The Landscape of the Tumor-Infiltrating Immune Cell and Prognostic Nomogram in Colorectal Cancer

Abstract

Tumor-infiltrating immune cells are associated with prognosis and immunotherapy targets in colorectal cancer (CRC). The recently developed CIBERSORT method allows immune cell analysis by deconvolution of high-throughput data onto gene expression. In this study, we analyzed the relative proportions of immune cells in GEO (94 samples) and TCGA (522 samples) CRC data based on the CIBERSORT method. A total of 22 types of tumor-infiltrating immune cells were evaluated. Combined with GEO and TCGA data, it was found that naive B cells, M2 macrophages, and resting mast cells were highly expressed in normal tissues, while M0 macrophages, M1 macrophages, activated mast cells, and neutrophils were highly expressed in tumors. Moreover, we constructed a prognostic model by infiltrating immune cells that showed high specificity and sensitivity in both the training (AUC of 5-year survival = 0.699) and validation (AUC of 5-year survival = 0.844) sets. This provides another basis for clinical prognosis. The results of multiple immunofluorescence detection showed that there were differences in the results of bioinformatics analysis. Neutrophils were highly expressed in normal tissues, and M2 macrophages were highly expressed in tumor tissues. Collectively, our data suggested that infiltrating immune cells in CRC may be an important determinant of prognosis and immunotherapy.

Keywords: CIBERSORT; colorectal cancer; immunotherapy; nomogram; prognosis.

FIGURE 1

Landscape of immune infiltration in normal and CRC tissues based on GEO data. (A) Composition of immune infiltration between paired cancer and normal tissues. The height of the colored column represents the proportion of immune cells. (B) Heat map of the 22 immune cell proportions. The genes shown in red are upregulated, and the genes in green are downregulated. The horizontal axis shows the clustering information of samples which were divided into two major clusters. (C) Correlation matrix of 22 immune cell expressions. The redder the color, the higher the positive correlation. The bluer the color, the higher the negative correlation.

FIGURE 2

Differential expression of immune cells in cancer and normal tissues based on the GEO database. (A) Expression of different immune cells in normal and cancer tissues. The white dots inside the violin indicate the median value. (B) Expression of immune cells is lower in cancer tissues than in paired normal tissues. (C) Immune cells are mainly expressed in cancer tissues. (D) Expression of immune cells is higher in cancer tissues than in paired normal tissues.

FIGURE 3

Landscape of immune infiltration in normal and CRC tissues based on TCGA data. (A) Heat map of the 22 immune cell expressions. The genes shown in red are upregulated, and the genes in green are downregulated. (B) Violin map of the 22 immune cell expressions in normal and CRC tissues. (C) Correlation matrix of 22 immune cell expressions based on TCGA data. The redder the color, the higher the positive correlation. The bluer the color, the higher the negative correlation.

FIGURE 4

Relationship between tumor-infiltrating immune cells and clinical characters of CRC. (A) Survival plots of naive B cells. Data were analyzed using the Kaplan–Meier plotter. Patients with naive B cell expression above the median are indicated in the red line and below the median in the green line. (B) Immune cells with the highest correlation with CRC stages. (C) Immune cells with the highest correlation with lymph node metastasis in CRC. (D) Immune cells with the highest correlation with distant metastasis in CRC.

FIGURE 5

Nomogram for patients with CRC. (A) Nomogram for predicting 3- and 5-year survival for CRC patients based on tumor-infiltrating immune cells. (B) Kaplan–Meier estimates of patients’ survival status and time using the median risk score cut-off, which divided patients into low-risk and high-risk groups based on the training cohort. (C) ROC analysis of the sensitivity and specificity of the survival time by the tumor-infiltrating immune cells based on the risk score for the training cohort. (D) Calibration curve for the prediction of 3-year overall survival based on the training cohort. (E) Calibration curve for the prediction of 5-year overall survival based on the training cohort.

FIGURE 6

Multiplex immunofluorescence detection of different marker expressions. (A) Detection of CD19, CD163, and C-kit expression in tumor and normal tissues by multiplex immunofluorescence. (B) Detection of CD68, NOS2, mast cell tryptase, and CD16 expressions in tumor and normal tissues by multiplex immunofluorescence.