Related Experiment Video
Updated: Jan 13, 2026
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Immunogenomic classification and nomogram development and validation for colorectal cancer survival prediction based
Guangzhe Shen1, Fangze Wei2, Tixian Xiao2
1Department of Anorectal, First Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, China.
Background:
Colorectal cancer (CRC) is the third most common digestive cancer, and immunity plays an important role in the development of CRC. In this study, we aimed to explore immune-related prognostic biomarkers.
Methods:
We first explored the immune microenvironment and identified a high immune score cluster and a low immune score cluster on the basis of the immune score calculated by single-sample gene set enrichment analysis (ssGSEA). We also explored the differentially expressed genes (DEGs) between the high- and low-immune score groups and utilized weighted gene coexpression network analysis (WGCNA) to explore the gene sets with the highest correlation with prognosis. DEGs between paracancerous and tumor tissues were discovered on the basis of the RNA sequencing results, and immune-related differentially expressed genes (IRDEGs) were obtained by the intersection of the DEGs with the prognosis-related gene sets determined by WGCNA. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression (multi Cox) were used to screen risk factors for overall survival in CRC patients, and a risk score model and nomogram were constructed. We also assessed the relationships among immune infiltration, cancer transcription factor targets and risk factors in the prognostic model.
Results:
Based on the immune enrichment, we divided CRC patients into high and low immune score groups, and the low-immune score group had a better survival rate. The brown cluster of WGCNA is associated with prognosis, the hub DEGs between the brown cluster and RNA sequencing, and we explore the 26 genes. Based on hub DEGs, we developed and validated a risk score model, areas under the curve (AUCs) of 1-, 3-, and 5-year survival were 0.75,0.73, and 0.65, indicating good accuracy and the low-risk score group had a better prognosis(P<0.0001). Combined risk score and clinical information, the nomogram also has good prediction value, the AUC for 1-year survival was 0.809, that for 3-year survival was 0.804, and that for 5-year survival was 0.794.
Conclusions:
Immunogenomic classification can identify different immune statuses of CRC patients and predict survival.
