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使用关联规则挖掘算法,揭示乳头甲状腺癌早期和晚期的关键分子机制
Seyed Mahdi Hosseiniyan Khatibi1,2,3, Sepideh Zununi Vahed2, Hamed Homaei Rad3
1Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical Sciences, Tabriz, Iran.
PloS one
|November 2, 2023
概括
机器学习确定了早期和晚期乳腺甲状腺癌 (PTC) 的关键信使RNA (mRNA). 这些发现有助于理解分子机制,并开发针对甲状腺癌的向治疗方法.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 甲状腺癌 (TC) 是一种普遍存在的内分泌恶性瘤,在全球范围内排名为女性的第六大癌症.
- 了解早期和晚期乳头甲状腺癌 (PTC) 的分子驱动因素对于改善治疗策略和患者生存至关重要.
研究的目的:
- 使用机器学习算法识别与乳腺甲状腺癌 (PTC) 早期和晚期相关的显著信使RNA (mRNA).
- 阐明PTC进展背后的主导分子机制.
主要方法:
- 数据预处理,包括清理和规范化,然后使用t测试和二进制非主导排序遗传算法II (NSGAII) 进行特征选择.
- 使用机器学习和深度学习算法评估所选特征,包括支持矢量机 (SVM) 分类.
- 协会规则采矿的应用,以精确确定影响PTC阶段的关键基因.
主要成果:
- 在基于已识别的mRNA的基础上,SVM分类器在区分早期和晚期PTC时获得了83.5%的准确性和0.78的AUC.
- 对早期PTC发现的关键基因包括ZNF518B,DTD2和CCAR1.1.
- 晚期PTC的重要基因包括lnc-DNAJB6-7:7,RP11-484D2.3,以及MSL3P1.1.
结论:
- 这项研究突出了潜在的候选基因,这些基因对PTC的早期和晚期都至关重要.
- 这些发现为确定新的治疗点提供了基础,以加强有效PTC治疗的开发.
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