Abstract Prostate cancer (PCa) shows tremendous heterogeneity which makes it difficult to identify patients with an increased risk of disease recurrence. A better understanding of the biological mechanism of prostate cancer formation and progression is crucial for the discovery of new markers for this disease. In recent years it has become apparent that different non-coding RNAs are also implicated in prostate cancer. Several microRNAs are now associated with progression and classification of prostate cancer and other malignancies. The role played in progression and differentiation of distinct PCa subtypes by a recently discovered class of non-coding RNAs, called large intergenic non-coding RNAs (lincRNAs), has remained unexplored. LincRNAs are believed to have major consequences on gene expression patterns through epigenetic mechanisms. Thousands of lincRNAs have been identified in human tissues, but only few have been functionally characterized. To assess the role of lincRNAs in PCa, we analysed the expression pattern of nearly 28000 Entrez genes and 7500 unique lincRNAs in 56 primary PCas and 5 normal prostate tissues, using Agilent 8x60k arrays. Unsupervised clustering over 1610 lincRNAs, selected after filtering out non informative probes, classified 61 samples into 5 distinct classes. Anova analysis was done to identify genes specifically over or under expressed in each cluster and was followed by functional annotation analysis. Normal samples were separated into a cluster characterized by the down regulation of genes involved in chemotaxis and intracellular signaling cascade. The four tumor clusters showed up regulation of distinct biological processes, like cell cycle, chromatin organization and immune response, together with deregulation of MAP-kinase signaling through EGFR and members of the RAS family oncogene. These results show that sample classification based on lincRNA profiling is able to separate tumors into subgroups with distinct biological processes. Citation Format: Maurizia Mello-Grand, Vijay K. Singh, Chiara Ghimenti, Maria Scatolini, Nicole Longoni, Laura Curti, Andrea Zitella, Paolo Gontero, Carlo V. Catapano, Giuseppina M. Carbone, Giovanna Chiorino. LincRNA expression data analysis identifies prostate tumor subtypes with distinct biological processes abstract. In: Proceedings of the AACR Special Conference on Advances in Prostate Cancer Research; 2012 Feb 6-9; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(4 Suppl):Abstract nr C21.