Article ; Online: A transcriptomic dataset used to derive biomarkers of chemically induced histone deacetylase inhibition (HDACi) in human TK6 cells
Data in Brief, Vol 36, Iss , Pp 107097- (2021)
2021
Abstract: Transcriptomic biomarkers facilitate mode of action analysis of toxicants by detecting specific patterns of gene expression perturbations. We identified an 81-gene transcriptomic biomarker of histone deacetylase inhibitors (HDACi) using whole ... ...
| Abstract | Transcriptomic biomarkers facilitate mode of action analysis of toxicants by detecting specific patterns of gene expression perturbations. We identified an 81-gene transcriptomic biomarker of histone deacetylase inhibitors (HDACi) using whole transcriptome data sets of TK6 human lymphoblastoid cells generated by Templated Oligo-Sequencing (TempO-Seq) after 4 h of exposure to 20 reference compounds (10 HDACi and 10 non-HDACi) [1]. The biomarker, named TGx-HDACi, was derived using the nearest shrunken centroid (NSC) method and can distinguish HDACi from non-HDACi compounds based on the expression pattern across the 81 genes. The classification capability of TGx-HDACi was evaluated by NSC probability analysis of 11 external validation compounds (4 HDACi and 7 non-HDACi) with a probability cut-off of 90%. Thus far, TGx-HDACi has demonstrated 100% accuracy in classifying the reference and validation compounds as HDACi or non-HDACi. Of the 81 TGx-HDACi genes, 19 genes are part of the S1500+ gene panel containing 2753 genes, developed for toxicological assessments [2]. Herein, we assessed the classification performance of the biomarker with this reduced gene set to determine if TGx-HDACi can be applied to analyze S1500+ gene expression profiles. The 20 reference compounds and 11 validation compounds were correctly classified as HDACi or non-HDACi by the NSC probability analysis, principal component analysis, and hierarchical clustering based on the expression of the 19 genes, demonstrating 100% accuracy. |
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| Keywords | Transcriptomic biomarker ; Toxicogenomics ; Predictive toxicology ; Histone deacetylase inhibition ; TempO-Seq ; Epigenetics ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Science (General) ; Q1-390 |
| Subject code | 572 |
| Language | English |
| Publishing date | 2021-06-01T00:00:00Z |
| Publisher | Elsevier |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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