The inflammatory microenvironment in colorectal neoplasia

Peer reviewedPublisher PD

pcaGoPromoter - An R Package for Biological and Regulatory Interpretation of Principal Components in Genome-Wide Gene Expression Data

Analyzing data obtained from genome-wide gene expression experiments is challenging due to the quantity of variables, the need for multivariate analyses, and the demands of managing large amounts of data. Here we present the R package pcaGoPromoter, which facilitates the interpretation of genome-wide expression data and overcomes the aforementioned problems. In the first step, principal component analysis (PCA) is applied to survey any differences between experiments and possible groupings. The next step is the interpretation of the principal components with respect to both biological function and regulation by predicted transcription factor binding sites. The robustness of the results is evaluated using cross-validation, and illustrative plots of PCA scores and gene ontology terms are available. pcaGoPromoter works with any platform that uses gene symbols or Entrez IDs as probe identifiers. In addition, support for several popular Affymetrix GeneChip platforms is provided. To illustrate the features of the pcaGoPromoter package a serum stimulation experiment was performed and the genome-wide gene expression in the resulting samples was profiled using the Affymetrix Human Genome U133 Plus 2.0 chip. Array data were analyzed using pcaGoPromoter package tools, resulting in a clear separation of the experiments into three groups: controls, serum only and serum with inhibitor. Functional annotation of the axes in the PCA score plot showed the expected serum-promoted biological processes, e.g., cell cycle progression and the predicted involvement of expected transcription factors, including E2F. In addition, unexpected results, e.g., cholesterol synthesis in serum-depleted cells and NF-κB activation in inhibitor treated cells, were noted. In summary, the pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. These tools give an overview of the input data via PCA, functional interpretation by gene ontology terms (biological processes), and an indication of the involvement of possible transcription factors

Presence of activating KRAS mutations correlates significantly with expression of tumour suppressor genes DCN and TPM1 in colorectal cancer

<p>Abstract</p> <p>Background</p> <p>Despite identification of the major genes and pathways involved in the development of colorectal cancer (CRC), it has become obvious that several steps in these pathways might be bypassed by other as yet unknown genetic events that lead towards CRC. Therefore we wanted to improve our understanding of the genetic mechanisms of CRC development.</p> <p>Methods</p> <p>We used microarrays to identify novel genes involved in the development of CRC. Real time PCR was used for mRNA expression as well as to search for chromosomal abnormalities within candidate genes. The correlation between the expression obtained by real time PCR and the presence of the <it>KRAS </it>mutation was investigated.</p> <p>Results</p> <p>We detected significant previously undescribed underexpression in CRC for genes <it>SLC26A3</it>, <it>TPM1 </it>and <it>DCN</it>, with a suggested tumour suppressor role. We also describe the correlation between <it>TPM1 </it>and <it>DCN </it>expression and the presence of <it>KRAS </it>mutations in CRC. When searching for chromosomal abnormalities, we found deletion of the <it>TPM1 </it>gene in one case of CRC, but no deletions of <it>DCN </it>and <it>SLC26A3 </it>were found.</p> <p>Conclusion</p> <p>Our study provides further evidence of decreased mRNA expression of three important tumour suppressor genes in cases of CRC, thus implicating them in the development of this type of cancer. Moreover, we found underexpression of the <it>TPM1 </it>gene in a case of CRCs without <it>KRAS </it>mutations, showing that <it>TPM1 </it>might serve as an alternative path of development of CRC. This downregulation could in some cases be mediated by deletion of the <it>TPM1 </it>gene. On the other hand, the correlation of <it>DCN </it>underexpression with the presence of <it>KRAS </it>mutations suggests that <it>DCN </it>expression is affected by the presence of activating <it>KRAS </it>mutations, lowering the amount of the important tumour suppressor protein decorin.</p

A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

Background: Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature's relevance to a classification task.Results: We apply POS, along-with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.Conclusions: A novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along-with a novel gene score are exploited to produce the selected subset of genes

A Population Proportion approach for ranking differentially expressed genes

<p>Abstract</p> <p>Background</p> <p>DNA microarrays are used to investigate differences in gene expression between two or more classes of samples. Most currently used approaches compare mean expression levels between classes and are not geared to find genes whose expression is significantly different in only a subset of samples in a class. However, biological variability can lead to situations where key genes are differentially expressed in only a subset of samples. To facilitate the identification of such genes, a new method is reported.</p> <p>Methods</p> <p>The key difference between the Population Proportion Ranking Method (PPRM) presented here and almost all other methods currently used is in the quantification of variability. PPRM quantifies variability in terms of inter-sample ratios and can be used to calculate the relative merit of differentially expressed genes with a specified difference in expression level between at least some samples in the two classes, which at the same time have lower than a specified variability within each class.</p> <p>Results</p> <p>PPRM is tested on simulated data and on three publicly available cancer data sets. It is compared to the t test, PPST, COPA, OS, ORT and MOST using the simulated data. Under the conditions tested, it performs as well or better than the other methods tested under low intra-class variability and better than t test, PPST, COPA and OS when a gene is differentially expressed in only a subset of samples. It performs better than ORT and MOST in recognizing non differentially expressed genes with high variability in expression levels across all samples. For biological data, the success of predictor genes identified in appropriately classifying an independent sample is reported.</p

Predicting cancer involvement of genes from heterogeneous data

<p>Abstract</p> <p>Background</p> <p>Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.</p> <p>Results</p> <p>We implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature.</p> <p>Conclusion</p> <p>Our approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks. </p

An immunohistochemical perspective of PPARβ and one of its putative targets PDK1 in normal ovaries, benign and malignant ovarian tumours

Peroxisome proliferator-activated receptor β (PPARβ) is a member of the nuclear hormone receptor family and is a ligand-activated transcription factor with few known molecular targets including 3-phosphoinositide-dependent protein kinase 1(PDK1). In view of the association of PPARβ and PDK1 with cancer, we have examined the expression of PPARβ and PDK1 in normal ovaries and different histological grades of ovarian tumours. Normal ovaries, benign, borderline, grades 1, 2 and 3 ovarian tumours of serous, muciuous, endometrioid, clear cell and mixed subtypes were analysed by immunohistochemistry for PPARβ and PDK1 expression. All normal ovarian tissues, benign, borderline and grade 1 tumours showed PPARβ staining localised in the epithelium and stroma. Staining was predominantly nuclear, but some degree of cytoplasmic staining was also evident. Approximately 20% of grades 2 and 3 tumours lacked PPARβ staining, whereas the rest displayed some degree of nuclear and cytoplasmic staining of the scattered epithelium and stroma. The extent of epithelial and stromal PPARβ staining was significantly different among the normal and the histological grades of tumours (χ2=59.25, d.f.=25, P<0.001; χ2=64.48, d.f.=25, P<0.001). Significantly different staining of PPARβ was observed in the epithelium and stroma of benign and borderline tumours compared with grades 1, 2 and 3 tumours (χ2=11.28, d.f.=4, P<0.05; χ2=16.15, d.f.=4, P<0.005). In contrast, PDK1 immunostaining was absent in 9 out of 10 normal ovaries. Weak staining for PDK1 was observed in one normal ovary and 40% of benign ovarian tumours. All borderline and malignant ovarian tumours showed positive cytoplasmic and membrane PDK1 staining. Staining of PDK1 was confined to the epithelium and the blood vessels, and no apparent staining of the stroma was evident. Significantly different PDK1 staining was observed between the benign/borderline and malignant ovarian tumours (χ2=22.45, d.f.=5, P<0.001). In some borderline and high-grade tumours, staining of the reactive stroma was also evident. Our results suggest that unlike the colon, the endometrial, head and neck carcinomas, overexpression of PPARβ does not occur in ovarian tumours. However, overexpression of PDK1 was evident in borderline and low- to high-grade ovarian tumours and is consistent with its known role in tumorigenesis

Gene Expression Profiles of Colonic Mucosa in Healthy Young Adult and Senior Dogs

Background: We have previously reported the effects of age and diet on nutrient digestibility, intestinal morphology, and large intestinal fermentation patterns in healthy young adult and senior dogs. However, a genome-wide molecular analysis of colonic mucosa as a function of age and diet has not yet been performed in dogs. Methodology/Principal Findings: Colonic mucosa samples were collected from six senior (12-year old) and six young adult (1-year old) female beagles fed one of two diets (animal protein-based vs. plant protein-based) for 12 months. Total RNA in colonic mucosa was extracted and hybridized to Affymetrix GeneChipH Canine Genome Arrays. Results indicated that the majority of gene expression changes were due to age (212 genes) rather than diet (66 genes). In particular, the colonic mucosa of senior dogs had increased expression of genes associated with cell proliferation, inflammation, stress response, and cellular metabolism, whereas the expression of genes associated with apoptosis and defensive mechanisms were decreased in senior vs. young adult dogs. No consistent diet-induced alterations in gene expression existed in both age groups, with the effects of diet being more pronounced in senior dogs than in young adult dogs. Conclusion: Our results provide molecular insight pertaining to the aged canine colon and its predisposition to dysfunction and disease. Therefore, our data may aid in future research pertaining to age-associated gastrointestinal physiologica

Calcium Prevents Tumorigenesis in a Mouse Model of Colorectal Cancer

Calcium has been proposed as a mediator of the chemoprevention of colorectal cancer (CRC), but the comprehensive mechanism underlying this preventive effect is not yet clear. Hence, we conducted this study to evaluate the possible roles and mechanisms of calcium-mediated prevention of CRC induced by 1,2-dimethylhydrazine (DMH) in mice.For gene expression analysis, 6 non-tumor colorectal tissues of mice from the DMH + Calcium group and 3 samples each from the DMH and control groups were hybridized on a 4×44 K Agilent whole genome oligo microarray, and selected genes were validated by real-time polymerase chain reaction (PCR). Functional analysis of the microarray data was performed using KEGG and Gene Ontology (GO) analyses. Hub genes were identified using Pathway Studio software.The tumor incidence rates in the DMH and DMH + Calcium groups were 90% and 40%, respectively. Microarray gene expression analysis showed that S100a9, Defa20, Mmp10, Mmp7, Ptgs2, and Ang2 were among the most downregulated genes, whereas Per3, Tef, Rnf152, and Prdx6 were significantly upregulated in the DMH + Calcium group compared with the DMH group. Functional analysis showed that the Wnt, cell cycle, and arachidonic acid pathways were significantly downregulated in the DMH + Calcium group, and that the GO terms related to cell differentiation, cell cycle, proliferation, cell death, adhesion, and cell migration were significantly affected. Forkhead box M1 (FoxM1) and nuclear factor kappa-B (NF-κB) were considered as potent hub genes.In the DMH-induced CRC mouse model, comprehensive mechanisms were involved with complex gene expression alterations encompassing many altered pathways and GO terms. However, how calcium regulates these events remains to be studied