Detecting common gene expression patterns in multiple cancer outcome entities.
Most oncological microarray studies focus on molecular distinctions in different cancer entities. Recently, researchers started using microarrays for investigating molecular commonalities of multiple cancer types. This poses novel bioinformatics challenges. In this paper we describe a method that detects common molecular mechanisms in different cancer entities. The method extends previously described concepts by introducing Meta-Analysis Pattern Matches. In an analysis of four prognostic cancer studies, involving breast cancer, leukemia, and mesothelioma, we are able to identify 42 genes that show consistent up- or down-regulation in patients with a poor disease outcome. These genes complement the set of previously published candidates for universal prognostic markers in cancer.
Yang
Xinan
X
Computational Diagnostics Group, Department of Computational Molecular Biology, MPI for Molecular Genetics, Ihnestr. 73, 14195 Berlin, Germany.
Bentink
Stefan
S
Spang
Rainer
R
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis / Q000378:metabolism*; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins