AURKB/CDC37 complex promotes clear cell renal cell carcinoma progression via phosphorylating MYC and constituting an AURKB/E2F1-positive feedforward loop

As the second most common malignant tumor in the urinary system, renal cell carcinoma (RCC) is imperative to explore its early diagnostic markers and therapeutic targets. Numerous studies have shown that AURKB promotes tumor development by phosphorylating downstream substrates. However, the functional effects and regulatory mechanisms of AURKB on clear cell renal cell carcinoma (ccRCC) progression remain largely unknown. In the current study, we identified AURKB as a novel key gene in ccRCC progression based on bioinformatics analysis. Meanwhile, we observed that AURKB was highly expressed in ccRCC tissue and cell lines and knockdown AURKB in ccRCC cells inhibit cell proliferation and migration in vitro and in vivo. Identified CDC37 as a kinase molecular chaperone for AURKB, which phenocopy AURKB in ccRCC. AURKB/CDC37 complex mediate the stabilization of MYC protein by directly phosphorylating MYC at S67 and S373 to promote ccRCC development. At the same time, we demonstrated that the AURKB/CDC37 complex activates MYC to transcribe CCND1, enhances Rb phosphorylation, and promotes E2F1 release, which in turn activates AURKB transcription and forms a positive feedforward loop in ccRCC. Collectively, our study identified AURKB as a novel marker of ccRCC, revealed a new mechanism by which the AURKB/CDC37 complex promotes ccRCC by directly phosphorylating MYC to enhance its stability, and first proposed AURKB/E2F1-positive feedforward loop, highlighting AURKB may be a promising therapeutic target for ccRCC.


Figure S2 .
Figure S2.Correlation analysis of key genes with ccRCC survival.(A) TCGA data were used to calculate the influence of key gene expression on prognostic data of ccRCC, including OS, DSS and PFI.(B) Cox regression map was used to investigate the influence of key genes on prognostic types of ccRCC.

Figure S3 .
Figure S3.Expression and clinical correlation analysis of key genes.(A) Expression of key genes in ccRCC.The correlation between key genes expression and clinical stage in the TCGA databases.The expression of key genes in different topography (B), lymph node metastasis (C), distant

Figure S4 .
Figure S4.AURKB is up-regulated in ccRCC and promotes the proliferation and migration of ccRCC cells in vitro in vivo.(A) mRNA expression levels of AURKB in 786-O and CAKI-1 cells were detected by qRT-PCR to confirm the knockdown efficiency of both siRNAs.(B) Flow

Figure S5 .
Figure S5.CDC37 phenocopy AURKB in ccRCC.(A) mRNA expression levels of CDC37 in 786-O and CAKI-1 cells were detected by qRT-PCR to confirm the knockdown efficiency of both

Figure S7 .
Figure S7.MYC proteins were quantified and plotted.(A) MYC proteins were quantified and plotted in 786-O cells.(B) MYC proteins were quantified and plotted in CAKI-1cells.

Figure S8 .
Figure S8.Regulation of CDC37, AURKB, MYC and CCND1 expression in synchronized cells.(A) Western blotting was used to detect the expression changes of MYC after AURKB knockdown in synchronized ccRCC cells.(B) Western blotting was used to detect the expression changes of AURKB and MYC after CDC37 knockdown in synchronized ccRCC cells.(C) CCND1 mRNA levels in synchronized 786-O and CAKI-1 cells with AURKB knockdown were detected by qRT-PCR.(D) CCND1 mRNA levels in synchronized 786-O and CAKI-1 cells with CDC37 knockdown were detected by qRT-PCR.*p < 0.05, **p < 0.01, ***p < 0.001.

Table S1 . DATA and R language description 1. TCGA data download website and citation website:
TCGA data download from UCSC XENA (https://xena.ucsc.edu)

. The survival dataset： dataset: phenotype -Curated survival data hub: https
://tcga.xenahubs.netCurated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics".The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS(overall survial), PFI( progression-free interval), DFI(disease-free interval), and DSS(disease-specific survival) for each TCGA cancer type.