Comprehensive Analysis of Tumor-Infiltrating Immune Cells and Relevant Therapeutic Strategy in Esophageal Cancer

Abstract

A growing body of evidence has indicated that behaviors of cancers are defined by not only intrinsic activities of tumor cells but also tumor-infiltrating immune cells (TIICs) in the tumor microenvironment. However, it still lacks a well-structured and comprehensive analysis of TIICs and its therapeutic value in esophageal cancer (EC). The proportions of 22 TIICs were evaluated between 150 normal tissues and 141 tumor tissues of EC by the CIBERSORT algorithm. Besides, correlation analyses between proportions of TIICs and clinicopathological characters, including age, gender, histologic grade, tumor location, histologic type, LRP1B mutation, TP53 mutation, tumor stage, lymph node stage, and TNM stage, were conducted. We constructed a risk score model to improve prognostic capacity with 5 TIICs by least absolute shrinkage and selection operator (lasso) regression analysis. The risk score = -1.86∗plasma + 2.56∗T cell follicular helper - 1.37∗monocytes - 3.64∗activated dendritic cells - 2.24∗resting mast cells (immune cells in the risk model mean the proportions of immune cell infiltration in EC). Patients in the high-risk group had significantly worse overall survival than these in the low-risk group (HR: 2.146, 95% CI: 1.243-3.705, p = 0.0061). Finally, we identified Semustine and Sirolimus as two candidate compounds for the treatment of EC based on CMap analysis. In conclusion, the proportions of TIICs may be important to the progression, prognosis, and treatment of EC.

Figure 1

Relative distributions of infiltrated immune cells between normal and tumor tissues and correlation of TIICs in EC. (a) Distribution of 22 infiltrated immune cells in 150 normal tissues and 141 tumor tissues of EC. Each bar represented the relative proportion of infiltrated immune cells of one tissue. (b) Violin plot of infiltrated immune cells between normal and tumor tissues. The blue color represented normal tissue, and the red represented tumor tissues. Inner violin plot showed the quartile, median, and third quartile. (c) Correlation analysis among each TIIC in esophagus cancer. The blue color represented negative correlation, and the red represented positive correlation.

Figure 2

Correlations between clinicopathological features and proportions of TIICs in EC. Comparison of significantly different percentages of TIICs for different age (a), gender (b), histologic grade (c), tumor location (d), histologic type (e), LRP1B mutation status (f), TP53 mutation (g), tumor stage (h), lymph node stage (i), and TNM stage (j).

Figure 3

Establishment of the risk score model with TIICs. (a, b) The process of constructing the lasso regression model with size and coefficients by multivariate cox regression. (c) Coefficients of TIICs in the risk model. (d) The survivorship curve of risk model stratified by TIICs. (e) Distributions of five immune cell infiltration involved in the risk model, survival status, and survival time in the low- and high-risk groups.

Figure 4

Univariate (a) and multivariate (b) cox regression analyses of clinicopathological features and the risk model.

Figure 5

Gene ontology and gene set enrichment analyses between low- and high-risk groups stratified by the risk model. (a) The top ten gene ontology-enriched annotations between the low- and high-risk groups. (b) The top three enriched gene sets of the high-risk group. (c) The top three enriched gene sets of the low-risk group. BP: biological process; CC: cellular component; MF: molecular function.

Figure 6

Identification of two candidate compounds for the treatment of EC based on CMap analysis. (a) Identification of core genes from the top three enriched gene sets of the high-risk group. (b) Identification of core genes from the top three enriched gene sets of the low-risk group.