Prediction of lymph node involvement in breast cancer from primary tumor tissue using gene expression profiling and miRNAs
- 580 Downloads
The aim of this study was to investigate whether lymph node involvement in breast cancer is influenced by gene or miRNA expression of the primary tumor. For this purpose, we selected a very homogeneous patient population to minimize heterogeneity in other tumor and patient characteristics. First, we compared gene expression profiles of primary tumor tissue from a group of 96 breast cancer patients balanced for lymph node involvement using Affymetrix Human U133 Plus 2.0 microarray chip. A model was built by weighted Least-Squares Support Vector Machines and validated on an internal and external dataset. Next, miRNA profiling was performed on a subset of 82 tumors using Human MiRNA-microarray chips (Illumina). Finally, for each miRNA the number of significant inverse correlated targets was determined and compared with 1000 sets of randomly chosen targets. A model based on 241 genes was built (AUC 0.66). The AUC for the internal dataset was 0.646 and 0. 651 for the external datasets. The model includes multiple kinases, apoptosis-related, and zinc ion-binding genes. Integration of the microarray and miRNA data reveals ten miRNAs suppressing lymph node invasion and one miRNA promoting lymph node invasion. Our results provide evidence that measurable differences in gene and miRNA expression exist between node negative and node positive patients and thus that lymph node involvement is not a genetically random process. Moreover, our data suggest a general deregulation of the miRNA machinery that is potentially responsible for lymph node invasion.
KeywordsBreast cancer Microarrays miRNA Lymph node Prediction
AS is supported by a grant from Research Foundation Flanders (FWO) and Pfizer Oncology.
- 16.Martinez-Ramos D, Escrig-Sos J, Alcalde-Sanchez M, Torrela-Ramos A, Salvador-Sanchis JL (2009) Disease-free survival and prognostic significance of metastatic lymph node ratio in T1–T2 N positive breast cancer patients. A population registry-based study in a European country. World J Surg 33:1659–1664PubMedCrossRefGoogle Scholar
- 19.Cawley GC (2006) Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. Proc Int Joint Conf Neural Netw 1661–1668Google Scholar
- 45.Abdelmohsen K, Kim MM, Srikantan S, et al. (2010) miR-519 suppresses tumor growth by reducing HuR levels. Cell Cycle 9(7):1538–4101Google Scholar