Prognostic genes are key molecules informative for cancer prognosis and treatment. Previous studies have focused on the properties of individual prognostic genes, but have lacked a global view of their system-level properties. Here we examined their properties in gene co-expression networks for four cancer types using data from The Cancer Genome Atlas. We found that prognostic mRNA genes tend not to be hub genes (genes with an extremely high connectivity), and this pattern is unique to the corresponding cancer-type specific network. In contrast, the prognostic genes are enriched in modules (., a group of highly interconnected genes), especially in module genes conserved across different cancer co-expression networks. The target genes of prognostic miRNA genes show similar patterns. We identified the modules enriched in various prognostic genes, some of which show cross-tumor conservation. Given the cancer types surveyed, our study presents a view of emergent properties of prognostic genes.

Objectives Systemic Sclerosis (SSc) is a fibrotic disease attributed to both genetic susceptibility and environmental factors. Our studies tried to demonstrate how human fibroblasts with SSc associated genetic variants respond to time-course and dose-response expression of the extracellular matrix (ECM) genes with silica particle stimulation. Methods A total of 200 fibroblast strains were examined for ECM gene expression after stimulation by silica particles. The fibroblasts were genetically profiled with Immunochip assays, and followed by whole-genome genotype imputation. Associations of genotypes and gene expressions were first analyzed in a Caucasian cohort, and then validated by a meta-analysis which combines the results from Caucasian, Blacks and Hispanics. We applied the linear mixed model for longitudinal data analysis to identify genetic variants associated with ECM genes’ expressions; we implemented haplotype-based longitudinal association test on identified loci region as a validation approach. Results SNP rs58905141 of TNFAIP3 was consistently associated with time-course and/or dose-response expressions of MMP3 gene and MMP1 gene of the fibroblasts stimulated with silica particles in both Caucasian only and meta-analysis. The haplotype-based analysis validated the association signals. Conclusions A genetic variant of TNFAIP3 is strongly associated with the silica-induced profibrotic response of the fibroblasts. In silico functional analysis based on ENCODE revealed that rs58905141 might affect binding activities of the transcription factors for TNFAIP3. This is the first genome-wide study of interaction between genetic and environmental factors in a complex SSc fibroblast model.