Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma

Abstract

Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted using edgeR algorithm, and functional enrichment analysis showed that these IRGs were primarily enriched in inflammatory- and immune-related processes. "Cytokine-cytokine receptor interaction" and "PI3K-AKT signaling pathway" were the most enriched KEGG pathways. 27 differentially expressed IRGs were significantly correlated with the overall survival (OS) of patients using univariate Cox regression analysis. A prognostic risk signature that comprises seven IRGs (GCCR, FGF8, CLEC4M, PTH, SLC10A2, NPPC, and FGF4) was developed with effective predictive performance by multivariable Cox stepwise regression analysis. Most importantly, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters, and also validated in two independent LSCC cohorts (GSE4573 and GSE17710). Potential molecular mechanisms and tumor immune landscape of these IRGs were investigated through computational biology. Analysis of tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinct immune landscape in high- and low-risk group. The study was the first time to construct IRG-based immune signature in the recognition of disease progression and prognosis of LSCC patients.

Keywords: immune-related genes; lung squamous cell carcinoma; prognosis; risk score; signature.

FIGURE 1

Identification of the differentially expressed immune-related genes. (A) Heatmap of differentially expressed genes between LSCC and non-tumors tissues. (B) Heatmap of differentially expressed IRGs between LSCC and non-tumors tissues. (C) Volcano plot of all the differentially expressed genes. (D) Volcano plot of the differentially expressed IRGs.

FIGURE 2

GO terms and pathways analysis of the differentially expressed IRGs. (A) The significant enriched biological processes. (B) The significant enriched molecular functions. (C) The significant enriched cellular components. (D) The significant enriched KEGG pathways.

FIGURE 3

GO terms and pathways analysis of the differentially expressed OS-related IRGs. (A) The significant enriched biological processes. (B) The significant enriched molecular functions. (C) The significant enriched KEGG pathways.

FIGURE 4

Three modules identified through protein-protein interaction network analysis. (A) S1PR1 module. (B) EDNRB module. (C) FGFR4 module. The color of a node in each module reflects its log transformed fold change, and the circle size represented as adjusted P-value.

FIGURE 5

Mutation landscape of OS-related differentially expressed IRGs.

FIGURE 6

Transcription factors-mediated regulatory network. (A) Differentially expressed TFs. (B) Heatmap of differentially expressed TFs between LSCC and non-tumors tissues. (C) The transcription regulatory network according to the clinically relevant IRGs and differentially expressed TFs. The circle in a node reflects clinically relevant IRGs and triangle represented as differentially expressed TFs. The shades of color reflect the correlation.

FIGURE 7

Development of the prognostic signature based on the clinically relevant IRGs. (A) The hazard ratio of model genes. (B) Distribution of patients’ risk scores. (C) Patients’ survival time along with risk score. (D) The expression of the seven model genes in high- and low-risk groups.

FIGURE 8

The prognostic signature predicted the OS of LSCC patients. (A) Patients in high-risk groups have shorter OS. (B) The receiver operating characteristics (ROC) curve of prognostic utility of the signature for 5 years. (C) The receiver operating characteristics (ROC) curve of prognostic utility of the signature for 3 years. (D) The prognostic utility of the signature in test LSCC cohort (GSE4573, n = 130). (E) The prognostic utility of the signature in test LSCC cohort (GSE17710, n = 56).

FIGURE 9

Prognostic utility of signature in LSCC patients with different clinical parameters. (A) The prognostic utility of the signature in LSCC patients with different T stages. (B) The prognostic utility of the signature in LSCC patients with different node statuses. (C) The prognostic utility of the signature in LSCC patients with different M stages. (D) The prognostic utility of the signature in male and female LSCC patients. (E) The prognostic utility of the signature in LSCC patients with different clinical tumor stages. (F) The prognostic utility of the signature in LSCC patients with different age groups.

FIGURE 10

The expression of immune checkpoint molecules in high- and low-risk groups. (A) CTLA-4; (B) PD-L1; (C) LAG-3; (D) TIM-3.

FIGURE 11

Relative infiltrating proportion of immune cells in high- and low-risk groups. Blue violin reflects high-risk groups, and red violin represents low-risk groups.

FIGURE 12

Pearson’s correlation of the risk score and infiltration abundance of six types of immune cells. (A) B cell; (B) CD4 cell; (C) CD8 cell; (D) neutrophil cell; (E) macrophage cell; (F) dendritic cell.