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解开口腔状细胞癌的预后生物标志物:基于可解释的人工智能的方法

Atika Dogra¹, Yasha Hasija²

¹Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi 110042, India; Department of Research, Rajiv Gandhi Cancer Institute and Research Centre, Sector-5, Rohini, Delhi 110085, India.

Cancer genetics

|July 25, 2025

概括

这项研究确定了利用可解释的人工智能 (XAI) 预测口腔状细胞癌 (OSCC) 复发的关键基因和微RNA. 这些发现为改善口腔癌患者的治疗结果提供了潜在的预后标志物.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
人工智能的人工智能

背景情况:

口腔癌,特别是口腔状细胞癌 (OSCC),是一个重大的全球卫生挑战,死亡率高.
患者的不良预后经常与疾病复发和转移有关,这凸显了需要可靠的预后生物标志物.
目前的管理策略需要通过识别预测疾病进展和患者存活率的标记来加强.

研究的目的:

为了确定口腔状细胞癌 (OSCC) 复发的新型预后生物标志物.
为了利用可解释的人工智能 (XAI) 发现预测基因签名.
探索侵略性OSCC的潜在治疗点和诊断标记.

主要方法:

使用的微阵列数据 (GSE31056) 包括OSCC和正常口腔组织样本.
应用极端梯度增强 (EGB) 机器学习模型与夏普利添加式扩展 (XAI) 集成.
执行二进制分类,以区分复发和非复发的OSCC病例.

主要成果:

确定了与OSCC复发相关的20个重要基因,包括FAM49B,TTC39A,IFI16,ANGPTL4,HSPH1,GRIA2和SERF2.
这些基因涉及到关键的细胞过程,如生长,入侵,亡和整体生存.
基因微RNA网络分析突出显示hsa-let-7b-5p,hsa-miR-27a-3p和hsa-miR-124-3p是关键的相互作用微RNA.

结论:

已识别的基因和微RNA显示出作为OSCC的预后标记物的前景.
这些发现有助于更深入地了解推动OSCC进展的分子途径.
这项研究为开发向疗法和改善侵袭性口腔癌的管理开辟了道路.

关键词:

可解释的人工智能机器学习是机器学习.口腔状细胞癌的癌症.预测生物标志物预测生物标志物这是一个小RNARNA.

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