基于基因表达的结肠直肠癌预测使用机器学习和SHAP分析.
Yulai Yin1, Zhen Yang1, Xueqing Li1,2
1School of Medicine, Nankai University, Tianjin 300071, China.
Genes
|January 28, 2026
概括
使用机器学习开发了一种用于结直肠癌 (CRC) 的新型遗传诊断模型. 这种十基因模型显示出高预测性能,为早期CRC检测和干预提供了潜力.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 在瘤学瘤学.
背景情况:
- 结肠直肠癌 (CRC) 是一个重大的全球健康挑战.
- 准确和早期诊断对于有效的治疗和改善患者结果至关重要.
- 基因标记器为开发精确的诊断工具提供了一个有希望的途径.
研究的目的:
- 开发和验证强大的结直肠癌 (CRC) 基因诊断模型.
- 通过综合生物信息学分析,识别与CRC相关的关键基因.
- 利用机器学习来预测CRC风险和诊断.
主要方法:
- 使用TCGA数据库进行差异基因表达分析.
- 孟德尔随机化分析与eQTL和CRC结果数据.
- 使用9个机器学习算法开发和验证诊断模型,包括XGBoost.
- 基因选择基于差异表达,随机化分析和机器学习模型的重要性.
主要成果:
- 通过孟德尔随机化识别了3716个差异表达基因 (DEG) 和121个CRC相关基因.
- 建立了一个最终的十基因签名 (RIF1,GDPD5,DBNDD1,RCCD1,CLDN5,ASCL2,IFITM3,IFITM1,SMPDL3A,SUCLG2).
- XGBoost模型的AUC值为0.990,最终模型的AUC值为0.9875 (培训) 和0.9601 (验证).
- IFITM1和DBNDD1被确定为最有影响力的基因.
结论:
- 在CRC中的基因表达特征反映了增强的细胞增殖,新陈代谢和免疫逃避.
- 开发的十基因遗传诊断模型显示了CRC的强大预测性能.
- 这种模型对早期CRC诊断,干预和第三层预防策略具有重大潜力.
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