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- W2098694310 abstract "Prostate cancer (PCa) is the second most commonly diagnosed malignancy in men in the Western world and the second leading cause of cancer-related deaths among men worldwide. Although most cancers have the potential to metastasize under appropriate conditions, PCa favors the skeleton as a primary site of metastasis, suggesting that the bone microenvironment is conducive to its growth. PCa metastasis proceeds through a complex series of molecular events that include angiogenesis at the site of the original tumor, local migration within the primary site, intravasation into the blood stream, survival within the circulation, extravasation of the tumor cells to the target organ and colonization of those cells within the new site. In turn, each one of these steps involves a complicated chain of events that utilize multiple protein-protein interactions, protein signaling cascades and transcriptional changes. Despite the urgent need to improve current biomarkers for diagnosis, prognosis and drug resistance, advances have been slow. Global gene expression methods such as gene microarrays and RNA sequencing enable the study of thousands of genes simultaneously and allow scientists to examine molecular pathways of cancer pathogenesis. In this review, we summarize the current literature that explored high-throughput transcriptome analysis toward the advancement of biomarker discovery for PCa. Novel biomarkers are strongly needed to enable more accurate detection of PCa, improve prediction of tumor aggressiveness and facilitate the discovery of new therapeutic targets for tailored medicine. Promising molecular markers identified from gene expression profiling studies include HPN, CLU1, WT1, WNT5A, AURKA and SPARC." @default.
- W2098694310 created "2016-06-24" @default.
- W2098694310 creator A5012395081 @default.
- W2098694310 creator A5026487959 @default.
- W2098694310 creator A5069593736 @default.
- W2098694310 date "2013-07-22" @default.
- W2098694310 modified "2023-10-18" @default.
- W2098694310 title "Prostate cancer invasion and metastasis: insights from mining genomic data" @default.
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