Journal of gastrointestinal oncology · 2026
Metabolic-cell-death gene trio predicts survival and cuproptosis sensitivity in colorectal cancer.
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Zhuang Wei, Zhang Ya, Liu Zhengyong, Yan Wenxue, Wei Tao, Deng Qiuping, Liu Qi
Аннотация
BACKGROUND: Metabolic cell death (MCD) modulates colorectal cancer (CRC) progression, yet its prognostic value remains unexplored. We aimed to build an MCD-centred gene signature for outcome prediction and precision therapy.
METHODS: Transcriptomes of 1,174 CRC patients were integrated. Weighted gene co-expression network analysis, differential expressions and least absolute shrinkage and selection operator (LASSO) + random survival forest were successively applied to derive a three-gene (CDKN2A/MPC1/AHCY) risk model. Functional, immune-infiltration, drug-sensitivity and genomic analyses were performed, followed by validation in fresh clinical specimens and cell lines.
RESULTS: Integrative metabolic-death transcriptomics identified CDKN2A, MPC1 and AHCY as the hub drivers of CRC. Their three-gene signature robustly stratified patients into high- and low-risk subsets [3-year area under the curve (AUC) 0.83-0.85, P<0.001]. High-risk tumors were enriched for extracellular matrix (ECM)-receptor-interaction pathways, displayed abundant myeloid-derived suppressor cell (MDSC) infiltration and were more vulnerable to AZD8186, AZ960 and JAK inhibitors. Guided by these in-silico findings, we functionally confirmed that CDKN2A silencing markedly repressed proliferation, invasion and migration of SW480/HCT116 cells and potentiated cuproptosis via up-regulation of lipoylated DLAT/DLST and CTR1.
CONCLUSIONS: We report the first MCD-derived prognostic platform for CRC that simultaneously predicts survival and therapeutic response. Targeting CDKN2A-enhanced cuproptosis represents a promising metabolic-precision strategy for high-risk patients.
METHODS: Transcriptomes of 1,174 CRC patients were integrated. Weighted gene co-expression network analysis, differential expressions and least absolute shrinkage and selection operator (LASSO) + random survival forest were successively applied to derive a three-gene (CDKN2A/MPC1/AHCY) risk model. Functional, immune-infiltration, drug-sensitivity and genomic analyses were performed, followed by validation in fresh clinical specimens and cell lines.
RESULTS: Integrative metabolic-death transcriptomics identified CDKN2A, MPC1 and AHCY as the hub drivers of CRC. Their three-gene signature robustly stratified patients into high- and low-risk subsets [3-year area under the curve (AUC) 0.83-0.85, P<0.001]. High-risk tumors were enriched for extracellular matrix (ECM)-receptor-interaction pathways, displayed abundant myeloid-derived suppressor cell (MDSC) infiltration and were more vulnerable to AZD8186, AZ960 and JAK inhibitors. Guided by these in-silico findings, we functionally confirmed that CDKN2A silencing markedly repressed proliferation, invasion and migration of SW480/HCT116 cells and potentiated cuproptosis via up-regulation of lipoylated DLAT/DLST and CTR1.
CONCLUSIONS: We report the first MCD-derived prognostic platform for CRC that simultaneously predicts survival and therapeutic response. Targeting CDKN2A-enhanced cuproptosis represents a promising metabolic-precision strategy for high-risk patients.