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Novel discoveries of biomarkers predictive of drug-specific responses not only play a pivotal role in revealing the drug mechanisms in cancers, but are also critical to personalized medicine. In this study, we identified drug-specific biomarkers by integrating protein expression data, drug treatment data and survival outcome of 7076 patients from The Cancer Genome Atlas (TCGA). We first defined cancer-drug groups, where each cancer-drug group contains patients with the same cancer and treated with the same drug. For each protein, we stratified the patients in each cancer-drug group by high or low expression of the protein, and applied log-rank test to examine whether the stratified patients show significant survival difference. We examined 336 proteins in 98 cancer-drug groups and identified 65 protein-cancer-drug combinations involving 55 unique proteins, where the protein expression levels are predictive of drug-specific survival outcomes. Some of the identified proteins were supported by published literature. Using the gene expression data from TCGA, we found the mRNA expression of ∼11% of the drug-specific proteins also showed significant correlation with drug-specific survival, and most of these drug-specific proteins and their corresponding genes are strongly correlated. 

Long-time evolution has shaped a harmonious host-microbiota symbiosis consisting of intestinal microbiota in conjunction with the host immune system. Inflammatory bowel disease (IBD) is a result of the dysbiotic microbial composition together with aberrant mucosal immune responses, while the underlying mechanism is far from clear. In this report, we creatively proposed that when correlating with the host metabolism, functional microbial communities matter more than individual bacteria. Based on this assumption, we performed a systematic analysis to characterize the co-metabolism of host and gut microbiota established on a set of newly diagnosed Crohn’s disease (CD) samples and healthy controls. From the host side, we applied gene set enrichment analysis on host mucosal proteome data to identify those host pathways associated with CD. At the same time, we applied community detection analysis on the metagenomic data of mucosal microbiota to identify those microbial communities, which were assembled for a functional purpose. Then, the correlation analysis between host pathways and microbial communities was conducted. We discovered two microbial communities negatively correlated with IBD enriched host pathways. The dominant genera for these two microbial communities are known as health-benefits and could serve as a reference for designing complex beneficial microorganisms for IBD treatment. The correlated host pathways are all relevant to MHC antigen presentation pathways, which hints toward a possible mechanism of immune-microbiota cross talk underlying IBD.