Clinical progression of colorectal cancers (CRC) may occur in parallel with distinctive signaling alterations. We designed multidirectional analyses integrating microarray-based data with biostatistics and bioinformatics to elucidate the signaling and metabolic alterations underlying CRC development in the adenoma-carcinoma sequence. Studies were performed on normal mucosa, adenoma, and carcinoma samples obtained during surgery or colonoscopy. Collections of cryostat sections prepared from the tissue samples were evaluated by a pathologist to control the relative cell type content. The measurements were done using Affymetrix GeneChip HG-U133plus2, and probe set data was generated using two normalization algorithms: MAS5.0 and GCRMA with least-variant set (LVS). The data was evaluated using pair-wise comparisons and data decomposition into singular value decomposition (SVD) modes. The method selected for the functional analysis used the Kolmogorov-Smirnov test. Expressional profiles obtained in 105 samples of whole tissue sections were used to establish oncogenic signaling alterations in progression of CRC, while those representing 40 microdissected specimens were used to select differences in KEGG pathways between epithelium and mucosa. Based on a consensus of the results obtained by two normalization algorithms, and two probe set sorting criteria, we identified 14 and 17 KEGG signaling and metabolic pathways that are significantly altered between normal and tumor samples and between benign and malignant tumors, respectively. Several of them were also selected from the raw microarray data of 2 recently published studies (GSE4183 and GSE8671). Although the proposed strategy is computationally complex and labor–intensive, it may reduce the number of false results.

Histone post-translational modifications (PTMs) play an important role in the regulation of the expression of genes, including those involved in cancer development and progression. However, our knowledge of PTM patterns in human tumours is limited.

Irritable bowel syndrome (IBS) is a chronic functional disorder and its development may be linked, directly and indirectly, to intestinal dysbiosis. Here we investigated the interactions between IBS symptoms and the gut microbiome, including the relation to rifaximin (1200 mg daily; 11.2 g per a treatment). We recruited 72 patients, including 31 with IBS-D (diarrhea), 11 with IBS-C (constipation), and 30 with IBS-M (mixed constipation and diarrhea) and 30 healthy controls (HCs). Of them, 68%, 64%, and 53% patients with IBS-D, IBS-C, and IBS-M, respectively, achieved 10–12 week-term improvement after the rifaximin treatment. Stool samples were collected before and after the treatment, and fecal microbiotic profiles were analyzed by deep sequencing of 16S rRNA, while stool metabolic profiles were studied by hydrogen 1-nuclear magnetic resonance (¹H-NMR) and gas chromatography–mass spectrometry (GC-MS). Of 26 identified phyla, only Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria were consistently found in all samples. Bacteroidetes was predominant in fecal samples from HCs and IBS-D and IBS-M subjects, whereas Firmicutes was predominant in samples from IBS-C subjects. Species richness, but not community diversity, differentiated all IBS patients from HCs. Metabolic fingerprinting, using NMR spectra, distinguished HCs from all IBS patients. Thirteen metabolites identified by GC-MS differed HCs and IBS patients. However, neither metagenomics nor metabolomics analyses identified significant differences between patients with and without improvement after treatment.