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Updated: Dec 12, 2025
Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
Published on: September 25, 2011
A five-immune-related genes-based prognostic signature for colorectal cancer
Lin Zhu1, Haiying Wang2, Zuofen Wang3
1Department of Anorectal Surgery, ZiBo Central Hospital, Zibo, Shandong 255036, PR China.
Background:
Colorectal cancer (CRC) is a common malignancy with high morbidity and mortality. Prognosis of CRC is highly heterogeneous which hinders the making of appropriate clinical decision tremendously.
Aims:
We here propose to comprehensively define prognosis of CRC patients in the context of mRNA levels of immune-related genes (IRGs).
Methods:
CRC samples were acquired from both the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). IRGs were obtained from ImmProt database. Univariate Cox-regression analysis was adopted to screen prognostically significant IRGs for CRC patients. LASSO Cox-regression analysis was used to identify the optimal prognostic IRGs and construct the prognostic model. Multivariate Cox-regression analysis was performed to determine the independence of associations of specific factors with CRC's OS. Nomogram that combines the independent prognostic factors was constructed to predict CRC patients' 1-year, 3-years, and 5-years OS probability.
Results:
There were a total of 76 differentially expressed IRGs (DEGs) in CRC tumor compared with paracancerous samples. Five out of those DEGs were significantly associated with the CRC patients' OS probability and used for the construction of prognostic model. CRC samples were divided into high-risk and low-risk group based on prognostic model. Significant differences in OS probability were obtained between high-risk and low-risk CRC patients. Additionally, a nomogram containing risk score, age and stage could effectively predict long-term OS probability of CRC patients.
Conclusion:
In conclusion, we developed a robust IRGs-based prognostic signature which should be helpful for the understanding of heterogeneity of OS probability and the future clinical research.
