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Updated: Sep 20, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Establishing an 8-gene immune prognostic model based on TP53 status for lung adenocarcinoma
Guodong Wu1, Youyu Wang1, Yanhui Wan1
1Thoracic Surgery, Shenzhen Second People's Hospital, Shenzhen, China.
Background:
Lung adenocarcinoma (LUAD) results in a majority of cancer burden worldwide. TP53 is the most commonly mutated in LUAD. This study aimed to reveal the relation between TP53 and tumor microenvironment (TME) for improving LUAD treatment.
Methods:
Differentially expressed genes (DEGs) related to immunity were analyzed between TP53-WT and TP53-MUT groups. Least absolute shrinkage and selection operator (LASSO) Cox regression was applied to screen prognostic DEGs. Two independent datasets were included to evaluate the robustness of the prognostic model.
Results:
An 8-gene prognostic model containing ANLN, CCNB1, DLGAP5, FAM83A, GJB2, NAPSA, SFTPB, and SLC2A1 was established based on DEGs. LUAD samples were classified into high- and low-risk groups with differential overall survival in the two datasets. M0 macrophages, M1 macrophages, and activated memory CD4 T cells were more enriched in high-risk group. Immune checkpoints of PDCD1, LAG3, and CD274 were also high-expressed in high-risk group.
Conclusion:
The study improved the understanding of the role of TP53 in the TME modulation. The 8-gene model had robust performance to predict LUAD prognosis in clinical practice. In addition, the eight prognostic genes may also serve as potential targets for designing therapeutic drugs for LUAD patients.
