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脂质代谢相关的结肠癌四基因预后模型
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Abstract:
目的：通过生物信息学方法构建脂质代谢相关基因异构模型，探讨其在结肠癌患者中的应用价值。方法：从TCGA (The Cancer Genome Atlas, TCGA)和GEO (Gene Expression Omnibus, GEO)数据库中收集临床信息和RNA测序数据。对筛选出的脂质代谢相关差异表达基因进行Lasso-Cox回归分析，建立结肠癌预后模型。并构建了预后模型的诺模图，以分析其在评估结肠癌患者生存期和临床分期中的价值。结果：分析了722个FAM相关基因在结肠癌和正常组织中的表达差异，并通过Lasso-Cox回归分析构建了基于4个FAM基因的结肠癌预后预测模型，并在验证集中验证了该模型的实用性。此外，基于预后模型计算的风险分数被验证为结肠癌患者的独立预后因素。我们进一步构建了由风险评分特征、年龄和美国癌症联合委员会分期组成的诺模图，供临床应用。结论：本研究通过生物信息学方法，构建了一个结肠癌四基因的预后模型。并系统揭示了该模型在结肠癌中的临床预测作用。本研究的发现，进一步增强了FAM相关基因在结肠癌中的预后价值。
Objective: To construct a model of lipid metabolism related gene isomerism by bioinformatics method, and to explore its application value in patients with colon cancer. Methods: Clinical information and RNA sequencing data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Lasso-Cox regression analysis was performed on the screened differentially expressed genes related to lipid metabolism to establish a prognostic model of colon cancer. The nomogram of the prognostic model was constructed to analyze its value in evaluating the survival time and clinical stage of colon cancer patients. Results: The expression differences of 722 FAM-related genes in colon cancer and normal tissues were analyzed. The prognostic prediction model of colon cancer based on four FAM genes was constructed by Lasso-Cox regression analysis, and the practicability of the model was verified in the validation set. In addition, the risk score calculated based on the prognostic model was validated as an independent prognostic factor for colon cancer patients. We further constructed a nomogram consisting of risk score characteristics, age, and American Joint Committee on Cancer staging for clinical application. Conclusion: In this study, a prognostic model of four genes in colon cancer was constructed by bioinformatics methods. The clinical predictive effect of the model in colon cancer was systematically revealed. The findings of this study further enhanced the prognostic value of FAM-related genes in colon cancer.
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