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

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.

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A 13-gene risk score system and a nomogram survival model for predicting the prognosis of clear cell renal cell carcinoma

Urologic Oncology Seminars and Original Investigations ◽

10.1016/j.urolonc.2019.12.022 ◽

2020 ◽

Vol 38 (3) ◽

pp. 74.e1-74.e11

Author(s):

Chao Zhang ◽

Fubo Wang ◽

Fei Guo ◽

Chen Ye ◽

Yue Yang ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Renal Cell ◽

Clear Cell ◽

Survival Model ◽

Cell Renal Cell Carcinoma ◽

Score System ◽

Risk Score System

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

BioMed Research International ◽

10.1155/2021/9972968 ◽

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.

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A nomogram based on 9-lncRNAs signature for improving prognostic prediction of clear cell renal cell carcinoma

Cancer Cell International ◽

10.1186/s12935-019-0928-5 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 6

Author(s):

Wen Jiang ◽

Qing Guo ◽

Chenghe Wang ◽

Yu Zhu

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Cell Renal Cell Carcinoma ◽

Prognostic Prediction

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

Pharmacogenomics and Personalized Medicine ◽

10.2147/pgpm.s337695 ◽

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

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

Frontiers in Medicine ◽

10.3389/fmed.2021.777507 ◽

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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