A Novel Immune-Related Prognostic Model for Response to Immunotherapy and Survival in Patients With Lung Adenocarcinoma - Zheng, Yujia; Tian, He; Zhou, Zheng; Xiao, Chu; Liu, Hengchang; Liu, Yu; Wang, Liyu; Fan, Tao; Zheng, Bo; Tan, Fengwei; Xue, Qi; Gao, Gengshu; et al. (Frontiers in Cell and Developmental Biology, 2021)
Lung adenocarcinoma is one of the most malignant diseases worldwide. The immune checkpoint inhibitors targeting PD-1 and PD-L1 has changed the paradigm of lung cancer treatment, however, there are still patients who are resistant. Further exploration of the immune infiltration status of LUAD is necessary for better clinical management. In our study, the CIBERSORT method was used to calculate 22 immune cells in LUAD in TCGA. We clustered LUAD based on immune infiltration status by consensus clustering. The differentially expressed genes (DEGs) between cold and hot tumor group were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Last, we constructed a Cox regression model and validated. We found that the infiltration of M0 macrophage cells and follicular helper T cells predicted an unfavorable overall survival of patients. Consensus clustering of 22 immune cells identified 5 clusters with different patterns of immune cells infiltration, stromal cells infiltration, and tumor purity. Based on the immune scores we classified these 5 clusters into hot tumors and cold tumors, which are different in transcription profiles. Hot tumors are enriched in cytokine-cytokine receptor interaction, while cold tumors are enriched in metabolic pathways. Based on the hub genes and prognostic related genes, we developed a cox regression model to predict the overall survival of patients with LUAD and validated in other 3 datasets. We developed an immune-related signature that can predict the prognosis of patients which might facilitate the clinical application of immunotherapy in LUAD.