International journal of genomics · 2025
Risk Prediction of Colon Cancer Metastasis and Bioinformatics Analysis of Aspirin Treatment.
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Li Jing, Yu Xinyue, Shao Jingjing, Zhang Hui, Yu Jiawei
Аннотация
BACKGROUND: Metastasis is a major adverse prognostic factors of colon cancer. Aspirin chemoprophylaxis may improve outcomes for metastatic colon cancer patients. This study aimed to determine the impact of metastasis-related molecular subtypes on prognosis and aspirin chemoprophylaxis benefit.
METHODS: We obtained differentially expressed metastasis-related genes in cancer and normal tissues. A weighted gene co-expression network (WGCNA) was constructed by differentially expressed genes. Lasso-Cox regression identified key prognostic genes within relevant modules, establishing a risk score model. Transcription factors regulating module genes were explored. Aspirin-interacting genes were identified using the Comparative Toxicogenomics Database (CTD) and validated via cellular experiments.
RESULTS: WGCNA analysis of 2062 metastasis-related genes revealed significant correlations between blue/yellow modules and colon cancer. A risk score model based on blue module genes predicted overall survival and 1-, 3-, and 5-year survival rates. Transcription factor analysis implicated the E2F family in blue module regulation and NFκB1/STAT3 in yellow module regulation. CTD analysis showed persistent upregulation of NOX4, CXCL8, CXCL5, GDF15, and MMP13 post-aspirin treatment. Cellular experiments confirmed aspirin downregulated metastasis-related genes (E2F1, CCNE1, VEGFA, MMP3) in colon cancer.
CONCLUSION: We developed a validated metastasis gene predictive model. Colon cancer patients with upregulated NOX4, CXCL8, CXCL5, GDF15, or MMP13 may not benefit from aspirin chemoprophylaxis. Conversely, patients showing aspirin-induced downregulation of E2F1, CCNE1, VEGFA, and MMP3 may derive chemoprophylactic benefit.
METHODS: We obtained differentially expressed metastasis-related genes in cancer and normal tissues. A weighted gene co-expression network (WGCNA) was constructed by differentially expressed genes. Lasso-Cox regression identified key prognostic genes within relevant modules, establishing a risk score model. Transcription factors regulating module genes were explored. Aspirin-interacting genes were identified using the Comparative Toxicogenomics Database (CTD) and validated via cellular experiments.
RESULTS: WGCNA analysis of 2062 metastasis-related genes revealed significant correlations between blue/yellow modules and colon cancer. A risk score model based on blue module genes predicted overall survival and 1-, 3-, and 5-year survival rates. Transcription factor analysis implicated the E2F family in blue module regulation and NFκB1/STAT3 in yellow module regulation. CTD analysis showed persistent upregulation of NOX4, CXCL8, CXCL5, GDF15, and MMP13 post-aspirin treatment. Cellular experiments confirmed aspirin downregulated metastasis-related genes (E2F1, CCNE1, VEGFA, MMP3) in colon cancer.
CONCLUSION: We developed a validated metastasis gene predictive model. Colon cancer patients with upregulated NOX4, CXCL8, CXCL5, GDF15, or MMP13 may not benefit from aspirin chemoprophylaxis. Conversely, patients showing aspirin-induced downregulation of E2F1, CCNE1, VEGFA, and MMP3 may derive chemoprophylactic benefit.