Liver metastasis is one of the major causes of death in colorectal cancer (CRC) patients. To understand this process, we investigated whether the gene expression profiling of matched colorectal carcinomas and liver metastases could reveal key molecular events involved in tumor progression and metastasis. We performed experiments using a cDNA microarray containing 17,104 genes with the following tissue samples: paired tissues of 25 normal colorectal mucosa, 27 primary colorectal tumors, 13 normal liver and 27 liver metastasis, and 20 primary colorectal tumors without liver metastasis. To remove the effect of normal cell contamination, we selected 4,583 organ-specific genes with a false discovery rate (FDR) of 0.0067% by comparing normal colon and liver tissues using significant analysis of microarray, and these genes were excluded from further analysis. We then identified and validated 46 liver metastasis-specific genes with an accuracy of 83.3% by comparing the expression of paired primary colorectal tumors and liver metastases using prediction analysis of microarray. The 46 selected genes contained several known oncogenes and 2 ESTs. To confirm that the results correlated with the microarray expression patterns, we performed RT-PCR with WNT5A and carbonic anhydrase II. Additionally, we observed that 21 of the 46 genes were differentially expressed (FDR 5 2.27%) in primary tumors with synchronous liver metastasis compared with primary tumors without liver metastasis. We scanned the human genome using a cDNA microarray and identified 46 genes that may play an important role in the progression of liver metastasis in CRC. ' 2007 Wiley-Liss, Inc.Key words: colorectal cancer; liver metastasis; gene expression profiling; cDNA microarray Colorectal cancer (CRC) is one of the most common causes of cancer-related deaths, and it causes death primarily through liver metastasis.1 Recent progress in diagnosis and treatment has enabled clinicians to save the lives of many patients at early stages of the disease, but the prognosis for patients with advanced disease or systemic metastasis is still very poor. About 30% of recurrent CRC patients have liver metastasis, and more than 70% of them are not candidates for the curative resection.2 Therefore, predicting the metastatic potential of a primary tumor could help in improving patient survival by identifying those who should receive intensive postoperative follow-up and adjuvant chemotherapy.Most cancers, including CRC, might have a single clonal origin at the initial stage of the disease, but a malignant tumor contains multiple cell populations with different properties, such as growth rate, karyotype, immunogenicity, sensitivity to various drugs and the ability to invade and develop metastases.3 This heterogeneity is the major obstacle to effective treatment of CRC because of the variation in clinical patterns and treatment efficacies. Cells that have acquired the ability to regulate their adhesion or motility are of special concern because of their high potential to initiat...

BackgroundIn the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy. To identify a radiosensitive gene signature and elucidate related signaling pathways, four different microarray experiments were reanalyzed before radiotherapy.ResultsRadiosensitivity profiling data using clonogenic assay and gene expression profiling data from four published microarray platforms applied to NCI-60 cancer cell panel were used. The survival fraction at 2 Gy (SF2, range from 0 to 1) was calculated as a measure of radiosensitivity and a linear regression model was applied to identify genes or a gene set with a correlation between expression and radiosensitivity (SF2). Radiosensitivity signature genes were identified using significant analysis of microarrays (SAM) and gene set analysis was performed using a global test using linear regression model. Using the radiation-related signaling pathway and identified genes, a genetic network was generated. According to SAM, 31 genes were identified as common to all the microarray platforms and therefore a common radiosensitivity signature. In gene set analysis, functions in the cell cycle, DNA replication, and cell junction, including adherence and gap junctions were related to radiosensitivity. The integrin, VEGF, MAPK, p53, JAK-STAT and Wnt signaling pathways were overrepresented in radiosensitivity. Significant genes including ACTN1, CCND1, HCLS1, ITGB5, PFN2, PTPRC, RAB13, and WAS, which are adhesion-related molecules that were identified by both SAM and gene set analysis, and showed interaction in the genetic network with the integrin signaling pathway.ConclusionsIntegration of four different microarray experiments and gene selection using gene set analysis discovered possible target genes and pathways relevant to radiosensitivity. Our results suggested that the identified genes are candidates for radiosensitivity biomarkers and that integrin signaling via adhesion molecules could be a target for radiosensitization.

ABCB1 genotypes may be a predictor of paclitaxel activity as well as a prognostic factor in metastatic breast cancer patients.

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