A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes.

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.

Detection of Novel Genomic Markers for Predicting Prognosis in Hepatocellular Carcinoma Patients by Integrative Analysis of Copy Number Aberrations and Gene Expression Profiles: Results from a Long-Term Follow-Up.

The aim of this study was to explore novel genomic biomarkers predicting hepatocellular carcinoma (HCC) prognosis by integrative analysis of DNA copy number aberrations (CNAs) and gene expression profiles. Array comparative genomic hybridization and expression array were performed on 45 and 31 HCC samples, respectively. To identify functionally important genes, concordant results of DNA copy number and gene expression were retrieved by integrative analysis. Cox regression analysis indicated that the CNAs in 192 genomic regions were significantly associated with overall survival (OS; p < 0.05). Integrative analysis capturing concordant results demonstrated that the low expression of TLE4 (p = 0.041) and XPA (p = 0.006) was associated with poor OS. In the analysis of tumor recurrence, 514 genomic regions with CNAs were associated with recurrence. Integrative analysis revealed that the overexpression of 16 genes, including FGR (p = 0.003), RELA (p = 0.049), LTBP3 (p = 0.050), and RIN1 (p = 0.023), was significantly associated with shorter time to tumor recurrence. On multivariate analysis, FGR and XPA were independent risk factors of early recurrence and poor OS, respectively. Integrated analysis of CNAs and gene expression profiles correlated with long-term follow-up data successfully identified potential prognostic markers predicting survival and tumor recurrence in patients with HCC who underwent surgical resection.

Patterns of gene expression associated with Pten deficiency in the developing inner ear.

In inner ear development, phosphatase and tensin homolog (PTEN) is necessary for neuronal maintenance, such as neuronal survival and accurate nerve innervations of hair cells. We previously reported that Pten conditional knockout (cKO) mice exhibited disorganized fasciculus with neuronal apoptosis in spiral ganglion neurons (SGNs). To better understand the genes and signaling networks related to auditory neuron maintenance, we compared the profiles of differentially expressed genes (DEGs) using microarray analysis of the inner ear in E14.5 Pten cKO and wild-type mice. We identified 46 statistically significant transcripts using significance analysis of microarrays, with the false-discovery rate set at 0%. Among the DEGs, expression levels of candidate genes and expression domains were validated by quantitative real-time RT-PCR and in situ hybridization, respectively. Ingenuity pathway analysis using DEGs identified significant signaling networks associated with apoptosis, cellular movement, and axon guidance (i.e., secreted phosphoprotein 1 (Spp1)-mediated cellular movement and regulator of G-protein signaling 4 (Rgs4)-mediated axon guidance). This result was consistent with the phenotypic defects of SGNs in Pten cKO mice (e.g., neuronal apoptosis, abnormal migration, and irregular nerve fiber patterns of SGNs). From this study, we suggest two key regulatory signaling networks mediated by Spp1 and Rgs4, which may play potential roles in neuronal differentiation of developing auditory neurons.