scholarly journals NFIB promotes the migration and progression of kidney renal clear cell carcinoma by regulating PINK1 transcription

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10848
Author(s):  
Ninghua Wang ◽  
Jing Yuan ◽  
Fei Liu ◽  
Jun Wei ◽  
Yu Liu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Transcriptional Level ◽  
Nuclear Factor I ◽  
Cancer Genome Atlas ◽  
Metastasis Rate

Kidney renal clear cell carcinoma (KIRC) is the most common and aggressive type of renal cell carcinoma. Due to high mortality rate, high metastasis rate and chemical resistance, the prognosis of KIRC patients is poor. Therefore, it is necessary to study the mechanisms of KIRC development and to develop more effective prognostic molecular biomarkers to help clinical patients. In our study, we used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to investigate that the expression of nuclear factor I B (NFIB) is significantly higher in KIRC than in adjacent tissues. Moreover, NFIB expression levels are associated with multiple clinical pathological parameters of KIRC, and KIRC patients with high NFIB expression have poor prognosis, suggesting that NFIB may play vital roles in the malignant development of KIRC. Further studies demonstrated that NFIB could promote the progression and metastasis of KIRC and participate in the regulation of PTEN induced kinase 1 (PINK1). Furthermore, we used chromatin immunoprecipitation (ChIP) experiments to confirm that NFIB binds to the PINK1 promoter and regulates its expression at the transcriptional level. Further experiments also confirmed the important roles of PINK1 in promoting the development of tumors by NFIB. Hence, our data provide a new NFIB-mediated regulatory mechanism for the tumor progression of KIRC and suggest that NFIB can be applied as a new predictor and therapeutic target for KIRC.

OSkirc: a web tool for identifying prognostic biomarkers in kidney renal clear cell carcinoma

2019 ◽  
Vol 15 (27) ◽  
pp. 3103-3110 ◽  
Author(s):  
Longxiang Xie ◽  
Qiang Wang ◽  
Yifang Dang ◽  
Linna Ge ◽  
Xiaoxiao Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Carcinoma ◽  
Web Application ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
P Value ◽  
Survival Plot

Aim: To develop a free and quick analysis online tool that allows users to easily investigate the prognostic potencies of interesting genes in kidney renal clear cell carcinoma (KIRC). Patients & methods: A total of 629 KIRC cases with gene expression profiling data and clinical follow-up information are collected from public Gene Expression Omnibus and The Cancer Genome Atlas databases. Results: One web application called Online consensus Survival analysis for KIRC (OSkirc) that can be used for exploring the prognostic implications of interesting genes in KIRC was constructed. By OSkirc, users could simply input the gene symbol to receive the Kaplan–Meier survival plot with hazard ratio and log-rank p-value. Conclusion: OSkirc is extremely valuable for basic and translational researchers to screen and validate the prognostic potencies of genes for KIRC, publicly accessible at http://bioinfo.henu.edu.cn/KIRC/KIRCList.jsp

scholarly journals CORO6 Promotes Cell Growth and Invasion of Clear Cell Renal Cell Carcinoma via Activation of WNT Signaling

2020 ◽  
Author(s):  
Xinjun Wang ◽  
Yiming Xiao ◽  
Zhijian Yan ◽  
Guangcheng Luo
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Growth ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract BackgroundRenal cell carcinoma (RCC) constitutes the most lethal type of genitourinary cancer. Understanding of RCC tumor biology helps to identify novel targets and develop directed treatments for patients with this type of cancer. MethodsBioinformatical Analysis of The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma dataset. Western blotting and qPCR were used to examine the expression levels of interested genes. Flow cytometry, MTT, BrdU staining, wound healing assay and transwell invasion assay were applied to determine the biological functions of CORO6 in ccRCC cells. The in vivo effect of CORO6 on ccRCC development was validated with xenografted mouse model.ResultsAnalysis from both The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma dataset and our RCC samples demonstrated that the expression level of CORO6 was significantly higher in RCC patients than in normal kidney tissues, and its level was highly associated with tumor stage and grade. Importantly, CORO6 expression level was an independent predictor of tumor metastasis and overall survival in RCC patients. Our cell line data also confirmed that CORO6 knockdown could suppress RCC cell growth as well as cell migration and invasion. The depletion of CORO6 led to cell cycle arrest at the G0/G1 phase and caused cell apoptosis. Further, mechanistic dissection showed that CORO6 mediated RCC cell growth, and cell invasion relied on WNT signaling. Moreover, the in vivo data suggested that CORO6 knockdown indeed suppressed RCC tumor growth. ConclusionsOverall, our study defines the oncogenic role of CORO6 in RCC progression and provides a rationale for developing CORO6-targeted therapies for improved treatment of RCC patients.

scholarly journals Identification and Validation of Two Novel Prognostic lncRNAs in Kidney Renal Clear Cell Carcinoma

2018 ◽  
Vol 48 (6) ◽  
pp. 2549-2562 ◽  
Author(s):  
Jukun Song ◽  
Juxiang Peng ◽  
Chen Zhu ◽  
Guohui Bai ◽  
Yongda Liu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Molecular Biomarkers ◽  
The Cancer Genome Atlas ◽  
Aberrant Expression ◽  
Cerna Network ◽  
Poor Treatment ◽  
Cancer Genome Atlas

Background/Aims: Kidney renal clear cell carcinoma (KIRC) is one of the most fatal malignancies due to late diagnosis and poor treatment. To improve its prognosis, a screening for molecular biomarkers of KIRC is urgently needed. Long non-coding RNAs (lncRNAs) play important roles in tumorigenesis and prognosis of cancers. However, it is not clear whether lncRNAs can be used as molecular biomarkers in predicting the survival of KIRC patients. Methods: In this study, our aim was to identify lncRNAs/mRNAs signatures and their prognostic values in KIRC. The aberrant expression profile of mRNAs and lncRNAs in 529 KIRC tissues and 72 adjacent non-tumor pancreatic tissues were obtained from the Cancer Genome Atlas (TCGA). A weighted gene co-expression network analysis (WGCNA) of two key lncRNAs was constructed. We constructed an aberrant lncRNA-mRNA-miRNA ceRNA network in CESC. In addition, Gene Ontology (GO) and KEGG pathway analysis were performed. Results: Using lncRNA/mRNA expression profiling data, the overall analysis revealed that two novel lncRNA signatures (DNM1P35 and MIR155HG) and several mRNAs were found to be significantly correlated with KIRC patient’s overall analysis. Based on the target gene of the two lncRNA in co-expression network, the GO and KEGG analysis were also performed. A dysregulated lncRNA-related ceRNA network was also observed. Conclusion: These results suggested that the two novel lncRNAs signatures may act as independent prognostic biomarkers for predicting the survival of KIRC patient.

scholarly journals Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Tongjun Gu ◽  
Xiwu Zhao
Keyword(s):  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Genomic Data ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Cell Renal Cell Carcinoma ◽  
Unsupervised Deep Learning ◽  
Cancer Genome Atlas

Abstract Clear cell renal cell carcinoma (ccRCC) is highly heterogeneous and is the most lethal cancer of all urologic cancers. We developed an unsupervised deep learning method, stacked denoising autoencoders (SdA), by integrating multi-platform genomic data for subtyping ccRCC with the goal of assisting diagnosis, personalized treatments and prognosis. We successfully found two subtypes of ccRCC using five genomics datasets for Kidney Renal Clear Cell Carcinoma (KIRC) from The Cancer Genome Atlas (TCGA). Correlation analysis between the last reconstructed input and the original input data showed that all the five types of genomic data positively contribute to the identification of the subtypes. The first subtype of patients had significantly lower survival probability, higher grade on neoplasm histology and higher stage on pathology than the other subtype of patients. Furthermore, we identified a set of genes, proteins and miRNAs that were differential expressed (DE) between the two subtypes. The function annotation of the DE genes from pathway analysis matches the clinical features. Importantly, we applied the model learned from KIRC as a pre-trained model to two independent datasets from TCGA, Lung Adenocarcinoma (LUAD) dataset and Low Grade Glioma (LGG), and the model stratified the LUAD and LGG patients into clinical associated subtypes. The successful application of our method to independent groups of patients supports that the SdA method and the model learned from KIRC are effective on subtyping cancer patients and most likely can be used on other similar tasks. We supplied the source code and the models to assist similar studies at https://github.com/tjgu/cancer_subtyping.

GALNT14 expression in renal clear cell carcinoma and its effect on prognosis

2021 ◽  
Author(s):  
Yuqin Wei ◽  
Fan Wu ◽  
Shengfeng Zhang ◽  
Yanlin Tan ◽  
Qunying Wu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Regulatory Pathways ◽  
Cox Analysis ◽  
The Relationship

Abstract Background The expression of GALNT14 in kidney renal clear cell carcinoma (KIRC) and its clinical significance remains unknown. Methods The KIRC data expressed by GALNT14 was downloaded from The Cancer Genome Atlas (TCGA) database. The expression of GALNT14 was analyzed by R software, Perl software and online analysis database. The relationship between GALNT14 expression and clinicopathological features in KIRC was analyzed by univariate, multivariate Cox regression and some databases. Gene Expression Profling Interactive Analysis (GEPIA), Starbase v3.0, UALCAN, and Kaplan-Meier were used to analyze the relationship between GALNT14 expression and overall survival (OS) in KIRC. UALCAN detects the expression of GALNT14 methylation in KIRC. Linkedomics and Genemania were used to analyze the gene co-expression of GALNT14. Gene Set Enrichment Analysis (GSEA) was performed to search for potential regulatory pathways. Results We found that GALNT14 was overexpressed in KIRC (p=1.433e-25). Patients with high GALNT14 expression in KIRC had a better prognosis than patients with low GALNT14 expression (p=0.008). In addition, high GALNT14 expression in KIRC was significantly associated with low T stage and positive OS (p<0.05). Univariate Cox analysis showed that GALNT14 was positively correlated with OS (p<0.001). Multivariate Cox analysis showed that GALNT14 was associated with OS (p<0.001), age (p=0.01) and histological grade (p=0.02). GALNT14 methylation is low expressed in KIRC (p<0.001). GSEA analysis showed that GALNT14 was enriched in histidine metabolism, peroxisome, and renin-angiotensin system pathways. Conclusion GALNT14 can be used as an independent prognostic factor for renal clear cell carcinoma and a potential target for clinical diagnosis and treatment of KIRC.

scholarly journals Comprehensive analysis of the expression and prognosis for COL4A family genes in kidney renal clear cell carcinoma

2021 ◽  
Author(s):  
Jun Qian ◽  
Bizhi Tu ◽  
Yaya Jia ◽  
Laifu Wei ◽  
Fei Gao ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Survival Time ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Comprehensive Analysis ◽  
Tumor Tissues ◽  
Open Access Database ◽  
Cancer Genome Atlas ◽  
Almost All

Abstract Background COL4A family genes are a group of genes related to the extracellular matrix, which have been proven to be associated with various cancers. However, its relationship with kidney renal clear cell carcinoma (KIRC) has not been reported. Methods Hence, we obtained the data of differential expression and survival time of COL4A genes in KIRC from an online open-access database including ONCOMINE, UALCAN, GEPIA, Cancer Genome Atlas (TCGA) database, cBioPortal, Metascape, and STRING. Results We found significant overexpression in COL4A1, COL4A2 while COL4A 3, COL4A4, COL4A5, and COL4A6 has decreased in tumor tissues. Moreover, almost all COL4A family genes obviously correlated with individual cancer stages of KIRC; and higher expression of COL4A1/2/3/BP/4 was found to accompany better overall survival time (OS) while COL4A5 with a lower OS in KIRC patients. We also found that the COL4A genes altered group had longer OS and DFS than unaltered teams.

scholarly journals Comprehensive Analysis of the Immune Infiltrates of Pyroptosis in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhuolun Sun ◽  
Changying Jing ◽  
Xudong Guo ◽  
Mingxiao Zhang ◽  
Feng Kong ◽  
...  
Keyword(s):  
High Risk ◽  
Cell Carcinoma ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Kidney renal clear cell carcinoma (KIRC) has long been identified as a highly immune-infiltrated tumor. However, the underlying role of pyroptosis in the tumor microenvironment (TME) of KIRC remains poorly described. Herein, we systematically analyzed the prognostic value, role in the TME, response to ICIs, and drug sensitivity of pyroptosis-related genes (PRGs) in KIRC patients based on The Cancer Genome Atlas (TCGA) database. Cluster 2, by consensus clustering for 24 PRGs, presented a poor prognosis, likely because malignancy-related hallmarks were remarkably enriched. Additionally, we constructed a prognostic prediction model that discriminated well between high- and low-risk patients and was further confirmed in external E-MTAB-1980 cohort and HSP cohort. By further analyzing the TME based on the risk model, higher immune cell infiltration and lower tumor purity were found in the high-risk group, which presented a poor prognosis. Patients with high risk scores also exhibited higher ICI expression, indicating that these patients may be more prone to profit from ICIs. The sensitivity to anticancer drugs that correlated with model-related genes was also identified. Collectively, the pyroptosis-related prognosis risk model may improve prognostic information and provide directions for current research investigations on immunotherapeutic strategies for KIRC patients.

scholarly journals LY96 Upregulation Promotes Kidney Renal Clear Cell Carcinoma (KIRC)

2021 ◽  
Author(s):  
Ji-li Xu ◽  
Yong Guo
Keyword(s):  
Transcription Factors ◽  
Cell Carcinoma ◽  
Dna Methyltransferase ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Related Factors ◽  
Beneficial Effects ◽  
Highly Correlated

Abstract Background: LY96 has been reported to be relevant with kidney inflammatory injury but the function of this gene in kidney renal clear cell carcinoma (KIRC) remains unknown.Methods: Various online tools were applied to analyze the roles of LY96 in KIRC using data from the Cancer Genome Atlas. Differential LY96 expression and overall survival (OS) based on different expression levels were analyzed through Oncomine and GEPIA tools. The alterations, related genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathways of LY96 were explored via cBioPortal and STRING database. LinkedOmics and Cistrome DB Toolkit were utilized to identify targets of kinase, miRNAs, and transcription factors. The relationship between LY96 and some associated genes or regulatory factors was displayed via GeneMANIA and TIMER tool. TISIDB revealed correlations between LY96 expression and immune-associated factors in the tumor microenvironment. Results: High LY96 expression level was observed in KIRC and associated with poor prognosis and diverse clinical characteristics. LY96 often amplified in KIRC and was mostly linked to the inflammatory response. Several highly correlated genes, kinase targets, transcription factors, and DNA methyltransferase that may interact with LY96 were all identified. Our study also demonstrated that various immune-related factors were relevant to LY96 in KIRC. Conclusions: Our study has shown the complex relationships between LY96 and KIRC from diverse angles. High LY96 expression had an adverse effect on the prognosis of KIRC. To find effective demethylation agents and transcription factors inhibitors targeting LY96 may have beneficial effects on the survival of KIRC patients.

scholarly journals Identification of Tumor Microenvironment-Related Genes in Kidney Renal Clear Cell Carcinoma Patients via Bioinformatic Analysis

2021 ◽  
Author(s):  
Rongjiong Zheng ◽  
Yaosen SHao ◽  
Mingming Wang ◽  
Yeli Tang ◽  
Meiling Hu
Keyword(s):  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment

Abstract BackgroundTumor microenvironment has been implicated in the development and progression of cancers. However, the prognostic significance of tumor microenvironment-related genes in kidney renal clear cell carcinoma (KIRC) remains unclear. MethodsIn this study, we obtained and analyzed gene expression profiles from The Cancer Genome Atlas database. Stromal and immune scores were calculated based on the ESTIMATE algorithm. ResultsIn the discovery series of 537 patients, we identified a list of differentially expressed genes which was significantly associated with prognosis in KIRC patients. Protein-protein interaction networks and functional enrichment analysis were both performed, indicating that these identified genes were related to the immune response. ConclusionsThe tumor microenvironment-related genes could serve as the potential biomarkers for KIRC.

Sign in / Sign up

Export Citation Format

Share Document