Identification of high-quality cancer prognostic markers and metastasis network modules.
Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. Thus far, no algorithm has been able to successfully generate cancer prognostic gene signatures with high accuracy and robustness in order to identify these patients. In this paper, we developed an algorithm that identifies prognostic markers using tumour gene microarrays focusing on metastasis-driving gene expression signals. Application of the algorithm to breast cancer samples identified prognostic gene signature sets for both estrogen receptor (ER) negative (-) and positive (+) subtypes. A combinatorial use of the signatures allowed the stratification of patients into low-, intermediate- and high-risk groups in both the training set and in eight independent testing sets containing 1,375 samples. The predictive accuracy for the low-risk group reached 87-100%. Integrative network analysis identified modules in which each module contained the genes of a signature and their direct interacting partners that are cancer driver-mutating genes. These modules are recurrent in many breast tumours and contribute to metastasis.
Computational Chemistry and Bioinformatics Group, Biotechnology Research Institute, National Research Council Canada, Montreal, Quebec, Canada.
Jie
J
Li
Anne E G
AE
Lenferink
Yinghai
Y
Deng
Catherine
C
Collins
Qinghua
Q
Cui
Enrico O
EO
Purisima
Maureen D
MD
O'Connor-McCourt
Edwin
E
Wang
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis; D011960:Receptors, Estrogen / Q000235:genetics / Q000378:metabolism
D014408:Biomarkers, Tumor; D011960:Receptors, Estrogen