This article is part of the supplement: 22nd International Conference on Genome Informatics: Systems Biology
Exploring molecular links between lymph node invasion and cancer prognosis in human breast cancer
1 Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, Republic of Korea
2 Current address: Department of Computer Science and Engineering, University of California at San Diego, 9500 Gilman Dr. La Jolla, CA 92093-0404, USA
3 Department of Bioengineering, University of California at San Diego, 9500 Gilman Dr. La Jolla, CA 92093, USA
Citation and License
BMC Systems Biology 2011, 5(Suppl 2):S4 doi:10.1186/1752-0509-5-S2-S4Published: 14 December 2011
Lymph node invasion is one of the most powerful clinical factors in cancer prognosis. However, molecular level signatures of their correlation are remaining poorly understood. Here, we propose a new approach, monotonically expressed gene analysis (MEGA), to correlate transcriptional patterns of lymph node invasion related genes with clinical outcome of breast cancer patients.
Using MEGA, we scored all genes with their transcriptional patterns over progression levels of lymph node invasion from 278 non-metastatic breast cancer samples. Applied on 65 independent test data, our gene sets of top 20 scores (positive and negative correlations) showed significant associations with prognostic measures such as cancer metastasis, relapse and survival. Our method showed better accuracy than conventional two class comparison methods. We could also find that expression patterns of some genes are strongly associated with stage transition of pathological T and N at specific time. Additionally, some pathways including T-cell immune response and wound healing serum response are expected to be related with cancer progression from pathway enrichment and common motif binding site analyses of the inferred gene sets.
By applying MEGA, we can find possible molecular links between lymph node invasion and cancer prognosis in human breast cancer, supported by evidences of feasible gene expression patterns and significant results of meta-analysis tests.