scholarly journals Gene Signature Associated With Bromodomain Genes Predicts the Prognosis of Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Junwan Lu ◽  
Changrui Qian ◽  
Yongan Ji ◽  
Qiyu Bao ◽  
Bin Lu
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Cell Carcinoma ◽  
Epigenetic Regulation ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Survival Prediction

Bromodomain (BRD) proteins exhibit a variety of activities, such as histone modification, transcription factor recruitment, chromatin remodeling, and mediator or enhancer complex assembly, that affect transcription initiation and elongation. These proteins also participate in epigenetic regulation. Although specific epigenetic regulation plays an important role in the occurrence and development of cancer, the characteristics of the BRD family in renal clear cell carcinoma (KIRC) have not been determined. In this study, we investigated the expression of BRD family genes in KIRC at the transcriptome level and examined the relationship of the expression of these genes with patient overall survival. mRNA levels of tumor tissues and adjacent tissues were extracted from The Cancer Genome Atlas (TCGA) database. Seven BRD genes (KAT2A, KAT2B, SP140, BRD9, BRPF3, SMARCA2, and EP300) were searched by using LASSO Cox regression and the model with prognostic risk integration. The patients were divided into two groups: high risk and low risk. The combined analysis of these seven BRD genes showed a significant association with the high-risk groups and lower overall survival (OS). This analysis demonstrated that total survival could be predicted well in the low-risk group according to the time-dependent receiver operating characteristic (ROC) curve. The prognosis was determined to be consistent with that obtained using an independent dataset from TCGA. The relevant biological functions were identified using Gene Set Enrichment Analysis (GSEA). In summary, this study provides an optimized survival prediction model and promising data resources for further research investigating the role of the expression of BRD genes in KIRC.

scholarly journals Identification of Autophagy-Related Long Non-Coding RNA Prognostic Signature for Clear Cell Renal Cell Carcinoma

2020 ◽  
Author(s):  
Yankang Cui ◽  
Shaobo Zhang ◽  
Chenkui Miao ◽  
Chao Liang ◽  
Xiaochao Chen ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Cell Carcinoma ◽  
Prognostic Value ◽  
Clear Cell ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Independent Predictive Factor ◽  
Cell Renal Cell Carcinoma

Abstract Background: Studies over the past decade have shown that long non-coding RNAs (lncRNAs) play an essential role in the tumorigenesis and progression of kidney renal clear cell carcinoma (KIRC). Meanwhile, autophagy has been demonstrated to regulate KIRC pathogenesis and targeting therapy resistance. However, the prognostic value of autophagy-related lncRNAs in KIRC patients has not been reported before.Methods: In this study, we obtained transcriptome data of 611 KIRC cases from the TCGA database and 258 autophagy-related mRNAs from the HADb database to identify autophagy-related lncRNAs by co-expression network. A prognostic model was then established basing on these autophagy-related lncRNAs, dividing patients into high-risk and low-risk groups. Survival analysis, clinical variables dependent receiver operating characteristic (ROC) analyses, univariate/multivariate Cox analyses, and clinical correlation analysis were performed based on risk signature with R language. Gene set enrichment analysis (GSEA) was then performed to investigate the potential mechanism of the risk signature promoting KIRC progression with GSEA software. Results: A total of 17 lncRNAs were screened out and all these lncRNAs were found significantly related to KIRC patients' overall survival in subsequent survival analyses. Besides, the overall survival time in the high-risk group was much poorer than in the low-risk group. The ROC analysis revealed that the prognostic value of risk signature was better than age, gender, grade, and N stage. Univariate/multivariate analyses suggested that the risk signature was an independent predictive factor for KIRC patients. Immune-related pathways were dramatically enriched in high-risk patients according to GSEA. Conclusions: In summary, our identified 17 autophagy-related lncRNAs had prognostic value for KIRC patients which may function in Immunomodulation.

scholarly journals Establishment and Validation of a Prognostic Risk Model for Autophagy-Related Genes in Clear Cell Renal Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Wenkai Han ◽  
Xiaoyan Xu ◽  
Kai Che ◽  
Guofeng Ma ◽  
Danxia Li ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Factor ◽  
Cell Carcinoma ◽  
Risk Model ◽  
Clear Cell ◽  
Risk Group ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
High Risk Group ◽  
Low Risk

Background. Autophagy plays an essential role in tumorigenesis. At present, due to the unclear role of autophagy in renal clear cell carcinoma, we studied the potential value of autophagy-related genes (ARGs) in renal clear cell carcinoma (ccRCC). Methods. We obtained all ccRCC data from The Cancer Genome Atlas (TCGA). We extracted the expression data of ARGs for difference analysis and carried out biological function analysis on the different results. The autophagy risk model was constructed. The 5-year survival rate was assessed using the model, and the predictive power of the model was evaluated from multiple perspectives. Cox regression analysis was use to assess whether the model could be an independent prognostic factor. Finally, the correlation between the model and clinical indicators is analyzed. Results. The patients were divided into the high-risk group and low-risk group according to the median of autophagy risk score, and the results showed that the prognosis of the low-risk group was better than that of a high-risk group. The validation results of external data sets show that our model has good predictive value for ccRCC patients. The model can be an independent prognostic factor. Finally, the results show that our model has a stable predictive ability. Conclusion. The autophagy gene model we constructed can be used as an excellent prognostic indicator for ccRCC. Our study provides the possibility of individualized and precise treatment for ccRCC patients.

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.

GALNT14 expression in renal clear cell carcinoma and its effect on prognosis

2021 ◽  
Author(s):  
Yuqin Wei ◽  
Fan Wu ◽  
Shengfeng Zhang ◽  
Yanlin Tan ◽  
Qunying Wu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Regulatory Pathways ◽  
Cox Analysis ◽  
The Relationship

Abstract Background The expression of GALNT14 in kidney renal clear cell carcinoma (KIRC) and its clinical significance remains unknown. Methods The KIRC data expressed by GALNT14 was downloaded from The Cancer Genome Atlas (TCGA) database. The expression of GALNT14 was analyzed by R software, Perl software and online analysis database. The relationship between GALNT14 expression and clinicopathological features in KIRC was analyzed by univariate, multivariate Cox regression and some databases. Gene Expression Profling Interactive Analysis (GEPIA), Starbase v3.0, UALCAN, and Kaplan-Meier were used to analyze the relationship between GALNT14 expression and overall survival (OS) in KIRC. UALCAN detects the expression of GALNT14 methylation in KIRC. Linkedomics and Genemania were used to analyze the gene co-expression of GALNT14. Gene Set Enrichment Analysis (GSEA) was performed to search for potential regulatory pathways. Results We found that GALNT14 was overexpressed in KIRC (p=1.433e-25). Patients with high GALNT14 expression in KIRC had a better prognosis than patients with low GALNT14 expression (p=0.008). In addition, high GALNT14 expression in KIRC was significantly associated with low T stage and positive OS (p<0.05). Univariate Cox analysis showed that GALNT14 was positively correlated with OS (p<0.001). Multivariate Cox analysis showed that GALNT14 was associated with OS (p<0.001), age (p=0.01) and histological grade (p=0.02). GALNT14 methylation is low expressed in KIRC (p<0.001). GSEA analysis showed that GALNT14 was enriched in histidine metabolism, peroxisome, and renin-angiotensin system pathways. Conclusion GALNT14 can be used as an independent prognostic factor for renal clear cell carcinoma and a potential target for clinical diagnosis and treatment of KIRC.

scholarly journals Comprehensive Analysis of the Immune Infiltrates of Pyroptosis in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhuolun Sun ◽  
Changying Jing ◽  
Xudong Guo ◽  
Mingxiao Zhang ◽  
Feng Kong ◽  
...  
Keyword(s):  
High Risk ◽  
Cell Carcinoma ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Kidney renal clear cell carcinoma (KIRC) has long been identified as a highly immune-infiltrated tumor. However, the underlying role of pyroptosis in the tumor microenvironment (TME) of KIRC remains poorly described. Herein, we systematically analyzed the prognostic value, role in the TME, response to ICIs, and drug sensitivity of pyroptosis-related genes (PRGs) in KIRC patients based on The Cancer Genome Atlas (TCGA) database. Cluster 2, by consensus clustering for 24 PRGs, presented a poor prognosis, likely because malignancy-related hallmarks were remarkably enriched. Additionally, we constructed a prognostic prediction model that discriminated well between high- and low-risk patients and was further confirmed in external E-MTAB-1980 cohort and HSP cohort. By further analyzing the TME based on the risk model, higher immune cell infiltration and lower tumor purity were found in the high-risk group, which presented a poor prognosis. Patients with high risk scores also exhibited higher ICI expression, indicating that these patients may be more prone to profit from ICIs. The sensitivity to anticancer drugs that correlated with model-related genes was also identified. Collectively, the pyroptosis-related prognosis risk model may improve prognostic information and provide directions for current research investigations on immunotherapeutic strategies for KIRC patients.

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 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.

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