SummaryRoughly 27% of miRNAs are commonly expressed in colonic tissue; of these, over 86% are dysregulated between carcinoma and normal tissue when applying a false discovery rate of 0.05. MiRNA expression from normal to adenoma to carcinoma varied by miRNA and its frequency of expression in the population.

MicroRNAs are thought to have an impact on cell proliferation, apoptosis, stress responses, maintenance of stem cell potency, and metabolism and are, therefore, important in the carcinogenic process. In this study, we examined 40 colon tumors, 30 rectal tumors, and 30 normal tissue samples (10 proximal colon, 10 distal colon, and 10 rectal paired with cancer cases) to examine miRNA expression profiles in colon and rectal tumors. MiRNA expression levels were adjusted for multiple comparisons; tumor tissue was compared with noncancerous tissue from the same site. A comparison of normal tissue showed 287 unique miRNAs that were significantly differentially expressed at the 1.5-fold level and 73 with over a two-fold difference in expression between colon and rectal tissue. Examination of miRNAs that were significantly differentially expressed at the 1.5-fold level by tumor phenotype showed 143 unique miRNAs differentially expression for microsatellite instability positive (MSI+) colon tumors; 129 unique miRNAs differentially expressed for CpG Island Methylator Phenotype positive (CIMP+) colon tumors; 135 miRNAs were differentially expressed for KRAS2-mutated colon tumors, and 139 miRNAs were differentially expressed for TP53-mutated colon tumors. Similar numbers of differentially expressed miRNAs were observed for rectal tumors, although the miRNAs differentially expressed differed. There were 129 unique miRNAs for CIMP+, 143 unique miRNAs for KRAS2-mutated, and 136 unique miRNAs for TP53-mutated rectal tumors. These results suggest the importance of miRNAs in colorectal cancer and the need for studies that can confirm these results and provide insight into the diet, lifestyle, and genetic factors that influence miRNA expression.

OBJECTIVES:MicroRNAs (miRNAs) are small, non-protein-coding RNA molecules that are commonly dysregulated in colorectal tumors. The objective of this study was to identify smaller subsets of highly predictive miRNAs.METHODS:Data come from population-based studies of colorectal cancer conducted in Utah and the Kaiser Permanente Medical Care Program. Tissue samples were available for 1,953 individuals, of which 1,894 had carcinoma tissue and 1,599 had normal mucosa available for statistical analysis. Agilent Human miRNA Microarray V.19.0 was used to generate miRNA expression profiles; validation of expression levels was carried out using quantitative PCR. We used random forest analysis and verified findings with logistic modeling in separate data sets. Important microRNAs are identified and bioinformatics tools are used to identify target genes and related biological pathways.RESULTS:We identified 16 miRNAs for colon and 17 miRNAs for rectal carcinoma that appear to differentiate between carcinoma and normal mucosa; of these, 12 were important for both colon and rectal cancer, hsa-miR-663b, hsa-miR-4539, hsa-miR-17-5p, hsa-miR-20a-5p, hsa-miR-21-5p, hsa-miR-4506, hsa-miR-92a-3p, hsa-miR-93-5p, hsa-miR-145-5p, hsa-miR-3651, hsa-miR-378a-3p, and hsa-miR-378i. Estimated misclassification rates were low at 4.83% and 2.5% among colon and rectal observations, respectively. Among independent observations, logistic modeling reinforced the importance of these miRNAs, finding the primary principal components of their variation statistically significant (P<0.001 among both colon and rectal observations) and again producing low misclassification rates. Repeating our analysis without those miRNAs initially identified as important identified other important miRNAs; however, misclassification rates increased and distinctions between remaining miRNAs in terms of classification importance were reduced.CONCLUSIONS:Our data support the hypothesis that while many miRNAs are dysregulated between carcinoma and normal mucosa, smaller subsets of these miRNAs are useful and informative in discriminating between these tissues.

Heteroskedasticity is a common feature of financial time series and is commonly addressed in the model building process through the use of autoregressive conditional heteroskedastic and generalized autoregressive conditional heteroskedastic (GARCH) processes. More recently, multivariate variants of these processes have been the focus of research with attention given to methods seeking an efficient and economic estimation of a large number of model parameters. Because of the need for estimation of many parameters, however, these models may not be suitable for modelling now prevalent high-frequency volatility data. One potentially useful way to bypass these issues is to take a functional approach. In this article, theory is developed for a new functional version of the GARCH process, termed fGARCH. The main results are concerned with the structure of the fGARCH(1,1) process, providing criteria for the existence of strictly stationary solutions both in the space of square-integrable and continuous functions. An estimation procedure is introduced, and its consistency and asymptotic normality are verified. A small empirical study highlights potential applications to intraday volatility estimation.

Hundreds to thousands of genes are differentially expressed in tumors when compared to nontumor colonic tissue samples. We evaluated gene expression patterns to better understand differences in colon cancer by tumor site and tumor molecular phenotype. We analyzed RNA-seq data from tumor/normal paired samples from 175 colon cancer patients. We implemented a cross validation strategy with nonparametric tests to identify genes which displayed varying expression characteristics related to paired tumor/nontumor tissue across proximal and distal colon sites and by tumor molecular phenotypes, that is, TP53, KRAS, CpG Island Methylator Phenotype (CIMP), and microsatellite instability (MSI). We used Ingenuity Pathway Analysis (IPA) to determine networks associated with deregulated genes in our data. Genes showed significant differences in expression characteristics at the 0.01 level in both validation groups between tumor subsite (116 genes), CIMP high versus CIMP low (79 genes), MSI versus microsatellite stable (MSS) (49 genes), TP53-mutated versus not mutated (17genes), and KRAS-mutated versus not mutated (1 gene). Deregulated genes for CIMP high and MSI tumors were often down-regulated. In contrast to CIMP high and MSI tumors, genes that were deregulated in TP53 were likely to be up-regulated. ERK1, WNT, growth factors and inflammation-related factors were focal points of both CIMP and MSI IPA networks. The MUC family of genes was up-regulated MSI networks. Numerous genes showed differences in expression between proximal and distal tumors, nontumor proximal and distal tissue, and tumor molecular phenotype. Deregulated mucin genes appear to play an important role in MSI tumors.