Image2_Establishment and Application of a Prognostic Risk Score Model Based on Characteristics of Different Immunophenotypes for Lung Adenocarcinoma.TIF (7.17 MB)
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Image2_Establishment and Application of a Prognostic Risk Score Model Based on Characteristics of Different Immunophenotypes for Lung Adenocarcinoma.TIF

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posted on 25.04.2022, 04:27 authored by Hong Gao, Yanhong Liu, Yue Hu, Meiling Ge, Jie Ding, Qing Ye

Objective: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor. Tumor mutations and the immune microenvironment play important roles in LUAD development and progression. This study was aimed at elucidating the characteristics of patients with different tumor immune microenvironment and establishing a prediction model of prognoses and immunotherapy benefits for patients with LUAD.

Materials and Methods: We conducted a bioinformatics analysis on data from The Cancer Genome Atlas and Gene Expression Omnibus (training and test sets, respectively). Patients in the training set were clustered into different immunophenotypes based on tumor-infiltrating immune cells (TIICs). The immunophenotypic differentially expressed genes (IDEGs) were used to develop a prognostic risk score (PRS) model. Then, the model was validated in the test set and applied to evaluate 42 surgery patients with early LUAD.

Results: Patients in the training set were clustered into high (Immunity_H), medium (Immunity_M), and low (Immunity_L) immunophenotype groups. Immunity_H patients had the best survival and more TIICs than Immunity_L patients. Immunity_M patients had the worst survival, characterized by most CD8+ T and Treg cells and highest expression of PD-1 and PD-L1. The PRS model, which consisted of 14 IDEGs, showed good potential for predicting the prognoses of patients in both training and test sets. In the training set, the low-risk patients had more TIICs, higher immunophenoscores (IPSs) and lower mutation rates of driver genes. The high-risk patients had more mutations of DNA mismatch repair deficiency and APOBEC (apolipoprotein B mRNA editing enzyme catalytic polypeptide-like). The model was also a good indicator of the curative effect for immunotherapy-treated patients. Furthermore, the low-risk group out of 42 patients, which was evaluated by the PRS model, had more TIICs, higher IPSs and better progression-free survival. Additionally, IPSs and PRSs of these patients were correlated with EGFR mutations.

Conclusion: The PRS model has good potential for predicting the prognoses and immunotherapy benefits of LUAD patients. It may facilitate the diagnosis, risk stratification, and treatment decision-making for LUAD patients.

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