ArticleScientific reports2015
Identification of reproducible drug-resistance-related dysregulated genes in small-scale cancer cell line experiments.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- Loss of keratin 23 enhances growth inhibitory effect of melatonin in gastric cancer.Molecular medicine reports · 2024Article
- Transcriptomic analysis on the effects of melatonin in gastrointestinal carcinomas.BMC gastroenterology · 2020Article
- A rank-based algorithm of differential expression analysis for small cell line data with statistical control.Briefings in bioinformatics · 2019Article
- A robust qualitative transcriptional signature for the correct pathological diagnosis of gastric cancer.Journal of translational medicine · 2019Article
- Epigenomics alternations and dynamic transcriptional changes in responses to 5-fluorouracil stimulation reveal mechanisms of acquired drug resistance of colorectal cancer cells.The pharmacogenomics journal · 2018Article
- Statistically controlled identification of differentially expressed genes in one-to-one cell line comparisons of the CMAP database for drug repositioning.Journal of translational medicine · 2017Article
- Identifying reproducible cancer-associated highly expressed genes with important functional significances using multiple datasets.Scientific reports · 2016Article
- Electrochemistry-based approaches to low cost, high sensitivity, automated, multiplexed protein immunoassays for cancer diagnostics.The Analyst · 2016Review
- Identifying clinically relevant drug resistance genes in drug-induced resistant cancer cell lines and post-chemotherapy tissues.Oncotarget · 2015Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Researchers usually measure only a few technical replicates of two types of cell line, resistant or sensitive to a drug, and use a fold-change (FC) cut-off value to detect differentially expressed (DE) genes. However, the FC cut-off lacks statistical control and is biased towards the identification of genes with low expression levels in both cell lines. Here, viewing every pair of resistant-sensitive technical replicates as an experiment, we proposed an algorithm to identify DE genes by evaluating the reproducibility of the expression difference or FC between every two independent experiments without overlapping samples. Using four small datasets of cancer cell line resistant or sensitive to a drug, we demonstrated that this algorithm could efficiently capture reproducible DE genes significantly enriched in biological pathways relevant to the corresponding drugs, whereas many of them could not be found by the FC and other commonly used methods. Therefore, the proposed algorithm is an effective complement to current approaches for analysing small cancer cell line data.
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