SAGE analysis of human metastatic and non-metastatic prostate cancer xenograft sublines from a single patient. Free

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Malignant progression of prostate cancers results in metastasis, poor response to hormonal treatment and to chemotherapy. Identification of molecular markers predictive of malignant progression in primary prostate cancers is therefore of untmost importance for controlling this disease. We have previously reported the establishment of two transplantable prostate cancer tissue xenograft lines (a metastatic, PCa1-met, and a non-metastatic subline, PCa2) from a single patient’s primary tumor. In the present study we have subjected the two tumor sublines to Serial Analysis of Gene Expression (SAGE) with a view to identifying genes associated with metastatic and non-metastatic abilities. RNA was extracted from same generation and orthotopically grafted xenograft tissues. LongSAGE libraries were prepared and tags were sequenced, i.e. 132.163 tags (containing 35,186 unique tags) from the metastatic subline and 134,206 tags (containing 37,646 unique tags) from the non-metastatic subline. LongSAGE tags from the two libraries were annotated to cancer tissue (human) or stromal tissue (mouse), using human/mouse Genome and Unigene sequence data. A total of 28,567 unique tags were annotated in only human Genome sequences and 24,160 unique tags were annotated in only human Unigene sequences. The cancer (human) tags of the metastatic and non-metastatic sublines were then compared for identification of genes showing significant differences in expression. Various candidate genes were detected, including some not previously associated with prostate cancer. Some of these genes and their differential expressions were confirmed by real time PCR using various generations of the sublines. Xenografted tumor tissue sublines obtained from one patient appear to provide a good tool for screening genes that are responsible for a particular phenotype. This new approach, which avoids differences between individuals, overcomes the heterogeneity in patients’ tumor, could also be used for tumor-stroma specific expression pattern analysis. Supported by a Strategic Research Grant on Genomics and Proteomics of Metastatic Cancer from Cancer Research Society, Canada and a grant from the US Army Department of Defense, USAMRMC W81XWH-04-1-0290 (Y.Z.W.).