scholarly journals Identification And Validation of A Cigarette Smoke-Related Five-Gene Signature As A Prognostic Biomarker In Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Yefei Huang ◽  
Qinzhi Wang ◽  
Yu Tang ◽  
Zixuan Liu ◽  
Guixiang Sun ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Cigarette Smoke ◽  
Differentially Expressed Genes ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Gene Signature ◽  
Renal Clear Cell Carcinoma ◽  
Differentially Expressed ◽  
Dependent Manner ◽  
Rna Seq

Abstract Cigarette smoking greatly promotes the progression of kidney renal clear cell carcinoma (KIRC), however, the underlying molecular events has not been fully established. In this study, RCC cells were exposed to the tobacco specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK, nicotine-derived nitrosamine ketone) for 120 days, and then the soft agar colony formation, wound healing and transwell assays were used to explore characteristics of RCC cells. RNA-seq was used to explore differentially expressed genes. We found that NNK promoted RCC cell growth and migration in a dose-dependent manner, and RNA-seq explored 14 differentially expressed genes. In TCGA-KIRC cohort, Lasso regression and multivariate COX regression models screened and constructed a five-gene signature containing ANKRD1, CYB5A, ECHDC3, MT1E, and AKT1S1. This novel gene signature significantly associated with TNM stage, invasion depth, metastasis, and tumor grade. Moreover, when compared with individual genes, the gene signature contained a higher hazard ratio and therefore had a more powerful value for the prognosis of KIRC. A nomogram was also developed based on clinical features and the gene signature, which showed good application. Finally, AKT1S1, the most crucial component of the gene signature, was significantly induced after NNK exposure and its related AKT-mTOR signaling pathway was dramatically activated. Our findings supported that NNK exposure would promote the KIRC progression, and the novel cigarette smoke-related five-gene signature might serve as a highly efficient biomarker to identify progression of KIRC patients, AKT1S1 might play an important role in cigarette smoke exposure-induced KIRC progression.

scholarly journals A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

Hereditas ◽  
2020 ◽  
Vol 157 (1) ◽  
Author(s):  
Ling Chen ◽  
Zijin Xiang ◽  
Xueru Chen ◽  
Xiuting Zhu ◽  
Xiangdong Peng
Keyword(s):  
Overall Survival ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Gene Signature ◽  
Renal Clear Cell Carcinoma

scholarly journals Identification of prognostic genes in kidney renal clear cell carcinoma by RNA-seq data analysis

2017 ◽  
Vol 15 (4) ◽  
pp. 1661-1667 ◽  
Author(s):  
Yanqin Gu ◽  
Linfeng Lu ◽  
Lingfeng Wu ◽  
Hao Chen ◽  
Wei Zhu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Rna Seq

scholarly journals Prognostic and therapeutic implications of extracellular matrix associated gene signature in renal clear cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pankaj Ahluwalia ◽  
Meenakshi Ahluwalia ◽  
Ashis K. Mondal ◽  
Nikhil Sahajpal ◽  
Vamsi Kota ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Risk Group ◽  
Clear Cell Carcinoma ◽  
Gene Signature ◽  
Renal Clear Cell Carcinoma ◽  
High Risk Group ◽  
Elevated Expression

AbstractComplex interactions in tumor microenvironment between ECM (extra-cellular matrix) and cancer cell plays a central role in the generation of tumor supportive microenvironment. In this study, the expression of ECM-related genes was explored for prognostic and immunological implication in clear cell renal clear cell carcinoma (ccRCC). Out of 964 ECM genes, higher expression (z-score > 2) of 35 genes showed significant association with overall survival (OS), progression-free survival (PFS) and disease-specific survival (DSS). On comparison to normal tissue, 12 genes (NUDT1, SIGLEC1, LRP1, LOXL2, SERPINE1, PLOD3, ZP3, RARRES2, TGM2, COL3A1, ANXA4, and POSTN) showed elevated expression in kidney tumor (n = 523) compared to normal (n = 100). Further, Cox proportional hazard model was utilized to develop 12 genes ECM signature that showed significant association with overall survival in TCGA dataset (HR = 2.45; 95% CI [1.78–3.38]; p < 0.01). This gene signature was further validated in 3 independent datasets from GEO database. Kaplan–Meier log-rank test significantly associated patients with elevated expression of this gene signature with a higher risk of mortality. Further, differential gene expression analysis using DESeq2 and principal component analysis (PCA) identified genes with the highest fold change forming distinct clusters between ECM-rich high-risk and ECM-poor low-risk patients. Geneset enrichment analysis (GSEA) identified significant perturbations in homeostatic kidney functions in the high-risk group. Further, higher infiltration of immunosuppressive T-reg and M2 macrophages was observed in high-risk group patients. The present study has identified a prognostic signature with associated tumor-promoting immune niche with clinical utility in ccRCC. Further exploration of ECM dynamics and validation of this gene signature can assist in design and application of novel therapeutic approaches.

scholarly journals Antioxidant Gene Signature Impacts the Immune Infiltration and Predicts the Prognosis of Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Xueting Ren ◽  
Li Ma ◽  
Nan Wang ◽  
Ruina Zhou ◽  
Jianhua Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Gene Signature ◽  
Renal Clear Cell Carcinoma ◽  
Low Risk ◽  
Antioxidant Gene ◽  
Immune Infiltration

Background: Oxidative stress is related to oncogenic transformation in kidney renal clear cell carcinoma (KIRC). We intended to identify a prognostic antioxidant gene signature and investigate its relationship with immune infiltration in KIRC.Methods: With the support of The Cancer Genome Atlas (TCGA) database, we researched the gene expression and clinical data of KIRC patients. Antioxidant related genes with significant differences in expression between KIRC and normal samples were then identified. Through univariate and multivariate Cox analysis, a prognostic gene model was established and all patients were divided into high- and low-risk subgroups. Single sample gene set enrichment analysis was adopted to analyze the immune infiltration, HLA expression, and immune checkpoint genes in different risk groups. Finally, the prognostic nomogram model was established and evaluated.Results: We identified six antioxidant genes significantly correlated with the outcome of KIRC patients as independent predictors, namely DPEP1 (HR = 0.97, P &lt; 0.05), GSTM3 (HR = 0.97, P &lt; 0.05), IYD (HR = 0.33, P &lt; 0.05), KDM3B (HR = 0.96, P &lt; 0.05), PRDX2 (HR = 0.99, P &lt; 0.05), and PRXL2A (HR = 0.96, P &lt; 0.05). The high- and low-risk subgroups of KIRC patients were grouped according to the six-gene signature. Patients with higher risk scores had poorer prognosis, more advanced grade and stage, and more abundance of M0 macrophages, regulatory T cells, and follicular helper T cells. There were statistically significant differences in HLA and checkpoint gene expression between the two risk subgroups. The performance of the nomogram was favorable (concordance index = 0.766) and reliably predicted the 3-year (AUC = 0.792) and 5-year (AUC = 0.766) survival of patients with KIRC.Conclusion: The novel six antioxidant related gene signature could effectively forecast the prognosis of patients with KIRC, supply insights into the interaction between cellular antioxidant mechanisms and cancer, and is an innovative tool for selecting potential patients and targets for immunotherapy.

scholarly journals Construction and Validation of a Ferroptosis-related Prognostic Signature in Kidney Renal Clear Cell Carcinoma

2020 ◽  
Author(s):  
Zhuolun Sun ◽  
Changying Jing ◽  
Chutian Xiao ◽  
Mingxiao Zhang ◽  
Zhenqing Wang ◽  
...  
Keyword(s):  
High Risk ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Risk Group ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
High Risk Group ◽  
Differentially Expressed ◽  
Consensus Clustering ◽  
Prognostic Signature

Abstract Background: Kidney renal clear cell carcinoma (KIRC) is the most common and lethal renal cell carcinoma (RCC) histological subtype. Ferroptosis is a newly discovered programmed cell death and serves an essential role in tumor occurrence and development. The purpose of this study is to analyze ferroptosis-related gene (FRG) expression profiles and to construct a multi-gene signature for predicting the prognosis of KIRC patients.Methods:RNA-sequencing data and clinicopathological data of KIRC patients were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed FRGs between KIRC and normal tissues were identified using ‘limma’ package in R. GO and KEGG enrichment analyses were conducted to elucidate the biological functions and pathways of differentially expressed FRGs. Consensus clustering was used to investigate the relationship between the expression of FRGs and clinical phenotypes. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to screen genes related to prognosis and construct the optimal signature. Then, a nomogram was established to predict individual survival probability by combining clinical features and prognostic signature.Results: A total of 19 differentially expressed FRGs were identified. Consensus clustering identified two clusters of KIRC patients with distinguished prognostic. Functional analysis revealed that metabolism-related pathways were enriched, especially lipid metabolism. A 7-gene ferroptosis-related prognostic signature was constructed to stratify the TCGA training cohort into high- and low-risk groups where the prognosis was significantly worse in the high-risk group. The signature was identified as an independent prognostic indicator for KIRC. These findings were validated in the testing cohort, the entire cohort, and the International Cancer Genome Consortium (ICGC) cohort. We further demonstrated that the signature-based risk score was highly associated with the KIRC progression. Further stratified survival analysis showed that the high-risk group had a significantly lower overall survival (OS) rate than those in the low-risk group. Moreover, we constructed a nomogram that had a strong ability to forecast the OS of the KIRC patients.Conclusion: We constructed a ferroptosis-related prognostic signature, which might provide a reliable prognosis assessment tool for clinician to guide clinical decision-making and outcomes research.

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 Construction and Validation of a Ferroptosis-Related Prognostic Signature in Kidney Renal Clear Cell Carcinoma

2020 ◽  
Author(s):  
Zhuolun Sun ◽  
Changying Jing ◽  
Chutian Xiao ◽  
Mingxiao Zhang ◽  
Zhenqing Wang ◽  
...  
Keyword(s):  
High Risk ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Risk Group ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
High Risk Group ◽  
Differentially Expressed ◽  
Consensus Clustering ◽  
Prognostic Signature

Abstract Background: Kidney renal clear cell carcinoma (KIRC) is the most common and lethal renal cell carcinoma (RCC) histological subtype. Ferroptosis is a newly discovered programmed cell death and serves an essential role in tumor occurrence and development. The purpose of this study is to analyze ferroptosis-related gene (FRG) expression profiles and to construct a multi-gene signature for predicting the prognosis of KIRC patients.Methods:RNA-sequencing data and clinicopathological data of KIRC patients were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed FRGs between KIRC and normal tissues were identified using ‘limma’ package in R. GO and KEGG enrichment analyses were conducted to elucidate the biological functions and pathways of differentially expressed FRGs. Consensus clustering was used to investigate the relationship between the expression of FRGs and clinical phenotypes. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to screen genes related to prognosis and construct the optimal signature. Then, a nomogram was established to predict individual survival probability by combining clinical features and prognostic signature.Results: A total of 19 differentially expressed FRGs were identified. Consensus clustering identified two clusters of KIRC patients with distinguished prognostic. Functional analysis revealed that metabolism-related pathways were enriched, especially lipid metabolism. A 7-gene ferroptosis-related prognostic signature was constructed to stratify the TCGA training cohort into high- and low-risk groups where the prognosis was significantly worse in the high-risk group. The signature was identified as an independent prognostic indicator for KIRC. These findings were validated in the testing cohort, the entire cohort, and the International Cancer Genome Consortium (ICGC) cohort. We further demonstrated that the signature-based risk score was highly associated with the KIRC progression. Further stratified survival analysis showed that the high-risk group had a significantly lower overall survival (OS) rate than those in the low-risk group. Moreover, we constructed a nomogram that had a strong ability to forecast the OS of the KIRC patients.Conclusion: We constructed a ferroptosis-related prognostic signature, which might provide a reliable prognosis assessment tool for clinician to guide clinical decision-making and outcomes research.

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.

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