Candidate Gene and MicroRNA Biomarkers of Metastatic Renal Cell Carcinoma (MRCC) Response to Sunitinib

Backgrounds: The incidence of renal cancer is relatively insidious, and some patients have been metastatic renal cancer at the initial visit. Sunitinib is the first-line systemic therapy for patients with metastatic renal cell carcinoma, however, there is scant analysis of its effect on genes and microRNAs.Methods: In this study, 8 differentially expressed microRNAs and 112 differentially expressed genes were designated by analyzing mRNA and microRNA data sets and weighted correlation network analysis (WGCNA).Results: NIPSNAP1 gene showed the most co-expression with other genes. Through the intersection of the microRNA target gene with our differentially expressed genes, we got 26 genes. KEGG and GO analysis showed that these genes were predominantly concentrated in Pathways in cancer, Sphingolipid metabolism and Glycosaminoglycan degradation. After we set the 26 genes and gene of WGCNA do intersection, received six genes, respectively is NIPSNAP1, SDC4, TBC1D9, NEU1, STK40 and PLAUR. Conclusion: Through subsequent cell, molecular and flow cytometry experiments, we found the PLAUR would play a crucial role in renal cell carcinoma (RCC) resistant to sunitinib, which will be available for new ideas to forecast sunitinib resistance and reverse sunitinib resistance.

Establishment of a Prognostic Prediction and Drug Selection Model for Patients with Clear Cell Renal Cell Carcinoma by Multiomics Data Analysis

Rationale. Patients with clear cell renal cell cancer (ccRCC) may have completely different treatment choices and prognoses due to the wide range of heterogeneity of the disease. However, there is a lack of effective models for risk stratification, treatment decision-making, and prognostic prediction of renal cancer patients. The aim of the present study was to establish a model to stratify ccRCC patients in terms of prognostic prediction and drug selection based on multiomics data analysis. Methods. This study was based on the multiomics data (including mRNA, lncRNA, miRNA, methylation, and WES) of 258 ccRCC patients from TCGA database. Firstly, we screened the feature values that had impact on the prognosis and obtained two subtypes. Then, we used 10 algorithms to achieve multiomics clustering and conducted pseudotiming analysis to further validate the robustness of our clustering method, based on which the two subtypes of ccRCC patients were further subtyped. Meanwhile, the immune infiltration was compared between the two subtypes, and drug sensitivity and potential drugs were analyzed. Furthermore, to analyze the heterogeneity of patients at the multiomics level, biological functions between two subtypes were compared. Finally, Boruta and PCA methods were used for dimensionality reduction and cluster analysis to construct a renal cancer risk model based on mRNA expression. Results. A prognosis predicting model of ccRCC was established by dividing patients into the high- and low-risk groups. It was found that overall survival (OS) and progression-free interval (PFI) were significantly different between the two groups ( p < 0.01 ). The area under the OS time-dependent ROC curve for 1, 3, 5, and 10 years in the training set was 0.75, 0.72, 0.71, and 0.68, respectively. Conclusion. The model could precisely predict the prognosis of ccRCC patients and may have implications for drug selection for ccRCC patients.

Isothiocyanate From Moringa oleifera Seeds Inhibits the Growth and Migration of Renal Cancer Cells by Regulating the PTP1B-dependent Src/Ras/Raf/ERK Signaling Pathway

Moringa oleifera Lam. is a tropical and subtropical plant that has been used for centuries as both food and traditional medicine. 4-[(α-L-Rhamnosyloxy) benzyl] isothiocyanate (MIC-1) is an active substance in M. oleifera, with anti-cancer activity. However, whether MIC-1 exerts anti-renal cancer effects is unknown. Therefore, the aim of the present study was to evaluate the effects of MIC-1 on the growth and migration of renal cell carcinoma (RCC) cells and to identify the putative underlying mechanism. We found that, among 30 types of cancer cells, MIC-1 exerted the strongest growth inhibitory effects against 786-O RCC cells. In addition, MIC-1 (10 μM) significantly inhibited the growth of five RCC cell lines, including 786-O, OSRC-2, 769-P, SK-NEP-1, and ACHN cells, but was not toxic to normal renal (HK2) cells. Also, MIC-1 suppressed 786-O and 769-P cell migration and invasion abilities, and reduced the expression of matrix metalloproteinase (MMP)-2 and MMP-9. Furthermore, MIC-1 induced apoptosis and cell cycle arrest, increased Bax/Bcl-2 ratio, and decreased cell cycle-related protein expression in 786-O cells and 769-P cells. Molecular docking and small-molecule interaction analyses with PTP1B both showed that MIC-1 inhibited PTP1B activity by binding to its active site through hydrogen bonding and hydrophobic interactions. Additionally, MIC-1 could suppress the growth and migration of 786-O cells by inhibiting PTP1B-mediated activation of the Src/Ras/Raf/ERK signaling pathway. In vivo experiments further showed that MIC-1 markedly inhibited the growth of xenograft tumors in mice, and greatly increased Bax/Bcl-2 ratio in tumor tissues. In addition, MIC-1 had no effect on the PTP1B-dependent Src/Ras/Raf/ERK signaling pathway in HCT-116 cells, Hep-G2 cells, and A431 cells. Overall, our data showed that MIC-1 could be a promising, non-toxic, natural dietary supplement for the prevention and treatment of renal cancer.

Correction to: Long non-coding RNA UCA1 promotes malignant phenotypes of renal cancer cells by modulating the miR-182-5p/DLL4 axis as a ceRNA

An amendment to this paper has been published and can be accessed via the original article.