Dataset Information

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

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.

SUBMITTER: Wang S

PROVIDER: S-EPMC11311166 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature

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Publications

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

Wang Shuo S Yu Ziyi Z Cao Yudong Y Du Peng P Ma Jinchao J Ji Yongpeng Y Yang Xiao X Zhao Qiang Q Hong Baoan B Yang Yong Y Hai Yanru Y Li Junhui J Mao Yufeng Y Wu Shuangxiu S

Cancer control : journal of the Moffitt Cancer Center 20240101

<h4>Objectives</h4>Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC).<h4>Methods</h4>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 ...[more]

PMID: 39115042

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Dataset Information

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

Objectives

Methods

Results

Conclusion

Publications

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

Similar Datasets

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