scholarly journals Integrated bioinformatics analysis of the NEDD4 family reveals a prognostic value of NEDD4L in clear-cell renal cell cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11880
Author(s):  
Hui Zhao ◽  
Junjun Zhang ◽  
Xiaoliang Fu ◽  
Dongdong Mao ◽  
Xuesen Qi ◽  
...  
Keyword(s):  
Renal Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Cancer ◽  
Data Sets ◽  
Potential Therapeutic Target ◽  
Cell Renal Cell Carcinoma ◽  
Cellular Energy ◽  
Testing Data

The members of the Nedd4-like E3 family participate in various biological processes. However, their role in clear cell renal cell carcinoma (ccRCC) is not clear. This study systematically analyzed the Nedd4-like E3 family members in ccRCC data sets from multiple publicly available databases. NEDD4L was identified as the only NEDD4 family member differentially expressed in ccRCC compared with normal samples. Bioinformatics tools were used to characterize the function of NEDD4L in ccRCC. It indicated that NEDD4L might regulate cellular energy metabolism by co-expression analysis, and subsequent gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A prognostic model developed by the LASSO Cox regression method showed a relatively good predictive value in training and testing data sets. The result revealed that NEDD4L was associated with biosynthesis and metabolism of ccRCC. Since NEDD4L is downregulated and dysregulation of metabolism is involved in tumor progression, NEDD4L might be a potential therapeutic target in ccRCC.

scholarly journals A Novel Prognostic Model for Clear Cell Renal Cell Carcinoma with Differentially Expressed Immune-related lncRNA Pairs Based on the Cancer Genome Atlas

2021 ◽  
Author(s):  
Chen Zhao ◽  
Kewei Xiong ◽  
Fengming Liu ◽  
Xiangpan Li
Keyword(s):  
Renal Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
Clear Cell ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract Objective: To construct a novel prognostic model of immune-related lncRNA (irlncRNA) pairs in clear cell renal cell carcinoma (ccRCC). Methods: RNA-seq and clinical data were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed irlncRNAs (DEirlncRNAs) were obtained by co-expression strategy with immune genes. A 0-1 matrix was constructed according to DEirlncRNAs relevant expression levels. Univariate cox regression was used to select potential target pairs. Lasso regression with cross validation and multivariate cox regression were carried out to extract the final biomarker pairs for risk score calculation. Through calculating the optimal cutoff of AUCs, patients were divided into high and low risk group. Model validation was conducted by independent prognostic analysis, survival analysis, tumor-infiltrating and chemosensitivity analysis. Results: A total of 42 DEirlncRNAs were identified and 12 target pairs were included to construct the final model. The risk score were both significantly different according to univariate (p<0.001, HR=1.391, 95%CI [1.313–1.475]) and multivariate cox regression (p<0.001, HR=1.3104, 95%CI [1.227-1.399]). The AUC reached 0.765 at 1-year, 0.724 at 3-year and 0.785 at 5-year. Patients in the high-risk group had significantly poor survival, higher level of CD8+T infiltration, lower drug sensitivity of sunitinib and temsirolimus but higher sensitivity of lapatinib and pazopanib.Conclusion: The novel prognostic model constructed by paring irlncRNAs showed an effective clinical prediction in ccRCC patients.

scholarly journals Soluble PD-L1 Is an Independent Prognostic Factor in Clear Cell Renal Cell Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 667 ◽  
Author(s):  
Gorka Larrinaga ◽  
Jon Danel Solano-Iturri ◽  
Peio Errarte ◽  
Miguel Unda ◽  
Ana Loizaga-Iriarte ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Systemic Therapy ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Cancer ◽  
Tissue Expression ◽  
Cell Renal Cell Carcinoma ◽  
Tumor Center

(1). Background: Immunohistochemical (IHC) evaluation of programmed death-1 (PD-1) and its ligand (PD-L1) is being used to evaluate advanced malignancies with potential response to immune checkpoint inhibitors. We evaluated both plasma and tissue expression of PD-1 and PD-L1 in the same cohort of patients, including non-metastatic and metastatic clear cell renal cell carcinoma (CCRCC). Concomitant plasma and tissue expression of PD-1 and PD-L1 was evaluated with emphasis on diagnostic and prognostic implications. (2) Methods: we analyzed PD-1 and PD-L1 IHC expression in tumor tissues and soluble forms (sPD-1 and sPD-L1) in plasma from 89 patients with CCRCC, of which 23 were metastatic and 16 received systemic therapy. The primary endpoint was evaluation of overall survival using Kaplan-Meier analysis and the Cox regression model. Plasma samples from healthy volunteers were also evaluated. (3) Results: Interestingly, sPD-1 and sPD-L1 levels were lower in cancer patients than in controls. sPD-1 and sPD-L1 levels and their counterpart tissue expression both at the tumor center and infiltrating front were not associated. Higher expression of both PD-1 and PD-L1 were associated with tumor grade, necrosis and tumor size. PD-1 was associated to tumor stage (pT) and PD-L1 to metastases. sPD-1 and sPD-L1 were not associated with clinico-pathological parameters, although both were higher in patients with synchronous metastases compared to metachronous ones and sPD-L1 was also higher for metastatic patients compared to non-metastatic patients. sPD-1 was also associated with the International Metastatic Renal Cell Cancer Database Consortium (IMDC) prognostic groups in metastatic CCRCC and also to the Morphology, Attenuation, Size and Structure (MASS) response criteria in metastatic patients treated with systemic therapy, mainly tyrosine-kinase inhibitors. Regarding prognosis, PD-L1 immunostaining at the tumor center with and without the tumor front was associated with worse survival, and so was sPD-L1 at a cut-off >793 ng/mL. Combination of positivity at both the tissue and plasma level increased the level of significance to predict prognosis. (4) Conclusions: Our findings corroborate the role of PD-L1 IHC to evaluate prognosis in CCRCC and present novel data on the usefulness of plasma sPD-L1 as a promising biomarker of survival in this neoplasia.

scholarly journals Identification of DNA methylation signatures associated with poor outcome in lower-risk Stage, Size, Grade and Necrosis (SSIGN) score clear cell renal cell cancer

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Louis Y. El Khoury ◽  
Shuang Fu ◽  
Ryan A. Hlady ◽  
Ryan T. Wagner ◽  
Liguo Wang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Renal Cell ◽  
Lower Risk ◽  
Clear Cell ◽  
Cell Cancer ◽  
Low Risk ◽  
Cell Renal Cell Carcinoma ◽  
A Genome ◽  
Size Grade ◽  
Prognostic Power

Abstract Background Despite using prognostic algorithms and standard surveillance guidelines, 17% of patients initially diagnosed with low risk clear cell renal cell carcinoma (ccRCC) ultimately relapse and die of recurrent disease, indicating additional molecular parameters are needed for improved prognosis. Results To address the gap in ccRCC prognostication in the lower risk population, we performed a genome-wide analysis for methylation signatures capable of distinguishing recurrent and non-recurrent ccRCCs within the subgroup classified as ‘low risk’ by the Mayo Clinic Stage, Size, Grade, and Necrosis score (SSIGN 0–3). This approach revealed that recurrent patients have globally hypermethylated tumors and differ in methylation significantly at 5929 CpGs. Differentially methylated CpGs (DMCpGs) were enriched in regulatory regions and genes modulating cell growth and invasion. A subset of DMCpGs stratified low SSIGN groups into high and low risk of recurrence in independent data sets, indicating that DNA methylation enhances the prognostic power of the SSIGN score. Conclusions This study reports a global DNA hypermethylation in tumors of recurrent ccRCC patients. Furthermore, DMCpGs were capable of discriminating between aggressive and less aggressive tumors, in addition to SSIGN score. Therefore, DNA methylation presents itself as a potentially strong biomarker to further improve prognostic power in patients with low risk SSIGN score (0–3).

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 c-Met is a prognostic marker and potential therapeutic target in clear cell renal cell carcinoma

2013 ◽  
Vol 24 (2) ◽  
pp. 343-349 ◽  
Author(s):  
G.T. Gibney ◽  
S.A. Aziz ◽  
R.L. Camp ◽  
P. Conrad ◽  
B.E. Schwartz ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Prognostic Marker ◽  
Therapeutic Target ◽  
Renal Cell ◽  
Clear Cell ◽  
Potential Therapeutic Target ◽  
Cell Renal Cell Carcinoma

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.

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