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Cross-platform Data Analysis Reveals a Generic Gene Expression Signature for Microsatellite Instability in Colorectal Cancer
A. Pačínková, V. Popovici,
Language English Country United States
Document type Journal Article
NLK
Free Medical Journals
from 2013
PubMed Central
from 2013
Europe PubMed Central
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ProQuest Central
from 2013
Open Access Digital Library
from 2001-01-01
Open Access Digital Library
from 2012-12-04
Open Access Digital Library
from 2013-01-01
CINAHL Plus with Full Text (EBSCOhost)
from 2013-01-01
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Health & Medicine (ProQuest)
from 2013
Wiley-Blackwell Open Access Titles
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from 2013
PubMed
31008109
DOI
10.1155/2019/6763596
Knihovny.cz E-resources
- MeSH
- Data Analysis MeSH
- Colorectal Neoplasms genetics pathology MeSH
- Humans MeSH
- Microsatellite Instability * MeSH
- Microsatellite Repeats genetics MeSH
- Stomach Neoplasms genetics pathology MeSH
- Prognosis MeSH
- Gene Expression Regulation, Neoplastic genetics MeSH
- Gene Expression Profiling MeSH
- Transcriptome genetics MeSH
- High-Throughput Nucleotide Sequencing MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 - 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 - 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 - 0.94) and AUC = 0.83, 95% CI = (0.69 - 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 - 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples.
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