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 The long noncoding RNA urothelial carcinoma-associated 1 overexpression as a poor prognostic biomarker in clear cell renal cell carcinoma

Tumor Biology ◽  
2017 ◽  
Vol 39 (5) ◽  
pp. 101042831769837 ◽  
Author(s):  
Yang Wang ◽  
Wen Gao ◽  
Jiali Xu ◽  
Yizhi Zhu ◽  
Lingxiang Liu
Keyword(s):  
Renal Cell Carcinoma ◽  
Urothelial Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Noncoding Rna ◽  
Clear Cell ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Long noncoding RNA urothelial carcinoma-associated 1 has previously played important roles in cancer. However, its role is still unknown in clear cell renal cell carcinoma. We utilized the most recent molecular and clinical data of clear cell renal cell carcinoma from The Cancer Genome Atlas project, and the relationship between urothelial carcinoma-associated 1 expression and the clinicopathological features was analyzed. Our results indicated that urothelial carcinoma-associated 1 overexpression was associated with male ( p = 0.003), wild-type PBRM1 ( p = 0.021), and BAP1 mutation ( p = 0.022) in clear cell renal cell carcinoma, although lower expression was found in tumors compared with normal controls, validated in tumor tissues from The Cancer Genome Atlas and 21 clear cell renal cell carcinoma patients at our hospital. Moreover, urothelial carcinoma-associated 1 overexpression indicated poor prognosis independently (Hazard Ratio [HR]: 1.92, p = 0.000) in clear cell renal cell carcinoma; it might be a potential detrimental gene considered as a predictive biomarker involved in clear cell renal cell carcinoma.

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.

scholarly journals Caspase 4 Overexpression in Clear Cell Renal Cell Carcinoma and Its Prognostic Role

2020 ◽  
Author(s):  
Lingfeng Meng ◽  
Zijian Tian ◽  
Xingbo Long ◽  
Tongxiang Diao ◽  
Maolin Hu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract Background: Caspase 4 (CASP4) dysregulation is related to the occurrence, development, and outcome of many malignant tumors, but its role in clear cell renal cell carcinoma (ccRCC) is unclear. This study was conducted to investigate the expression level of CASP4 in tumor tissues and its relationship with clinical prognosis of patients with ccRCC. Methods: First, the Oncomine and The Cancer Genome Atlas databases were used to determine CASP4 mRNA expression in ccRCC and its association with ccRCC prognosis. We then performed immunohistochemical staining and evaluation of 30 paired ccRCC and adjacent normal tissues to confirm these results. The correlation between CASP4 expression and ccRCC prognosis was evaluated using Kaplan-Meier analysis, and related genes and pathways were obtained from The Cancer Genome Atlas database by gene set enrichment analysis and gene set variation analysis. Finally, we explored the co-expression of genes with CASP4 in ccRCC. Results: CASP4 mRNA expression in ccRCC was significantly higher than that in normal tissues (p < 0.001). Kaplan-Meier analysis showed that the overall survival of patients with ccRCC showing high CASP4 expression was significantly reduced (p < 0.001). We then used external datasets (Gene Expression Omnibus database and patients from our center) to verify the level of CASP4 expression and survival differences (all p < 0.05). We also found that differential expression levels of CASP4 were correlated with pathological grade and clinical TNM stage (all p < 0.05). Conclusions: Overall, our study shows that CASP4 is highly expressed in ccRCC and is an important factor affecting prognosis. Thus, CASP4 may be a potential prognostic biomarker of ccRCC.

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.

Prognostic value of genomic signatures in metastatic Clear Cell Renal Cell Carcinoma (mRCC) using The Cancer Genome Atlas (TCGA) data.

2015 ◽  
Vol 33 (15_suppl) ◽  
pp. 4560-4560
Author(s):  
Guillermo de Velasco ◽  
Andre Poisl Fay ◽  
Aedin Culhane ◽  
A. Ari Hakimi ◽  
Martin Henner Voss ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Clear Cell ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Genomic Signatures ◽  
Cancer Genome Atlas ◽  
Genome Atlas

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.

scholarly journals Ferroptosis-Related Gene-Based Prognostic Model and Immune Infiltration in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guo-Jiang Zhao ◽  
Zonglong Wu ◽  
Liyuan Ge ◽  
Feilong Yang ◽  
Kai Hong ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Clear Cell ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cancer Genome Atlas ◽  
Score Model ◽  
Genome Atlas

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Ferroptosis plays a vital role in ccRCC development and progression. We did an update of ferroptosis-related multigene expression signature for individualized prognosis prediction in patients with ccRCC. Differentially expressed ferroptosis-related genes in ccRCC and normal samples were screened using The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses and machine learning methods were employed to identify optimal prognosis-related genes. CARS1, CD44, FANCD2, HMGCR, NCOA4, SLC7A11, and ACACA were selected to establish a prognostic risk score model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly enriched in immune-related pathways; single-sample Gene Set Enrichment Analysis revealed several immune cells potentially related to ferroptosis. Kaplan–Meier survival analysis demonstrated that patients with high-risk scores had significantly poor overall survival (log-rank P = 7.815 × 10–11). The ferroptosis signature was identified as an independent prognostic factor. Finally, a prognostic nomogram, including the ferroptosis signature, age, histological grade, and stage status, was constructed. Analysis of The Cancer Genome Atlas-based calibration plots, C-index, and decision curve indicated the excellent predictive performance of the nomogram. The ferroptosis-related seven-gene risk score model is useful as a prognostic biomarker and suggests therapeutic targets for ccRCC. The prognostic nomogram may assist in individualized survival prediction and improve treatment strategies.

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