Minichromosome maintenance (MCM) proteins play an essential role in DNA replication. They have been shown to be overexpressed in various types of cancer. However, the role of this family in renal cell carcinoma (RCC) is widely unknown. In this study, we have identified a number of RCC datasets in the Gene Expression Omnibus database and also investigated the correlation between the expression levels of MCM genes and clinicopathological parameters. We found that the expression levels of MCM genes are positively correlated with one another. Expression levels of MCM2, MCM5, MCM6, and MCM7, but not of MCM3 and MCM4, were higher in RCC compared to paired adjacent normal tissue. Only the expression level of MCM4, but not of other MCMs, was positively correlated with tumor grade. In addition, a high-level expression of MCM2 in either primary tumor or metastases of RCC predicted a shorter disease-free survival time, while a high-level expression of MCM4 or MCM6 in primary tumor was also associated with poorer disease-free survival. Interestingly, we also demonstrated that patients with their primary RCC overexpressing 2 or more MCM genes had a shorter disease-free survival time, while those with RCC metastases overexpressing 3 or more MCM genes had a shorter disease-free survival. Importantly, we also demonstrated that overexpression of MCM genes is an independent predictor for survival in RCC patients. Our results suggest that MCM2–7 genes may be an important prognostic marker for patients with RCC.

Epithelial–mesenchymal transition (EMT), a reversible cellular program, is critically important in tumor progression and is regulated by a family of transcription factors, induction factors, and an array of signaling pathway genes. The prognostic role and biological functions of EMT-related lncRNAs in ccRCC are largely unknown. In the present study, we analyzed the gene expression data and clinical information retrieved from The Cancer Genome Atlas (TCGA) database (N=512) and International Cancer Genome Consortium (ICGC) database (N=90) which served as training and external validation dataset, respectively. Then, we constructed an EMT-related lncRNA risk signature based on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information. The Kaplan-Meier curve analysis revealed that patients in the low-risk and high-risk groups exhibited significant divergence in the overall survival (OS) and disease-free survival (DFS) of ccRCC, as was confirmed in the validation dataset. The Cox regression analysis of the clinical factors and risk signature in the OS and DFS demonstrated that the risk signature can be utilized as an independent prognostic predictor. Moreover, we developed an individualized prognosis prediction model relying on the nomogram and receive operator curve (ROC) analysis based on the independent factors. The Gene Set Enrichment Analysis (GSEA) indicated that patients in the low-risk group were associated with adherens junction, focal adhesion, MAPK signaling pathway, pathways in cancer, and renal cell carcinoma pathway. In addition, we identified three robust subtypes (named C1, C2 and C3) of ccRCC with distinct clinical characteristics and prognostic role in the TCGA dataset and ICGC dataset. Among them, C1 was associated with a better survival outcome, whereas C2 and C3 was associated with a worse survival outcome and have more advanced-stage patients. Moreover, C2 was more likely to respond to immunotherapy and was sensitive to chemo drugs, this may provide insights to clinicians to develop an individualized treatment. Collectively, this work developed a reliable EMT-related lncRNA risk signature that can independently predict the OS and DFS of ccRCC. Besides, we identified three stable molecular subtypes based on the EMT-related lncRNA expression, which may comprehensively be vital in elucidating the underlying molecular mechanism of ccRCC.

Cumulative studies have shown that RNA binding proteins (RBPs) play an important role in numerous malignant tumors and related to the occurrence and progression of tumors. However, the role of RBPs in the kidney renal clear cell carcinoma (KIRC) has been not fully understood. In this study, we firstly downloaded gene expression data and corresponding clinical information of KIRC from the Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC), respectively. A total of 137 differentially expressed RBPs (DERBPs) were then identified between normal and tumor tissue, including 38 down regulated RBPs and 99 up-regulated RBPs. Nine RBPs (EIF4A1, RPL36A, EXOSC5, RPL28, RPL13, RPS19, RPS2, EEF1A2, OASL) were served as prognostic genes and exploited to construct a prognostic model through survival analysis. The Kaplan-Meier curves analysis showed that low risk group had a better survival outcome when compared with high risk group. The area under curve (AUC) value of the prognostic model was 0.713 in TCGA dataset (train dataset) and 0.706 in ICGC dataset (validation dataset), respectively, confirming a good prognostic model. The prognostic model was also identified an independent prognostic factor for KIRC survival by performing cox regression analysis. In addition, we also built a nomogram rely on age and prognostic model, and internal validation in the TCGA data set. The clinical benefit of the prognostic model was revealed by decision curve analysis (DCA). Gene set enrichment analysis revealed several crucial pathways (ERBB signaling pathway, pathways in cancer, MTOR signaling pathway, WNT signaling pathway and TGF BETA signaling pathway) that may explain the underlying mechanisms of KIRC. Furthermore, potential drugs for KIRC treatment were predicted by Connectivity Map (Cmap) database based on DERBPs, and several important drugs such as depudecin and vorinostat that could reverse KIRC gene expression, which may provide reference for the treatment of KIRC. In summary, we developed and validated a robust Nine-RBP signature for KIRC prognosis prediction. A nomogram with risk score and age can be applied to promote the individualized prediction of overall survival in patients with KIRC. Moreover, the two drugs including depudecin and vorinostat may contribute to KIRC treatment.