bioRxivBioinformaticsPreprint
Comprehensive Analysis of the Expression and Prognosis for PSMBs in Clear Cell Renal Cell Carcinoma
Ning JiangiD, Yulin ZhangiD, Zihan ZhaoiD, Tianhang LiiD, Tianyao LiuiD, Hongqian GuoiD, Rong Li YangiD
Abstract
BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most common malignances with an ever-increasing incidence and high mortality. Although comprehensive therapy has made great progress in recent years, the prognosis of most ccRCC patients is still poor. The proteasome {beta} subunits (PSMBs) are a group of genes composing the ubiquitin-proteasome pathway, which is the principal mechanism of protein catabolism in mammalian cytoplasm and nucleus and is essential for the regulation of almost all cellular processes. Growing evidence show s that the PSMBs play an important role in the occurrence and progression of various cancers. However, the expression and prognostic value of PSMBs in ccRCC have not been evaluated. MethodsTo solve this issue, ONCOMINE, UALCAN, Human Protein Atlas, cBioPortal, Metascape, GeneMANIA, STRING, DAVID 6.8, TIMER were used in this study. ResultsThe transcriptional expression levels of PSMB2/4/7/8/9/10 in ccRCC tissues were found to be significantly upregulated, and the mRNA expression levels of PSMB1/2/3/4/8/9/10 were associated with advanced tumor stage and tumor grade. Besides, higher mRNA expression of PSMB1/2/3/4/5/6/7/10 were associated with a shorter overall survival (OS). Furthermore, the protein expression level of PSMB4/7/8/9/10 in ccRCC was higher than normal renal tissues. Significant relationships were also found between the expression of PSMBs and the infiltration of immune-related cells. ConclusionsThe present study implied that PSMB4 and PSMB10 might act as potential therapy targets and prognostic biomarkers in ccRCC, which may contribute to available knowledge, improve the treatment designs, and enhance the accuracy of prognosis for patients with ccRCC.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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