Human genome u95a array

Manufactured by Thermo Fisher Scientific

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The Human Genome U95A Array is a high-density oligonucleotide array designed to analyze the expression of approximately 12,600 genes. It provides a comprehensive survey of the human genome and enables researchers to monitor the expression levels of multiple genes simultaneously.

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Lab products found in correlation

Human genome u133 plus 2.0 array, by Thermo Fisher Scientific (3 mentions) Human genome u133a array, by Thermo Fisher Scientific (1 mentions) Human genome u95 version 2 array, by Thermo Fisher Scientific (1 mentions) Humanht 12 v4.0 expression beadchip, by Thermo Fisher Scientific (1 mentions) Rneasy spin column, by Qiagen (1 mentions) Genechip software mas 5, by Thermo Fisher Scientific (1 mentions) Human genome u133a plus 2.0 array, by Thermo Fisher Scientific (1 mentions)

6 protocols using human genome u95a array

Integrated Gene Expression Analysis of OA

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The following gene expression profile datasets were downloaded from the GEO database: GSE55457, GSE55235, GSE12021, GSE10575, and GSE1919. The five GEO gene datasets contained 37 OA samples and 36 control samples (the corresponding information is shown in Table 1); all rheumatoid arthritis (RA) samples were excluded. The profiles of GSE55457, GSE55235, and GSE12021 were based on the GPL96 [HG-U133A] Affymetrix Human Genome U133A Array. The profile of GSE10575 was based on the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. The profile of GSE1919 was based on the GPL91 [HG_U95A] Affymetrix Human Genome U95A Array.

Table 1

Sample statistics of the five GEO gene datasets

Dataset ID	OA	Control	Total
GSE55457	10	10	20
GSE55235	10	10	20
GSE12021	10	9	19
GSE1919	5	5	10
GSE10575	2	2	4

Duan Z.X., Li Y.S., Tu C., Xie P., Li Y.H., Qi L, & Li Z.H. (2020). Identification of a potential gene target for osteoarthritis based on bioinformatics analyses. Journal of Orthopaedic Surgery and Research, 15, 228.

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Comprehensive HNSCC Transcriptome Analysis

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The RNA-seq expression data and clinical data of 525 HNSCC patients in the training group were downloaded from the TCGA dataset¹, which was used as the training cohort. Two independent datasets were used as the validation group were downloaded from the GEO dataset in this study, including GSE65858 (N = 270, Illumina HumanHT-12 V4.0 expression bead chip) (Wichmann et al., 2015 (link)) and GSE2379 (N = 34, Affymetrix Human Genome U95 Version 2 Array and Affymetrix Human Genome U95A Array) (Cromer et al., 2004 (link)). For each dataset, the probe ID was converted to the corresponding gene symbol according to its annotation file without making further standardization. If more than one probe matches the same gene, the average value is calculated as the expression value of the gene. The immune-related gene list was download from the ImmPort database².

He J., Fang X, & Han M. (2021). Discovery and Verification of an Immune-Related Gene Pairs Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma. Frontiers in Genetics, 12, 654657.

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Profiling Gene Expression in Rheumatoid Arthritis Synovium

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The gene expression profile data of RA synovium were downloaded from the Gene Expression Omnibus (GEO, access number: GSE1919) database.¹⁵ Briefly, synovial tissue samples were collected from eight RA patients undergoing synovectomy or arthroplasty, and from 15 normal subjects who died from fatal accidents, and were matched for age and gender. All donors were Caucasians living in Berlin, Germany. Total ribonucleic acid (RNA) was isolated from the synovium using RNeasy spin columns (Qiagen, Hilden, Germany). The isolated total RNA was labelled and hybridised to Affymetrix Human Genome U95A Array (containing 12 600 oligonucleotide probes) following standard protocol. Global normalisation of raw signal intensities was performed by GeneChip software (MAS 5.0, Affymetrix). Data analysis was conducted using DMT 3.0 software (Affymetrix). A detailed description of study subjects, experimental procedures and analysis approaches can be found in the previous study.^{4 (link)}

Xiao X., Hao J., Wen Y., Wang W., Guo X, & Zhang F. (2016). Genome-wide association studies and gene expression profiles of rheumatoid arthritis: An analysis. Bone & Joint Research, 5(7), 314-319.

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Integrative Analysis of NSCLC Transcriptomes

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The Gene Expression Omnibus database (GEO) is a public functional database for high- throughput screening of gene expression data, microarray data, and gene chips. In this study, we recovered genome expression datasets from GEO (Affymetrix Human Genome U133A Plus 2.0 Array, Affymetrix Human Genome U95A Array, and Affymetrix Human Genome U133 Plus 2.0 Array) [GSE1987, GSE17073, GSE 54495, GSE118370]. The GSE1987, GSE17073, GSE 54495, and GSE118370 datasets contain 28, two, 17, and six NSCLC tissue samples and 9, 10, 13, and six non-cancer tissue samples, respectively (Table 1). The software tools used in this study are listed in Supplementary Table 1.

Altaf R., Ilyas U., Ma A, & Shi M. (2023). Identification and validation of differentially expressed genes for targeted therapy in NSCLC using integrated bioinformatics analysis. Frontiers in Oncology, 13, 1206768.

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Microarray Data Exploration from GEO Repository

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GEO¹ is a public repository containing high throughout sequencing and microarray data sets. We selected three gene expression microarray datasets (GSE38713, GSE1919, and GSE12251) from the GEO database. The GSE38713 and GSE12251 datasets were available on the GPL570 platform (HG-U133_Plus_2; Affymetrix Human Genome U133 Plus 2.0 Array), while GSE1919 was accessible on the GPL91 platform (HG_U95A; Affymetrix Human Genome U95A Array).

Chen Y., Li H., Lai L., Feng Q, & Shen J. (2020). Identification of Common Differentially Expressed Genes and Potential Therapeutic Targets in Ulcerative Colitis and Rheumatoid Arthritis. Frontiers in Genetics, 11, 572194.

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Analyzing Insulin Sensitivity in Skeletal Muscle

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We downloaded and re-analyzed a previous published mRNA expression microarray data set measured on Affymetrix Human Genome U95A Array (Affymetrix, Santa Clara, CA, USA) from the Gene Expression Omnibus (GEO) database (GSE22309) [5 (link)]. This data was measured in human skeletal muscle cell of human individuals: 20 insulin sensitive, 20 insulin resistant, and 15 diabetic patients for before and after insulin treatment. We used only the insulin sensitive group. Total of 12,626 probes were monitored, and we considered genes which are protein-coding gene.

Jung H., Han S, & Kim S. (2015). The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations. Genomics & Informatics, 13(3), 76-80.

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