Dataset Information

Identification of HIPK3 as a potential biomarker and an inhibitor of clear cell renal cell carcinoma.

ABSTRACT: Invasion and metastasis are the main causes of poor prognosis in patients with clear cell renal cell carcinoma (ccRCC). The homeodomain interacting protein kinases (HIPKs) can regulate cell proliferation and apoptosis. Little is known about the prognostic role of HIPKs in ccRCC. Here we use Kaplan-Meier survival analysis and multivariate analysis to analyze the correlation of overall survival (OS) and disease-free survival (DFS). ROC curves analyzed the relationship between clinicopathological parameters and HIPK3 expression in ccRCC. Univariate analysis and multivariate analysis confirmed that the expression of HIPK3 was associated with OS (HR, 0.701; P=0.041) and DFS (HR, 0.630; P=0.012). Low HIPK3 expression was a poor prognostic factor and HIPK3 expression was significantly down-regulated in ccRCC cancer tissues when compared with normal renal tissues. In vitro cell results also confirmed that HIPK3 over-expression could inhibit tumor growth and malignant characteristics. The results indicate that low expression of HIPK3 in ccRCC tissues is significantly associated with poor survival rates in tumor patients, and HIPK3 may be used as a valuable biomarker and inhibitor of ccRCC.

SUBMITTER: Xiao W

PROVIDER: S-EPMC7906163 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Identification of HIPK3 as a potential biomarker and an inhibitor of clear cell renal cell carcinoma.

Xiao Wen W Wang Tao T Ye Yuzhong Y Wang Xuegang X Chen Bin B Xing Jinchun J Yang Hongmei H Zhang Xiaoping X

Aging 20210120 3

Invasion and metastasis are the main causes of poor prognosis in patients with clear cell renal cell carcinoma (ccRCC). The homeodomain interacting protein kinases (HIPKs) can regulate cell proliferation and apoptosis. Little is known about the prognostic role of HIPKs in ccRCC. Here we use Kaplan-Meier survival analysis and multivariate analysis to analyze the correlation of overall survival (OS) and disease-free survival (DFS). ROC curves analyzed the relationship between clinicopathological p ...[more]

PMID: 33495417

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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: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 HIPK3 as a potential biomarker and an inhibitor of clear cell renal cell carcinoma.

Publications

Identification of HIPK3 as a potential biomarker and an inhibitor of clear cell renal cell carcinoma.

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