A bioinformatic approach to the study of gene, microRNA expression and alternative splicing regulation in colorectal cancer progression and liver metastasis

Expression profiles are increasingly used in cancer research, and a growing number of microarray data is available in public databases such as Gene Expression Omnibus (GEO) and ArrayExpress. Therefore, it is possible to compare studies with similar research aims through a meta-analysis approach. Colorectal cancer (CRC) is a very common disease and represents a fundamental biological model of tumorigenesis. Many experiments comparing the expression profiles of normal colonic mucosa and colorectal cancer (CRC) samples have been conducted and results are available. Using A-MADMAN, a web application that allows the retrieval, annotation, organization and analysis of public available gene expression datasets, we downloaded from the GEO and collected the largest-collected normal colon, adenoma and primary colorectal tumor, with and without metastasis, gene expression dataset which includes 1,795 experiments pertaining to 27 distinct series, obtained from two different generations of Affymetrix, HG-U133A and HG-U133 plus2.0. After we performed the quality control of the downloaded data and have designed three different workflows for signal reconstruction, we conducted preliminary analysis of differential expression. In this first project we focused on right and left colon differences. In particularly, we analyzed normal, adenoma and tumor expression profiles focusing on changes among topologically different samples, under the hypothesis that the use of site-specific normal tissue as reference for the study of right and left tumor samples may facilitate to shed light on specific cell functions, pathways and regulatory circuits altered in each tumor type, and help deepening our understanding of this complex disease. From preliminary data, we concluded that the workflow based on the virtual chips production and on a chip-specific background correction is the most robust. Higher numbers of differentially expressed genes were identified when comparing, respectively, tumor and adenoma versus normal tissue (647 and 683 genes). These tissue samples were then pairwise compared by topology to identify those genes that are differentially expressed, when the matched by topology set of normal samples is considered. We found 72 genes colon left-specific and 1183 colon-right specific in the comparison from adenoma with normal tissues, and 46 genes and 69 genes, respectively, in colon left- and colon right- specific in the comparison from CRC and adenoma tissues. We focused also to the functional information associated to gene expression differences between tumor and normal tissue samples, compared considering only right or left samples, to identify specific functions and pathways involved with cancer present, such as DNA repair, adhesion and cell interaction. In the second project we reconstructed regulatory networks of colon cancer by integrating gene expression, alternative splicing and microRNA (miRNAs) expression data. We carried out a genome-wide integrative analysis of miRNA and genes and exon expression profiles in tissues of 55 colon cancer patients, comprehensive of normal colon mucosa, primary tumor and liver metastasis. GeneChip Human Exon 1.0 ST (Affymetrix) and Affymetrix GeneChip miRNA arrays have been used to obtain high quality gene, exon and miRNA expression quantification respectively. We analyzed both differential gene expression and alternative splicing applying AltAnalyze software to exon-level analysis, identifying 33,740 genes involved in alternative splicing events. We considered a custom set of expression signals of 449,810 probesets, using MIDAS and FIRMA statistics, and we combined the results with MMBGX software output, to identify a more restricted, but perhaps more robust set of candidate alternative splicing events, possibly relevant for colon cancer biology. When comparing liver metastasis with normal colon tissue whereas 182 genes were identified; comparable numbers of genes were identified when comparing metastasis versus colorectal tumor and colorectal tumor versus normal tissue (51 and 10 respectively). We validated two genes with alternative splicing events, VCL and CALD1. We then identified respectively 62, 63 and 11 differentially expressed miRNAs in tumors and metastases compared to normal tissue, and comparing metastasis with primary tumors. To assess the reproducibility and robustness of the miRNA signature identified, we measured by qRT-PCR the expression of 5 miRNA in all samples (hsa miR-150, hsa miR-10b, has miR-146a, miR-210 and has miR-122). For each considered contrast, KEGG pathways enriched in genes resulting supported target of differentially expressed miRNAs were identified. The integrated analysis of miRNA and genes expression profiles with target predictions was performed with the purpose of reconstruct post-transcriptional regulatory networks governing tumor and metastases development. Considering expression profiles in the same set of samples of 305 miRNAs and 12,748 genes with variable expression profile, we reconstructed post-transcriptional regulatory networks involving modulated miRNAs. In particular, considering the network associated to the tumor versus normal contrast, we experimentally validated miR-145 – c-Myc and miR-182 – ENTPD5 relationships. This latter is new and may have a relevant pathogenetic role. Looking at our results, we can say that the regulatory signatures that affect tumor progression are complex and difficult to interpret. They involve interactions involving different modulated miRNAs that control the expression of multiple genes belonging to the same pathway in different ways, and alternative transcripts of genes that are differentially expressed in normal tissues, tumors and metastases.