A Novel Defined Pyroptosis-Related Gene Prognostic Index for Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
xuexiang Li ◽  
Yarong Song ◽  
Bing Liu ◽  
Liang Chen ◽  
dingheng Lu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Risk Score ◽  
Immune Checkpoint Inhibitor ◽  
Cox Regression ◽  
Clear Cell ◽  
Checkpoint Inhibitor ◽  
Cell Renal Cell Carcinoma ◽  
Checkpoint Inhibitor Therapy ◽  
Score Model ◽  
Immune Score

Abstract Background: Clear cell renal cell carcinoma (ccRCC), a common pathological subtype of renal cancer with high aggressiveness, has been reported to be associated with chronic inflammation. Pyroptosis, a newly discovered inflammatory form of programmed cell death, can aggravate the inflammatory response. However, the influence of pyroptosis-related genes on ccRCC patient outcomes is yet unknown.Methods: In this study, 43 differentially expressed pyroptosis-related hub genes were identified by analysing The Cancer Genome Atlas–Kidney Renal Clear Cell Carcinoma dataset. The risk-score model was selected using the least absolute shrinkage and selection operator Cox regression and Cox multivariate methods, and all patients were divided into two risk subgroups based on the risk score. Prognostic value of the risk-score model was verified through survival curve, receiver operating characteristic curve and risk curve. Gene ontology and Kyoto Encyclopaedia of Genes and Genomes analyses suggested that the differentially expressed genes between the two subgroups were enriched in immune-mediated categories. Furthermore, the relationship between the risk-score model and ESTIMATE immune score and immunophenoscore was analysed. Finally, Nomogram was constructed based on the results of cox regression analyses. Results: The training cohort and the validation cohort enrolled 346 and 148 ccRCC patients respectively. The risk-score model was constructed by two genes (AIM2 and GSDMB). The area under curve of the ROC curve in two cohorts were both greater than 0.6. The grade and risk score were selected as independent factors and used to construct a nomogram to predict ccRCC patients' survival rate with the c-index of 0.68. Moreover, high-risk score subgroup was associated with a higher immune score and a lower percentage of PBRM1 mutations. The risk score was positively related to the degree of immune infiltration of CD8+ T, T follicular helper, gamma delta T, and regulatory T cells, and patients with a higher risk score were more likely to benefit from immune checkpoint inhibitor therapy. Conclusion: The risk-score model based on pyroptosis-related genes constructed in our study is a promising biomarker to predict the prognosis, molecular and immune characteristics, and immune benefit from immune checkpoint inhibitor therapy in ccRCC patients.

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 Construction and Validation of a Prognostic Nomogram for Clear Cell Renal Cell Carcinoma Based on DNA Methylation-driven Differentially Expressed Genes

2020 ◽  
Author(s):  
Zheng Wang ◽  
Yanlong Zhang ◽  
Shuaishuai Fan ◽  
Yuan Ji ◽  
Jianchao Ren ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma ◽  
Score Model

Abstract Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent type of kidney cancer. This study aimed to establish a nomogram to predict ccRCC prognosis.Methods: By integrating DNA methylation (DNAm) data and gene expression profiles of ccRCC obtained from The Cancer Genome Atlas (TCGA), DNAm-driven genes were identified by differential and correlation analyses. Next, risk genes were selected by multiple algorithms (univariate Cox and Kaplan-Meier survival analyses) and various databases (TCGA, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and The Human Protein Atlas (HPA)). A risk score model was established by multivariate Cox analyses. ConsensusPathDB and Gene Set Enrichment Analysis (GSEA) were used to identify the biological functions of the selected genes. After comprehensively evaluating the clinical data, we established and assessed a dynamic nomogram available on a webserver.Results: In total, 220 differentially expressed DNAm-driven genes were identified, and five-gene signature (EPB41L4B, HHLA2, IFI16, CMTM3, and XAF1) was related to overall survival (OS). Next, we integrated the DNAm-driven genes into the prognostic risk score model and found that age, histologic grade, pathological stage, and risk level were correlated with OS in ccRCC patients. Based on these variables, a dynamic nomogram was established to predict the ccRCC prognosis. Finally, Functional enrichment analysis showed that the functions of these genes were relevant to immune reactions.Conclusions: We identified a 5 DNAm-driven gene signature whose altered status was highly correlated with ccRCC patient OS. We constructed a dynamic nomogram to provide individualized survival predictions for ccRCC patients.

scholarly journals Identification of a three-m6A related gene risk score model as a potential prognostic biomarker in clear cell renal cell carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8827 ◽  
Author(s):  
Yiqiao Zhao ◽  
Zijia Tao ◽  
Xiaonan Chen
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Experimental Studies ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis

Background Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent malignancies worldwide, N6-methyladenosine (m6A) has been shown to play important roles in regulating gene expression and phenotypes in both health and disease. Here, our purpose is to construct a m6A-regulrator-based risk score (RS) for prediction of the prognosis of ccRCC. Methods We used clinical and expression data of m6A related genes from The Cancer Genome Atlas (TCGA) dataset and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to develop an RS to predict survival of patients with ccRCC, and analyzed correlations between RS and other clinical indicators such as age, grade and stage. Validation of this RS was then engaged in another cohort, E-MTAB-1980 from the ArrayExpress dataset. Finally, we used quantitative real-time PCR to analyze the expression profile of genes consists of the RS. Results A three-gene RS including METTL3, METTL14 and HNRNPA2B1 which can predict overall survival (OS) of ccRCC patients from TCGA. After applying this RS into the validation cohort from Arrayexpress, we found that it successfully reproduced the result; furthermore, the results of PCR validation were in line with our analysis. Conclusion To sum up, our study has identified an RS composed of m6A related genes that may predict the prognosis of ccRCC patients, which might be helpful for future therapeutic strategies. Our results call for further experimental studies for validations.

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

2021 ◽  
Author(s):  
Yingkai Hong ◽  
Mingen Lin ◽  
Dehua Ou ◽  
Zhuangkai Huang ◽  
Peilin Shen
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Risk Group ◽  
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 kidney is an iron-metabolism-related organ, exploring and assessing the clinical value of ferroptosis, an iron-dependent regulated cell death, is practical and significant. Methods Prognostic ferroptosis-related differentially expressed genes (DEGs) were identified from KIRC cohort in TCGA database, from which a prognostic signature was established using the Lasso-penalized Cox regression analysis. Each patient in the KIRC cohort and the E-MTAB-1980 cohort (from the ArrayExpress database) was assigned with a calculated signature-correlated risk score and categorized to be either in 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 base on overall survival (OS) in both cohorts. Lastly, risk-related DEGs were identified in both cohorts and applied with the enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration. Results Within 60 ferroptosis-related genes, 32 prognostic DEGs were identified, from which we constructed a prognostic 12-gene signature including CARS1, HMGCR, CHAC1, GOT1, CD44, STEAP3, AKR1C1, CBS, DPP4, FANCD2, SLC1A5 and NCOA4. Patients in both cohorts were divided into high- and low-risk group which were visually distributed in two sets and with positive-risk-related mortality. The K-M survival and the ROC curves validated the signature as prognostic valuable 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 term in GO and KEGG not only shown a highly iron correlation, but also, interesting, an immunity relevancy of 3 immune cells (macrophages, mast cells and regulatory T cell) and 1 immune-related function (antigen processing cell co-stimulation). Conclusion We established a novel 12 ferroptosis-related-gene signature which was proved as an independent prognostic predictor for OS and inferred as relating to tumor immunity in ccRCC, however, the underlying mechanism is still poorly characterized and needed further exploration.

scholarly journals Integrated analysis of the functions of RNA binding proteins in clear cell renal cell carcinoma

2020 ◽  
Author(s):  
Xiaoliang Hua ◽  
Juan Chen ◽  
Shengdong Ge ◽  
Haibing Xiao ◽  
Li Zhang ◽  
...  
Keyword(s):  
Risk Score ◽  
Rna Binding ◽  
Cox Regression ◽  
Clear Cell ◽  
Rna Binding Proteins ◽  
Diagnostic Value ◽  
Cell Renal Cell Carcinoma ◽  
Kaplan Meier ◽  
Score Model ◽  
Cluster 2

Abstract Background: RNA binding proteins (RBPs) dysregulation is involved in the process es of various tumor. However, the roles of RBPs in clear cell renal cell carcinoma (ccRCC) remain poorly understand. Systematic exploration of the roles of RBPs in ccRCC may provide new insights for the treatments of ccRCC. Methods: Expression data of RBPs was obtained from The Cancer Genome Atlas database. The roles of RBPs in ccRCC were systematically investigated using consensus clustering methods. Differentially expressed RBPs between normal and tumor tissues were obtained. Protein-protein interaction (PPI) network was constructed using “STRING” software. The expression levels of hub genes were validated in The Human Protein Atlas (HPA) database and receiver operating characteristic (ROC) curves were used to evaluate diagnostic value . Univariate and Lasso Cox regression and Kaplan–Meier curves were used to screen the most useful prognostic genes. Multivariate Cox regression was performed to construct a risk score model. The efficiency of the model was evaluated using time-dependent ROC and Kaplan–Meier curves, and validated in E-MTAB-3267 set. Results: Two clusters were identified based on the expression similarity of RBPs, and the cluster 2 was closely correlated with the malignancy of ccRCC. Several oncogenic pathways, including epithelial mesenchymal transition, G2M checkpoint, KRAS signaling and IL6 JAK STAT3 signaling were enriched in cluster 2. In addition, we obtained 115 differently expressed RBPs in ccRCC, comprising 71 up-regulated and 44 down-regulated ones. Ten hub RBPs with good diagnostic value were obtained from PPI network and validated in HPA database. Ten RBPs were identified as survival-related genes and used to construct a risk score model. The model could be used to stratify patients with different prognosis. We found high-risk patients tended to be advanced stage, high grade, high pathological T staging and could be an independent risk factor for overall survival of ccRCC patients. Conclusions: We identified ten RBPs with diagnostic value, which might be the potential diagnostic biomarkers for ccRCC. A risk score model was established to stratify patients and could be used as a complementation for clinical factors to guide clinical practice in the future.

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.

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.

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