Background: Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate. Results: We developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level. Conclusion: PAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.

Gene Expression database of Normal and Tumor tissues 2 (GENT2) is an updated version of GENT, which has provided a user-friendly search platform for gene expression patterns across different normal and tumor tissues compiled from public gene expression data sets. We refactored GENT2 with recent technologies such as Apache Lucene indexing for fast search and Google Web Toolkit (GWT) framework for a user-friendly web interface. Now, GENT2 contains more than 68,000 samples and has several new useful functions. First, GENT2 now provides gene expression across 72 different tissues compared to 57 in GENT. Second, with increasing importance of tumor subtypes, GENT2 provides an option to study the differential expression and its prognostic significance based on tumor subtypes. Third, whenever available, GENT2 provides prognostic information of a gene of interest. Fourth, GENT2 provides a meta-analysis of survival information to provide users more reliable prognostic value of a gene of interest. In conclusion, with these significant improvements, GENT2 will continue to be a useful tool to a wide range of researchers. GENT2 is freely available at http://gent2.appex.kr .

Gastric cancer is a heterogeneous cancer, making treatment responses difficult to predict. Here we show that we identify two distinct molecular subtypes, mesenchymal phenotype (MP) and epithelial phenotype (EP), by analyzing genomic and proteomic data. Molecularly, MP subtype tumors show high genomic integrity characterized by low mutation rates and microsatellite stability, whereas EP subtype tumors show low genomic integrity. Clinically, the MP subtype is associated with markedly poor survival and resistance to standard chemotherapy, whereas the EP subtype is associated with better survival rates and sensitivity to chemotherapy. Integrative analysis shows that signaling pathways driving epithelial-to-mesenchymal transition and insulin-like growth factor 1 (IGF1)/IGF1 receptor (IGF1R) pathway are highly activated in MP subtype tumors. Importantly, MP subtype cancer cells are more sensitive to inhibition of IGF1/IGF1R pathway than EP subtype. Detailed characterization of these two subtypes could identify novel therapeutic targets and useful biomarkers for prognosis and therapy response.

Nonalcoholic fatty liver disease (NAFLD) is a major risk factor for liver cancer; therefore, its prevention is an important clinical goal. Ablation of phosphatase and tensin homolog (PTEN) or the protein kinase Hippo signaling pathway induces liver cancer via activation of AKT or the transcriptional regulators YAP/TAZ, respectively; however, the potential for crosstalk between the PTEN/AKT and Hippo/YAP/TAZ pathways in liver tumorigenesis has thus far remained unclear. Here, we have shown that deletion of both PTEN and SAV1 in the liver accelerates the development of NAFLD and liver cancer in mice. At the molecular level, activation of YAP/TAZ in the liver of Pten^–/– Sav1^–/– mice amplified AKT signaling through the upregulation of insulin receptor substrate 2 (IRS2) expression. Both ablation of YAP/TAZ and activation of the Hippo pathway could rescue these phenotypes. A high level of YAP/ TAZ expression was associated with a high level of IRS2 expression in human hepatocellular carcinoma (HCC). Moreover, treatment with the AKT inhibitor MK-2206 or knockout of IRS2 by AAV-Cas9 successfully repressed liver tumorigenesis in Pten^–/– Sav1^–/– mice. Thus, our findings suggest that Hippo signaling interacts with AKT signaling by regulating IRS2 expression to prevent NAFLD and liver cancer progression and provide evidence that impaired crosstalk between these 2 pathways accelerates NAFLD and liver cancer.

Background: Some oncogenes such as ERBB2 and EGFR are over-expressed in only a subset of patients. Cancer outlier profile analysis is one of computational approaches to identify outliers in gene expression data. A database with a large sample size would be a great advantage when searching for genes over-expressed in only a subset of patients. Description: GENT (Gene Expression database of Normal and Tumor tissues) is a web-accessible database that provides gene expression patterns across diverse human cancer and normal tissues. More than 40000 samples, profiled by Affymetrix U133A or U133plus2 platforms in many different laboratories across the world, were collected from public resources and combined into two large data sets, helping the identification of cancer outliers that are over-expressed in only a subset of patients. Gene expression patterns in nearly 1000 human cancer cell lines are also provided. In each tissue, users can retrieve gene expression patterns classified by more detailed clinical information. Conclusions: The large samples size (>24300 for U133plus2 and >16400 for U133A) of GENT provides an advantage in identifying cancer outliers. A cancer cell line gene expression database is useful for target validation by in vitro experiment. We hope GENT will be a useful resource for cancer researchers in many stages from target discovery to target validation. GENT is available at http://medicalgenome.kribb.re.kr/GENT/ or http://genome.kobic.re.kr/GENT/ .

Skeletal muscle degenerates progressively, losing mass (sarcopenia) over time, which leads to reduced physical ability and often results in secondary diseases such as diabetes and obesity. The regulation of gene expression by microRNAs is a key event in muscle development and disease. To understand genome‐wide changes in microRNAs and mRNAs during muscle aging, we sequenced microRNAs and mRNAs from mouse gastrocnemius muscles at two different ages (6 and 24 months). Thirty‐four microRNAs (15 up‐regulated and 19 down‐regulated) were differentially expressed with age, including the microRNAs miR‐206 and ‐434, which were differentially expressed in aged muscle in previous studies. Interestingly, eight microRNAs in a microRNA cluster at the imprinted Dlk1‐Dio3 locus on chromosome 12 were coordinately down‐regulated. In addition, sixteen novel microRNAs were identified. Integrative analysis of microRNA and mRNA expression revealed that microRNAs may contribute to muscle aging through the positive regulation of transcription, metabolic processes, and kinase activity. Many of the age‐related microRNAs have been implicated in human muscular diseases. We suggest that genome‐wide microRNA profiling will expand our knowledge of microRNA function in the muscle aging process.