scholarly journals A microRNA‐clinical prognosis model to predict the overall survival for kidney renal clear cell carcinoma

2021 ◽  
Author(s):  
Yating Zhan ◽  
Rongrong Zhang ◽  
Chunxue Li ◽  
Xuantong Xu ◽  
Kai Zhu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Clinical Prognosis ◽  
Prognosis Model

scholarly journals AC010973.2 Promotes Cell Proliferation and Is One of Six Stemness-Related Genes That Predict Overall Survival of Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Yingqing Liu ◽  
Yuang Wei ◽  
Xu Zhang ◽  
Xiaohan Ren ◽  
Jiawei Wang ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Learning Algorithm ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Prognosis Model

Abstract Background Extensive research has revealed that tumor stemness plays a central role in promoting tumor progression. However, the underlying involvement of stemness-related genes in renal clear cell carcinoma (ccRCC) remains controversial. Methods The data used for bioinformatics analysis were downloaded from The Cancer Genome Atlas database. The R software, SPSS and GraphPad Prism 8 were used for mapping and statistical analysis. Results We first quantified the stemness index of each patient through a machine learning algorithm. Then, we identified the differentially expressed genes between high and low stemness index as stemness-related genes. Based on these genes, we finally established a stable and effective prognosis model to predict patients' overall survival using a random forest algorithm (Training cohort; 1-year AUC: 0.67; 3-year AUC: 0.79; 5-year AUC: 0.73; Validation cohort; 1-year AUC: 0.66; 3-year AUC: 0.71; 5-year AUC: 0.7). The model genes include AC010973.2, RNU6-125P, AP001209.2, Z98885.1, KDM5C-IT1 and AL021368.3. The gene AC010973.2 was selected for further research for its highest importance. In vitro experiments demonstrated that AC010973.2 is highly expressed in ccRCC tissue and cell lines. Meanwhile, knockdown of AC010973.2 could significantly hamper the proliferation of ccRCC cells according to the colony formation and CCK8 assays. Conclusion In summary, our finding indicated that the stemness-related gene AC01097.3 is closely associated with patients' survival and could remarkably facilitate cell proliferation in ccRCC, making it potential to be a novel therapeutic target.

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 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 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 Novel prognostic prediction model constructed through machine learning on the basis of methylation-driven genes in kidney renal clear cell carcinoma

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Weihao Tang ◽  
Yiling Cao ◽  
Xiaoke Ma
Keyword(s):  
Prediction Model ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Epigenetic Modification ◽  
Clinical Information ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Prognosis Model

Abstract Kidney renal clear cell carcinoma (KIRC) is a common tumor with poor prognosis and is closely related to many aberrant gene expressions. DNA methylation is an important epigenetic modification mechanism and a novel research target. Thus, exploring the relationship between methylation-driven genes and KIRC prognosis is important. The methylation profile, methylation-driven genes, and methylation characteristics in KIRC was revealed through the integration of KIRC methylation, RNA-seq, and clinical information data from The Cancer Genome Atlas. The Lasso regression was used to establish a prognosis model on the basis of methylation-driven genes. Then, a trans-omics prognostic nomogram was constructed and evaluated by combining clinical information and methylated prognosis model. A total of 242 methylation-driven genes were identified. The Gene Ontology terms of these methylation-driven genes mainly clustered in the activation, adhesion, and proliferation of immune cells. The methylation prognosis prediction model that was established using the Lasso regression included four genes in the methylation data, namely, FOXI2, USP44, EVI2A, and TRIP13. The areas under the receiver operating characteristic curve of 1-, 3-, and 5-year survival rates were 0.810, 0.824, and 0.799, respectively, in the training group and 0.794, 0.752, and 0.731, respectively, in the testing group. An easy trans-omics nomogram was successfully established. The C-indices of the nomogram in the training and the testing groups were 0.8015 and 0.8389, respectively. The present study revealed the overall perspective of methylation-driven genes in KIRC and can help in the evaluation of the prognosis of KIRC patients and provide new clues for further study.

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 A 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10183
Author(s):  
Yong Zou ◽  
Chuan Hu
Keyword(s):  
Cell Carcinoma ◽  
Clinical Outcomes ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Clinical Prognosis ◽  
Immune Related Gene ◽  
Immune Related Genes

Kidney renal clear cell carcinoma (KIRC) is the leading cause of kidney cancer-related deaths. Currently, there are no studies in tumor immunology investigating the use of signatures as a predictor of overall survival in KIRC patients. Our study attempts to establish an immune-related gene risk signature to predict clinical outcomes in KIRC. A total of 528 patients from The Cancer Genome Atlas (TCGA) database were included in our analysis and randomly divided into training (n = 315) and testing sets (n = 213). We collected 1,534 immune-related genes from the Immunology Database and Analysis Portal as candidates to construct our signature. LASSO-COX was used to find gene models with the highest predictive ability. We used survival and Cox analysis to test the model’s independent prognostic ability. Univariate analysis identified 650 immune-related genes with prognostic abilities. After 1,000 iterations, we choose 14 of the most frequent and stable immune-related genes as our signature. We found that the signature was associated with M stage, T stage, and pathological staging. More importantly, the signature can independently predict clinical prognosis in KIRC patients. Gene Set Enrichment Analysis (GSEA) showed an association between our signature and critical metabolism pathways. Our research established a model based upon 14 immune-related genes that predicted the prognosis of KIRC patients based on tumor immune microenvironments.

scholarly journals Using Genomic and Transcriptome Analyses to Identify the Role of the Oxidative Stress Pathway in Renal Clear Cell Carcinoma and Its Potential Therapeutic Significance

2021 ◽  
Vol 2021 ◽  
pp. 1-38
Author(s):  
Xiangyu Che ◽  
Xiaochen Qi ◽  
Yingkun Xu ◽  
Qifei Wang ◽  
Guangzhen Wu
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Free Radicals ◽  
Cell Carcinoma ◽  
Immune Cell ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Body ◽  
Prognosis Model

Oxidative stress (OS) refers to endogenous and/or exogenous stimulation when the balance between oxidation and antioxidants in the body is disrupted, resulting in excessive production of free radicals. Excessive free radicals exert a series of negative effects on the body, which can result in the oxidation of and infliction of damage on biological molecules and further cause cell death and tissue damage, which are related to many pathological processes. Pathways related to OS have always been the focus of medical research. Several studies are being conducted to develop strategies to treat cancer by exploring the OS pathways. Therefore, this study is aimed at determining the correlation between the OS pathway and kidney renal clear cell carcinoma (KIRC) through bioinformatics analysis, at proving the effect of common anticancer drugs on the OS pathway, and at constructing a prognosis model of patients with KIRC based on several genes with the strongest correlation between the OS pathway and KIRC. We first collected and analyzed gene expression and clinical information of related patients through TCGA database. Then, we divided the samples into three clusters according to their gene expression levels obtained through cluster analysis. Using these three clusters, we performed GDSC drug analysis and GSEA analysis and examined the correlation among the OS pathway, histone modification, and immune cell infiltration. We also analyzed the response of anti-PD-1 and anti-CTLA-4 to the OS pathway. Thereafter, we used LASSO regression to select the most suitable nine genes, combined with the clinicopathological characteristics to establish the prognosis model of patients with KIRC, and verified the scientific precision of the model. Finally, tumor mutational burden was calculated to verify whether patients would benefit from immunotherapy. The results of this study may provide a reference for the establishment of treatment strategies for patients with KIRC.

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