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在食道癌症中功能性识别和预测lncRNAs

Lu Han¹, Zhikuan Wang², Congyong Li³

¹Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China; Medical School of Chinese PLA, Beijing, China.

Computers in biology and medicine

|August 23, 2023

概括

一种新的基于变压器的方法准确地识别了与食道癌相关的长非编码RNA (lncRNA),有助于诊断和治疗. 这种计算方法增强了对癌症进展和免疫微环境中的lncRNAs的理解.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
分子生物学分子生物学

背景情况:

食道癌是一种致命的疾病,预后不好,需要新的诊断和治疗生物标志物.
长非编码RNAs (lncRNAs) 与食道癌的发展和进展有关.
与癌症相关的lncRNAs的实验识别是耗时的,突出了对高效计算方法的需求.

研究的目的:

开发和验证一种基于变压器的计算方法,用于识别食道癌相关的lncRNAs.
探索已识别的lncRNA及其向基因在食道癌症中的功能性作用.
研究这些lncRNA与瘤免疫微环境和相关生物通路的关联.

主要方法:

基于变压器的深度学习模型被用于lncRNA识别.
使用曲线下的面积 (AUC) 和精度回忆曲线下的面积 (AUPR) 度量来评估性能.
对预测的 lncRNA 基因进行了功能性丰富分析.

主要成果:

提出的基于变压器的方法实现了卓越的性能,AUC为0.87和AUPR为0.83.
鉴定到的 lncRNA 标显示出与食道癌有很强的关联.
丰富分析显示,基因参与关键通路,对食道癌的发生,发展和预后至关重要.

结论:

基于变压器的方法是有效和准确的,用于识别食道癌症相关的lncRNAs.
已识别的lncRNA及其点在免疫微环境和癌症进展中起着重要作用.
这种方法为生物标志物发现和理解食道癌的发病过程提供了宝贵的工具.

关键词:

深度学习是一种深度学习.食道癌是食道癌的一个类型.免疫细胞是免疫细胞.这是一个LncRNARNA.预测预后预测预测

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