The Gene Expression Landscape of Prostate Cancer BM Reveals Close Interaction with the Bone Microenvironment

Bone metastatic (BM) prostate cancer (PCa) belongs to the most lethal form of PCa, and therapeutic options are limited. Molecular profiling of metastases contributes to the understanding of mechanisms defining the bone metastatic niche. Our aim was to explore the transcriptional profile of PCa BM and to identify genes that drive progression. Paraffin-embedded tissues of 28 primary PCa and 30 BM were submitted to RNA extraction and analyzed by RNA sequencing using the Nanostring nCounter gene expression platform. A total of 770 cancer-related genes were measured using the Nanostring (TM) PanCancer progression panel. Gene Ontology (GO), KEGG, Reactome, STRING, Metascape, PANTHER, and Pubmed were used for data integration and gene annotation. We identified 116 differentially expressed genes (DEG) in BM compared to primaries. The most significant DEGs include CD36, FOXC2, CHAD, SPP1, MMPs, IBSP, and PTX3, which are more highly expressed in BM, and ACTG2, MYH11, CNN1, FGF2, SPOCK3, and CHRDL1, which have a lower expression. DEGs functionally relate to extracellular matrix (ECM) proteoglycans, ECM-receptors, cell-substrate adhesion, cell motility as well as receptor tyrosine kinase signaling and response to growth factors. Data integration and gene annotation of 116 DEGs were used to build a gene platform which we termed Manually Annotated and Curated Nanostring-data Platform. In summary, our results highlight the significance of certain genes in PCa BM to which essential pro-metastatic functions could be ascribed. Data from this study provide a comprehensive platform of genes that are related to PCa BM and provide evidence for further investigations.

Automated summary

Bone metastatic prostate cancer is a highly lethal form with limited therapeutic options. This study identified 116 differentially expressed genes in bone metastases compared to primary tumors, with functions related to extracellular matrix, cell motility, and signaling pathways. Data integration and gene annotation were used to build a manually annotated and curated Nanostring-data platform for further investigations.