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. 2021 Mar 19:9:651406.
doi: 10.3389/fcell.2021.651406. eCollection 2021.

A Novel Immune-Related Prognostic Model for Response to Immunotherapy and Survival in Patients With Lung Adenocarcinoma

Yujia Zheng  1 , He Tian  1 , Zheng Zhou  1 , Chu Xiao  1 , Hengchang Liu  2 , Yu Liu  1 , Liyu Wang  1 , Tao Fan  1 , Bo Zheng  3 , Fengwei Tan  1 , Qi Xue  1 , Gengshu Gao  1 , Chunxiang Li  1 , Jie He  1
Affiliations

A Novel Immune-Related Prognostic Model for Response to Immunotherapy and Survival in Patients With Lung Adenocarcinoma

Yujia Zheng et al. Front Cell Dev Biol. .

Abstract

Lung adenocarcinoma is one of the most malignant diseases worldwide. The immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and programmed cell death-ligand 1 (PD-L1) have changed the paradigm of lung cancer treatment; however, there are still patients who are resistant. Further exploration of the immune infiltration status of lung adenocarcinoma (LUAD) is necessary for better clinical management. In our study, the CIBERSORT method was used to calculate the infiltration status of 22 immune cells in LUAD patients from The Cancer Genome Atlas (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. 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 five clusters into hot 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 three datasets. In conclusion, we developed an immune-related signature that can predict the prognosis of patients, which might facilitate the clinical application of immunotherapy in LUAD.

Keywords: immune infiltration; immunotherapy; lung adenocarcinoma; prognosis; risk prediction model; signature.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic flowchart showed the analysis strategy.
Figure 2
Figure 2
Correlation of immune infiltration and clinical parameters in lung adenocarcinoma (LUAD). (A) Forest plot showed the correlation of immune infiltration and overall survival (OS). (B) Forest plot showed the correlation of immune infiltration progression-free interval (PFI). (C–G) The Kaplan–Meier diagram showed the correlation of infiltration of immune cells and overall survival (OS).
Figure 3
Figure 3
Immune cell infiltration pattern in tumor and normal tissue. (A) Barplot showed the distribution of 22 immune cells in normal tissue. (B) Barplot showed the distribution of 22 immune cells in tumor tissue. (C) Boxplot showed the 22 immune cells infiltration in normal and tumor tissue.
Figure 4
Figure 4
Correlation of immune cells in the tumor and normal tissues. (A) Corrplot showed the correlation of 22 immune cells in tumor tissues. (B) Corrplot showed the correlation of 22 immune cells in normal tissues. (C) Heatmap showed the clusters of immune cells. (D) Delta diagram showed the clusters with under area.
Figure 5
Figure 5
Immune subtyping of lung adenocarcinoma (LUAD). (A) Heatmap showed the immune clusters of LUAD. (B–D) Expression of the immune score, stromal score, and tumor purity in the five clusters.
Figure 6
Figure 6
Alterations of signaling in hot and cold tumors. (A) Immune infiltration in hot and cold tumors analyzed by TIMER. (B) Differentially expressed genes between hot and cold tumors. (C,D) Gene Ontology (GO) enrichment analysis in hot and cold tumors. (E,F) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis in hot and cold tumors. ****p < 0.001; ns, not significant.
Figure 7
Figure 7
Identification of hub and prognostic-related genes. (A) Protein–protein interaction (PPI) network of upregulated genes in the hot tumor. (B) PPI network of downregulated genes in the hot tumor. (C,D) Least absolute shrinkage and selection operator (LASSO) and partial likelihood deviance coefficient profiles of the selected genes. (E) Multivariate Cox analysis showed the hazard ratios (HRs) of selected genes with forest plots.
Figure 8
Figure 8
Construction and validation of risk predicting model for overall survival. (A) Survival status in the training cohort. (B) Receiver operating characteristic (ROC) curve of 1, 2, and 3 years of the training cohort. (C–E) Kaplan–Meier survival curve showed the validation of risk predicting model in three external datasets.
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