Abstract

Abnormal expression or mutation of RNA splicing proteins are widely observed in human cancers. Here, we identified poly(U) binding splicing factor 60 (PUF60) as one of the most differentially expressed genes out of 97 RNA splicing proteins between normal and bladder cancer tissues by bioinformatics analysis of TCGA bladder cancer expression data. The expression of PUF60 was significantly higher in tumor tissues, while high PUF60 expression was associated with malignant phenotypes of bladder cancer and shorter survival time. Moreover, we identified aurora kinase A (AURKA) as a new downstream target of PUF60 in bladder cancer cells. PUF60 knockdown significantly inhibited cell viability and colony formation capacity in bladder cancer cells, whereas AURKA overexpression reversed this inhibition effect. Overexpression of PUF60 significantly promoted cell viability and colony formation in bladder cancer cells, while treatment with AURKA specific inhibitor reversed this promotive effect. Mechanistically, PUF60 specifically bound to the AURKA promoter, thereby activating its transcription and expression. Furthermore, we showed that there was a significant positive correlation between PUF60 and AURKA expression in bladder cancer tissues, and PUF60 and AURKA expression contributed to tumor progression and malignant phenotypes in the patients with bladder cancer. Collectively, these results indicate that the PUF60/AURKA axis plays a key role in regulating tumorigenesis and progression of bladder cancer, and may be a potential prognostic biomarker and therapeutic target for bladder cancer patients.

Highlights

  • Bladder cancer is the sixth most common cancer in men and the tenth in both sexes worldwide

  • We confirmed the expression of poly(U) binding splicing factor 60 (PUF60) in TCGA datasets, and found significantly higher expression of PUF60 mRNA in carcinoma tissues compared to normal bladder tissues (Figure 1B)

  • We confirmed that the protein and mRNA expression of PUF60 were overexpressed in bladder cancer by analyzing our tissue microarray data and expression data from Oncomine database and GEO database

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Summary

Introduction

Bladder cancer is the sixth most common cancer in men and the tenth in both sexes worldwide. The tumor, node and metastasis (TNM) classification system was the most commonly used approach in risk stratification and management of bladder cancer patients. It has been updated many times over the last few decades, it still has limitations in predicting therapy response and outcome among bladder cancer patients [3, 4]. It is urgent to find new biomarkers for predicting the outcomes of bladder cancer, which may lead to a better management of bladder cancer patients. Molecular subtypes based on the gene expression profile in bladder cancer has aroused attention worldwide, and it is promising to identify gene signatures which can better predict survival time and therapy response [5,6,7]

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