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综合性长非编码RNA分析和宫癌的复发预测使用复发神经网络

Geeitha Senthilkumar¹, Renuka Pitchaimuthu¹, Prabu Sankar Panneerselvam²

¹Department of Information Technology, M. Kumarasamy College of Engineering, Thalavapalayam, Karur 639113, Tamil Nadu, India.

Diagnostics (Basel, Switzerland)

|November 27, 2025

概括

一个新的9个长的非编码RNA (lncRNA) 签名,结合深度学习,有效地根据患者患子宫癌复发风险进行分层,提高了预后准确性.

科学领域:

在瘤学瘤学.
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

再发性子宫癌对患者的生存构成重大威胁.
准确的预后模型对于识别高风险个体至关重要.
目前的模型需要通过集成的临床和分子数据来增强.

研究的目的:

将临床数据与GSE44001数据集集集成,以确定宫癌复发风险因素.
将患者分为高风险,中风险和低风险组.
为了识别长非编码RNA (lncRNA) 基因签名的复发性宫癌.

主要方法:

利用来自NCBI GEO数据库的GSE44001数据集,专注于138名复发性宫癌患者.
使用GENCODE注释工具过和分析长非编码RNA (lncRNAs).
采用最小绝对收缩选择运算器 (LASSO) 来根据lncRNA表达和系数进行特征选择和风险值分配.

主要成果:

一个复发神经网络 (RNN) 长期短期记忆模型显示出预后价值,高风险患者的无复发生存时间较短 (p < 0.05).
特定的lncRNA标记物 (ATXN8OS,C5orf60,INE1) 与疾病复发有关.
其他标记物 (KCNQ1DN,LOH12CR2,RFPL1S,KCNQ1OT1,EMX2OS) 与早期或中等阶段的诊断相关.

结论:

一个九个lncRNA签名显示出预测宫癌复发的巨大潜力.
这种 lncRNA 签名与深度学习的整合提供了一个强大的风险分层方法.
这种方法可以帮助识别可能受益于加强监测或治疗的患者.

关键词:

生物标志物生物标志物长非编码RNA是什么意思预后预后预后再发性子宫癌的发生.经常性的神经网络.

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...

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

Non-LTR Retrotransposons

Non-LTR Retrotransposons

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