Epigenetic modulation of cytokine expression in gastric cancer: influence on angiogenesis, metastasis and chemoresistance

Cytokines are proteins that act in the immune response and inflammation and have been associated with the development of some types of cancer, such as gastric cancer (GC). GC is a malignant neoplasm that ranks fifth in incidence and third in cancer-related mortality worldwide, making it a major public health issue. Recent studies have focused on the role these cytokines may play in GC associated with angiogenesis, metastasis, and chemoresistance, which are key factors that can affect carcinogenesis and tumor progression, quality, and patient survival. These inflammatory mediators can be regulated by epigenetic modifications such as DNA methylation, histone protein modification, and non-coding RNA, which results in the silencing or overexpression of key genes in GC, presenting different targets of action, either direct or mediated by modifications in key genes of cytokine-related signaling pathways. This review seeks insight into the relationship between cytokine-associated epigenetic regulation and its potential effects on the different stages of development and chemoresistance in GC

Landscape of Genome-Wide DNA Methylation of Colorectal Cancer Metastasis.

Colorectal cancer is a heterogeneous disease caused by both genetic and epigenetics factors. Analysing DNA methylation changes occurring during colorectal cancer progression and metastasis formation is crucial for the identification of novel epigenetic markers of patient prognosis. Genome-wide methylation sequencing of paired samples of colon (normal adjacent, primary tumour and lymph node metastasis) showed global hypomethylation and CpG island (CGI) hypermethylation of primary tumours compared to normal. In metastasis we observed high global and non-CGI regions methylation, but lower CGI methylation, compared to primary tumours. Gene ontology analysis showed shared biological processes between hypermethylated CGIs in metastasis and primary tumours. After complementary analysis with The Cancer Genome Atlas (TCGA) cohort, FIGN, HTRA3, BDNF, HCN4 and STAC2 genes were found associated with poor survival. We mapped the methylation landscape of colon normal tissues, primary tumours and lymph node metastasis, being capable of identified methylation changes throughout the genome. Furthermore, we found five genes with potential for methylation biomarkers of poor prognosis in colorectal cancer patients

Genome-wide and gene-specific epigenomic platforms for hepatocellular carcinoma biomarker development trials

The majority of the epigenomic reports in hepatocellular carcinoma have focused on identifying novel differentially methylated drivers or passengers of the oncogenic process. Few reports have considered the technologies in place for clinical translation of newly identified biomarkers. The aim of this study was to identify epigenomic technologies that need only a small number of samples to discriminate HCC from non-HCC tissue, a basic requirement for biomarker development trials. To assess that potential, we used quantitative Methylation Specific PCR, oligonucleotide tiling arrays, and Methylation BeadChip assays. Concurrent global DNA hypomethylation, gene-specific hypermethylation, and chromatin alterations were observed as a hallmark of HCC. A global loss of promoter methylation was observed in HCC with the Illumina BeadChip assays and the Nimblegen oligonucleotide arrays. HCC samples had lower median methylation peak scores and a reduced number of significant promoter-wide methylated probes. Promoter hypermethylation of RASSF1A, SSBP2, and B4GALT1 quantified by qMSP had a sensitivity ranging from 38% to 52%, a specificity of 100%, and an AUC from 0.58 to 0.75. A panel combining these genes with HCC risk factors had a sensitivity of 87%, a specificity of 100%, and an AUC of 0.91

Genome-Wide and Gene-Specific Epigenomic Platforms for Hepatocellular Carcinoma Biomarker Development Trials

The majority of the epigenomic reports in hepatocellular carcinoma have focused on identifying novel differentially methylated drivers or passengers of the oncogenic process. Few reports have considered the technologies in place for clinical translation of newly identified biomarkers. The aim of this study was to identify epigenomic technologies that need only a small number of samples to discriminate HCC from non-HCC tissue, a basic requirement for biomarker development trials. To assess that potential, we used quantitative Methylation Specific PCR, oligonucleotide tiling arrays, and Methylation BeadChip assays. Concurrent global DNA hypomethylation, gene-specific hypermethylation, and chromatin alterations were observed as a hallmark of HCC. A global loss of promoter methylation was observed in HCC with the Illumina BeadChip assays and the Nimblegen oligonucleotide arrays. HCC samples had lower median methylation peak scores and a reduced number of significant promoter-wide methylated probes. Promoter hypermethylation of RASSF1A, SSBP2, and B4GALT1 quantified by qMSP had a sensitivity ranging from 38% to 52%, a specificity of 100%, and an AUC from 0.58 to 0.75. A panel combining these genes with HCC risk factors had a sensitivity of 87%, a specificity of 100%, and an AUC of 0.91

DNA promoter methylation as a diagnostic and therapeutic biomarker in gallbladder cancer

Abstract Gallbladder cancer is an infrequent neoplasia with noticeable geographical variations in its incidence around the world. In Chile, it is the main cause of death owing to cancer in women over 40 years old, with mortality rates up to 16.5 per 100,000 cases. The prognosis is poor with few therapeutic options; in advanced cases there is only a 10% survival at 5 years. Several studies mention the possible role of DNA methylation in gallbladder carcinogenesis. This epigenetic modification affects tumor suppressor genes involved in regulation pathways, cell cycle control, cell adhesion and extracellular matrix degradation, in a sequential and cumulative way. Determining DNA methylation patterns would allow them to be used as biomarkers for the early detection, diagnosis, prognosis and/or therapeutic selection in gallbladder cancer.</p

Novel Methylation Biomarkers for Colorectal Cancer Prognosis

Colorectal cancer (CRC) comprises the third most common cancer worldwide and the second regarding number of deaths. In order to make a correct and early diagnosis to predict metastasis formation, biomarkers are an important tool. Although there are multiple signaling pathways associated with cancer progression, the most recognized are the MAPK pathway, p53 pathway, and TGF-β pathway. These pathways regulate many important functions in the cell, such as cell cycle regulation, proliferation, differentiation, and metastasis formation, among others. Changes in expression in genes belonging to these pathways are drivers of carcinogenesis. Often these expression changes are caused by mutations; however, epigenetic changes, such as DNA methylation, are increasingly acknowledged to play a role in the deregulation of oncogenic genes. This makes DNA methylation changes an interesting biomarkers in cancer. Among the newly identified biomarkers for CRC metastasis INHBB, SMOC2, BDNF, and TBRG4 are included, all of which are highly deregulated by methylation and closely associated with metastasis. The identification of such biomarkers in metastasis of CRC may allow a better treatment and early identification of cancer formation in order to perform better diagnostics and improve the life expectancy

Role of lncRNAs in the Development of an Aggressive Phenotype in Gallbladder Cancer

Long non-coding RNAs are sequences longer than 200 nucleotides that are involved in different normal and abnormal biological processes exerting their effect on proliferation and differentiation, among other cell features. Functionally, lncRNAs can regulate gene expression within the cells by acting at transcriptional, post-transcriptional, translational, or post-translational levels. However, in pathological conditions such as cancer, the expression of these molecules is deregulated, becoming elements that can help in the acquisition of tumoral characteristics in the cells that trigger carcinogenesis and cancer progression. Specifically, in gallbladder cancer (GBC), recent publications have shown that lncRNAs participate in the acquisition of an aggressive phenotype in cancer cells, allowing them to acquire increased malignant capacities such as chemotherapy resistance or metastasis, inducing a worse survival in these patients. Furthermore, lncRNAs are useful as prognostic and diagnostic biomarkers since they have been shown to be differentially expressed in tumor tissues and serum of individuals with GBC. Therefore, this review will address different lncRNAs that could be promoting malignant phenotypic characteristics in GBC cells and lncRNAs that may be useful as markers due to their capability to predict a poor prognosis in GBC patients

Tools for Sequence-Based miRNA Target Prediction: What to Choose?

MicroRNAs (miRNAs) are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists