A seven-gene prognostic model related to immune checkpoint PD-1 revealing overall survival in patients with lung adenocarcinoma
Background: We aimed to identify the immune checkpoint Programmed cell death 1 (PD-1)-related gene signatures to predict the overall survival of lung adenocarcinoma (LUAD). Methods: RNA-seq datasets associated with LUAD as well as clinical information were downloaded from the Gene Expression Om...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
AIMS Press
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/9cc2abbe6c56479fb27f4d39c28a668e |
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| Sumario: | Background:
We aimed to identify the immune checkpoint Programmed cell death 1 (PD-1)-related gene signatures to predict the overall survival of lung adenocarcinoma (LUAD).
Methods:
RNA-seq datasets associated with LUAD as well as clinical information were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Based on the expression level of PD-1, Kaplan-Meier (K-M) survival analysis was performed to divide samples into PD-1 high- and low- expression groups. Then, differentially expressed genes (DEGs) between high- and low- expression groups were identified. Meanwhile, samples were divided into the high and low immune infiltration groups according to score of immune cell, followed by screening of DEGs between these two groups. Subsequently, DEGs related to both PD-1 expression and immune infiltration was integrated to obtain the overlapping genes. Lasso COX regressions were implemented to construct prognostic signatures. The prognostic model was validated using an independent GEO dataset and TCGA cohorts. In addition, the predictive ability of the seven-gene prognostic model with other molecular biomarkers was compared.
Results:
A seven-gene signature (DPT, ITGAD, CLECL1, SYT13, DUSP26, AMPD1, and NELL2) related to PD-1 was developed through Lasso Cox regression. Univariate and multivariate regression analyses indicated that the constructed risk model was an independent prognostic factor. K-M survival analysis indicated that patients in the high risk group had significantly worse prognosis than those in the low risk group. Further, the results of validation analysis showed that this model was reliable and effective.
Conclusions:
The constructed prognostic model can predict overall survival in LUAD patients with great predictive performance, and it may be applied for diagnosis and adjuvant treatment of LUAD in clinical trials. |
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