Information about the distribution of specific gene hits among the different GBM subtypes has been obtained from The Cancer Genome Atlas (TCGA) through the ‘Expression box plot (Affymetrix HT HG U133A)’ and ‘Expression box plot (Affymetrix Human Exon 1.0 ST)’ graphs on the Betastasis website (www.betastasis.com ) that organize patients’ samples according to their GBM subtypes.
Ht hg u133a
Manufactured by Thermo Fisher Scientific
The HT HG U133A is a gene expression microarray platform from Thermo Fisher Scientific. It is designed to analyze the expression levels of over 22,000 well-characterized human genes and transcripts. The HT HG U133A utilizes high-density oligonucleotide probe arrays to provide a comprehensive view of the human transcriptome.
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Lab products found in correlation
Ht human genome u133a array, by Thermo Fisher Scientific (2 mentions)
Human exon 1.0 st, by Thermo Fisher Scientific (1 mentions)
Snp 6, by Illumina (1 mentions)
Infinium humanmethylation27, by Illumina (1 mentions)
Humanht 12 v4.0 expression beadchip, by Illumina (1 mentions)
Hiseq rna seq platform, by Illumina (1 mentions)
Sas version 9.3 statistical software, by SAS Institute (1 mentions)
10 protocols using ht hg u133a
1
Glioblastoma Subtype Gene Expression
2
Prognostic Significance of SOX2 and PROM1 in GBM
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Analysis of bulk tumor RNA-sequencing was performed on 169 GBM tumor samples and 5 normal control samples obtained from The Cancer Genome Atlas Project (TCGA). HTSeq-FPKM files, clinical, and metadata were downloaded using the GDC Data Transfer Tool and GDC data portal from the National Cancer Institute per the data manifest. A log2(FPKM + 1) transformation was then performed on all FPKM values and normal sample controls were removed. GBM tumor samples were then separated based on the expression of individual genes at the median: separating high SOX2 expressing tumors (above the median) from low SOX2 expressing tumors (below the median), and high PROM1 expressing tumors (above the median) from low PROM1 expressing tumors (below the median). Survival analysis (Log-rank (Mantel-Cox) test) was then performed, where tumors were classified based on high or low expression of PROM1 or SOX2 alone and in combination with MGMT methylation status. MGMT methylation status was mapped to samples by matching the submitter IDs to case IDs where data was available from Brennan et al. [11 (link)]. For two-gene correlations, the Betastasis two-gene scatterplot tool (Affymetrix HT HG U133A) and the glioblastoma Rembrandt (GEO GSE108476) dataset were used, with log-2 (FPKM + 1) transformation.
3
Analyzing Glioma Subtypes through TCGA-REMBRANDT Data
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Detailed patient data were obtained from the The Cancer Genome Atlas (TCGA) REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT) databases publicly available at the Project Betastasis website4 . The graphical representation of gene expression data for CaMK2 isoforms in different glioma subtypes were downloaded from the REMBRANDT. The transcriptome analysis and Gene survival association (Affymetrix HT HG U133A) for Glioblastoma subtypes were downloaded from TCGA portal in betastasis4. The patient survival curves and corresponding Log rank test p-values were obtained by setting “median range” of survival in 453 patients.
4
Isolation and Profiling of Human Aortic Endothelial Cells
5
TCGA Glioblastoma Transcriptomic Analysis
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The collection of the data from TCGA (TCGA Research, 2008) was compliant with all applicable laws, regulations, and policies for the protection of human subjects, and necessary ethical approvals were obtained. Experimental and clinical data were downloaded (https://tcga-data.nci.nih.gov/ ), as described in TCGA Research Network. For analysis of mature microRNA, precursor microRNA, and gene expression in glioblastoma, we used normalization of data and aggregation at the feature level as designated by the TCGA glioblastoma the “Level3.” Transcript expression data have been generated from experiments on three different platforms: Affymetrix HT HG U133A (10 + 488 patient samples × 12,042 features). Data were analyzed using free available portals (GlioVis; GBM-BioDP; Betastasis) as a resource for accessing and displaying interactive views of TCGA data associated with glioblastoma.
6
Comprehensive Molecular Profiling of Tumors
7
Glioblastoma Molecular Profiling via TCGA
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From The Cancer Genome Atlas (TCGA) website, level 3 normalized and processed gene expression dataset for 49 genes coding for ligands, receptors, co-receptors, destruction complex, transcriptional effectors, antagonists, downstream targets, tumor suppressors, and apoptotic genes involved in SHH, as well as Wnt/β-catenin canonical and non-canonical Wnt signaling pathways (Table 1 ) was compiled. In all, data belonging to a total of 431 GBM and 10 normal tissue samples were downloaded. The microarray platform used was Affymetrix HT_HG-U133A platform and the GBM samples were primary GBM samples.
8
Integrated Liver Cancer Transcriptomics
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The gene expression and clinical data covering age, gender, stage, and survival data were rooted in the GEO (https://www.ncbi.nlm.nih.gov/geo/ ), ICGC (https://icgc.org/ ), and TCGA databases (https://tcga-data.nci.nih.gov/ ). Gene expression and clinical data of the GSE14520 series (Roessler et al., 2010 (link)) and the GSE76427 series (Grinchuk et al., 2018 (link)) originated from the GEO database. Gene expression and clinical data of GSE14520 based on the GPL3921 platform [HT_HG-U133A] (Affymetrix HT Human Genome U133A Array), including 241 non-tumor specimens and 247 tumor specimens (including 227 paired HCC specimens). Gene expression and clinical data of GSE76427 based on the GPL10558 platform (Illumina HumanHT-12 V4.0 expression bead chip), including 52 non-tumor specimens and 115 tumor specimens (including 52 paired HCC specimens). The gene expression files (ICGC-LIRI-JP) of the ICGC database based on the Illumina HiSeq RNA Seq platform, including 202 non-tumor specimens and 243 tumor specimens (including 202 paired HCC specimens), were obtained from the ICGC database. The RNA sequencing and clinical data (TCGA-LIHC-FPKM) based on the Illumina HiSeq RNA-Seq platform, including 50 non-tumor specimens and 374 tumor specimens (including 50 paired HCC specimens), were obtained from the TCGA database.
9
Predictive Value of miRNA Expression on Survival in Cancer Patients
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The MM miR gene expression data and clinical data were downloaded from the TCGA website with the platform Affymetrix HT_HGU133A. Gene expression data and matching clinical data were available for 87 patients. The TCGA miR gene expression data were extracted and merged with clinical outcome data for further analysis. We examined the predictive value of various miRs on survival treating individual miR gene expression as a continuous measurement as well as a categorical variable, that is, by collapsing miR gene expression values into two groups: lower 50% (44 patients) and upper 50% (43 patients) from the median value. The Kaplan–Meier method was used to estimate the overall survival, and the difference of survival among groups was examined using a log‐rank test. The Cox proportional hazards model was performed to estimate the predictive value of miR gene expression on survival, and the hazard ratio was obtained from this model. Analyses were performed using sas version 9.3 statistical software (SAS Institute Inc., Cary, NC, USA) or r 2.1.15 (https://www.r-project.org/ ).
10
Integrative Transcriptomic and Epigenomic Analysis
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Gene expression and clinical files were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/ ) and TCGA databases (https://portal.gdc.cancer.gov/repository ). DNA methylation and copy number variation (CNV) files were also obtained from TCGA (Wang et al., 2016 (link)). The gene expression and clinical files of GSE14520 (Chen et al., 2021 (link)) originated from the GEO database and are based on the GPL3921 platform [HT_HG-U133A] (Affymetrix HT Human Genome U133A Array), covering 241 normal and 247 tumor samples. The mRNA expression of TCGA (TPM format) includes 50 normal and 374 tumor samples. DNA methylation data of TCGA have 50 normal and 379 tumor samples. The “sva” R package was applied for data correction and normalization.
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