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基于多个机器学习模型,在子宫内膜癌中开发与衰老相关的lncRNA特征

Jie Lin¹, Xuemei Lei¹, Yanhong Li¹

¹Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China.

Frontiers in genetics

|December 12, 2025

概括

使用七种与衰老相关的lncRNAs (srlncRNAs) 的新模型准确预测子宫内膜癌 (EC) 的预后. 这种签名还表明了EC患者的潜在治疗策略,包括免疫疗法和化疗.

科学领域:

在瘤学瘤学.
分子生物学分子生物学
基因组学就是基因组学.

背景情况:

与衰老相关的长非编码RNAs (srlncRNAs) 与各种癌症有关.
在子宫内膜癌 (EC) 病原发生过程中,srlncRNAs的具体作用尚未被理解.
这项研究旨在开发使用srlncRNAs进行EC预后和治疗的预测模型.

研究的目的:

使用与衰老相关的长非编码RNA (srlncRNAs) 来构建子宫内膜癌 (EC) 预后的预测模型.
在EC中识别具有预后和治疗意义的srlncRNAs.
探索与EC. srlncRNA签名相关的潜在机制和免疫景观.

主要方法:

下载了来自CellAge的TCGA和srlncRNA的基因表达和临床数据.
构建了一个与衰老相关的mRNA-lncRNA联合表达网络,并利用Cox,Lasso和机器学习进行模型开发.
采用名录,基因组丰富分析 (GSEA),CIBERSORT和患者衍生器官 (PDO) 药物敏感性测试.

主要成果:

一个七个srlncRNA签名 (AL121906.2,AP002761.4,BX322234.1,LINC00662,LINC00908,VIM-AS1,ZNF236-DT) 已被开发和验证为EC预后预测高准确度 (AUC风险得分=0.769).
这种包含年龄,学年级和风险分数的诺莫图谱准确地预测了EC患者的结果.
低风险患者表现出增加的免疫透和抗PD-1/抗CTLA4疗法的潜在益处;特定的化疗 (Paclitaxel,gemcitabine,cisplatin) 基于srlncRNA表达表现出有效性.

结论:

开发的srlncRNA签名作为预测子宫内膜癌 (EC) 预后的强大工具.
这种签名可以指导个性化治疗策略,包括免疫疗法和化疗选择.
对srlncRNA功能的进一步调查可能会揭示EC的新型治疗点.

关键词:

在TCGA中,TCGA就是TCGA.细胞衰老细胞衰老这是子宫内膜癌的癌症.免疫疗法免疫疗法患者获得的瘤器官预后预后预后签名签名签名签名

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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...