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整合机器学习和神经网络用于识别与PANoptosis相关的lncRNA分子亚型,并构建头部和部状细胞癌的预测模型

Zhenzhen Wang¹, Lixin Cheng¹, Juntao Huang²

¹Department of Otolaryngology Head and Neck Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, China.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

|June 24, 2024

概括

这项研究开发了一个机器学习模型,使用与PANoptosis相关的长非编码RNA (PRLs) 来预测头部和部状细胞癌 (HNSCC) 患者的生存率. 该模型有助于识别瘤亚型并指导临床治疗策略.

科学领域:

在瘤学瘤学.
基因组学就是基因组学.
细胞死亡研究细胞死亡研究

背景情况:

一种新的细胞死亡途径PANoptosis与瘤进展有关.
长非编码RNAs (lncRNAs) 在各种癌症中起着至关重要的作用,包括HNSCC.
准确的预后预测对于有效的HNSCC管理至关重要.

研究的目的:

探索与PANoptosis相关的lncRNAs (PRLs) 与头部和部状细胞癌 (HNSCC) 之间的关联.
开发基于机器学习的神经网络模型,用于HNSCC患者的预后预测.
确定HNSCC预后和潜在治疗点的新生物标志物.

主要方法:

使用TCGA数据库用于HNSCC队列数据.
应用非负矩阵因子化 (NMF) 来识别差异表达的预后PRLs.
采用了五种机器学习算法来选择核心PRL并构建一个神经网络预测模型 (PLRS系统).
进行了生存分析 (Kaplan-Meier),ROC分析,功能丰富,免疫透,TMB和治疗反应评估.

主要成果:

确定了347个PRL,其中225个以HNSCC不同的表达.
NMF分析显示了两个患者群,其中一个显示出更好的预后和更高的免疫细胞透率.
使用13个交互式PRL构建了一个预测模型,实现AUC为0.735-0.740.
低风险评分 (PLRS) 患者表现出更好的生存率,增强的免疫细胞/功能丰富,更低的TMB和更好的免疫治疗敏感性.

结论:

为HNSCC建立了一个新的神经网络预后模型 (PLRS).
该模型有效地预测了生存率,并确定了HNSCC患者的瘤亚型.
该系统为HNSCC的临床治疗策略提供了宝贵的见解.

关键词:

头部和部状细胞癌瘤机器学习机器学习这就是PANoptosis.预测预后预后在 lncRNA 中,

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相关概念视频

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...