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Updated: Sep 18, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Mito-fission gene prognostic model for colorectal cancer
Chao Liu1,2, Sheng Xu1, Yuanyuan Liu3
1Departments of Gastrointestinal, Hernia and Enterofistula Surgery, The People's Hospital of Guangxi Zhuang Autonomous Region, Nannning, Guangxi Province, China.
Background:
Dysregulated cellular metabolism is one of the major causes of colorectal cancer (CRC), including mitochondrial fission. Therefore, this study focuses on the specific regulatory mechanisms of mitochondrial dysfunction on CRC, which will provide theoretical guidance for CRC in the future.
Methods:
The Cancer Genome Atlas (TCGA)-CRC dataset, GSE103479 dataset and 40 mitochondrial fission-related genes (MFRGs) were downloaded in this study. The differentially expressed genes (DEGs) were analyzed in TCGA-CRC samples. Using MFRGs scores as traits, key module genes associated with its scores were screened by weighted gene co-expression network analysis (WGCNA). Then, differentially expressed MFRGs (DE-MFRGs) were obtained by intersecting DEGs and key module genes. Next, DE-MFRGs were subjected to univariate Cox, least absolute shrinkage and selection operator (LASSO), multivariate Cox and stepwise regression analysis to scree hub genes and to construct the risk model. The risk model was validated in GSE103479. Finally, the hub genes were comprehensively investigated through a multi-faceted approach encompassing clinical characteristic analysis, Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and drug sensitivity prediction. Subsequently, the expression levels of the identified key genes were validated utilizing quantitative real-time fluorescence PCR (qRT-PCR), reinforcing the findings and ensuring their accuracy.
Results:
The 49 DE-MFRGs were gained by intersecting 3,310 DEGs and 1,952 key module genes. Then, CCDC68, FAM151A and MC1R were screened as hub genes. Also, the risk model validated in GSE103479 showed that the higher the risk score, the worse the survival of CRC patients. Furthermore, T/N/M stages were differences in risk scores between subgroups of clinical characteristics. The memory CD4+ T cell and plasma cell were more significant differences in the low-risk group samples. The 51 drugs were showed a better response in the high-risk group patients. RT-qPCR validation results showed that CCDC68 and FAM151A were down-regulated in CRC, while MC1R was up-regulated, consistent with the validation set results. And FAM151A and MC1R showed highly significant difference between CRC and normal samples (P < 0.0001).
Conclusion:
In this study, we found CCDC68, FAM151A and MC1R as potential hub genes in CRC, and analyzed the molecular mechanism of mitochondrial affecting CRC, which would provide theoretical reference value for CRC.
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