Genome-wide expression profiling in colorectal cancer focusing on lncRNAs in the adenoma-carcinoma transition.

BACKGROUND: Long non-coding RNAs (lncRNAs) play a fundamental role in colorectal cancer (CRC) development, however, lncRNA expression profiles in CRC and its precancerous stages remain to be explored. We aimed to study whole genomic lncRNA expression patterns in colorectal adenoma-carcinoma transition and to analyze the underlying functional interactions of aberrantly expressed lncRNAs. METHODS: LncRNA expression levels of colonic biopsy samples (20 CRCs, 20 adenomas (Ad), 20 healthy controls (N)) were analyzed with Human Transcriptome Array (HTA) 2.0. Expression of a subset of candidates was verified by qRT-PCR and in situ hybridization (ISH) analyses. Furthermore, in silico validation was performed on an independent HTA 2.0, on HGU133Plus 2.0 array data and on the TCGA COAD dataset. MiRNA targets of lncRNAs were predicted with miRCODE and lncBase v2 algorithms and miRNA expression was analyzed on miRNA3.0 Array data. MiRNA-mRNA target prediction was performed using miRWALK and c-Met protein levels were analyzed by immunohistochemistry. Comprehensive lncRNA-mRNA-miRNA co-expression pattern analysis was also performed. RESULTS: Based on our HTA results, a subset of literature-based CRC-associated lncRNAs showed remarkable expression changes already in precancerous colonic lesions. In both Ad vs. normal and CRC vs. normal comparisons 16 lncRNAs, including downregulated LINC02023, MEG8, AC092834.1, and upregulated CCAT1, CASC19 were identified showing differential expression during early carcinogenesis that persisted until CRC formation (FDR-adjusted p < 0.05). The intersection of CRC vs. N and CRC vs. Ad comparisons defines lncRNAs characteristic of malignancy in colonic tumors, where significant downregulation of LINC01752 and overexpression of UCA1 and PCAT1 were found. Two candidates with the greatest increase in expression in the adenoma-carcinoma transition were further confirmed by qRT-PCR (UCA1, CCAT1) and by ISH (UCA1). In line with aberrant expression of certain lncRNAs in tumors, the expression of miRNA and mRNA targets showed systematic alterations. For example, UCA1 upregulation in CRC samples occurred in parallel with hsa-miR-1 downregulation, accompanied by c-Met target mRNA overexpression (p < 0.05). CONCLUSION: The defined lncRNA sets may have a regulatory role in the colorectal adenoma-carcinoma transition. A subset of CRC-associated lncRNAs showed significantly differential expression in precancerous samples, raising the possibility of developing adenoma-specific markers for early detection of colonic lesions.

Gene promoter and exon DNA methylation changes in colon cancer development - mRNA expression and tumor mutation alterations.

BACKGROUND: DNA mutations occur randomly and sporadically in growth-related genes, mostly on cytosines. Demethylation of cytosines may lead to genetic instability through spontaneous deamination. Aims were whole genome methylation and targeted mutation analysis of colorectal cancer (CRC)-related genes and mRNA expression analysis of TP53 pathway genes. METHODS: Long interspersed nuclear element-1 (LINE-1) BS-PCR followed by pyrosequencing was performed for the estimation of global DNA metlyation levels along the colorectal normal-adenoma-carcinoma sequence. Methyl capture sequencing was done on 6 normal adjacent (NAT), 15 adenomatous (AD) and 9 CRC tissues. Overall quantitative methylation analysis, selection of top hyper/hypomethylated genes, methylation analysis on mutation regions and TP53 pathway gene promoters were performed. Mutations of 12 CRC-related genes (APC, BRAF, CTNNB1, EGFR, FBXW7, KRAS, NRAS, MSH6, PIK3CA, SMAD2, SMAD4, TP53) were evaluated. mRNA expression of TP53 pathway genes was also analyzed. RESULTS: According to the LINE-1 methylation results, overall hypomethylation was observed along the normal-adenoma-carcinoma sequence. Within top50 differential methylated regions (DMRs), in AD-N comparison TP73, NGFR, PDGFRA genes were hypermethylated, FMN1, SLC16A7 genes were hypomethylated. In CRC-N comparison DKK2, SDC2, SOX1 genes showed hypermethylation, while ERBB4, CREB5, CNTN1 genes were hypomethylated. In certain mutation hot spot regions significant DNA methylation alterations were detected. The TP53 gene body was addressed by hypermethylation in adenomas. APC, TP53 and KRAS mutations were found in 30, 15, 21% of adenomas, and in 29, 53, 29% of CRCs, respectively. mRNA expression changes were observed in several TP53 pathway genes showing promoter methylation alterations. CONCLUSIONS: DNA methylation with consecutive phenotypic effect can be observed in a high number of promoter and gene body regions through CRC development.

Construction of a multiplex mutation hot spot PCR panel: the first step towards colorectal cancer genotyping on the GS Junior platform.

Background: To support cancer therapy, development of low cost library preparation techniques for targeted next generation sequencing (NGS) is needed. In this study we designed and tested a PCR-based library preparation panel with limited target area for sequencing the top 12 somatic mutation hot spots in colorectal cancer on the GS Junior instrument. Materials and Methods: A multiplex PCR panel was designed to amplify regions of mutation hot spots in 12 selected genes (APC, BRAF, CTNNB1, EGFR, FBXW7, KRAS, NRAS, MSH6, PIK3CA, SMAD2, SMAD4, TP53). Amplicons were sequenced on a GS Junior instrument using ligated and barcoded adaptors. Eight samples were sequenced in a single run. Colonic DNA samples (8 normal mucosa; 33 adenomas; 17 adenocarcinomas) as well as HT-29 and Caco-2 cell lines with known mutation profiles were analyzed. Variants found by the panel on APC, BRAF, KRAS and NRAS genes were validated by conventional sequencing. Results: In total, 34 kinds of mutations were detected including two novel mutations (FBXW7 c.1740:C>G and SMAD4 c.413C>G) that have not been recorded in mutation databases, and one potential germline mutation (APC). The most frequently mutated genes were APC, TP53 and KRAS with 30%, 15% and 21% frequencies in adenomas and 29%, 53% and 29% frequencies in carcinomas, respectively. In cell lines, all the expected mutations were detected except for one located in a homopolymer region. According to re-sequencing results sensitivity and specificity was 100% and 92% respectively. Conclusions: Our NGS-based screening panel denotes a promising step towards low cost colorectal cancer genotyping on the GS Junior instrument. Despite the relatively low coverage, we discovered two novel mutations and obtained mutation frequencies comparable to literature data. Additionally, as an advantage, this panel requires less template DNA than sequence capture colon cancer panels currently available for the GS Junior instrument.

Aberrant DNA methylation of WNT pathway genes in the development and progression of CIMP-negative colorectal cancer.

The WNT signaling pathway has an essential role in colorectal carcinogenesis and progression, which involves a cascade of genetic and epigenetic changes. We aimed to analyze DNA methylation affecting the WNT pathway genes in colorectal carcinogenesis in promoter and gene body regions using whole methylome analysis in 9 colorectal cancer, 15 adenoma, and 6 normal tumor adjacent tissue (NAT) samples by methyl capture sequencing. Functional methylation was confirmed on 5-aza-2'-deoxycytidine-treated colorectal cancer cell line datasets. In parallel with the DNA methylation analysis, mutations of WNT pathway genes (APC, ß-catenin/CTNNB1) were analyzed by 454 sequencing on GS Junior platform. Most differentially methylated CpG sites were localized in gene body regions (95% of WNT pathway genes). In the promoter regions, 33 of the 160 analyzed WNT pathway genes were differentially methylated in colorectal cancer vs. normal, including hypermethylated AXIN2, CHP1, PRICKLE1, SFRP1, SFRP2, SOX17, and hypomethylated CACYBP, CTNNB1, MYC; 44 genes in adenoma vs. NAT; and 41 genes in colorectal cancer vs. adenoma comparisons. Hypermethylation of AXIN2, DKK1, VANGL1, and WNT5A gene promoters was higher, while those of SOX17, PRICKLE1, DAAM2, and MYC was lower in colon carcinoma compared to adenoma. Inverse correlation between expression and methylation was confirmed in 23 genes, including APC, CHP1, PRICKLE1, PSEN1, and SFRP1. Differential methylation affected both canonical and noncanonical WNT pathway genes in colorectal normal-adenoma-carcinoma sequence. Aberrant DNA methylation appears already in adenomas as an early event of colorectal carcinogenesis.

DNA hypermethylation and decreased mRNA expression of MAL, PRIMA1, PTGDR and SFRP1 in colorectal adenoma and cancer.

BACKGROUND: Colorectal cancer (CRC) development is accompanied by changes in expression for several genes; but the details of the underlying regulatory procesess remain unknown. Our aims were to assess the role of epigenetic processes in tumour formation and to identify characteristic DNA methylation and miRNA alterations in the colorectal adenoma-carcinoma sequence. METHODS: Whole genome expression profiling was performed on colonic biopsy samples (49 healthy normal, 49 colorectal adenoma (AD), 49 CRC); on laser capture microdissected (LCM) epithelial and stromal cells from 6 CRC-normal adjacent tissue (NAT) samples pairs, and on demethylated human CRC cell lines using HGU133 Plus 2.0 microarrays (Affymetrix). Methylation status of genes with gradually altering expression along the AD-CRC sequence was further analysed on 10-10 macrodissected and 5-5 LCM samples from healthy colon, from adenoma and from CRC biopsy samples using bisulfite-sequencing PCR (BS-PCR) followed by pyrosequencing. In silico miRNA prediction for the selected genes was performed with miRWALK algorithm, miRNA expression was analysed on 3 CRC-NAT sample pairs and 3 adenoma tissue samples using the Human Panel I + II (Exiqon). SFRP1 immunohistochemistry experiments were performed. RESULTS: A set of transcripts (18 genes including MAL, SFRP1, SULT1A1, PRIMA1, PTGDR) showed decreasing expression (p < 0.01) in the biopsy samples along the adenoma-carcinoma sequence. Three of those (COL1A2, SFRP2, SOCS3) showed hypermethylation and THBS2 showed hypomethylation both in AD and in CRC samples compared to NAT, while BCL2, PRIMA1 and PTGDR showed hypermethylation only in the CRC group. miR-21 was found to be significantly (p < 0.01) upregulated in adenoma and tumour samples compared to the healthy colonic tissue controls and could explain the altered expression of genes for which DNA methylation changes do not appear to play role (e.g. BCL2, MAL, PTGS2). Demethylation treatment could upregulate gene expression of genes that were found to be hypermethylated in human CRC tissue samples. Decreasing protein levels of SFRP1 was also observed along the adenoma-carcinoma sequence. CONCLUSION: Hypermethylation of the selected markers (MAL, PRIMA1, PTGDR and SFRP1) can result in reduced gene expression and may contribute to the formation of colorectal cancer.

Bisulfite-Based DNA Methylation Analysis from Recent and Archived Formalin-Fixed, Paraffin Embedded Colorectal Tissue Samples.

We aimed to test the applicability of formalin-fixed and paraffin-embedded (FFPE) tissue samples for gene specific DNA methylation analysis after using two commercially available DNA isolation kits. Genomic DNA was isolated from 5 colorectal adenocarcinomas and 5 normal adjacent tissues from "recent", collected within 6 months, and "archived", collected more than 5 years ago, FFPE tissues using either High Pure FFPET DNA Isolation kit or QIAamp DNA FFPE Tissue kit. DNA methylation analysis of MAL, SFRP1 and SFRP2 genes, known to be hypermethylated in CRC, was performed using methylation-sensitive high resolution melting (MS-HRM) analysis and sequencing. QIAamp (Q) method resulted in slightly higher recovery in archived (HP: 1.22 ± 3.18 µg DNA; Q: 3.00 ± 4.04 µg DNA) and significantly (p < 0.05) higher recovery in recent samples compared to High Pure method (HP) (HP: 4.10 ± 2.91 µg DNA; Q: 11.51 ± 7.50 µg DNA). Both OD260/280 and OD260/230 ratios were lower, but still high in the High Pure isolated archived and recent samples compared to those isolated with QIAamp. Identical DNA methylation patterns were detected for all 3 genes tested by MS-HRM with both isolation kits in the recent group. However, despite of higher DNA recovery in QIAamp slightly more reproducible methylation results were obtained from High Pure isolated archived samples. Sequencing confirmed DNA hypermethylation in CRCs. In conclusion, reproducible DNA methylation patterns were obtained from recent samples using both isolation kits. However, long term storage may affect the reliability of the results leading to moderate differences between the efficiency of isolation kits.

rCUR: an R package for CUR matrix decomposition.

BACKGROUND: Many methods for dimensionality reduction of large data sets such as those generated in microarray studies boil down to the Singular Value Decomposition (SVD). Although singular vectors associated with the largest singular values have strong optimality properties and can often be quite useful as a tool to summarize the data, they are linear combinations of up to all of the data points, and thus it is typically quite hard to interpret those vectors in terms of the application domain from which the data are drawn. Recently, an alternative dimensionality reduction paradigm, CUR matrix decompositions, has been proposed to address this problem and has been applied to genetic and internet data. CUR decompositions are low-rank matrix decompositions that are explicitly expressed in terms of a small number of actual columns and/or actual rows of the data matrix. Since they are constructed from actual data elements, CUR decompositions are interpretable by practitioners of the field from which the data are drawn. RESULTS: We present an implementation to perform CUR matrix decompositions, in the form of a freely available, open source R-package called rCUR. This package will help users to perform CUR-based analysis on large-scale data, such as those obtained from different high-throughput technologies, in an interactive and exploratory manner. We show two examples that illustrate how CUR-based techniques make it possible to reduce significantly the number of probes, while at the same time maintaining major trends in data and keeping the same classification accuracy. CONCLUSIONS: The package rCUR provides functions for the users to perform CUR-based matrix decompositions in the R environment. In gene expression studies, it gives an additional way of analysis of differential expression and discriminant gene selection based on the use of statistical leverage scores. These scores, which have been used historically in diagnostic regression analysis to identify outliers, can be used by rCUR to identify the most informative data points with respect to which to express the remaining data points.