Immunotherapy response and microenvironment provide biomarkers of immunotherapy options for patients with lung adenocarcinoma

Abstract

Background: Immunotherapy has been a promising approach option for lung cancer. Method: All the open-accessed data was obtained from the Cancer Genome Atlas (TCGA) database. All the analysis was conducted using the R software analysis. Results: Firstly, the genes differentially expressed in lung cancer immunotherapy responders and non-responders were identified. Then, the lung adenocarcinoma immunotherapy-related genes were determined by LASSO logistic regression and SVM-RFE, respectively. A total of 18 immunotherapy response-related genes were included in our investigation. Subsequently, we constructed the logistics score model. Patients with high logistics score had a better clinical effect on immunotherapy, with 63.2% of patients responding to immunotherapy, while only 12.1% of patients in the low logistics score group responded to immunotherapy. Moreover, we found that pathways related to immunotherapy were mainly enriched in metabolic pathways such as fatty acid metabolism, bile acid metabolism, oxidative phosphorylation, and carcinogenic pathways such as KRAS signaling. Logistics score was positively correlated with NK cells activated, Mast cells resting, Monocytes, Macrophages M2, dendritic cells resting, dendritic cells activated and eosinophils, while was negatively related to Tregs, macrophages M0, macrophages M1, and mast cells activated. In addition, ERVH48-1 was screened for single-cell exploration. The expression of ERVH48-1 increased in patients with distant metastasis, and ERVH48-1 was associated with pathways such as pancreas beta cells, spermatogenesis, G2M checkpoints and KRAS signaling. The result of quantitative real-time PCR showed that ERVH48-1 was upregulated in lung cancer cells. Conclusion: Our study developed an effective signature to predict the immunotherapy response of lung cancer patients.

Keywords: ERVH48-1; immunotherapy response; lung adenocarcinoma; predictive model; tumor environment.

FIGURE 1

Identification of the immunotherapy characteristic genes. Notes: (A) TIDE analysis was performed to evaluate the immunotherapy response of each patient. (B) Volcano plots depicted differentially expressed genes between immunotherapy responders and non-responders; (C,D): LASSO logistic regression analysis based on differentially expressed genes between immunotherapy responders and non-responders. (E) SVM-RFE analysis based on differentially expressed genes between immunotherapy responders and non-responders; (F,G): The intersection of LASSO logistics regression and SVM-RFE algorithms identified 18 immunotherapy characteristic genes.

FIGURE 2

Evaluation of the prediction performance of immunotherapy characteristic genes. Notes: (A) Expression level of identified 18 characteristic genes in immunotherapy responders and non-responders; (B–S): Prediction performance of 18 characteristic genes in LUAD immunotherapy response.

FIGURE 3

Logistics regression model. Notes: (A) Logistic regression model was constructed based on the identified characteristic genes; (B) Differences of logistics score between immunotherapy responders and nonresponders; (C) ROC curve was utilized to assess the performance of Logistics score in predicting the response to immunotherapy in LUAD patients; (D) Proportion of immunotherapy responders and non-responders in patients with high and low logistics score; (E) The level of key immune checkpoints in patients with high and low logistics score.

FIGURE 4

Biological enrichment analysis. Notes: (A) GSEA analysis between high and low logistics score based on the Hallmark gene set; (B) GO analysis between high and low logistics score.

FIGURE 5

Immune infiltration analysis. Notes: (A) The CIBERSORT algorithm was utilized to quantify the immune infiltration in the LUAD tumor microenvironment; (B) Correlation of logistics score and quantified immune cells; (C) Infiltration level of quantified immune cells in patients with high and low logistics score; (D–G): Level of TMB, MSI, mRNAsi and EREG-mRNAsi in patients with high and low logistics score.

FIGURE 6

Further exploration of ERVH48-1. Notes: (A) Univariate Cox regression analysis of the characteristic genes; (B,C): Difference in disease-specific survival and progression-free survival in patients with high and low logistics score; (D–I): The expression of ERVH48-1 in populations with different clinical characteristics; (J) Pathway enrichment analysis of ERVH48-1.

FIGURE 7

Single-cell analysis of ERVH48-1. Notes: (A,B): The ERVH48-1 expression in different cell subgroups; (C): The level of key immune checkpoints in patients with high and low logistics score.