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Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.
Wang, Shuo; Yu, Ziyi; Cao, Yudong; Du, Peng; Ma, Jinchao; Ji, Yongpeng; Yang, Xiao; Zhao, Qiang; Hong, Baoan; Yang, Yong; Hai, Yanru; Li, Junhui; Mao, Yufeng; Wu, Shuangxiu.
Afiliación
  • Wang S; Urological Department, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
  • Yu Z; Urological Department, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
  • Cao Y; Urological Department, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
  • Du P; Urological Department, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
  • Ma J; Urological Department, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
  • Ji Y; Urological Department, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
  • Yang X; Urological Department, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
  • Zhao Q; Urological Department, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
  • Hong B; Urological Department, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
  • Yang Y; Urological Department, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
  • Hai Y; Genetron Health (Beijing) Technology, Co. Ltd, Beijing, China.
  • Li J; Genetron Health (Beijing) Technology, Co. Ltd, Beijing, China.
  • Mao Y; Genetron Health (Beijing) Technology, Co. Ltd, Beijing, China.
  • Wu S; Genetron Health (Beijing) Technology, Co. Ltd, Beijing, China.
Cancer Control ; 31: 10732748241272713, 2024.
Article en En | MEDLINE | ID: mdl-39115042
ABSTRACT

OBJECTIVES:

Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC).

METHODS:

In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression.

RESULTS:

A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group.

CONCLUSION:

The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Células Renales / Nomogramas / Microambiente Tumoral / Neoplasias Renales Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Cancer Control Asunto de la revista: NEOPLASIAS Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Células Renales / Nomogramas / Microambiente Tumoral / Neoplasias Renales Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Cancer Control Asunto de la revista: NEOPLASIAS Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos