根据生物信息学分析和机器学习,识别与NSCLC存活相关的lncRNA和mRNA
Wei Yue1, Jing Wang1, Bo Lin1,2
1Innovation Centre for Information, Binjiang Institute of Zhejiang University, Hangzhou 310053, China.
Aging
|May 2, 2024
概括
这项研究确定了用于预测非小细胞肺癌 (NSCLC) 存活率的关键基因 (CDC6,CEP55,TYMS). 这些发现为NSCLC患者提供了潜在的预后生物标志物,有助于个性化治疗策略.
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:
- 非小细胞肺癌 (NSCLC) 是主要的肺癌亚型.
- 确定预后生物标志物对于NSCLC患者的管理和治疗至关重要.
- 现有的生物标志物需要改进,以准确预测NSCLC.
研究的目的:
- 识别与NSCLC存活相关的长非编码RNA (lncRNA) 和信使RNA (mRNA).
- 利用分子数据开发一个强大的NSCLC预测模型.
- 确定影响NSCLC患者结果的关键基因.
主要方法:
- 利用癌症基因组图谱 (TCGA) 和基因表达总汇 (GEO) 数据集用于NSCLC表达概况.
- 应用生存分析,基因本体学,基因和基因组的京都百科全书路径分析,和考克斯回归.
- 采用机器学习算法 (LASSO,RFE,随机森林) 并构建了一个蛋白质-蛋白质相互作用 (PPI) 网络.
- 开发了一个基于已识别的特征mRNAs的Nomogram预测模型.
主要成果:
- 确定了8种与生存相关的lncRNA和262种与生存相关的mRNA.
- 使用机器学习过了30个独立的预后mRNA和17个特征的mRNA.
- 在Nomogram模型中,ROC值为0.835 (训练) 和0.767 (测试).
- 鉴定出CDC6,CEP55和TYMS是与NSCLC存活相关的关键基因.
结论:
- CDC6,CEP55和TYMS是影响NSCLC存活的关键分子因素.
- 开发的Nomogram模型显示了预测NSCLC预后的潜力.
- 进一步的实验室验证证实了CDC6,CEP55和TYMS在NSCLC进展中的作用.
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