  The analysis of cancer omics data is a "classic" problem, however, still
remains challenging. Advancing from early studies that are mostly focused on a
single type of cancer, some recent studies have analyzed data on multiple
"related" cancer types/subtypes, examined their commonality and difference, and
led to insightful findings. In this article, we consider the analysis of
multiple omics datasets, with each dataset on one type/subtype of "related"
cancers. A Community Fusion (CoFu) approach is developed, which conducts marker
selection and model building using a novel penalization technique,
informatively accommodates the network community structure of omics
measurements, and automatically identifies the commonality and difference of
cancer omics markers. Simulation demonstrates its superiority over direct
competitors. The analysis of TCGA lung cancer and melanoma data leads to
interesting findings
