Breast cancer is a heterogeneous disease characterized by multiple genetic alterations leading to the activation of growth factor signaling pathways that promote cell proliferation. Platelet-derived growth factor-C (PDGF-C) is overexpressed in various malignancies; however, the involvement of PDGF-C in breast cancers and the mechanisms underlying PDGF-C deregulation remain unclear. Here, we show that PDGF-C is overexpressed in clinical breast cancers and correlates with poor prognosis. PDGF-C up-regulation was mediated by the human embryonic lethal abnormal vision-like protein HuR, which stabilizes the PDGF-C transcript by binding to two predicted AU-rich elements (AREs) in the 3'-untranslated region (3'-UTR). HuR is up-regulated in hydrogen peroxide-treated or ultraviolet-irradiated breast cancer cells. Clinically, HuR levels are correlated with PDGF-C expression and histological grade or pathological tumor-node-metastasis (pTNM) stage. Our OPEN ACCESSInt. J. Mol. Sci. 2014, 15 20307 findings reveal a novel mechanism underlying HuR-mediated breast cancer progression, and suggest that HuR and PDGF-C are potential molecular candidates for targeted therapy of breast cancers.

Background Colon adenocarcinoma (COAD) is one of the most common malignant tumors, with high incidence and mortality rates worldwide. Reliable prognostic biomarkers are needed to guide clinical practice. Methods Comprehensive gene expression with alternative splicing (AS) profiles for each patient was downloaded using the SpliceSeq database from The Cancer Genome Atlas. Cox regression analysis was conducted to screen for prognostic AS events. The R package limma was used to screen differentially expressed genes (DEGs) between normal and tumor samples in the COAD cohort. A Venn plot analysis was performed between DEGs and prognostic AS events, and the DEGs that co-occurred with prognostic AS events (DEGAS) were identified. The top 30 most-connected DEGAS in protein–protein interaction analysis were identified through Cox proportional hazards regression to establish prognostic models. Results In total, 350 patients were included in the study. A total of 22,451 AS events were detected, of which 2004 from 1439 genes were significantly associated with survival time. By overlapping these 1439 genes with 6455 DEGs, 211 DEGs with AS events were identified. After the construction of the protein–protein interaction network, the top 30 hub genes were included in a multivariate analysis. Finally, a risk score based on 12 genes associated with overall survival was established ( P < 0.05). The area under the curve was 0.782. The risk score was an independent predictor ( P < 0.001). Conclusions By exploring survival-associated AS events, a powerful prognostic predictor consisting of 12 DEGAS was built. This study aims to propose a novel method to provide treatment targets for COAD and guide clinical practice in the future.