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Updated: Jan 16, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Development of a prognostic risk model for colorectal cancer based on microsatellite stability-associated genes
Xuefeng Zheng1, Yunduan He1, Zhan Tuo1
1Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
Background:
Colorectal cancer (CRC) patients have a high recurrence rate, impacting survival. Microsatellite instability (MSI) is strongly linked to CRC development, making the MSI-related prognostic genes crucial for diagnosis and treatment.
Methods:
This study used CRC datasets, including TCGA-CRC, GSE17537, GSE39582, and GSE18088. We analyzed differential expression between CRC and control samples, and between MSS and MSI-H samples. Key genes were identified through a co-expression network and used to develop a prognostic risk model. The model's performance was validated in GSE17537, and independent prognostic factors were identified to construct a survival nomogram. We also explored pathways linked to the risk groups and their association with the tumor immune microenvironment, and predicted potential therapeutic agents for CRC.
Results:
We identified 11 prognostic genes (CHGB, FABP4, PLIN4, PLIN1, RPRM, C7, AQP8, C2CD4A, APLP1, ADH1B, and CD36) and developed a CRC risk model that showed significant survival differences in the TCGA-CRC cohort and GSE17537, with AUCs over 0.6 at 3, 5, and 7 years. Independent prognostic factors included risk score, age, tumor stage, and pathological N, and a nomogram was created for survival prediction. The identified genes may influence CRC through various pathways and are linked to immune responses. Bleomycin emerged as a potential treatment, with CHGB and RPRM regulated by non-coding RNAs and transcription factors, possibly affecting CRC development.
Conclusions:
Our analysis of microsatellite stability-associated genes in CRC highlights their impact on TIME, clinicopathological features, and prognosis, providing new insights into predicting prognosis and developing personalized treatments.

