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Prostate cancer (PCa) refers to one of the most common tumors in male's genitourinary system. Emerging research has confirmed that circRNAs play an important role in the occurrence and development of tumors. However, the correlation between circular RNA circITGA7 and PCa still remains unclear. Here, the role of circITGA7 in PCa was explored and the underlying mechanism was investigated as well. The circRNA expression profiles in PCa and the paracancerous tissues were established by high-throughput sequencing. The expression levels of circITGA7 in PCa tissues and cells were detected by qRT-PCR. Cell Counting Kit-8, colony formation, EdU, and flow cytometry assays were used to detect the effects of circITGA7 on PCa cell proliferation. To further explore the underlying mechanisms, bioinformatics analysis on downstream target genes was carried out. RNA immunoprecipitation and dual-luciferase reporter assays were used to verify the direct relationship between miR-370-3p and circITGA7 or P21CIP1. The present results demonstrated that circITGA7 was downregulated in PCa tissues and cells. Gain- or loss-of-function assays showed that circITGA7 inhibited the proliferation of PCa cells in vivo and in vitro. Mechanically, circITGA7 served as a sponge for miR-370-3p, and miR-370-3p could target P21CIP1 in PCa cells. The inhibition of cell proliferation induced by circITGA7 could be reversed by transfecting miR-370-3p mimic. Collectively, our data indicated that circITGA7 played an important role in inhibiting tumor proliferation in PCa and might be a potential therapeutic target. [ABSTRACT FROM AUTHOR]

Renal cell carcinoma (RCC), which was one of the most common malignant tumors in urinary system, had gradually increased incidence and mortality in recent years. Although significant advances had been made in molecular and biology research on the pathogenesis of RCC, effective treatments and prognostic indicators were still lacking. In order to predict the prognosis of RCC better, we identified 17 genes that were associated with the overall survival (OS) of RCC patients from The Cancer Genome Atlas (TCGA) dataset and a 17-gene signature was developed. Through SurvExpress, we analyzed the expression differences of the 17 genes and their correlation with the survival of RCC patients in five datasets (ZHAO, TCGA, KIPAN, KIRC, and KIRP), and then evaluated the survival prognostic significance of the 17-gene signature for RCC. Our results showed that the 17-gene signature had a predictive prognostic value not only in single pathologic RCC, but also in multiple pathologic types of RCC. In conclusion, the 17-gene signature model was related to the survival of RCC patients and could help predict the prognosis with significant clinical implications. [ABSTRACT FROM AUTHOR]