Hg u133a

Manufactured by Thermo Fisher Scientific

Sourced in United States

The HG-U133A is a DNA microarray product from Thermo Fisher Scientific. It is designed for gene expression analysis and provides comprehensive coverage of the human genome. The HG-U133A microarray contains over 22,000 probe sets, allowing for the measurement of the expression levels of a large number of human genes.

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Close spelling variants of this product

HG-U133A 2.0 (16 citations) HG-U133A array (16 citations) HG-U133A GeneChips (14 citations) HG-U133A] Affymetrix Human Genome U133A Array (12 citations) HT HG U133A (10 citations) HG-U133A 2.0 microarray (4 citations) HG U133A 2.0 arrays (4 citations) HG-U133A microarray platform (3 citations) Human Genome U133A Array (HG-U133A) platform (3 citations) HG-U133A 2.0 plates (2 citations)

65 protocols using hg u133a

Glioblastoma Gene Expression Profiling

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GBM gene expression profiles and corresponding clinical data were obtained from three public databases: the TCGA dataset (http://cancergenome.nih.gov), the GEO GSE4271 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4271) and the CGGA dataset (http://cgga.org.cn). All these datasets were generated on Affymetrix platform HG-U133a.
Based on TCGA dataset, we selected 195 patients received post-operationally combined radio-chemotherapy to regain a new cohort, TCGA 195 patient cohort, as training set to identify the gene expression signature. This dataset is a subset of TCGA dataset, and the data was also generated on Affymetrix platform HG-U133a. On the other hand, the TCGA 529 patient cohort, GSE4271 dataset (54 patients) and CGGA dataset (144 patients) were included as validation sets.

Ye N., Jiang N., Feng C., Wang F., Zhang H., Bai H.X., Yang L., Su Y., Huang C., Wanggou S, & Li X. (2019). Combined Therapy Sensitivity Index Based on a 13-Gene Signature Predicts Prognosis for IDH Wild-type and MGMT Promoter Unmethylated Glioblastoma Patients. Journal of Cancer, 10(22), 5536-5548.

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Analyzing INPP5F Expression and Copy Number in Glioblastoma

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For expression analysis, gene expression array data of 389 glioblastomas and non-brain tumors generated using Affymetrix HG_U133A platform was downloaded from TCGA data portal (https://tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp). The .CEL files were normalized and detection calls were generated using MAS5 algorithm in R ver. 2.12. The INPP5F expression data across all the patients were extracted and plot was created using Prism 6. INPP5F expression was also analyzed with REMBRANDT dataset of 577 brain tumors. For copy number analysis, 447 glioblastoma gene centered copy number data were downloaded from TCGA data portal. The copy number data of INPP5F and its neighboring genes BAG3 and MCMBP on chromosome 10 were extracted and the heatmap of these genes were created in MATLAB R2009b.

Kim H.S., Li A., Ahn S., Song H, & Zhang W. (2014). Inositol Polyphosphate-5-Phosphatase F (INPP5F) inhibits STAT3 activity and suppresses gliomas tumorigenicity. Scientific Reports, 4, 7330.

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Comparative Analysis of p54nrb Expression in Cancers

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To analyze the expressional level of p54nrb gene in cervical tumors, colon carcinoma, and melanoma, expressional levels were compared to control samples by using Oncomine database (Thermo Fisher Scientific). In the study by Scotto et al RNA isolated from 29 cervical carcinoma cases (20 primary tumors enriched for tumor cells by microdissection and 9 cell lines) and 20 microdisssected normal cervical squamous epithelial cells were used for expression studies. [23 (link)]. RNA expressional levels were detected by employing Affymetrix high density microarrays. The array data are available from the NCBI Gene Expression Omnibus using series accession number GSE7803. In the study of Notterman et al gene expression of 18 colon adenocarcinoma and 18 paired normal tissue was measured by using the Human 6500 GeneChip Set (Affymetrix, Santa Clara, CA USA) as previously described [24 (link)]. In the study bz Talantov et al the RNA was isolated from 7 normal skin and 45 cutaneous melanoma samples as previously described [25 (link)]. The microarray scanning was performed with high-densitiy oligonucleotide microarrays (HG_U133A, Affymetrix).

Eichler M., Distler U., Nasrullah U., Krishnan A., Kaulich M., Husnjak K., Eberhardt W., Rajalingam K., Tenzer S., Pfeilschifter J, & Imre G. (2022). The caspase-2 substrate p54nrb exhibits a multifaceted role in tumor cell death susceptibility via gene regulatory functions. Cell Death & Disease, 13(4), 386.

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Breast Cancer Molecular Subtypes Network Analysis

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The network architecture of breast cancer molecular subtypes has been previously analyzed (de Anda-Jáuregui et al., 2016 (link)). There, network inference was carried out by using data on 493 microarray expression profiles for breast cancer samples processed on the Affymetrix HGU133A platform. Mutual information calculations were performed by means of the ARACNe algorithm (Margolin et al., 2006 (link)). PAM50-subtyped gene expression datasets were obtained as in de Anda-Jáuregui et al. (2015 (link)). From PAM50 algorithm we conserved the HER2+ subtypes only. In this work, we built upon such transcriptional network structure to carry out posterior analyses. As a result of this network inference, nodes represent genes in the transcriptional space and edges is the statistical dependence between two genes, which is a robust measure of the degree of co-expression existing in any couple of genes, and edges the statistical dependence between two genes, which is a robust measure of the degree of co-expression existing in any pair of genes.

Alcalá-Corona S.A., Espinal-Enríquez J., de Anda-Jáuregui G, & Hernández-Lemus E. (2018). The Hierarchical Modular Structure of HER2+ Breast Cancer Network. Frontiers in Physiology, 9, 1423.

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Validating HGSOC Methylation Patterns in TCGA

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Illumina Human Methylation27 Beadchip data on HGSOC from The Cancer Genome Atlas data portal (http://cancergenome.nih.gov/dataportal) (“TCGA Cohort”) was used for independent validation of correlations observed in the Hammersmith cohort. Level 2 expression data on Affymetrix HGU133A microarrays, level 3 methylation data and annotated clinical data were obtained. The expression microarray data was pre-processed and normalised across samples.

Bonito N.A., Borley J., Wilhelm-Benartzi C.S., Ghaem-Maghami S, & Brown R. (2016). Epigenetic regulation of the homeobox gene MSX1 associates with platinum resistant disease in high grade serous epithelial ovarian cancer. Clinical cancer research : an official journal of the American Association for Cancer Research, 22(12), 3097-3104.

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Breast Cancer Survival Analysis of DPP Genes

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Gene expression levels correlations of mRNAs of DPP genes with breast cancer patients’ survival, such as relapse-free survival (RFS), were investigated using the KM plotter database (https://kmplot.com/, accessed on 1 May 2021) [38 (link)]. The breast cancer database was established using gene expression data and survival information of 2898 patients acquired from the Gene Expression Omnibus (GEO) (Affymetrix HGU133A microarrays platform). The numbers of patients in high- and low-risk groups were also displayed along with the survival duration on the horizontal axis. Poor survival status of patients was based on log-rank p values smaller than 0.05 for statistically significant differences between low and high mRNA expression of the target genes. The HR ratio was displayed as a mean, together with 95% confidence intervals (CI). All analyses in the KM plotter database were performed with default parameters for calculating survival curves, log-rank p values, as well as hazard ratios (HRs) with 95% CIs.

Choy T.K., Wang C.Y., Phan N.N., Khoa Ta H.D., Anuraga G., Liu Y.H., Wu Y.F., Lee K.H., Chuang J.Y, & Kao T.J. (2021). Identification of Dipeptidyl Peptidase (DPP) Family Genes in Clinical Breast Cancer Patients via an Integrated Bioinformatics Approach. Diagnostics, 11(7), 1204.

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Exploring EFEMP2 Expression and Breast Cancer Outcomes

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Oncomine (

https://www.oncomine.org

) is the world’s largest oncogene chip database and integrated data mining platform, aimed at exploring genetic information about cancer. We used this tool to compare EFEMP2 expression between clinical breast cancer specimens and normal control samples. A Kaplan–Meier plotter (

http://kmplot.com/analysis/

) was used to assess the relevance of gene expression on clinical outcomes of breast cancer patients. A background database was established using the gene expression data and relapse free and overall survival information downloaded from the Gene Expression Omnibus (GEO) (Affymetrix HGU133A and HGU133+2 microarrays), European Genome-phenome Archive (EGA) and the Cancer Genome Analysis (TCGA).18 (link) This online tool was used to assess the correlation between EFEMP2 expression and breast cancer patient survival.

Kang N., Zhou J., Xu J., Zhou D, & Shi W. (2019). EFEMP2 Inhibits Breast Cancer Invasion And Metastasis In Vitro And In Vivo. OncoTargets and therapy, 12, 8915-8933.

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Intestinal Transcriptome Analysis

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The molecular analysis was started by extracting total RNA from the collected fragments of the large intestine stored in RNAlater (Qiagen, Hilden, Germany) at a temperature of −80 °C. In further stages of the study, RNA was the array for the assessment of intestinal transcriptome, using expression microarrays HG-U133A (Affymetrix^®, Santa Clara, CA, USA) and validation of selected transcripts.

Janikowska G., Janikowski T., Plato M., Mazurek U., Orchel J., Opiłka M, & Lorenc Z. (2023). Histaminergic System and Inflammation-Related Genes in Normal Large Intestine and Adenocarcinoma Tissues: Transcriptional Profiles and Relations. International Journal of Molecular Sciences, 24(5), 4913.

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Transcriptome Analysis of Relapsed Leukemia

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Total RNA was extracted with TriReagent (Molecular Research Center, Inc.) from freshly isolated or cryopreserved mononuclear cell suspensions from patient bone marrow aspirates obtained at diagnosis. In the COG relapsed cohort, an RNA sample was extracted from leukemia cells from patient bone marrow aspirates obtained at the time of original diagnosis and again at the time of disease recurrence. All gene expression microarrays were performed by the St. Jude Children’s Research Hospital, Hartwell Center for Bioinformatics & Biotechnology. High-quality RNA was hybridized to the HG-U133A (GPL96) or HG-U133 Plus 2.0 (GPL570) oligonucleotide microarrays in accordance with the manufacturer’s protocol (Affymetrix). These microarrays contain 22,283 or 54,675 gene probe sets, representing approximately 18,400 or 47,400 human transcripts, respectively. Gene expression data were MAS5⁴¹ processed using the affy⁴² Bioconductor⁴³ R-project package or using Affymetrix Microarray Suite version 5.0^{44 ,45} as previously described^{28 (link)}.

Paugh S.W., Bonten E.J., Savic D., Ramsey L.B., Thierfelder W.E., Gurung P., Malireddi R.K., Actis M., Mayasundari A., Min J., Coss D.R., Laudermilk L.T., Panetta J.C., McCorkle J.R., Fan Y., Crews K.R., Stocco G., Wilkinson M.R., Ferreira A.M., Cheng C., Yang W., Karol S.E., Fernandez C.A., Diouf B., Smith C., Hicks J.K., Zanut A., Giordanengo A., Crona D., Bianchi J.J., Holmfeldt L., Mullighan C.G., den Boer M.L., Pieters R., Jeha S., Dunwell T.L., Latif F., Bhojwani D., Carroll W.L., Pui C.H., Myers R.M., Guy R.K., Kanneganti T.D., Relling M.V, & Evans W.E. (2015). NALP3 inflammasome up-regulation and CASP1 cleavage of the glucocorticoid receptor causes glucocorticoid resistance in leukemia cells. Nature genetics, 47(6), 607-614.

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Differential Expression Analysis of Osteosarcoma

Cited 2 times

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Raw probe-level data (.CEL files) from Affymetrix HG U133A microarray experiments were collected from the Gene Expression Omnibus (GEO) and reanalyzed in order to evaluate human osteoblast patient samples. The series GSE16088 (130 (link)) included array data for 14 OS tissue samples, whereas the series GSE14359 (131 (link)) included array data for 10 OS tissue samples, four metastatic lung OS tissue samples, and a normal bone tissue sample. Normal osteoblast states were examined utilizing the GSE39262 series. The CEL files were read using the R package “oligo” (142 (link)). Averages of the probe set of values were normalized using the robust multichip algorithm (RMA) method of “oligo” R package, as well as heatmap and PCA plots. The “limma” R package was used to do differential expression analysis (143 (link)). Genes with adjusted p-values ≤ 0.05 and log2 foldchanges ≥2 were determined to be differentially expressed.

Capobianco E., McGaughey V., Seraphin G., Heckel J., Rieger S, & Lisse T.S. (2023). Vitamin D inhibits osteosarcoma by reprogramming nonsense-mediated RNA decay and SNAI2-mediated epithelial-to-mesenchymal transition. Frontiers in Oncology, 13, 1188641.

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