Prognostic Risk Model of Immune-Related Genes in Colorectal Cancer

Abstract

Purpose: We focused on immune-related genes (IRGs) derived from transcriptomic studies, which had the potential to stratify patients' prognosis and to establish a risk assessment model in colorectal cancer.

Summary: This article examined our understanding of the molecular pathways associated with intratumoral immune response, which represented a critical step for the implementation of stratification strategies toward the development of personalized immunotherapy of colorectal cancer. More and more evidence shows that IRGs play an important role in tumors. We have used data analysis to screen and identify immune-related molecular biomarkers of colon cancer. We selected 18 immune-related prognostic genes and established models to assess prognostic risks of patients, which can provide recommendations for clinical treatment and follow-up. Colorectal cancer (CRC) is a leading cause of cancer-related death in human. Several studies have investigated whether IRGs and tumor immune microenvironment (TIME) could be indicators of CRC prognoses. This study aimed to develop an improved prognostic signature for CRC based on IRGs to predict overall survival (OS) and provide new therapeutic targets for CRC treatment. Based on the screened IRGs, the Cox regression model was used to build a prediction model based on 18-IRG signature. Cox regression analysis revealed that the 18-IRG signature was an independent prognostic factor for OS in CRC patients. Then, we used the TIMER online database to explore the relationship between the risk scoring model and the infiltration of immune cells, and the results showed that the risk model can reflect the state of TIME to a certain extent. In short, an 18-IRG prognostic signature for predicting CRC patients' survival was firmly established.

Keywords: TCGA; colorectal cancer; immune prognostic signature; immune-related gene; tumor immune microenvironment.

FIGURE 1

Differentially expressed immune-related genes (IRGs) and cancer-related transcription factors (CRTFs) in colorectal cancer (CRC). (A) Heatmap of differentially expressed genes in CRC. The color from green to red represents the progression from low expression to high expression. (B) Volcano plot of differentially expressed genes in CRC. The red dots in the plot represent upregulated genes, and green dots represent downregulated genes with statistical significance. Black dots represent no differentially expressed genes in CRC. (C) Heatmap of significantly differentially expressed IRGs in CRC. (D) Heatmap of significantly differentially expressed cancer-related transcription factors in CRC. The color from green to red represents the progression from low expression to high expression. (E) Volcano plot of differentially expressed IRGs. (F) Volcano plot of differentially expressed cancer-related transcription factors.

FIGURE 2

Screening of IRGs related to significant prognosis in CRC. Forest plot showing the prognostic immune-related genes, P values, and hazard ratios. Green dots represent low risk factors, and red dots represent high risk factors.

FIGURE 3

The main regulatory network constructed based on prognosis-related IRGs and CRTFs. (A) The main regulatory network was constructed using Cytoscape for visualization. The circulars represent differentially expressed prognostic immune-related genes, and the purple triangles represent prognosis-related cancer-related transcription factors, respectively. The red circulars represent high-risk genes, and the green circulars represent low-risk genes. Red lines represent positive correlations and green lines represent negative correlations. (B) The PPI network was predicted using the Search Tool for the Retrieval of Interacting Genes online database. Analyzing the functional interactions between proteins may provide insights into the mechanisms of generation or development of CRC.

FIGURE 4

Interaction network and biological process analysis of the hub genes. (A) Top 10 hub genes screened from the regulatory network. (B) Top 10 hub genes and their first neighbors that are screened from the regulatory network. Hub gene is shown in red to orange on the left. The first neighboring node is shown in blue. The right picture shows the characteristics of the genes in the left picture. The green ones are low-risk genes. The red ones are high-risk genes. The triangles are transcription factors. (C) The biological process analysis of genes in the network was constructed using BiNGO. The color depth of nodes refers to the corrected P value of ontologies. The size of nodes refers to the number of genes that are involved in the ontologies. P < 0.01 was considered statistically significant. (D) The biological process analysis of hub genes was constructed using BiNGO. P < 0.05 was considered statistically significant. (E) Ten-hub-gene enrichment plots from Gene Set Enrichment Analysis (GSEA).

FIGURE 5

Construction of an immune-related prognostic signature for CRC. (A) The risk score distribution of CRC patients in The Cancer Genome Atlas (TCGA) database. (B) Survival status and duration of patients. (C) Heatmap of the expression of 18 immune-related genes in CRC patients. (D) Survival curves for the low-risk and high-risk groups. (E) The receiver operating characteristic curve (ROC) analysis predicted overall survival using the risk score. The forecast time is 1, 3, and 5 years.

FIGURE 6

Independence of immune-related prognostic signature from clinical factors. (A) Forest plot for univariate analysis of overall survival of TCGA CRC patients. (B) Forest plot for multivariate analysis of overall survival of TCGA CRC patients. Red dots represent high-risk factors.

FIGURE 7

Clinical characteristics correlation analysis. Clinical characteristics correlation analysis of genes in the risk score model (P < 0.05).

FIGURE 8

Relationships between the risk score model and infiltration abundances of six types of immune cells.