ArticleBMC genomics2019
A qualitative transcriptional signature for predicting microsatellite instability status of right-sided Colon Cancer.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- MSIMEP: Predicting microsatellite instability from microarray DNA methylation tumor profiles.iScience · 2023Article
- Interpretable systems biomarkers predict response to immune-checkpoint inhibitors.Patterns (New York, N.Y.) · 2021Article
- RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples.Frontiers in oncology · 2021Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundMicrosatellite instability (MSI) accounts for about 15% of colorectal cancer and is associated with prognosis. Today, MSI is usually detected by polymerase chain reaction amplification of specific microsatellite markers. However, the instability is identified by comparing the length of microsatellite repeats in tumor and normal samples. In this work, we developed a qualitative transcriptional signature to individually predict MSI status for right-sided colon cancer (RCC) based on tumor samples.
resultsUsing RCC samples, based on the relative expression orderings (REOs) of gene pairs, we extracted a signature consisting of 10 gene pairs (10-GPS) to predict MSI status for RCC through a feature selection process. A sample is predicted as MSI when the gene expression orderings of at least 7 gene pairs vote for MSI; otherwise the microsatellite stability (MSS). The classification performance reached the largest F-score in the training dataset. This signature was verified in four independent datasets of RCCs with the F-scores of 1, 0.9630, 0.9412 and 0.8798, respectively. Additionally, the hierarchical clustering analyses and molecular features also supported the correctness of the reclassifications of the MSI status by 10-GPS.
conclusionsThe qualitative transcriptional signature can be used to classify MSI status of RCC samples at the individualized level.
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