Analysis of Large-Scale mRNA Expression Data Sets by Genetic Algorithms

2006 ◽  
pp. 51-66
Author(s):  
Chia Huey Ooi ◽  
Patrick Tan
Keyword(s):  
Genetic Algorithms ◽  
Mrna Expression ◽  
Large Scale ◽  
Data Sets ◽  
Expression Data ◽  
Mrna Expression Data

scholarly journals Normalization strategies for mRNA expression data in cartilage research

2008 ◽  
Vol 16 (8) ◽  
pp. 947-955 ◽  
Author(s):  
K. Fundel ◽  
J. Haag ◽  
P.M. Gebhard ◽  
R. Zimmer ◽  
T. Aigner
Keyword(s):  
Mrna Expression ◽  
Expression Data ◽  
Mrna Expression Data

ABCG2 and TOP-1 mRNA expression as predictive biomarkers for adjuvant FOLFIRI treatment in stage III colon cancer patients: Results from the PETAAC-3 prospective randomized clinical trial.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11594-11594
Author(s):  
Nils Brunner ◽  
Jan Stenvang ◽  
Eva Budinska ◽  
Sune Boris Nygaard
Keyword(s):  
Colon Cancer ◽  
Mrna Expression ◽  
Cancer Patients ◽  
Adjuvant Treatment ◽  
Stage Iii ◽  
Expression Data ◽  
P Values ◽  
Mrna Expression Data ◽  
Stage Iii Colon Cancer ◽  
Significant Difference

11594 Background: FOLFIRI as adjuvant treatment in primary colon cancer was previously tested in two pivotal prospective randomized clinical trials (PETACC-3 and CALGB 89803), both of which failed to demonstrate significant beneficial effects when adding irinotecan to 5FU. As a consequence, FOLFIRI is presently not used as adjuvant treatment for colon cancer. Methods: The study included 580 patients with mRNA expression data performed on tumor samples (FFPE) from stage III colon cancer patients enrolled in the PETACC-3 study, which randomized the patients to 5FU plus Leucovorin +/- irinotecan. Primary end-points were recurrence-free survival (RFS) and overall survival (OS). Median ABCG2 and the 75 percentile TOP-1 mRNA expression data were used to allocate the patients into one of two groups: One with high ABCG2 expression (above median) and low TOP-1 expression (below 75 percentile) (n = 167) and another group including all other combinations of these two genes. Kaplan Meier curves and Cox proportional hazards model were used to visualize differences between groups and calculate p-values (log-rank test). Results: The survival statistics showed a significant difference for both RFS (HR: 0.63 (0.44-0.92); p = 0.017) and OS (HR: 0.6 (0.39-0.93); p = 0.021) between the two groups when the patients received FOLFIRI. In contrast, no significant differences were observed between the groups when patients received 5FU and Leucovorin alone (p-values: RFS: 0.58; OS: 0.75). Conclusions: We here show that the combination of two independent gene expression abundance with a strong association to irinotecan treatment (high ABCG2 drug efflux pump and low TOP-1, the latter being the target for irinotecan) identified a group of stage III colon cancer patients who will not benefit from FOLFIRI adjuvant treatment while patients with other combinations of expression of these two genes appear to significantly benefit from adjuvant FOLFIRI treatment. The lack of a similar effect in patients receiving treatment with 5FU and Leucovorin only, points to a predictive value of ABCG2 and TOP-1 measurements.

scholarly journals A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information

2014 ◽  
Vol 13s7 ◽  
pp. CIN.S16348 ◽  
Author(s):  
Zixing Wang ◽  
Wenlong Xu ◽  
Haifeng Zhu ◽  
Yin Liu
Keyword(s):  
Liver Cancer ◽  
Mrna Expression ◽  
Target Prediction ◽  
Bayesian Framework ◽  
External Information ◽  
Sequence Information ◽  
Expression Data ◽  
Microrna Target ◽  
Mrna Expression Data ◽  
Mirna Targets

MicroRNAs (miRNAs) are small regulatory RNAs that play key gene-regulatory roles in diverse biological processes, particularly in cancer development. Therefore, inferring miRNA targets is an essential step to fully understanding the functional properties of miRNA actions in regulating tumorigenesis. Bayesian linear regression modeling has been proposed for identifying the interactions between miRNAs and mRNAs on the basis of the integrated sequence information and matched miRNA and mRNA expression data; however, this approach does not use the full spectrum of available features of putative miRNA targets. In this study, we integrated four important sequence and structural features of miRNA targeting with paired miRNA and mRNA expression data to improve miRNA-target prediction in a Bayesian framework. We have applied this approach to a gene-expression study of liver cancer patients and examined the posterior probability of each miRNA-mRNA interaction being functional in the development of liver cancer. Our method achieved better performance, in terms of the number of true targets identified, than did other methods.

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