Dataset Information

Identification of novel prognostic biomarkers in renal cell carcinoma.

ABSTRACT:

Objective

To identify novel prognostic biomarkers in renal cell carcinoma (RCC).

Results

12 coding genes and one miRNA were finally identified as prognostic biomarkers. All of them were related to a poor prognosis. Lower expression levels of the coding genes were observed in higher clinical stages. Prognostic signatures including 7 biomarkers were identified. Patients in the high-risk group had worse survival than those in the low-risk group. The areas under the curves in different years indicated that it was a valuable signature in prognosis. It was found that elevated WDR72 inhibited the survival and invasion of 786-O and 769P cells in vitro.

Conclusions

Thirteen prognostic biomarkers of RCC were identified. Among them, 7 biomarkers comprised a signature to evaluate the RCC prognosis. WDR72 was a cancer suppressor and a potential therapeutic target in RCC.

Methods

Differentially expressed genes/miRNAs (DEGs/DEMs) and prognosis-related genes/miRNAs were acquired from public database. Prognostic biomarkers were identified by overlapping the significant DEGs/DEMs and prognosis-related genes/miRNAs. The associations between these biomarkers and the clinical stages were analyzed. All of these prognostic biomarkers were further investigated with multi-variable Cox regression. Finally, the inhibitory effect of WDR72 on the growth and invasion of RCC cells was studied.

SUBMITTER: Zou Y

PROVIDER: S-EPMC7803519 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of novel prognostic biomarkers in renal cell carcinoma.

Zou Yuanzhang Y Lu Qiu Q Yao Qin Q Dong Di D Chen Binghai B

Aging 20201121 24

<h4>Objective</h4>To identify novel prognostic biomarkers in renal cell carcinoma (RCC).<h4>Results</h4>12 coding genes and one miRNA were finally identified as prognostic biomarkers. All of them were related to a poor prognosis. Lower expression levels of the coding genes were observed in higher clinical stages. Prognostic signatures including 7 biomarkers were identified. Patients in the high-risk group had worse survival than those in the low-risk group. The areas under the curves in differen ...[more]

PMID: 33234734

Similar Datasets

Project description:The purpose of the present study was to screen potential pathogenic biomarkers of clear cell renal cell carcinoma (ccRCC) via microarray analysis. The mRNA and microRNA (miRNA) expression profiles of GSE96574 and GSE71302 were downloaded from the Gene Expression Omnibus (GEO) database, as well as the methylation profile of GSE61441. A total of 5 ccRCC tissue samples and 5 normal kidney tissue samples were contained in each profile of GSE96574 and GSE71302, and 46 ccRCC tissue samples and 46 normal kidney tissue samples were involved in GSE61441. The differentially expressed genes (DEGs) and the differentially expressed miRNAs (DEMs) were obtained via limma package in ccRCC tissues compared with normal kidney tissues. The Two Sample t-test and the Beta distribution test were used to identify the differentially methylated sites (DMSs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform the Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs. The targets of the DEMs were screened with the miRWalk database, and the further combination analyses of DEGs, DEMs and DMSs were conducted. Additionally, reverse transcription PCR (RT-PCR) and methylation-specific PCR (MS-PCR) were performed to detect the mRNA level and methylation status of HAPLN1. The mRNA levels of hsa-miR-204 and hsa-miR-218 were tested by RT-PCR. A total of 2,172 DEGs, 202 DEMs and 2,172 DMSs were identified in RCC samples compared with normal samples. The DEGs were enriched in 1,015 GO terms and 69 KEGG pathways. A total of 10,601 miRNA-gene pairs were identified in at least 5 algorithms of the miRWalk database. A total of 143 overlaps were identified between the DEGs and the differentially methylated genes. Furthermore, the DEGs were involved in 851 miRNA-gene pairs, including 127 pairs in which the target genes were negatively associated with their corresponding DEMs and DMSs. HAPLN1 was lowly expressed and highly methylated in ccRCC tissues, while hsa-miR-204 and hsa-miR-218 were highly expressed. The results of the present study indicated that HAPLN1, hsa-miR-204 and hsa-miR-218 may be involved in the pathogenesis of ccRCC.

Project description:Background: Up frameshift protein 1 (UPF1) is a key component of nonsense-mediated mRNA decay (NMD) of mRNA containing premature termination codons (PTCs). The dysregulation of UPF1 has been reported in various cancers. However, the expression profile of UPF1 and its clinical significance in clear cell renal cell carcinoma (ccRCC) remains unclear. Methods: In order to detect UPF1 expression in ccRCC and its relationship with the clinical features of ccRCC, bulk RNA sequencing data were analyzed from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and ArrayExpress databases. The impact of UPF1 on the immune microenvironment of ccRCC was evaluated by multiple immune scoring algorithms to identify the cell groups that typically express UPF1 using ccRCC single cell sequencing (scRNA) data. In addition, genes co-expressed with UPF1 were identified by the weighted gene correlation network analysis (WGCNA), followed by KEGG and Reactome enrichment analysis. A series of functional experiments were performed to assess the roles of UPF1 in renal cancer cells. Finally, pan-cancer analysis of UPF1 was also performed. Results: Compared with normal tissues, the expression levels of UPF1 mRNA and protein in tumor tissues of ccRCC patients decreased significantly. In addition, patients with low expression of UPF1 had a worse prognosis. Analysis of the immune microenvironment indicated that UPF1 immune cell infiltration was closely related and the ccRCC scRNA-seq data identified that UPF1 was mainly expressed in macrophages. WGCNA analysis suggested that the functions of co-expressed genes are mainly enriched in cell proliferation and cellular processes. Experimental tests showed that knockdown of UPF1 can promote the invasion, migration and proliferation of ccRCC cells. Lastly, pan-cancer analysis revealed that UPF1 disorders were closely associated with various cancer outcomes. Conclusions: UPF1 may play a tumor suppressive role in ccRCC and modulate the immune microenvironment. The loss of UPF1 can predict the prognosis of ccRCC, making it a promising biomarker and providing a new reference for prevention and treatment.

Project description:BackgroundAntiangiogenic agents that specifically target vascular endothelial growth factor receptor (VEGFR), such as sunitinib, have been utilized as the standard therapy for metastatic clear cell renal cell carcinoma (ccRCC) patients. However, most patients eventually show no responses to the targeted drugs, and the mechanisms for the resistance remain unclear. This study is aimed to identify pivotal molecules and to uncover their potential functions involved in this adverse event in ccRCC treatment.MethodsTwo datasets, GSE64052 and GSE76068, were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the limma package in R software. The gene set enrichment analysis (GSEA) was conducted using clusterProfiler package. A protein-protein interaction (PPI) network was built using the STRING database and Cytoscape software. Kaplan-Meier survival curves were plotted using R software. qRT-PCR and Western blotting were used to detect the MX2 and pathway expression in RCC cell lines. Sunitinib-resistant cell lines were constructed, and loss-of-function experiments were conducted by knocking down MX2. All statistical analyses were performed using R version 3.6.1 and SPSS 23.0.ResultsA total of 760 DEGs were derived from two datasets in GEO database, and five hub genes were identified, among which high-level MX2 exhibited a pronounced correlation with poor overall survival (OS) in sunitinib-resistant ccRCC patients. Clinical correlation analysis and Gene Set Variation Analysis (GSVA) on MX2 showed that the upregulation of MX2 was significantly related to the malignant phenotype of ccRCC, and it was involved in several pathways and biological processes associated with anticancer drug resistance. qRT-PCR and Western blotting revealed that MX2 was distinctly upregulated in sunitinib-resistant RCC cell lines. Colony formation assay and Cell Counting Kit-8 (CCK8) assay showed that MX2 strongly promoted resistant capability to sunitinib of ccRCC cells.ConclusionMX2 is a potent indicator for sunitinib resistance and a therapeutic target in ccRCC patients.

Dataset Information

Identification of novel prognostic biomarkers in renal cell carcinoma.

Objective

Results

Conclusions

Methods

Publications

Identification of novel prognostic biomarkers in renal cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets