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Ensemble of gene signatures identifies novel biomarkers in colorectal cancer activated through PPAR? and TNF? signaling.

ABSTRACT: We describe a novel bioinformatic and translational pathology approach, gene Signature Finder Algorithm (gSFA) to identify biomarkers associated with Colorectal Cancer (CRC) survival. Here a robust set of CRC markers is selected by an ensemble method. By using a dataset of 232 gene expression profiles, gSFA discovers 16 highly significant small gene signatures. Analysis of dichotomies generated by the signatures results in a set of 133 samples stably classified in good prognosis group and 56 samples in poor prognosis group, whereas 43 remain unreliably classified. AKAP12, DCBLD2, NT5E and SPON1 are particularly represented in the signatures and selected for validation in vivo on two independent patients cohorts comprising 140 tumor tissues and 60 matched normal tissues. Their expression and regulatory programs are investigated in vitro. We show that the coupled expression of NT5E and DCBLD2 robustly stratifies our patients in two groups (one of which with 100% survival at five years). We show that NT5E is a target of the TNF-? signaling in vitro; the tumor suppressor PPAR? acts as a novel NT5E antagonist that positively and concomitantly regulates DCBLD2 in a cancer cell context-dependent manner.

SUBMITTER: Pagnotta SM

PROVIDER: S-EPMC3795784 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Ensemble of gene signatures identifies novel biomarkers in colorectal cancer activated through PPARγ and TNFα signaling.

Pagnotta Stefano Maria SM Laudanna Carmelo C Pancione Massimo M Sabatino Lina L Votino Carolina C Remo Andrea A Cerulo Luigi L Zoppoli Pietro P Manfrin Erminia E Colantuoni Vittorio V Ceccarelli Michele M

PloS one 20130819 8

We describe a novel bioinformatic and translational pathology approach, gene Signature Finder Algorithm (gSFA) to identify biomarkers associated with Colorectal Cancer (CRC) survival. Here a robust set of CRC markers is selected by an ensemble method. By using a dataset of 232 gene expression profiles, gSFA discovers 16 highly significant small gene signatures. Analysis of dichotomies generated by the signatures results in a set of 133 samples stably classified in good prognosis group and 56 sam ...[more]

PMID: 24133572

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Project description:BackgroundAberrant DNA methylation acts epigenetically to skew the gene transcription rate up or down, contributing to cancer etiology. A gap in our understanding concerns the epigenomics of stagewise cancer progression. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer (CRC).MethodsThe methylation β-matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with AJCC stages, and analysed for stage-salient genes using an ensemble of approaches involving stage-differentiated modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against controls (adjusted p-value <0.001 and |log fold-change of M-value| >2), and then filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by matching with clinical data and performing Kaplan-Meier survival analysis to evaluate the impact of methylation patterns of consensus stage-salient biomarkers on disease prognosis.ResultsWe found significant genome-wide changes in methylation patterns in cancer cases relative to controls agnostic of stage. The stage-differentiated models yielded the following consensus salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were significantly hypermethylated in the promoter regions, indicating down-regulation of expression and implying a putative CpG island Methylator Phenotype (CIMP) manifestation. A prognostic signature consisting of FBN1 and FOXG1 survived all the analytical filters, and represents a novel early-stage epigenetic biomarker / target.ConclusionsWe have designed and executed a workflow for stage-differentiated epigenomic analysis of colorectal cancer progression, and identified several stage-salient diagnostic biomarkers, and an early-stage prognostic biomarker panel. The study has led to the discovery of an alternative CIMP-like signature in colorectal cancer, reinforcing the role of CIMP drivers in tumor pathophysiology.

Project description:Stromal Derived Factor-1? (SDF-1?) and its cognate receptor CXCR4 play a key role in mediating breast cancer cell invasion and metastasis. Therefore, drugs able to inhibit CXCR4 activation may add critical tools to reduce tumor progression, especially in the most aggressive form of the breast cancer disease. Peroxisome Proliferator-Activated Receptor (PPAR) ?, a member of the nuclear receptor superfamily, has been found to downregulate CXCR4 gene expression in different cancer cells, however the molecular mechanism underlying this effect is not fully understood. Here, we identified a novel PPAR?-mediated mechanism that negatively regulates CXCR4 expression in both epithelial and stromal breast cancer cells. We found that ligand-activated PPAR? downregulated CXCR4 transcriptional activity through the recruitment of the silencing mediator of retinoid and thyroid hormone receptor (SMRT) corepressor onto a newly identified PPAR response element (PPRE) within the CXCR4 promoter in breast cancer cell lines. As a consequence, the PPAR? agonist rosiglitazone (BRL) significantly inhibited cell migration and invasion and this effect was PPAR?-mediated, since it was reversed in the presence of the PPAR? antagonist GW9662. According to the ability of cancer-associated fibroblasts (CAFs), the most abundant component of breast cancer stroma, to secrete high levels of SDF-1?, BRL reduced migratory promoting activities induced by conditioned media (CM) derived from CAFs and affected CXCR4 downstream signaling pathways activated by CAF-CM. In addition, CAFs exposed to BRL showed a decreased expression of CXCR4, a reduced motility and invasion along with a phenotype characterized by an altered morphology. Collectively, our findings provide novel insights into the role of PPAR? in inhibiting breast cancer progression and further highlight the utility of PPAR? ligands for future therapies aimed at targeting both cancer and surrounding stromal cells in breast cancer patients.

Project description:BACKGROUND: The application of high-throughput genomic tools in nutrition research is a widespread practice. However, it is becoming increasingly clear that the outcome of individual expression studies is insufficient for the comprehensive understanding of such a complex field. Currently, the availability of the large amounts of expression data in public repositories has opened up new challenges on microarray data analyses. We have focused on PPARalpha, a ligand-activated transcription factor functioning as fatty acid sensor controlling the gene expression regulation of a large set of genes in various metabolic organs such as liver, small intestine or heart. The function of PPARalpha is strictly connected to the function of its target genes and, although many of these have already been identified, major elements of its physiological function remain to be uncovered. To further investigate the function of PPARalpha, we have applied a cross-species meta-analysis approach to integrate sixteen microarray datasets studying high fat diet and PPARalpha signal perturbations in different organisms. RESULTS: We identified 164 genes (MDEGs) that were differentially expressed in a constant way in response to a high fat diet or to perturbations in PPARs signalling. In particular, we found five genes in yeast which were highly conserved and homologous of PPARalpha targets in mammals, potential candidates to be used as models for the equivalent mammalian genes. Moreover, a screening of the MDEGs for all known transcription factor binding sites and the comparison with a human genome-wide screening of Peroxisome Proliferating Response Elements (PPRE), enabled us to identify, 20 new potential candidate genes that show, both binding site, both change in expression in the condition studied. Lastly, we found a non random localization of the differentially expressed genes in the genome. CONCLUSION: The results presented are potentially of great interest to resume the currently available expression data, exploiting the power of in silico analysis filtered by evolutionary conservation. The analysis enabled us to indicate potential gene candidates that could fill in the gaps with regards to the signalling of PPARalpha and, moreover, the non-random localization of the differentially expressed genes in the genome, suggest that epigenetic mechanisms are of importance in the regulation of the transcription operated by PPARalpha.

Project description:BackgroundThe nuclear factor kappa B (NFκB) regulatory pathways downstream of tumor necrosis factor (TNF) play a critical role in carcinogenesis. However, the widespread influence of NFκB in cells can result in off-target effects, making it a challenging therapeutic target. Ensemble learning is a machine learning technique where multiple models are combined to improve the performance and robustness of the prediction. Accordingly, an ensemble learning model could uncover more precise targets within the NFκB/TNF signaling pathway for cancer therapy.MethodsIn this study, we trained an ensemble learning model on the transcriptome profiles from 16 cancer types in the TCGA database to identify a robust set of genes that are consistently associated with the NFκB/TNF pathway in cancer. Our model uses cancer patients as features to predict the genes involved in the NFκB/TNF signaling pathway and can be adapted to predict the genes for different cancer types by switching the cancer type of patients. We also performed functional analysis, survival analysis, and a case study of triple-negative breast cancer to demonstrate our model's potential in translational cancer medicine.ResultsOur model accurately identified genes regulated by NFκB in response to TNF in cancer patients. The downstream analysis showed that the identified genes are typically involved in the canonical NFκB-regulated pathways, particularly in adaptive immunity, anti-apoptosis, and cellular response to cytokine stimuli. These genes were found to have oncogenic properties and detrimental effects on patient survival. Our model also could distinguish patients with a specific cancer subtype, triple-negative breast cancer (TNBC), which is known to be influenced by NFκB-regulated pathways downstream of TNF. Furthermore, a functional module known as mononuclear cell differentiation was identified that accurately predicts TNBC patients and poor short-term survival in non-TNBC patients, providing a potential avenue for developing precision medicine for cancer subtypes.ConclusionsIn conclusion, our approach enables the discovery of genes in NFκB-regulated pathways in response to TNF and their relevance to carcinogenesis. We successfully categorized these genes into functional groups, providing valuable insights for discovering more precise and targeted cancer therapeutics.

Dataset Information

Ensemble of gene signatures identifies novel biomarkers in colorectal cancer activated through PPAR? and TNF? signaling.

Publications

Ensemble of gene signatures identifies novel biomarkers in colorectal cancer activated through PPARγ and TNFα signaling.

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