Prognostic biomarkers correlated with immune infiltration in non-small cell lung cancer

Abstract

Lung cancer is the leading cause of cancer-related mortality in men and women globally. Non-small cell lung cancer (NSCLC) is the most prevalent subtype, accounting for 85-90% of all cancers. Although there have been dramatic advances in therapeutic approaches in recent decades, the recurrence and metastasis rates of NSCLC are as high as 30-40% with the 5-year overall survival rate being less than 15%. Therefore, it is necessary to explore the pathogenesis of NSCLC at the genetic level and identify prognostic biomarkers and novel therapeutic targets. Here, we aimed to identify mutated genes with high frequencies in Chinese NSCLC patients using next-generation sequencing and to investigate their relationships with the tumor mutation burden (TMB) and tumor immune microenvironment. A total of 110 NSCLC patients were enrolled to profile the genetic variations. Mutations in EGFR (62.37%), TP53 (61.29%), LRP1B (13.98%), FAT1 (12.90%), KMT2D (11.83%), CREBBP (10.75%), and RB1 (9.68%) were most prevalent. TP53, LRP1B, KMT2D, and CREBBP mutations were all significantly associated with high TMB (P < 0.05 or P < 0.01). The infiltrating levels of immune cells and immune molecules were enriched significantly in the LRP1B mutation group. LRP1B mutations significantly correlated with stimulating and inhibitory immunoregulators. Gene set enrichment analysis revealed that cell cycle, the Notch signaling pathway, the insulin signaling pathway, and the mTOR signaling pathway are related to LRP1B mutations in the immune system. LRP1B mutations may be of clinical importance in enhancing the anti-tumor immune response and may be a promising biomarker for predicting immunotherapy responsiveness.

Keywords: lung cancer; next-generation sequencing; non-small cell lung cancer; prognosis; tumor immune microenvironment; tumor mutation burden.

Fig. 1

Genomic alternations of the Chinese patients with NSCLC tested by NGS. (A) The flowchart of this study. (B) Distribution of mutation type in NSCLC samples. (C) The frequency of main genetic alterations identified in NSCLC samples.

Fig. 2

Gene mutational landscape in NSCLC patients from TCGA cohort by using cBioPortal network tool. (A) The mutation frequencies of EGFR, TP53, LRP1B, FAT1, KMT2D, CREBBP, and RB1 in NSCLC from TCGA cohort. (B) The mutant sites of the above seven genes in NSCLC from TCGA cohort.

Fig. 3

Association of TMB with clinical features and gene mutation. (A) Significant difference was observed in age groups, not gender groups. (B) Most gene mutations were associated with a higher TMB, except for EGFR and FAT1. Ns, P ≥ 0.05; *P < 0.05; **P < 0.01.

Fig. 4

The gene mutation and survival analysis in NSCLC samples from TCGA cohort by using cBioPortal network tool. Kaplan–Meier survival curves showed the predictive value of LRP1B, FAT1, KMT2D, CREBBP and RB1 mutation for overall survival (A), disease‐free survival (B) and PFS (C) in the NSCLC patients.

Fig. 5

LPR1B mutation is correlated with tumor‐infiltrating immune cells in LUAD. (A) the 22 immune cells in each LUAD sample with LRP1B mutation were annotated by stacked bar chart using the CIBERSORT algorithm. (B) Violin plot for the different proportions of tumor‐infiltrating immune cells between LRP1B‐mutant groups and LRP1B‐wild groups in LUAD. Yellow color represents LRP1B‐wild group, and blue color represents LRP1B‐mutant group. (C) Correlation matrix of 22 types of fractions of tumor‐infiltrating immune cell in LUAD. The red color represents positive correlation and the blue color represents negative correlation.

Fig. 6

LPR1B mutation is correlated with tumor‐infiltrating immune cells in LUSC. (A) the 22 immune cells in each LUSC sample with LRP1B mutation were annotated by stacked bar chart using the CIBERSORT algorithm. (B) Violin plot for the different proportions of tumor‐infiltrating immune cells between LRP1B‐mutant groups and LRP1B‐wild groups in LUSC. Yellow color represents LRP1B‐wild group, and blue color represents LRP1B‐mutant group. (C) Correlation matrix of 22 types of fractions of tumor‐infiltrating immune cell in LUSC. The red color represents positive correlation and the blue color represents negative correlation.

Fig. 7

Association between LRP1B mutation and immunoregulators. (A) the heatmap showed the associations between LRP1B mutation and immunostimulators and immunoinhibitors in different cancers, respectively. Integrative analysis between LRP1B mutation with immunostimulators (B) and immunoinhibitors (C).

Fig. 8

Association between LRP1B mutation and chemokines. (A) the heatmap showed the associations between LRP1B mutation and chemokines and receptors in different cancers, respectively. Integrative analysis between LRP1B mutation with chemokines (B) and receptors (C).

Fig. 9

Representative pathways identified by GSEA was performed with the TCGA. Gene enrichment plots performed by functional enrichment of GO biological processes (A) and KEGG pathway enrichment analysis (B) showed that a series of gene sets in LRP1B‐mutant group. The P‐value is marked in each plot.