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Upregulation of CCNB2 and a novel lncRNAs-related risk model predict prognosis in clear cell renal cell carcinoma.
Ren, Congzhe; Wang, Qihua; Xu, Zhunan; Pan, Yang; Wang, Shangren; Liu, Xiaoqiang.
Afiliação
  • Ren C; Department of Urology, Tianjin Medical University General Hospital, Heping District, 154 Anshan Road, Tianjin, 300052, China.
  • Wang Q; Department of Urology, Tianjin Medical University General Hospital, Heping District, 154 Anshan Road, Tianjin, 300052, China.
  • Xu Z; Department of Urology, Tianjin Medical University General Hospital, Heping District, 154 Anshan Road, Tianjin, 300052, China.
  • Pan Y; Department of Urology, Tianjin Medical University General Hospital, Heping District, 154 Anshan Road, Tianjin, 300052, China.
  • Wang S; Department of Urology, Tianjin Medical University General Hospital, Heping District, 154 Anshan Road, Tianjin, 300052, China.
  • Liu X; Department of Urology, Tianjin Medical University General Hospital, Heping District, 154 Anshan Road, Tianjin, 300052, China. xiaoqiangliu1@163.com.
J Cancer Res Clin Oncol ; 150(2): 64, 2024 Feb 01.
Article em En | MEDLINE | ID: mdl-38300330
ABSTRACT

BACKGROUND:

Clear cell renal cell carcinoma (ccRCC) is the main type of renal cell carcinoma. Cyclin B2 (CCNB2) is a subtype of B-type cyclin that is associated with the prognosis of several cancers. This study aimed to identify the relationship between CCNB2 and progression of ccRCC and construct a novel lncRNAs-related model to predict prognosis of ccRCC patients.

METHODS:

The data were obtained from public databases. We identified CCNB2 in ccRCC using Kaplan-Meier survival analysis, univariate and multivariate Cox regression, and Gene Ontology analysis. External validation was then performed. The risk model was constructed based on prognostic lncRNAs by the LASSO algorithm and multivariate Cox regression. Receiver operating characteristics (ROC) curves were used to evaluate the model. Consensus clustering analysis was performed to re-stratify the patients. Finally, we analyzed the tumor-immune microenvironment and performed screening of potential drugs.

RESULTS:

CCNB2 associated with late clinicopathological parameters and poor prognosis in ccRCC and was an independent predictor for disease-free survival. In addition, CCNB2 shared the same expression pattern with known suppressive immune checkpoints. A risk model dependent on the expression of three prognostic CCNB2-related lncRNAs (SNHG17, VPS9D1-AS1, and ZMIZ1-AS1) was constructed. The risk signature was an independent predictor of ccRCC. The area under the ROC (AUC) curve for overall survival at 1-, 3-, 5-, and 8-year was 0.704, 0.702, 0.741, and 0.763. The high-risk group and cluster 2 had stronger immunogenicity and were more sensitive to immunotherapy.

CONCLUSION:

CCNB2 could be an important biomarker for predicting prognosis in ccRCC patients. Furthermore, we developed a novel lncRNAs-related risk model and identified two CCNB2-related molecular clusters. The risk model performed well in predicting overall survival and immunological microenvironment of ccRCC.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Carcinoma / Carcinoma de Células Renais / RNA Longo não Codificante / Neoplasias Renais Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Carcinoma / Carcinoma de Células Renais / RNA Longo não Codificante / Neoplasias Renais Idioma: En Ano de publicação: 2024 Tipo de documento: Article