scholarly journals A novel prognostic predictor for clear cell renal cell carcinoma constructed by weighted gene co-expression correlation network analysis

2020 ◽  
Author(s):  
Feng Li ◽  
Yi Jin ◽  
Peng Guo ◽  
Zhiyu Wang
Keyword(s):  
Renal Cell Carcinoma ◽  
Network Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Prognostic Signature ◽  
Cell Renal Cell Carcinoma ◽  
Clinical Prognosis ◽  
A Genome

Abstract Background Clear cell renal cell carcinoma (ccRCC) accounts the largest proportion of all types of renal tumor, it is highly invasive leading to shorter survival time of patients suffer from tumor. This study aims at constructing a new prognostic signature to provide a new reference for clinic work to adjust treatment protocols. Material and Methods Comprehensive gene expression profiles of ccRCC were downloaded from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA), a genome-wide profiling analysis, was applied to investigate key genes associated with clinical traits. Cox regression analysis was conducted to screen prognostic gene in ccRCC. LASSO analysis was used to enhance the robust of correlated genes. Then, a prognostic signature was developed as an independent factor to predict outcomes of ccRCC patients. Results The co-expression network was constructed by WGCNA, 26 modules were identified in total, including a module (grey) showed no significance. By associating the features with clinic features, we selected three modules (pink, purple and turquoise) significantly associated with the overall survival(OS) of ccRCC patients. Univariate Cox analysis for genes from each module separately and then we used LASSO regression to screen the significant genes for selecting potential prognosis element-genes. 8 genes (CAPRIN2, IFI44, LTV1, ZNF320, MTHFR, XPOT, BCL3 and PAX2) finally reserved by multivariate COX regression. ScoreW was defined as a prognosis modelestablished by the final potential prognosis 8 genes. Multivariate regression suggested the ScoreW was an independent prognosis predicting element. ROC curve method was performed and the AUC value of ScoreW showed satisfied and attractive clinical prognosis importance. Conclusion Our findings provided the framework of co-expression gene modules of ccRCC and identified valuable prognostic method might be detection for ccRCC patients.

scholarly journals N6-Methyladenosine-Related Long Non-coding RNA Signature Associated With Prognosis and Immunotherapeutic Efficacy of Clear-Cell Renal Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Tianming Ma ◽  
Xiaonan Wang ◽  
Jiawen Wang ◽  
Xiaodong Liu ◽  
Shicong Lai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differential Expression ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis

Increasing evidence suggests that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play important roles in cancer progression and immunotherapeutic efficacy in clear-cell renal cell carcinoma (ccRCC). In this study, we conducted a comprehensive ccRCC RNA-seq analysis using The Cancer Genome Atlas data to establish an m6A-related lncRNA prognostic signature (m6A-RLPS) for ccRCC. Forty-four prognostic m6A-related lncRNAs (m6A-RLs) were screened using Pearson correlation analysis (|R| > 0.7, p < 0.001) and univariable Cox regression analysis (p < 0.01). Using consensus clustering, the patients were divided into two clusters with different overall survival (OS) rates and immune status according to the differential expression of the lncRNAs. Gene set enrichment analysis corroborated that the clusters were enriched in immune-related activities. Twelve prognostic m6A-RLs were selected and used to construct the m6A-RLPS through least absolute shrinkage and selection operator Cox regression. We validated the differential expression of the 12 lncRNAs between tumor and non-cancerous samples, and the expression levels of four m6A-RLs were further validated using Gene Expression Omnibus data and Lnc2Cancer 3.0 database. The m6A-RLPS was verified to be an independent and robust predictor of ccRCC prognosis using univariable and multivariable Cox regression analyses. A nomogram based on age, tumor grade, clinical stage, and m6A-RLPS was generated and showed high accuracy and reliability at predicting the OS of patients with ccRCC. The prognostic signature was found to be strongly correlated to tumor-infiltrating immune cells and immune checkpoint expression. In conclusion, we established a novel m6A-RLPS with a favorable prognostic value for patients with ccRCC. The 12 m6A-RLs included in the signature may provide new insights into the tumorigenesis and allow the prediction of the treatment response of ccRCC.

scholarly journals Molecular Subtyping and Prognostic Prediction Based on the Ferroptosis-Related Long Non-Coding RNA Signature in Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
zhenpeng zhu ◽  
Cuijian Zhang ◽  
Jinqin Qian ◽  
Ninghan Feng ◽  
Weijie Zhu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Cancer Genome ◽  
Prognostic Signature ◽  
Immune Microenvironment ◽  
Cell Renal Cell Carcinoma ◽  
Molecular Subtyping

Abstract BackgroundRenal cell carcinoma is gradually characteristic of the accumulation of lipid-reactive oxygen species. And, the ferroptosis-related LncRNAs have been reported strongly correlated with tumorigenesis and progression. However, the further functions of ferroptosis-related LncRNAs in clear cell renal cell carcinoma have not been explored. MethodsAfter sample cleaning, data integration, and batch effect removal, we used the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) public database to screen out the expression and prognostic value of ferroptosis-related LncRNAs and then performed the molecular subtyping. The molecular subtyping was developed using the K-means. Then, the KEGG pathway enrichment and statistical analyses of the immune microenvironment and clusters were carried out. The outcomes indicated significant differences in the immune microenvironment and potential therapeutic targets between the two clusters. Then, LASSO Cox regression was performed to establish the 5-LncRNAs-based prognostic signature. The signature could accurately predict patient prognosis and be used as an independent clinical risk factor. And the 5 LncRNAs were validated in the cell lines and clinical samples. We then combined significant clinical parameters in multivariate Cox regression and prognostic signature to construct a clinical predictive nomogram, which provides appropriate guidance for predicting the overall survival of ccRCC patients.ResultsThe prognostic DEFRLncRNAs were identified, and 5 LncRNAs were finally utilized to establish the prognostic signature in the TCGA cohort, which was subsequently validated in the internal and external cohorts. Moreover, we conducted the molecular subtyping and divided the patients in the TCGA cohort into two clusters, showing the difference in the Hallmark pathways, immune infiltration, expression of the immune target, and drug therapy. Furthermore, the nomogram was established better to predict the clinical outcomes of the ccRCC patients. ConclusionsOur study conducted molecular subtyping and established a novel predictive signature based on the ferroptosis-related LncRNAs, which could accurately predict prognosis and potential therapeutic targets in ccRCC patients.

scholarly journals A novel 10 glycolysis-related genes signature could predict overall survival for clear cell renal cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qianwei Xing ◽  
Tengyue Zeng ◽  
Shouyong Liu ◽  
Hong Cheng ◽  
Limin Ma ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Regression Analyses ◽  
Cell Renal Cell Carcinoma

Abstract Background The role of glycolysis in tumorigenesis has received increasing attention and multiple glycolysis-related genes (GRGs) have been proven to be associated with tumor metastasis. Hence, we aimed to construct a prognostic signature based on GRGs for clear cell renal cell carcinoma (ccRCC) and to explore its relationships with immune infiltration. Methods Clinical information and RNA-sequencing data of ccRCC were obtained from The Cancer Genome Atlas (TCGA) and ArrayExpress datasets. Key GRGs were finally selected through univariate COX, LASSO and multivariate COX regression analyses. External and internal verifications were further carried out to verify our established signature. Results Finally, 10 GRGs including ANKZF1, CD44, CHST6, HS6ST2, IDUA, KIF20A, NDST3, PLOD2, VCAN, FBP1 were selected out and utilized to establish a novel signature. Compared with the low-risk group, ccRCC patients in high-risk groups showed a lower overall survival (OS) rate (P = 5.548Ee-13) and its AUCs based on our established signature were all above 0.70. Univariate/multivariate Cox regression analyses further proved that this signature could serve as an independent prognostic factor (all P < 0.05). Moreover, prognostic nomograms were also created to find out the associations between the established signature, clinical factors and OS for ccRCC in both the TCGA and ArrayExpress cohorts. All results remained consistent after external and internal verification. Besides, nine out of 21 tumor-infiltrating immune cells (TIICs) were highly related to high- and low- risk ccRCC patients stratified by our established signature. Conclusions A novel signature based on 10 prognostic GRGs was successfully established and verified externally and internally for predicting OS of ccRCC, helping clinicians better and more intuitively predict patients’ survival.

A prognostic nomogram based on competing endogenous RNA network for clear-cell renal cell carcinoma

2020 ◽  
Author(s):  
Yun Peng ◽  
Shangrong Wu ◽  
Zihan Xu ◽  
Dingkun Hou ◽  
Nan Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene Signature ◽  
Expression Data ◽  
Competing Endogenous Rna ◽  
Cell Renal Cell Carcinoma

Abstract Backgroud Clear-cell renal cell carcinoma (ccRCC) is stubborn to traditional chemotherapy and radiation treatment, which makes its clinical management a major challenge. Recently, we have made efforts to understand the etiology of ccRCC. Increasing evidence revealed that the competing endogenous RNA (ceRNA) were involved in the development of various tumor. However, it’s scant for studying on ccRCC, and a comprehensive analysis of prognostic model based on lncRNA-miRNA-mRNA ceRNA regulatory network of ccRCC with large-scale sample size and RNA‐sequencing expression data is still limited. Methods RNA‐sequencing expression data were taken out from GTEx database and TCGA database, A total of 354 samples with ccRCC and 157 normal controlled samples were included in our study. The ccRCC-specific genes were obtained from WGCNA and differential expression analysis. Following, the communication between mRNAs and lncRNAs and target miRNAs were predicted by MiRcode, starBase, miRTarBase, and TargetScan. A gene signature of eight genes was constructed by univariate Cox regression, lasso methods and multivariate Cox regression analysis. Results A total of 2191 mRNAs and 1377 lncRNAs was identified, and a dys-regulated ceRNA network for ccRCC was established using 7 mRNAs, 363 lncRNAs, and 3 miRNAs. Further, a gene signature in cluding 8 genes based on this ceRNA was constructed, meanwhile, a nomogram predicting 1-, 3-, 5-year survival probability containing both clinical characteristics and ccRCC-specific gene signatures was developed. Conclusion It could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision.

scholarly journals Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Maolin Hu ◽  
Jiangling Xie ◽  
Huiming Hou ◽  
Ming Liu ◽  
Jianye Wang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Methylation Level ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

Background. Few previous studies have comprehensively explored the level of DNA methylation and gene expression in ccRCC. The purpose of this study was to identify the key clear cell renal cell carcinoma- (ccRCC-) related DNA methylation-driven genes (MDG) and to build a prognostic model based on the level of DNA methylation. Methods. RNA-seq transcriptome data and DNA methylation data were obtained from The Cancer Genome Atlas. Based on the MethylMix algorithm, we obtain ccRCC-related MDG. The univariate and multivariate Cox regression analyses were employed to investigate the correlation between patient overall survival and the methylation level of each MDG. Finally, a prognosis risk score was established based on a linear combination of the regression coefficient derived from the multivariate Cox regression model (β) multiplied with the methylation level of the gene. Results. 19 ccRCC-related MDG were identified. Three MDG (NCKAP1L, EVI2A, and BATF) were further screened and integrated into a prognostic risk score model, risk score=3.710∗methylation level of NCKAP1L+−3.892∗methylation level of EVI2A+−3.907∗methylation level of BATF. The risk model was independent from conventional clinical characteristics as a prognostic factor for ccRCC (HR=1.221, 95% confidence interval: 1.063–1.402, and P=0.005). The joint survival analysis showed that the gene expression and methylation levels of the prognostic genes EVI2A and BATF were significantly related with prognosis. Conclusion. This study provided an important bioinformatics foundation for in-depth studies of ccRCC DNA methylation.

scholarly journals A Novel Immune-Related lncRNA-Based Model for Survival Prediction in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-37
Author(s):  
Zedan Zhang ◽  
Yanlin Tang ◽  
Yanjun Liu ◽  
Hongkai Zhuang ◽  
Enyu Lin ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
T Cells ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Functional Enrichment ◽  
Clinical Parameters ◽  
Training Set ◽  
Cell Renal Cell Carcinoma

Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction. Methods. The transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature. Results. Consequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8+ T cells, resting mast cells, and naïve B cells were significantly correlated with the signature. Conclusion. Therefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.

scholarly journals VHL substrate transcription factor ZHX2 as an oncogenic driver in clear cell renal cell carcinoma

Science ◽  
2018 ◽  
Vol 361 (6399) ◽  
pp. 290-295 ◽  
Author(s):  
Jing Zhang ◽  
Tao Wu ◽  
Jeremy Simon ◽  
Mamoru Takada ◽  
Ryoichi Saito ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Nuclear Factor Κb ◽  
Loss Of Function ◽  
Cell Renal Cell Carcinoma ◽  
A Genome

Inactivation of the von Hippel-Lindau (VHL) E3 ubiquitin ligase protein is a hallmark of clear cell renal cell carcinoma (ccRCC). Identifying how pathways affected by VHL loss contribute to ccRCC remains challenging. We used a genome-wide in vitro expression strategy to identify proteins that bind VHL when hydroxylated. Zinc fingers and homeoboxes 2 (ZHX2) was found as a VHL target, and its hydroxylation allowed VHL to regulate its protein stability. Tumor cells from ccRCC patients with VHL loss-of-function mutations usually had increased abundance and nuclear localization of ZHX2. Functionally, depletion of ZHX2 inhibited VHL-deficient ccRCC cell growth in vitro and in vivo. Mechanistically, integrated chromatin immunoprecipitation sequencing and microarray analysis showed that ZHX2 promoted nuclear factor κB activation. These studies reveal ZHX2 as a potential therapeutic target for ccRCC.

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