This article is part of the supplement: Selected articles from the Twelfth Asia Pacific Bioinformatics Conference (APBC 2014): Systems Biology
Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression
- Equal contributors
1 Department of Mathematics and Computer Science, Saint Louis University, Saint Louis, MO 63103, USA
2 Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA
3 Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
BMC Systems Biology 2014, 8(Suppl 1):S3 doi:10.1186/1752-0509-8-S1-S3Published: 24 January 2014
Recent global genomic analyses identified 69 gene sets and 12 core signaling pathways genetically altered in pancreatic cancer, which is a highly malignant disease. A comprehensive understanding of the genetic signatures and signaling pathways that are directly correlated to pancreatic cancer survival will help cancer researchers to develop effective multi-gene targeted, personalized therapies for the pancreatic cancer patients at different stages. A previous work that applied a LASSO penalized regression method, which only considered individual genetic effects, identified 12 genes associated with pancreatic cancer survival.
In this work, we integrate pathway information into pancreatic cancer survival analysis. We introduce and apply a doubly regularized Cox regression model to identify both genes and signaling pathways related to pancreatic cancer survival.
Four signaling pathways, including Ion transport, immune phagocytosis, TGFβ (spermatogenesis), regulation of DNA-dependent transcription pathways, and 15 genes within the four pathways are identified and verified to be directly correlated to pancreatic cancer survival. Our findings can help cancer researchers design new strategies for the early detection and diagnosis of pancreatic cancer.