scholarly journals Comprehensive analysis of lncRNA biomarkers in kidney renal clear cell carcinoma by lncRNA-mediated ceRNA network

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252452
Author(s):  
Ke Gong ◽  
Ting Xie ◽  
Yong Luo ◽  
Hui Guo ◽  
Jinlan Chen ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Cerna Network

Introduction Kidney renal clear cell carcinoma (KIRC) has a high incidence globally, and its pathogenesis remains unclear. Long non-coding RNA (lncRNA), as a molecular sponge, participates in the regulation of competitive endogenous RNA (ceRNA). We aimed to construct a ceRNA network and screened out possible lncRNAs to predict KIRC prognosis. Material and methods All KIRC data were downloaded from the TCGA database and screened to find the possible target lncRNA; a ceRNA network was designed. Next, GO functional enrichment and KEGG pathway of differentially expressed mRNA related to lncRNA were performed. We used Kaplan-Meier curve analysis to predict the survival of these RNAs. We used Cox regression analysis to construct a model to predict KIRC prognosis. Results In the KIRC datasets, 1457 lncRNA, 54 miRNA and 2307 mRNA were screened out. The constructed ceRNA network contained 81 lncRNAs, nine miRNAs, and 17 mRNAs differentially expressed in KIRC. Survival analysis of all differentially expressed RNAs showed that 21 lncRNAs, four miRNAs, and two mRNAs were related to the overall survival rate. Cox regression analysis was performed again, and we found that eight lncRNAs were related to prognosis and used to construct predictive models. Three lnRNAs from independent samples were meaningful. Conclusion The construction of ceRNA network was involved in the process and transfer of KIRC, and three lncRNAs may be potential targets for predicting KIRC prognosis.

scholarly journals High PON1 Expression Predicts Poor Prognosis In Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Chenxia Jiang ◽  
Xinyu Zhang ◽  
Xiaoyan Li ◽  
Jia Li ◽  
Hua Huang
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Factor ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Independent Prognostic Factor ◽  
Cox Regression Analysis

Abstract Background: Relevant study had demonstrated that Paraoxonase-1 (PON1) had relationship with occurrence and development of tumors which suggested that PON1 was a key gene in promoting tumor progression. However, the relationship between PON1 and Kidney renal clear cell carcinoma (KIRC) is still unclear so far. Methods: We downloaded relevant data about KIRC from TCGA dataset and compared it with normal renal tissues. Immunohistochemistry (IHC) was applied to analyze the expression of PON1. Univariate cox regression analysis and multivariate cox regression analysis were also utilized to analyze independent factors associated with prognosis. Gene set enrichment analysis was conducted to find the signaling pathways of PON1 in KIRC. Finally, we also investigated whether PON1 had relationship with immunity. Results: As shown in results, PON1 expression was decreased in KIRC compared with adjacent paracancer tissues. Immunohistochemistry (IHC) was utilized to find the expression of PON1. After survival analysis, the high expression of PON1 was significantly related to overall survival (P<0.001). Univariate/Multivariate cox regression analysis both revealed that PON1 could serve as an independent prognostic factor. To analyze overall survival (OS) of patients with KIRC, nomogram was developed. GSEA revealed that PON1 was correlated with homologous recombination. Besides, PON1 had few relationships with immunity. Conclusions: Our results revealed that PON1 could serve as an independent prognostic factor for KIRC, providing a novel target for KIRC future treatments.

scholarly journals The Construction and Exploration of the ceRNA Network and Patterns of Tumor-Infiltrating Immune Cells in Kidney Renal Clear Cell Carcinoma

2020 ◽  
Author(s):  
Weiwei Jia ◽  
Pengjia Li ◽  
Mingxia Ma ◽  
Xiaochen Niu ◽  
Lina Bai ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Immune Cells ◽  
Prognostic Model ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Model Based

Abstract Background: Kidney renal clear cell carcinoma is the malignant tumor with the highest incidence and poor prognosis in renal cell carcinoma. In view of its limited diagnostic strategies and poor prognosis, bioinformatics analysis has been used to explore the possible mechanisms of renal clear cell carcinoma and effective prognostic-related biomarkers.Method: The sequencing information of 3 types of RNA (mRNA, lncRNA and miRNA) in 539 cases of kidney renal clear cell carcinoma tumor tissues and 72 cases of normal tissues is obtained from the TCGA database. Heat map and volcano map of differentially expressed genes were drawn through R language; The CeRNA network was visualized by Cytoscape software (version 3.7.2). Methods such as univariate Cox regression analysis, lasso regression screening, and multivariate Cox regression analysis were used to construct a prognostic model based on the CeRNA network. The CIBERSORT algorithm was used to analyze the degree of infiltration of 22 kinds of immune cells from each sample of kidney renal clear cell carcinoma. Construction of a prognostic model based on tumor-infiltrating immune cells, The R "corrplot" software package was used for co-expression analysis based on the CeRNA network and tumor-infiltrating immune cells model.Results: There are 3074 differentially expressed mRNAs (1055 upregulated and 2019 downregulated), and 359 differentially expressed lncRNAs (71 upregulated and 280 downregulated) and 132 differentially expressed miRNAs (70 upregulated and 62 downregulated) that have been identified through differential analysis. A complete mRNA-miRNA-lncRNA (SIX1-hsa-miR-200b-3p-MALAT1) network was obtained based on the CeRNA network-based prognostic model construction. 2 immune cells (Mast cells resting, T cells follicular helper) were identified by constructing a prognostic model based on tumor-infiltrating immune cells. There was a negative correlation between lncRNA MALAT1 and Mast cells resting (R= -0.27, P<0.001); while there was a positive correlation between lncRNA MALAT1 and T cells follicular helper (R=0.23, P<0.001).Conclusion: Based on CeRNA network and tumor-infiltrating immune cells, we explored the possible mechanism of kidney renal clear cell carcinoma and obtained effective biomarkers for predicting prognosis by Bioinformatics analysis in this study.

scholarly journals Identification of a Prognostic Risk Signature of Kidney Renal Clear Cell Carcinoma Based on Regulating the Immune Response Pathway Exploration

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Guangzhen Wu ◽  
Yingkun Xu ◽  
Chenglin Han ◽  
Zilong Wang ◽  
Jiayi Li ◽  
...  
Keyword(s):  
Immune Response ◽  
Cell Carcinoma ◽  
Sensitivity And Specificity ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Lasso Regression ◽  
Regression Curve ◽  
Cox Regression Analysis

Purpose. To construct a survival model for predicting the prognosis of patients with kidney renal clear cell carcinoma (KIRC) based on gene expression related to immune response regulation. Materials and Methods. KIRC mRNA sequencing data and patient clinical data were downloaded from the TCGA database. The pathways and genes involved in the regulation of the immune response were identified from the GSEA database. A single factor Cox analysis was used to determine the association of mRNA in relation to patient prognosis P < 0.05 . The prognostic risk model was further established using the LASSO regression curve. The survival prognosis model was constructed, and the sensitivity and specificity of the model were evaluated using the ROC curve. Results. Compared with normal kidney tissues, there were 28 dysregulated mRNA expressions in KIRC tissues P < 0.05 . Univariate Cox regression analysis revealed that 12 mRNAs were related to the prognosis of patients with renal cell carcinoma. The LASSO regression curve drew a risk signature consisting of six genes: TRAF6, FYN, IKBKG, LAT2, C2, IL4, EREG, TRAF2, and IL12A. The five-year ROC area analysis (AUC) showed that the model has good sensitivity and specificity (AUC >0.712). Conclusion. We constructed a risk prediction model based on the regulated immune response-related genes, which can effectively predict the survival of patients with KIRC.

scholarly journals Hub Long Noncoding RNAs with m6A Modification for Signatures and Prognostic Values in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Gaoteng Lin ◽  
Huadong Wang ◽  
Yuqi Wu ◽  
Keruo Wang ◽  
Gang Li
Keyword(s):  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Noncoding Rnas ◽  
Renal Clear Cell Carcinoma ◽  
Expression Level ◽  
Molecular Signatures ◽  
Cox Regression Analysis ◽  
Qrt Pcr ◽  
Cerna Network

Background: N6-methyladenosine (m6A)–modified long noncoding RNAs (m6A-lncRNAs) have been proven to be involving in regulating tumorigenesis, invasion, and metastasis for a variety of tumors. The present study aimed to screen lncRNAs with m6A modification and investigate their biological signatures and prognostic values in kidney renal clear cell carcinoma (KIRC).Materials and Methods: lncRNA-seq, miRNA-seq, and mRNA-seq profiles of KIRC samples and the clinical characteristics of corresponding patients were downloaded from The Cancer Genome Atlas (TCGA). The R package “edgeR” was utilized to perform differentially expressed analysis on these profiles to gain DElncRNAs, DEmiRNAs, and DEmRNAs, respectively. The results of intersection of DElncRNAs and m6A-modified genes were analyzed by the weighted gene co-expression network analysis (WGCNA) to screen hub m6A-lncRNAs. Then, WGCNA was also used to construct an lncRNA-miRNA-mRNA (ceRNA) network. The Cox regression analysis was conducted on hub m6A-lncRNAs to construct the m6A-lncRNAs prognostic index (m6AlRsPI). Receiver operating characteristic (ROC) curve was used to assess the predictive ability of m6AlRsPI. The m6AlRsPI model was tested by internal and external cohorts. The molecular signatures and prognosis for hub m6A-lncRNAs and m6AlRsPI were analyzed. The expression level of hub m6A-lncRNAs in KIRC cell lines were quantified by qRT-PCR.Results: A total of 21 hub m6A-lncRNAs associated with tumor metastasis were identified in the light of WGCNA. The ceRNA network for 21 hub m6A-lncRNAs was developed. The Cox regression analysis was performed on the 21 hub m6A-lncRNAs, screening two m6A-lncRNAs regarded as independent prognostic risk factors. The m6AlRsPI was established based on the two m6A-lncRNAs as follows: (0.0006066 × expression level of LINC01820) + (0.0020769 × expression level of LINC02257). The cutoff of m6AlRsPI was 0.96. KM survival analysis for m6AlRsPI showed that the high m6AlRsPI group could contribute to higher mortality. The area under ROC curve for m6AlRsPI for predicting 3- and 5-year survival was 0.760 and 0.677, respectively, and the m6AlRsPI was also tested. The mutation and epithelial–mesenchymal transition (EMT) analysis for m6AlRsPI showed that the high m6AIRsPI group had more samples with gene mutation and had more likely caused EMT. Finally, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed for mRNAs interacted with the two m6A-lncRNAs, showing they were involved in the process of RNA splicing and regulation of the mRNA surveillance pathway. qRT-PCR analysis showed that the two m6A-lncRNAs were upregulated in KIRC.Conclusion: In the present study, hub m6A-lncRNAs were determined associated with metastasis in KIRC, and the ceRNA network demonstrated the potential carcinogenic regulatory pathway. Two m6A-lncRNAs associated with the overall survival were screened and m6AlRsPI was constructed and validated. Finally, the molecular signatures for m6AlRsPI and the two m6A-lncRNAs were analyzed to investigate the potential modulated processes in KIRC.

scholarly journals Angiogenesis Pathway in Kidney Renal Clear Cell Carcinoma and Its Prognostic Value for Cancer Risk Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiangyu Che ◽  
Wenyan Su ◽  
Xiaowei Li ◽  
Nana Liu ◽  
Qifei Wang ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Treatment Plan ◽  
Renal Clear Cell Carcinoma ◽  
Targeted Drugs ◽  
Cox Regression Analysis ◽  
Cluster 2 ◽  
Angiogenesis Pathway

Angiogenesis, a process highly regulated by pro-angiogenic and anti-angiogenic factors, is disrupted and dysregulated in cancer. Despite the increased clinical use of angiogenesis inhibitors in cancer therapy, most molecularly targeted drugs have been less effective than expected. Therefore, an in-depth exploration of the angiogenesis pathway is warranted. In this study, the expression of angiogenesis-related genes in various cancers was explored using The Cancer Genome Atlas datasets, whereupon it was found that most of them were protective genes in the patients with kidney renal clear cell carcinoma (KIRC). We divided the samples from the KIRC dataset into three clusters according to the mRNA expression levels of these genes, with the enrichment scores being in the order of Cluster 2 (upregulated expression) &gt; Cluster 3 (normal expression) &gt; Cluster 1 (downregulated expression). The survival curves plotted for the three clusters revealed that the patients in Cluster 2 had the highest overall survival rates. Via a sensitivity analysis of the drugs listed on the Genomics of Drug Sensitivity in Cancer database, we generated IC50 estimates for 12 commonly used molecularly targeted drugs for KIRC in the three clusters, which can provide a more personalized treatment plan for the patients according to angiogenesis-related gene expression. Subsequently, we investigated the correlation between the angiogenesis pathway and classical cancer-related genes as well as that between the angiogenesis score and immune cell infiltration. Finally, we used the least absolute shrinkage and selection operator (LASSO)–Cox regression analysis to construct a risk score model for predicting the survival of patients with KIRC. According to the areas under the receiver operating characteristic (ROC) curves, this new survival model based on the angiogenesis-related genes had high prognostic prediction value. Our results should provide new avenues for the clinical diagnosis and treatment of patients with KIRC.

scholarly journals Identification of the Prognostic LncRNA Biomarkers and Comprehensive Analysis of LncRNA-Mediated ceRNA Network for Uterine Corpus Endometrial Carcinoma

2021 ◽  
Author(s):  
Yiran Cai ◽  
Jin Cui ◽  
Huiqun Wu
Keyword(s):  
Regression Analysis ◽  
Endometrial Carcinoma ◽  
Noncoding Rna ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Uterine Corpus ◽  
Cerna Network

Abstract Background Given that long non-coding RNAs (lncRNAs) involved in the tumor initiation or progression of the endometrium and that competing endogenous RNA (ceRNA) plays an important role in increasingly more biological processes, lncRNA-mediated ceRNA is likely to function in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). Our present study aimed to explore the potential molecular mechanisms for the prognosis of UCEC through an lncRNA-mediated ceRNA network. Methods The transcriptome profiles and corresponding clinical profiles of UCEC dataset were retrieved from CPTAC and TCGA databases respectively. Differentially expressed genes (DEGs) in UCEC samples were identified via “Edge R” package. Then, an integrated bioinformatics analysis including functional enrichment analysis, tumor infiltrating immune cell(TIIC) analysis, Kaplan-Meier curve, Cox regression analysis were conducted to analyze the prognostic biomarkers. Results In the CPTAC dataset of UCEC, a ceRNA network comprised of 36 miRNAs, 123 lncRNAs and 124 targeted mRNAs was established, and 8 of 123 prognostic-related DElncRNAs(Differentially Expressed long noncoding RNA) were identified. While in the TCGA dataset, a ceRNA network comprised of 38 miRNAs, 83 lncRNAs and 110 targeted mRNAs was established, and 2 of 83 prognostic-related DElncRNAs were identified. After filtered by risk grouping and Cox regression analysis, 10 prognostic-related lncRNAs including LINC00443, LINC00483, C2orf48, TRBV11-2, MEG-8 were identified. In addition, 33 survival-related DEmRNAs(Differentially Expressed messager RNA) in two ceRNA networks were further validated in the HPA database. Finally, six lncRNA/miRNA/mRNA axes were established to elucidate prognostic regulatory roles in UCEC. Conclusion Several prognostic lncRNAs are identified and prognostic model of lncRNA-mediated ceRNA network is constructed, which promotes the understanding of UCEC development mechanisms and potential therapeutic targets.

scholarly journals Construction of a protein-based model for prognosis prediction of kidney renal clear cell carcinoma: an investigation based on functional proteomics data

2020 ◽  
Author(s):  
Huiming Jiang ◽  
Haibin Chen ◽  
Pei Wan ◽  
NanHui Chen
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Prognostic Parameters ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Prognosis Prediction ◽  
Protein Model

Abstract Background Several studies have shown prognostic value of gene-based models at mRNA level in kidney renal clear cell carcinoma (KIRC). However, protein-based models for prognosis prediction of KIRC are rarely reported, then we conduct this study. Methods Proteomics and clinical data of KIRC were acquired from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA), respectively. Prognosis-associated proteins were screened by univariate Cox regression analysis and log-rank test. Patients were grouped into training set and testing set. The protein-based prognostic model was constructed by lasso Cox regression analysis in training set and validated in test set and whole set. Results A five-protein model (ACC1, IGFBP2, MIG6, PEA15 and RAD51) was constructed for KIRC prognosis prediction. It could well classify KIRC patients into high-risk and low-risk group with significantly different survival. The results was validated in the testing set and whole set. Age, AJCC stage and risk score based on the five-protein model were identified as independent prognostic parameters and they were used to construct a nomogram. The calibration plot showed the nomogram had good agreement between predicted and actual outcomes. Time-dependent ROC curves revealed the nomogram performed best in predicting the 1-year, 3-year and 5-year overall survival (OS) compared with other independent prognostic parameters. DCA demonstrated the nomogram had obviously clinical net benefit. Furthermore, several cancer-related biological signaling pathways were enriched in the functional enrichment analysis. Conclusion Our study developed an effective protein-based model to predict the OS of KIRC, which may help clinicians to offer individual treatment.

scholarly journals Expression of BAG1 is associated with prognosis in kidney renal clear cell carcinoma based on bioinformatics

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hongrong Wu ◽  
Minjing Liu ◽  
Yuejun He ◽  
Guozhao Meng ◽  
Wanbei Guo ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Neoadjuvant Treatment ◽  
Renal Clear Cell Carcinoma ◽  
Cox Regression Analysis ◽  
Low Expression

Abstract Background BCL2 associated Athano-Gene 1 (BAG1) has been described to be involved in the development and progression of cancer. But the role of BAG1 in kidney renal clear cell carcinoma (KIRC) has remained largely unknown. Methods We performed bioinformatic analysis of data from TCGA and GEO dataset. The role of BAG1 in KIRC was explored by Logistic and Cox regression model. The molecular mechanisms of BAG1 was revealed by GSEA. Results The current study found that the KIRC tumor samples have a low level of BAG1 mRNA expression compared to the matched normal tissues based on TCGA data and GEO databases. Low expression of BAG1 in KIRC was significantly associated with Sex, clinical pathological stage, tumor-node-metastasis (TNM) stage, hemoglobin levels, cancer status and history of neoadjuvant treatment. Kaplan-Meier survival analysis indicated that KIRC patients with BAG1 high expression have a longer survival time than those with BAG1 low expression (p < 0.000). Cox regression analysis showed that BAG1 remained independently associated with overall survival, with a hazard ratio (HR) of 1.75(CI:1.05–2.90; p = 0.029). GSEA indicated that the signaling pathways including fatty acid metabolism and oxidative phosphorylation were differentially enriched in high BAG1 expression phenotype. Conclusions These findings suggested that BAG1 expression may act as a potential favorable prognostic marker and challenging therapeutic target.

scholarly journals A Five-Gene Signature Predicts Prognosis in Patients with Kidney Renal Clear Cell Carcinoma

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yueping Zhan ◽  
Wenna Guo ◽  
Ying Zhang ◽  
Qiang Wang ◽  
Xin-jian Xu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Clinical Information ◽  
Gene Signature ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Tcga Dataset

Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers with high mortality all over the world. Many studies have proposed that genes could be used to predict prognosis in KIRC. In this study, RNA expression data from next-generation sequencing and clinical information of 523 patients downloaded from The Cancer Genome Atlas (TCGA) dataset were analyzed in order to identify the relationship between gene expression level and the prognosis of KIRC patients. A set of five genes that significantly associated with overall survival time was identified and a model containing these five genes was constructed by Cox regression analysis. By Kaplan-Meier and Receiver Operating Characteristic (ROC) analysis, we confirmed that the model had good sensitivity and specificity. In summary, expression of the five-gene model is associated with the prognosis outcomes of KIRC patients, and it may have an important clinical significance.

scholarly journals Development of an Autophagy-Related Gene Prognostic Model and Nomogram for Estimating Renal Clear Cell Carcinoma Survival

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ying Wang ◽  
Yinhui Yao ◽  
Jingyi Zhao ◽  
Chunhua Cai ◽  
Junhui Hu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Bioinformatic Analysis ◽  
Renal Clear Cell Carcinoma ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Decision Curve Analysis ◽  
Cox Analysis

Background. Kidney renal clear cell carcinoma (KIRC) is a fatal malignancy of the urinary system. Autophagy is implicated in KIRC occurrence and development. Here, we evaluated the prognostic value of autophagy-related genes (ARGs) in kidney renal clear cell carcinoma. Materials and Methods. We analyzed RNA sequencing and clinical KIRC patient data obtained from TCGA and ICGC to develop an ARG prognostic signature. Differentially expressed ARGs were further evaluated by functional assessment and bioinformatic analysis. Next, ARG score was determined in 215 KIRC patients using univariable Cox and LASSO regression analyses. An ARG nomogram was built based on multivariable Cox analysis. The prognosis nomogram model based on the ARG signatures and clinicopathological information was evaluated for discrimination, calibration, and clinical usefulness. Results. A total of 47 differentially expressed ARGs were identified. Of these, 8 candidates that significantly correlated with KIRC overall survival were subjected to LASSO analysis and an ARG score built. Functional enrichment and bioinformatic analysis were used to reveal the differentially expressed ARGs in cancer-related biological processes and pathways. Multivariate Cox analysis was used to integrate the ARG nomogram with the ARG signature and clinicopathological information. The nomogram exhibited proper calibration and discrimination (C-index = 0.75, AUC = >0.7). Decision curve analysis also showed that the nomogram was clinically useful. Conclusions. KIRC patients and doctors could benefit from ARG nomogram use in clinical practice.

Sign in / Sign up

Export Citation Format

Share Document