用于mRNA替代拼接分析的机器学习框架识别了结直肠腺癌进展的签名
Uran Maimekov1, Mehdi Nosrati2, Ahmed Mahmoud2
1Rutgers Biomedical and Health Sciences, Rutgers School of Graduate Studies, Newark, NJ, 07039, USA.
Scientific reports
|February 3, 2026
概括
研究人员开发了一种机器学习模型,以识别与结直肠腺癌 (COAD) 无进展生存 (PFS) 相关的mRNA替代拼接 (AS) 事件. 这种AS-PFS签名有助于为个性化治疗策略分层患者.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 结肠直肠腺癌 (COAD) 患者对癌症进展风险的分类尽管取得了进展,但仍未得到充分发展.
- mRNA替代拼接 (AS) 在COAD发育和进展中起着关键作用.
- 在COAD进展中缺乏AS的综合性全基因组评估.
研究的目的:
- 开发一种机器学习框架,以确定与COAD患者无进展生存 (PFS) 相关的临床相关AS事件.
- 建立一个可靠的基于AS的风险签名,用于COAD患者分层.
主要方法:
- 来自TCGA-COAD队列 (n=266) 的RNA测序数据的分析.
- 整合考克斯比例危险 (PH) 分析和可靠的基于概率的生存 (RBSURV) 建模.
- 使用Kaplan-Meier生存分析和对TCGA和AC-ICAM队列的时间依赖ROC分析进行验证.
主要成果:
- 一个由五个事件组成的AS-PFS签名的识别 (OR52K1,SPIN3,NDUFV1,BMPR1A,ARPC4).
- 开发一个特定于患者的风险评分,将个人分为低风险和高风险组.
- 验证证实了与其他标记器相比,签名的独立预测价值.
结论:
- 开发的AS-PFS签名有效地根据PFS风险对COAD患者进行分层.
- 这种签名可以提高诊断时的患者分层.
- 它有可能在临床环境中为个性化治疗策略提供信息.
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