揭示与膀癌相关的circRNA生物标志物:结合深度学习和统计的混合模型
Yulian Ding1, Yi Pan2,3, Clarence Ronald Geyer4
1Central for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 Guangdong China.
Health information science and systems
|September 2, 2025
概括
这项研究使用深度学习模型确定了膀癌 (BC) 的新型循环RNA (circRNA) 生物标志物. 这些发现可能会为BC带来新的非侵入性诊断和预后工具.
科学领域:
- 癌症学
- 分子生物学
- 生物信息学
背景情况:
- 循环RNAs (circRNAs) 是关键的基因表达调节剂,涉及膀癌 (BC) 的发病.
- 它们的稳定性和体液中的存在表明它们可能是BC诊断和预后的非侵入性生物标志物.
研究的目的:
- 开发和验证用于识别与膀癌相关的新型circRNA生物标志物的计算策略.
- 利用集体模型整合深度学习和统计分析来发现生物标志物.
主要方法:
- 分析了454名BC患者和19名健康对照者的RNA测序数据.
- 使用CIRCexplorer3工具进行circRNA识别和预处理.
- 结合特征选择,特征提取和深度学习的整体模型被应用于识别BC相关的circRNA.
主要成果:
- 共有34个新型circRNA生物标志物被确定.
- 其中17个circRNA与以前经过验证的与BC相关的基因相对应.
- 其余的17个circRNA代表了膀癌的潜在新生物标志物.
结论:
- 该计算策略有效地确定了膀癌的新型circRNA生物标志物.
- 这些发现支持circRNAs作为BC早期诊断和预后的非侵入性生物标志物的潜力.
- 这项研究通过先进的计算方法推进了膀癌的非侵入性生物标志物发现.
相关概念视频
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