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 An improved clear cell renal cell carcinoma stage prediction model based on gene sets

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Fangjun Li ◽  
Mu Yang ◽  
Yunhe Li ◽  
Mingqiang Zhang ◽  
Wenjuan Wang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Model Based ◽  
Gene Sets

scholarly journals Establishment of a prognosis Prediction Model Based on Pyroptosis-Related Signatures Associated With the Immune Microenvironment and Molecular Heterogeneity in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Aimin Jiang ◽  
Jialin Meng ◽  
Yewei Bao ◽  
Anbang Wang ◽  
Wenliang Gong ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Molecular Heterogeneity ◽  
Cell Renal Cell Carcinoma ◽  
Prognosis Prediction ◽  
Model Based ◽  
Prognosis Prediction Model

BackgroundPyroptosis is essential for tumorigenesis and progression of neoplasm. However, the heterogeneity of pyroptosis and its relationship with the tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC) remain unclear. The purpose of the present study was to identify pyroptosis-related subtypes and construct a prognosis prediction model based on pyroptosis signatures.MethodsFirst, heterogenous pyroptosis subgroups were explored based on 33 pyroptosis-related genes and ccRCC samples from TCGA, and the model established by LASSO regression was verified by the ICGC database. Then, the clinical significance, functional status, immune infiltration, cell–cell communication, genomic alteration, and drug sensitivity of different subgroups were further analyzed. Finally, the LASSO-Cox algorithm was applied to narrow down the candidate genes to develop a robust and concise prognostic model.ResultsTwo heterogenous pyroptosis subgroups were identified: pyroptosis-low immunity-low C1 subtype and pyroptosis-high immunity-high C2 subtype. Compared with C1, C2 was associated with a higher clinical stage or grade and a worse prognosis. More immune cell infiltration was observed in C2 than that in C1, while the response rate in the C2 subgroup was lower than that in the C1 subgroup. Pyroptosis-related genes were mainly expressed in myeloid cells, and T cells and epithelial cells might influence other cell clusters via the pyroptosis-related pathway. In addition, C1 was characterized by MTOR and ATM mutation, while the characteristics of C2 were alterations in SPEN and ROS1 mutation. Finally, a robust and promising pyroptosis-related prediction model for ccRCC was constructed and validated.ConclusionTwo heterogeneous pyroptosis subtypes were identified and compared in multiple omics levels, and five pyroptosis-related signatures were applied to establish a prognosis prediction model. Our findings may help better understand the role of pyroptosis in ccRCC progression and provide a new perspective in the management of ccRCC patients.

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

PREOPERATIVE C-REACTIVE PROTEIN IS A SIGNIFICANT FACTOR OF RECURRENCE PREDICTION MODEL IN PATIENTS WITH NONMETASTATIC CLEAR CELL RENAL CELL CARCINOMA

2009 ◽  
Vol 181 (4S) ◽  
pp. 217-217
Author(s):  
Yasumasa Iimura ◽  
Kazutaka Saito ◽  
Yasuhisa Fujii ◽  
Jiro Kumagai ◽  
Yoshinobu Komai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
C Reactive Protein ◽  
Cell Renal Cell Carcinoma ◽  
Reactive Protein ◽  
Recurrence Prediction

scholarly journals Pan-cancer Analysis of NEDD4L and Its Tumor Suppressor Effects in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 12 (20) ◽  
pp. 6242-6253
Author(s):  
Huiyue Dong ◽  
Ling Zhu ◽  
Jingjing Sun ◽  
Yi Zhang ◽  
Qiang Cui ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Tumor Suppressor ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
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.

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