Modeling Oncogenic Signaling in Colon Tumors by Multidirectional Analyses of Microarray Data Directed for Maximization of Analytical Reliability

• Published in: PloS one Vol. 5; no. 10; p. e13091
• Main Authors: Skrzypczak, Magdalena, Goryca, Krzysztof, Rubel, Tymon, Paziewska, Agnieszka, Mikula, Michal, Jarosz, Dorota, Pachlewski, Jacek, Oledzki, Janusz, Ostrowsk, Jerzy
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.10.2010; Public Library of Science (PLoS)
• Subjects: Adenocarcinoma - genetics; Adenocarcinoma - metabolism; Adenoma; Algorithms; Alterations; Analysis; Arrays; Bioinformatics; Biometry; Classification; Colon; Colon cancer; Colonic Neoplasms - genetics; Colonic Neoplasms - metabolism; Colonoscopy; Colorectal cancer; Computational Biology/Systems Biology; Data collection; Data processing; Datasets; Decomposition; Epithelium; Functional analysis; Gastroenterology; Gastroenterology and Hepatology/Gastrointestinal Cancers; Gastrointestinal diseases; Gene expression; Genetics and Genomics/Gene Expression; Genomes; Hepatology; Humans; Intestinal Mucosa - metabolism; Kolmogorov-Smirnov test; Metabolic pathways; Metabolism; Mucosa; Oligonucleotide Array Sequence Analysis; Oncogenes; Oncology; Pathways; Principal components analysis; Reliability analysis; Reproducibility of Results; Signal Transduction; Signaling; Singular value decomposition; Sorting algorithms; Surgery; Tumors; Poland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0013091

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.