysis of DiFi Cells
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For global gene expression analysis, total RNA from parental DiFi, DiFi5, and DiFi-AG cells was extracted using Trizol Reagent (Life Technologies) and then subjected to clean-up and DNase digestion on an RNeasy spin column (QIAGEN). RNA was quantified using a UV spectrophotometer, and quality was assessed on agarose gel. Each RNA was then converted into double-strand cDNA using a T7 polymerase (New England BioLabs) and purified. Probes were prepared using standard Affymetrix protocols and hybridized to an Affymetrix HG-U133A array according to the manufacturer's instructions. Following hybridization, the array was scanned using a laser confocal scanner, and microarray image data were analyzed using DNA-Chip Analyzer (dChip), version 1.3, by the Sequencing and Microarray Facility at MD Anderson Cancer Center. Two independent microarray analyses were performed. The microarray data reported in this paper have been submitted to the gene expression omnibus (GEO) database at National Center for Biotechnology Information, with the access number GSE71210.
3
Comprehensive Transcriptomic Analysis of CN-AML
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All the microarray data used in our study were derived from the Gene Expression Omnibus (GEO) and were available for public downloading. Expression data for the 157 CN-AML patients in the primary cohort were detected using an Affymetrix HG-U133Plus 2.0 array [45 (link)], while the validating cohort of 162 CN-AML patients was evaluated using an Affymetrix HG-U133A array [49 (link)]. High-throughput sequencing data from The Cancer Genome Atlas (TCGA) were also used [50 (link)], including mRNA, microRNA and methylation data.
4
Microarray Analysis of Differentially Expressed Genes
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Microarray experiment data were analysed using R and Bioconductor packages.14 (link) Briefly, the human genome HGU133A array (Affymetrix) scans output was preprocessed using the affy package.15 The limma package was used to evaluate differential expressed genes.16 17 Gene annotations were performed using hgu133a.db, KEGG.db database packages.18–21 Genes were considered differentially expressed if they had a Benjamini and Hochberg (BH)-adjusted p value <1.5e-3 and >±2-fold change in gene expression. Bonferroni p value adjustments were also performed for comparison. Canonical pathways and functional networks that involve the differently expressed genes play were determined using the Ingenuity Pathway Analysis (IPA) catalogue of well-characterised metabolic and cell signalling cascades. Expression data can be accessed using accession number GSE47172 at the NCBI GEO database.
5
Integrative Analysis of Lung Cancer Transcriptomes
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Training datasets (GSE19804, GSE19188, GSE18842, GSE40791, GSE10072) based on the Affymetrix platform (Affymetrix HG-U133 Plus 2.0 array and HG-U133A array) and corresponding clinical information of lung cancer patients were retrieved from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo ). Three NSCLC genome-wide expression profiles were extracted from the following three datasets: GSE19804, which includes 60 paired tumor and normal tissues, GSE19188, including 91 tumor and 65 adjacent normal lung tissues, and GSE18842, which includes 46 tumors and 45 controls. Two LUAD genome-wide expression profiles were extracted from GSE40791 and GSE10072, with the former including 94 tumor and 100 non-tumor tissues and the latter containing 58 tumor and 49 non-tumor tissues.
Validation datasets were downloaded from the Cancer Genome Atlas (TCGA) data portal (http://tcga-data.nci.nih.gov ). We selected 349 tumor and 58 NTL samples, with both mRNA expression data and clinical features information available for performing the correlation analysis and survival analysis. Detailed clinical information of patients used in this study was shown in Table 1 .
Validation datasets were downloaded from the Cancer Genome Atlas (TCGA) data portal (
6
Affymetrix Gene Expression Profiling
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Global examination of gene expression was performed with an Affymetrix HG-U133A array (Affymetrix, Santa Clara, CA, USA) using standard conditions (16 h, 45 °C) as described [1 (link),2 (link)]. Arrays were washed and stained in a Fluidics Station 400 (Affymetrix) and scanned on a Gene Array Scanner 2500 (Agilent, Santa Clara, CA, USA). Raw fluorescence intensities from all hybridizations were normalized by applying variance stabilization with additional scaling. MAS5 and gcRMA expression values were calculated. Data and cluster analyses were performed using Affymetrix Microarray Suite 5.0 (MAS5) and GeneSpring GX software. Probes were made from total RNA following the guidelines given in the Affymetrix GeneChip Expression Analysis Technical Manual. An amount of 10 µg of fragmented, labelled cDNA was used for hybridization [1 (link),2 (link)].
7
Identifying Aging-Related Gene Signatures
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To define the aging-related gene expression signature, the probe IDs from each tissue were merged into categories defined by gene symbols. Those gene symbols were separated into genes with 2.5-fold up- and down-regulated expression in the young and old groups for the connectivity map. The two groups of gene symbols were converted into the probe IDs of the Affymetrix HG U133A array for connectivity map analysis [20 (link)]. The top 40-ranked chemicals with statistical significance were selected for each tissue and the lists were compared with each other to detect overlaps.
8
Lung Cancer Genomic Profiling
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Training data sets (GSE19804, GSE30219, GSE32863, GSE63459) based on the Affymetrix platform (Affymetrix HG-U133 Plus 2.0 array and HG-U133A array) and corresponding clinical information were retrieved from the Gene Expression Omnibus. Two non-small cell lung cancer (NSCLC) genome-wide expression profiles were extracted from GSE19804 (including 60 paired tumors and normal tissues) and GSE30219 (including 293 tumors and 14 non-tumor tissues). Two LUAD genome-wide expression profiles were extracted from GSE32863 (including 58 paired tumors and normal tissues) and GSE63459 (including 65 tumor tissues).
9
Differential Gene Expression Analysis of Glioblastoma Subtypes
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Level 3 probe collapsed, median normalized messenger RNA (mRNA) expression data (Affymetrix HG U133A array) was downloaded using the TCGA Data portal in June 2014. We identified genes that were differentially expressed by at least 1.2 foldchange between tumors with higher or lower than median CE parameter and performed Benjamini-Hochberg correction [25] . After correction, genes with p < 0.05 were considered significant. The list of differentially expressed genes were functionally annotated using the Database for Annotation, Visualization, and Integrated Discovery [26, 27] (DAVID; https://david.ncifcrf.gov/) to determine cellular processes mediated by the differentially expressed genes. Genomic subtypes (Proneural, Neural, Classical, Mesenchymal) were determined using single sample gene set enrichment analysis [5, 28] . Original source data used in this study had been anonymized according to standard HIPAA protocol and all contributing sites had approval by their institutional review board.
10
Comparative Analysis of RNA-Seq and Microarray Data
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TCGA data for ovarian cancer (OV) and lung
squamous cell carcinoma (LUSC) assayed by RNA-seq were downloaded
from xena.ucsc.edu, as estimated gene counts and TPMs for each gene
in each TCGA sample, analyzed via the TOIL pipeline.11 (link) TCGA data for OV and LUSC, assayed by Affymetrix HG U133a
arrays, were obtained from GEO (accession numbers GSE68661 and GSE68793 for
OV and LUSC, respectively) and were analyzed in R, using the methods
described above for Affymetrix arrays. For the computation of expression
ratios between samples, estimated counts from the TOIL pipeline were
input into edgeR, to obtain normalized gene expression in CPMs, allowing
for the comparison of expression ratios for genes between pairs of
samples. RNA-seq data from the CCLE2 (link) for
cell lines were downloaded as gene expression in TPMs from the EBI
expression atlas,12 (link) from data provided
on that portal, analyzed via the iRAP pipeline.13 (link)
squamous cell carcinoma (LUSC) assayed by RNA-seq were downloaded
from xena.ucsc.edu, as estimated gene counts and TPMs for each gene
in each TCGA sample, analyzed via the TOIL pipeline.11 (link) TCGA data for OV and LUSC, assayed by Affymetrix HG U133a
arrays, were obtained from GEO (accession numbers GSE68661 and GSE68793 for
OV and LUSC, respectively) and were analyzed in R, using the methods
described above for Affymetrix arrays. For the computation of expression
ratios between samples, estimated counts from the TOIL pipeline were
input into edgeR, to obtain normalized gene expression in CPMs, allowing
for the comparison of expression ratios for genes between pairs of
samples. RNA-seq data from the CCLE2 (link) for
cell lines were downloaded as gene expression in TPMs from the EBI
expression atlas,12 (link) from data provided
on that portal, analyzed via the iRAP pipeline.13 (link)
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