Development and validation of an individualized DNA repair-related gene signature in localized clear cell renal cell carcinoma

Author(s):  
Ying Xiong ◽  
Yu Qi ◽  
Wenyao Lin ◽  
Qi Bai ◽  
Li Liu ◽  
...  
2021 ◽  
Vol 11 ◽  
Author(s):  
Zhengtong Lv ◽  
Lin Qi ◽  
Xiheng Hu ◽  
Miao Mo ◽  
Huichuan Jiang ◽  
...  

BackgroundAccumulating evidences indicate significant alterations in the aerobic glycolysis in clear cell renal cell carcinoma (ccRCC). We aim to develop and validate a glycolysis-related genes signature for predicting the clinical outcomes of patients with ccRCC.MethodsmRNA expression profiling of ccRCC was obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. The protein expression levels of the core genes were obtained from the Human Protein Atlas database. We used four external independent data sets to verify the predictive power of the model for prognosis, tyrosine kinase inhibitor (TKI) therapy, and immunotherapy responses, respectively. Finally, we explored the potential mechanism of this signature through gene set enrichment analysis (GSEA).ResultsThrough the GSEA, glycolysis-related gene sets were significantly different between ccRCC tissues and normal tissues. Next, we identified and constructed a seven-mRNA signature (GALM, TGFA, RBCK1, CD44, HK3, KIF20A, and IDUA), which was significantly correlated with worse survival outcome and was an independent prognostic indicator for ccRCC patients. Furthermore, the expression levels of hub genes were validated based on the Human Protein Atlas databases. More importantly, the model can predict patients’ response to TKI therapy and immunotherapy. These findings were successfully validated in the external independent ccRCC cohorts. The mechanism exploration showed that the model may influence the prognosis by influencing tumor proliferation, base mismatch repair system and immune status of patients.ConclusionsOur study has built up a robust glycolysis-based molecular signature that predicts the prognosis and TKI therapy and immunotherapy responses of patients with ccRCC with high accuracy, which might provide important guidance for clinical assessment. Also, clinical investigations in large ccRCC cohorts are greatly needed to validate our findings.


2021 ◽  
Vol 11 ◽  
Author(s):  
Kaili Chang ◽  
Chong Yuan ◽  
Xueguang Liu

As a type of regulated cell death induced by Ras selective lethal (RSL) compounds such as erasti, ferroptosis is characterized by iron-dependent lipid peroxide accumulation to lethal levels. At present, little is known about the role of ferroptosis-related genes in clear-cell renal cell carcinoma (ccRCC). In the present study, the expression data of ferroptosis-related genes in ccRCC were obtained from the Cancer Genome Atlas (TCGA), and COX regression analysis was performed to construct a risk model of ferroptosis prognostic signature. The GEO database was used to verify the accuracy of the model. The following findings were made: the results reveal that the prognostic signature constructed by 11 ferroptosis genes (CARS, CD44, DPP4, GCLC, HMGCR, HSPB1, NCOA4, SAT1, PHKG2, GOT1, HMOX1) was significantly related to the overall survival (OS) of ccRCC patients based on the lowest Akaike information criterion (AIC); multivariate analysis indicates that ferroptosis-related gene prognostic signature was an independent prognostic factor in ccRCC patients; the calibration curve and c-index value (0.77) demonstrate that the nomogram with the signature could predict the survival of ccRCC patients; and enrichment analysis shows that the high-risk group were enriched in humoral immunity and receptor interaction pathways. The aforementioned findings indicate that the ferroptosis-related gene signature can accurately predict the prognosis of ccRCC patients and provide valuable insights for individualized treatment.


2021 ◽  
Vol 10 (1) ◽  
pp. 1933332
Author(s):  
Xiaomao Yin ◽  
Zaoyu Wang ◽  
Jianfeng Wang ◽  
Yunze Xu ◽  
Wen Kong ◽  
...  

2020 ◽  
Author(s):  
Yun Peng ◽  
Shangrong Wu ◽  
Zihan Xu ◽  
Dingkun Hou ◽  
Nan Li ◽  
...  

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.


Oncotarget ◽  
2016 ◽  
Vol 7 (50) ◽  
pp. 82712-82726 ◽  
Author(s):  
Jun Dai ◽  
Yuchao Lu ◽  
Jinyu Wang ◽  
Lili Yang ◽  
Yingyan Han ◽  
...  

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