scholarly journals A new survival model based on ferroptosis-related genes for prognostic prediction in clear cell renal cell carcinoma

Aging ◽  
2020 ◽  
Vol 12 (14) ◽  
pp. 14933-14948
Author(s):  
Guangzhen Wu ◽  
Qifei Wang ◽  
Yingkun Xu ◽  
Quanlin Li ◽  
Liang Cheng
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Survival Model ◽  
Cell Renal Cell Carcinoma ◽  
Model Based ◽  
Prognostic Prediction

scholarly journals A New Survival Model Based on ADAMTSs for Prognostic Prediction in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guangzhen Wu ◽  
Jianyi Li ◽  
Yingkun Xu ◽  
Xiangyu Che ◽  
Feng Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Model ◽  
Clear Cell ◽  
Survival Model ◽  
Cell Renal Cell Carcinoma ◽  
Cancer Types

The main purpose of this study was to explore the genetic variation, gene expression, and clinical significance of ADAMTSs (a disintegrin and metalloprotease domains with thrombospondin motifs) across cancer types. Analysis of data from the TCGA (The Cancer Genome Atlas) database showed that the ADAMTSs have extensive CNV (copy number variation) and SNV (single nucleotide variation) across cancer types. Compared with normal tissues, the methylation of ADAMTSs in cancer tissues is also significantly different, which affects the expression of ADAMTS gene and the prognosis of cancer patients. Through gene expression analysis, we found that ADAMTS family has significant changes in gene expression across cancer types and is closely related to the prognosis of carcinoma, especially in ccRCC (clear cell renal cell carcinoma). LASSO regression analysis was used to establish a prognostic model based on the ADAMTSs to judge the prognosis of patients with ccRCC. Multiple Cox regression analysis suggested that age, grade, stage, and risk score of the prognostic model of ccRCC were independent prognostic factors in patients with renal clear cell carcinoma. These findings indicate that the ADAMTSs-based survival model can accurately predict the prognosis of patients with ccRCC and suggest that ADAMTSs are a potential prognostic biomarker and therapeutic target in ccRCC.

scholarly journals A New Survival Model Based on Cholesterol Biosynthesis-Related Genes for Prognostic Prediction in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaochen Qi ◽  
Xin Lv ◽  
Xiaoxi Wang ◽  
Zihao Ruan ◽  
Peizhi Zhang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Cholesterol Biosynthesis ◽  
Kidney Tissue ◽  
Survival Model ◽  
Risk Scores ◽  
Cell Renal Cell Carcinoma

In our study, the value of cholesterol biosynthesis is related to clinical analysis in 32 cancer forms in the GSEA database facility. We have a mutation between 25 CBRGs. In The Cancer Genome Atlas database, clear cell renal cell carcinoma (ccRCC, n = 539 ) was upregulated or downregulated in 22 out of 25 cases ( n = 72 ) compared with normal kidney tissue. Then, using LASSO regression analysis, the survival model that is based on nine risk-related CBRGs (CYP51A1, HMGCR, HMGCS1, IDI1, FDFT1, SQLE, ACAT2, FDPS, and NSDHL) is established. ROC curves confirmed the good omen of the new survival mode, and the area under the curve is 0.72 (5 years) and 0.709 (10 years). High SQLE and ACAT2 expression and low NSDHL, FDPS, CYP51A1, FDFT1, HMGCS1, HMGCR, and IDI1 expression were closely related to patients with high-risk renal clear cell carcinoma. Two types of Cox regression, uni- and multivariate, were used to determine risk scores, age, staging, and grade as independent risk factors for prognosis in patients with clear cell renal cell carcinoma. The results showed the prediction model established by 9 selected CBRGs could predict the prognosis more accurately.

scholarly journals Systematic Pan-Cancer Analysis of KIF23 and a Prediction Model Based on KIF23 in Clear Cell Renal Cell Carcinoma (ccRCC)

2021 ◽  
Vol Volume 14 ◽  
pp. 1717-1729
Author(s):  
Xiaojie Bai ◽  
Yuanfei Cao ◽  
Xin Yan ◽  
Kurerban Tuoheti ◽  
Guowei Du ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Model Based ◽  
Pan Cancer

scholarly journals Identification of a Three-Glycolysis-Related lncRNA Signature Correlated With Prognosis and Metastasis in Clear Cell Renal Cell Carcinoma

2022 ◽  
Vol 8 ◽  
Author(s):  
Tinghao Li ◽  
Hang Tong ◽  
Junlong Zhu ◽  
Zijia Qin ◽  
Siwen Yin ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Prognostic Model ◽  
Clear Cell ◽  
Cancer Genome ◽  
Cell Renal Cell Carcinoma ◽  
Model Based ◽  
And Migration

The clear cell renal cell carcinoma (ccRCC) is not only a malignant disease but also an energy metabolic disease, we aimed to identify a novel prognostic model based on glycolysis-related long non-coding RNA (lncRNAs) and explore its mechanisms. With the use of Pearson correlation analysis between the glycolysis-related differentially expressed genes and lncRNAs from The Cancer Genome Atlas (TCGA) dataset, we identified three glycolysis-related lncRNAs and successfully constructed a prognostic model based on their expression. The diagnostic efficacy and the clinically predictive capacity of the signature were evaluated by univariate and multivariate Cox analyses, Kaplan–Meier survival analysis, and principal component analysis (PCA). The glycolysis-related lncRNA signature was constructed based on the expressions of AC009084.1, AC156455.1, and LINC00342. Patients were grouped into high- or low-risk groups according to risk score demonstrated significant differences in overall survival (OS) period, which were validated by patients with ccRCC from the International Cancer Genome Consortium (ICGC) database. Univariate Cox analyses, multivariate Cox analyses, and constructed nomogram-confirmed risk score based on our signature were independent prognosis predictors. The CIBERSORT algorithms demonstrated significant correlations between three-glycolysis-related lncRNAs and the tumor microenvironment (TME) components. Functional enrichment analysis demonstrated potential pathways and processes correlated with the risk model. Clinical samples validated expression levels of three-glycolysis-related lncRNAs, and LINC00342 demonstrated the most significant aberrant expression. in vitro, the general overexpression of LINC00342 was detected in ccRCC cells. After silencing LINC00342, the aberrant glycolytic levels and migration abilities in 786-O cells were decreased significantly, which might be explained by suppressed Wnt/β-catenin signaling pathway and reversed Epithelial mesenchymal transformation (EMT) process. Collectively, our research identified a novel three-glycolysis-related lncRNA signature as a promising model for generating accurate prognoses for patients with ccRCC, and silencing lncRNA LINC00342 from the signature could partly inhibit the glycolysis level and migration of ccRCC cells.

scholarly journals An optimal prognostic model based on gene expression for clear cell renal cell carcinoma

2020 ◽  
Vol 20 (3) ◽  
pp. 2420-2434 ◽  
Author(s):  
Dan Xu ◽  
Wantai Dang ◽  
Shaoqing Wang ◽  
Bo Hu ◽  
Lianghong Yin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Model ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Model Based

scholarly journals Development and Validation of an IL6/JAK/STAT3-Related Gene Signature to Predict Overall Survival in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Chuanchuan Zhan ◽  
Chao Xu ◽  
Jiajun Chen ◽  
Chong Shen ◽  
Jinkun Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Clinicopathological Features ◽  
Cell Renal Cell Carcinoma ◽  
Clinical Prognosis ◽  
Stat3 Pathway ◽  
Model Based ◽  
Prognosis Model

Background: Traditional clinicopathological features (TNM, pathology grade) are often insufficient in predictive prognosis accuracy of clear cell renal cell carcinoma (ccRCC). The IL6-JAK-STAT3 pathway is aberrantly hyperactivated in many cancer types, and such hyperactivation is generally associated with a poor clinical prognosis implying that it can be used as a promising prognosis indicator. The relation between the IL6-JAK-STAT3 pathway and ccRCC remains unknown.Methods: We evaluated the levels of various cancer hallmarks and filtered out the promising risk hallmarks in ccRCC. Subsequently, a prognosis model based on these hallmark-related genes was established via weighted correlation network analysis and Cox regression analysis. Besides, we constructed a nomogram based on the previous model with traditional clinicopathological features to improve the predictive power and accuracy.Results: The IL6-JAK-STAT3 pathway was identified as the promising risk hallmarks in ccRCC, and the pathway-related prognosis model based on five genes was built. Also, the nomogram we developed demonstrated the strongest and most stable survival predictive ability.Conclusion: Our study would provide new insights for guiding individualized treatment of ccRCC patients.

scholarly journals A long non-coding RNA signature to improve prognostic prediction in clear cell renal cell carcinoma

2019 ◽  
Vol 118 ◽  
pp. 109079 ◽  
Author(s):  
Jiarun Zhang ◽  
Xiaotong Zhang ◽  
Chiyuan Piao ◽  
Jianbin Bi ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Non Coding Rna ◽  
Prognostic Prediction ◽  
Long Non Coding Rna ◽  
Rna Signature
Sign in / Sign up

Export Citation Format

Share Document