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Transcriptomic effect marker patterns of genotoxins – a comparative study with literature data
Author(s) -
Kreuzer Katrin,
Frenzel Falko,
Lampen Alfonso,
Braeuning Albert,
Böhmert Linda
Publication year - 2020
Publication title -
journal of applied toxicology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.784
H-Index - 87
eISSN - 1099-1263
pISSN - 0260-437X
DOI - 10.1002/jat.3928
Subject(s) - context (archaeology) , omics , transcriptome , computational biology , standardization , microarray , biomarker discovery , biomarker , adverse outcome pathway , microarray analysis techniques , toxicogenomics , concordance , biology , bioinformatics , data science , computer science , proteomics , gene , gene expression , genetics , paleontology , operating system
Microarray approaches are frequently used experimental tools which have proven their value for example in the characterization of the molecular mode of action of toxicologically relevant compounds. In a regulatory context, omics techniques are still not routinely used, amongst others due to lacking standardization in experimental setup and data processing, and also due to issues with the definition of adversity. In order to exemplarily determine whether consensus transcript biomarker signatures for a certain toxicological endpoint can be derived from published microarray datasets, we here compared transcriptome data from human HepaRG hepatocarcinoma cells treated with different genotoxins, based on re‐analyzed datasets extracted from the literature. Comparison of the resulting data show that even with similarly‐acting compounds in the same cell line, considerable variation was observed with respect to the numbers and identities of differentially expressed genes. Greater concordance was observed when considering the whole data sets and biological functions associated with the genes affected. The present results highlight difficulties and possibilities in inter‐experiment comparisons of omics data and underpin the need for future efforts towards improved standardization to facilitate the use of omics data in risk assessment. Existing omics datasets may nonetheless prove valuable in establishing biological context information essential for the development of adverse outcome pathways.