scholarly journals A Risk Signature with Autophagy-Related Long Noncoding RNAs for Predicting the Prognosis of Clear Cell Renal Cell Carcinoma: Based on the TCGA Database and Bioinformatics

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yundong Xuan ◽  
Weihao Chen ◽  
Kan Liu ◽  
Yu Gao ◽  
Shidong Zuo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Noncoding Rna ◽  
Prognostic Value ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene List ◽  
Tumor Biology ◽  
Cell Renal Cell Carcinoma

Background. Disorders of autophagic processes have been reported to affect the survival outcome of clear cell renal cell carcinoma (ccRCC) patients. The purpose of our study was to identify and validate the candidate prognostic long noncoding RNA signature of autophagy. Methods. Transcriptome profiles were obtained from The Cancer Genome Atlas. The autophagy gene list was obtained from the Human Autophagy Database. Based on coexpression analysis, we obtained a list of autophagy-related lncRNAs (ARlncRNAs). GO enrichment analysis and KEGG pathway analysis were conducted to explore the functional annotation of these ARlncRNAs. Univariate and multivariate Cox regression analyses were conducted to elucidate the correlation between overall survival and the expression level of each ARlncRNAs. We then established a prognostic signature that was a linear combination of the regression coefficients from the multivariate Cox regression model ( β ) multiplied by the expression levels of the respective ARlncRNAs in the training cohort. The predictive performance was tested in the validation cohort. Additionally, the independence of the risk signature was assessed, and the relationship between the risk signature and conventional clinicopathological features was explored. Results. Seven autophagy-related lncRNAs with prognostic value (SNHG3, SNHG17, MELTF-AS1, HOTAIRM1, EPB41L4A-DT, AP003352.1, and AC145423.2) were identified and integrated into a risk signature, dividing patients into low-risk and high-risk groups. The risk signature was independent of conventional clinical characteristics as a prognostic indicator of ccRCC (HR, 1.074, 95% confidence interval: 1.036-1.113, p < 0.001 ) and was valuable in the prediction of ccRCC progression. Conclusion. Our risk signature has potential prognostic value in ccRCC, and these ARlncRNAs may play a significant role in ccRCC tumor biology.

scholarly journals Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

2021 ◽  
Vol 41 (8) ◽  
Author(s):  
Wei Ma ◽  
Manli Zhong ◽  
Xiaowu Liu
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Roc Curve ◽  
Renal Cell ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Kaplan Meier

Abstract Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC). Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model. Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways. Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.

scholarly journals A novel ferroptosis-related 12-gene signature predicts clinical prognosis and reveals immune relevancy in clear cell renal cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yingkai Hong ◽  
Mingen Lin ◽  
Dehua Ou ◽  
Zhuangkai Huang ◽  
Peilin Shen
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Prognostic Value ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene Signature ◽  
Cell Renal Cell Carcinoma ◽  
Prognostic Predictor

Abstract Background Clear cell renal cell carcinoma (ccRCC) is still highly aggressive and lethal even with various therapeutic approaches. As the kidney is an iron metabolism-related organ, exploring and assessing the clinical value of ferroptosis, an iron-dependent regulated cell death, is practical and important. Methods Prognostic ferroptosis-related differentially expressed genes (DEGs) were identified from the KIRC cohort in the cancer genome atlas (TCGA) database, from which a prognostic signature was established using Lasso-penalized Cox regression analysis. Each patient in the KIRC cohort and the E-MTAB-1980 cohort (from the ArrayExpress database) was assigned a calculated signature-correlated risk score and categorized to be either in the high- or low-risk group divided by the median risk score in the KIRC cohort. Then, the independent prognostic value of the signature was further assessed by Kaplan-Meier (K-M) survival, time-dependent receiver operating characteristic (ROC) and Cox regression analyses based on overall survival (OS) in both cohorts. Finally, risk-related DEGs were identified in both cohorts and subjected to enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration. Results Among 60 ferroptosis-related genes, 32 prognostic DEGs were identified, from which we constructed a prognostic 12-gene signature with CARS1, HMGCR, CHAC1, GOT1, CD44, STEAP3, AKR1C1, CBS, DPP4, FANCD2, SLC1A5 and NCOA4. Patients in both cohorts were divided into high- and low-risk groups, which were visually distributed in two sets and had positive-risk-related mortality. The K-M survival and the ROC curves validated that the signature has prognostic value with P < 0.05 and area under the curve > 0.7 in both cohorts, respectively. Multivariate Cox regression further confirmed the risk score as an independent prognostic predictor for OS. Commonly enriched terms in GO and KEGG not only showed a high iron correlation but also, interestingly, immune relevance of 3 immune cells (macrophages, mast cells and regulatory T cells) and 1 immune-related function (antigen processing cell co-stimulation). Conclusion We established a novel 12 ferroptosis-related-gene signature that was proven to be an independent prognostic predictor for OS and inferred to be related to tumour immunity in ccRCC; however, the underlying mechanism is still poorly characterized and needs further exploration.

scholarly journals The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

2021 ◽  
Vol 11 ◽  
Author(s):  
Xin Tang ◽  
Tong Pang ◽  
Wei-feng Yan ◽  
Wen-lei Qian ◽  
You-ling Gong ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Hierarchical Clustering ◽  
Renal Cell ◽  
Prognostic Value ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Calibration Curves

Background and purposeRadiomics is an emerging field of quantitative imaging. The prognostic value of radiomics analysis in patients with localized clear cell renal cell carcinoma (ccRCC) after nephrectomy remains unknown.MethodsComputed tomography images of 167 eligible cases were obtained from the Cancer Imaging Archive database. Radiomics features were extracted from the region of interest contoured manually for each patient. Hierarchical clustering was performed to divide patients into distinct groups. Prognostic assessments were performed by Kaplan–Meier curves, COX regression, and least absolute shrinkage and selection operator COX regression. Besides, transcriptome mRNA data were also included in the prognostic analyses. Endpoints were overall survival (OS) and disease-free survival (DFS). Concordance index (C-index), decision curve analysis and calibration curves with 1,000 bootstrapping replications were used for model’s validation.ResultsHierarchical clustering groups from nephrographic features and mRNA can divide patients into different prognostic groups while clustering groups from corticomedullary or unenhanced phase couldn’t distinguish patients’ prognosis. In multivariate analyses, 11 OS-predicting and eight DFS-predicting features were identified in nephrographic phase. Similarly, seven OS-predictors and seven DFS-predictors were confirmed in mRNA data. In contrast, limited prognostic features were found in corticomedullary (two OS-predictor and two DFS-predictors) and unenhanced phase (one OS-predictors and two DFS-predictors). Prognostic models combining both nephrographic features and mRNA showed improved C-index than any model alone (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively). In addition, decision curves and calibration curves also revealed the great performance of the novel models.ConclusionWe firstly investigated the prognostic significance of preoperative radiomics signatures in ccRCC patients. Radiomics features obtained from nephrographic phase had stronger predictive ability than features from corticomedullary or unenhanced phase. Multi-omics models combining radiomics and transcriptome data could further increase the predictive accuracy.

scholarly journals Prognostic value of immune-related genes in clear cell renal cell carcinoma

Aging ◽  
2019 ◽  
Vol 11 (23) ◽  
pp. 11474-11489 ◽  
Author(s):  
Bangbei Wan ◽  
Bo Liu ◽  
Yuan Huang ◽  
Gang Yu ◽  
Cai Lv
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Immune Related Genes

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.

scholarly journals Prognostic Value of an m5C RNA Methylation Regulator-Related Signature for Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol Volume 13 ◽  
pp. 6673-6687
Author(s):  
Hanrong Li ◽  
Huiming Jiang ◽  
Zhicheng Huang ◽  
Zhilin Chen ◽  
Nanhui Chen
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Rna Methylation

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.

Sign in / Sign up

Export Citation Format

Share Document