Mast cell-based molecular subtypes and signature associated with clinical outcome in early-stage lung adenocarcinoma

Abstract

Mast cells are a major component of the immune microenvironment in tumour tissues and modulate tumour progression by releasing pro-tumorigenic and antitumorigenic molecules. Regarding the impact of mast cells on the outcomes of patients with lung adenocarcinoma (LUAD) patient, several published studies have shown contradictory results. Here, we aimed at elucidating the role of mast cells in early-stage LUAD. We found that high mast cell abundance was correlated with prolonged survival in early-stage LUAD patients. The mast cell-related gene signature and gene mutation data sets were used to stratify early-stage LUAD patients into two molecular subtypes (subtype 1 and subtype 2). The neural network-based framework constructed with the mast cell-related signature showed high accuracy in predicting response to immunotherapy. Importantly, the prognostic mast cell-related signature predicted the survival probability and the potential relationship between TP53 mutation, c-MYC activation and mast cell activities. The meta-analysis confirmed the prognostic value of the mast cell-related gene signature. In summary, this study might improve our understanding of the role of mast cells in early-stage LUAD and aid in the development of immunotherapy and personalized treatments for early-stage LUAD patients.

Keywords: early-stage lung adenocarcinoma; immunotherapy; mast cell; prognosis.

Fig. 1

The association between mast cell abundance and clinical outcomes in early‐stage LUAD patients. (A) Schematic diagram of the study design. (B) The correlation among immune cell populations. (C–E) Kaplan–Meier curves for the OS of early‐stage LUAD patients showed that the patients with high mast cell abundance had a favourable outcome compared with the patients with low mast cell abundance in the TCGA, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31210 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50081 cohorts.

Fig. 2

Mast cell‐related gene signature identification. (A) WGCNA was performed to identify seven modules by unsupervised clustering. (B) A total of six modules (nongrey) were identified. The yellow module had the highest correlation (r = 0.73, P = 8e⁻⁵³) and was considered the most correlated with mast cells. (C) The gene significance and module membership of the genes in the yellow module exhibited a high correlation. (D) A total of 110 mast cell‐related genes were identified among the hub genes extracted from the yellow module. (E) The expression profile of the 110 mast cell‐related genes. (F) GO analysis was performed based on the 110 mast cell‐related genes.

Fig. 3

Molecular subtype identification according to the mast cell‐related gene signature. (A) Clustering heat map for intuitively visualizing the effect of sample clustering. (B) Univariate Cox analysis and Kaplan–Meier curves were used to evaluate the survival difference between the two molecular subtypes. (C) Average silhouette width between the two molecular subtypes. (D) The DEGs between two molecular subtypes. (E) GO analysis. (F) Upregulated hallmarks in the GSEA. (G) Downregulated hallmarks in the GSEA. (H) The immune cell population distribution in the subtype 1 and subtype 2. (I) The difference in immune cell population scores and the significances between the subtype 1 and subtype 2.

Fig. 4

Neural network‐based framework construction with the mast cell‐related gene signature. (A) Schematic diagram of the neural network. (B) The loss value in each epoch during training process in the validation cohort. (C) The confusion matrix in the testing cohort validated the accuracy of the network's prediction capacity. (D) The ROC plot in the testing data set validated the accuracy of the network's prediction capacity.

Fig. 5

Mast cell‐related signature‐based risk score calculation and the potential mechanism underlying the mast cells in early‐stage LUAD. (A) PCA of the key mast cell‐related genes. (B) The correlation between the prognostic signature‐based risk score and the mast cell ssGSEA score in early‐stage LUAD patients. (C) Univariate Cox analysis and Kaplan–Meier curves showed prolonged survival in patients with low‐risk scores compared with patients with high‐risk scores. (D) The correlation between the ssGSEA score of each hallmark gene and the risk score. (E) The correlation between the risk score and ssGSEA score in early‐stage LUAD patients. (F) The risk score distribution in patients with wild‐type or mutated TP53. P‐value was calculated with Mann–Whitney U‐test. (G) A Sankey plot was used to reveal the correlation between mast cell scores, prognostic signature‐based risk scores, immunotherapy response and clinical outcome. (H, I) Patients who received adjuvant therapies, including chemo(radio)therapy and targeted therapy, with low‐risk scores, exhibited prolonged overall survival.

Fig. 6

Association of the immune signature with early‐stage LUAD gene mutations. The distribution of gene mutations correlated with the prognostic signature‐based risk score. TP53 was the most important mutation according to the importance of ranking.

Fig. 7

Meta‐analysis and external validation of the prognostic value of the mast cell‐related signature. (A) Detailed information for the nine external validation cohorts. (B) A meta‐analysis revealed the overall prognostic value of the mast cell‐related signature.