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Accession IconGSE20916

Modeling oncogenic signaling in colon tumors by multidirectional analyses of microarray data

Organism Icon Homo sapiens
Sample Icon 144 Downloadable Samples
Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

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Description
Background. Most colorectal cancers (CRC) arise in a progression through adenoma to carcinoma phenotypes as a consequence of altered genetic information. Clinical progression of 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. Methodology/Principal Findings. Studies were performed on normal mucosa, adenoma, and CRC 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. RNA was isolated from 105 macro- and 40 microdissected specimens. The measurements were done using Affymetrix GeneChip HG-U133plus2, and probe set data were generated using two normalization algorithms: MAS5 and GCRMA with LVS. The data were evaluated using pair-wise comparisons and data decomposition into SVD modes. The method selected for the functional analysis used the Kolmogorov-Smirnov test. Based on a consensus of the results obtained by two tissue handling procedures, two normalization algorithms, and two probe set sorting criteria, we identified six KEGG signaling and metabolic pathways (cell cycle, DNA replication, p53 signaling pathway, purine metabolism, pyrimidine metabolism, and RNA polymerase) that are significantly altered in both macro- and microdissected tumor samples compared to normal colon. On the other hand, pathways altered between benign and malignant tumors were identified only in the macrodissected tissues. Conclusion/Significance. Multidirectional analyses of microarray data allow the identification of essential signaling alterations underlying CRC development. Although the proposed strategy is computationally complex and laborintensive, it may reduce the number of false results.
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