基于生物信息学识别肝细胞癌中的PANoptosis相关lncRNAs并构建预后模型
收稿日期: 2024-09-09
修回日期: 2024-11-29
网络出版日期: 2024-12-03
基金资助
国家自然科学基金项目(81773367);哈尔滨医科大学研究生科研与实践创新基金(YJSCX2023-123HYD)
Identification of PANoptosis-related lncRNAs in hepatocellular carcinoma based on bioinformatics and construction of a prognostic model
Received date: 2024-09-09
Revised date: 2024-11-29
Online published: 2024-12-03
Supported by
National Natural Science Foundation of China(81773367);Scientific Research and Practice Innovation Fund for Postgraduate of Harbin Medical University(YJSCX2023-123HYD)
PANoptosis是一种新的促炎程序性细胞死亡的方式，参与多种癌症的发生发展过程，但其在肝癌发生发展过程中的机制尚未被阐明。近年来的研究表明，长链非编码RNA (long non-coding RNAs，lncRNAs)在多种癌症的发生发展中起到了关键作用。本研究分别从癌症基因组图谱(The Cancer Genome Atlas，TCGA)数据库和基因表达综合数据库(Gene Expression Omnibus，GEO)数据中检索了肝癌数据集。结合肝癌数据集和前人的研究基础，通过相关性分析得到了PANoptosis相关lncRNAs。一致性聚类分析发现，肝细胞癌患者可以分为两种不同的亚型，即Cluster 1亚型和Cluster 2亚型。与Cluster 2亚型相比，Cluster 1亚型具有更好的预后和更高水平的免疫浸润。然后，对PANoptosis相关lncRNAs进行了Lasso-Cox降维分析，构建了风险评估模型用于预测肝细胞癌患者预后。Kaplan-Meier分析显示，低风险组患者生存率较高，受试者工作特征曲线(receiver operating characteristic curve，ROC)和校准曲线证实了模型具有良好的预测能力。这些发现有助于人们更加深入地理解PANoptosis相关lncRNAs在肝细胞癌发生发展中起到的关键作用，为后续的肝细胞癌治疗提供潜在的生物标志物和治疗靶点。
何锐, 郑秀娟, 王宁宁, 李旭颖, 李明其, 辇士晶, 王克维 . 基于生物信息学识别肝细胞癌中的PANoptosis相关lncRNAs并构建预后模型[J]. 遗传, 2025 , 47(4) : 456 -475 . DOI: 10.16288/j.yczz.24-208
PANoptosis, a novel form of pro-inflammatory programmed cell death, plays a role in the progression of various cancers. However, its mechanisms in hepatocellular carcinoma (HCC) remain unclear. Recent studies have highlighted the critical role of long non-coding RNAs (lncRNAs) in the development and progression of multiple cancers. In this study, we retrieve HCC datasets from the TCGA and GEO databases. We identify PANoptosis-related lncRNAs through correlation analysis based on HCC datasets and previous research. Consistent clustering analysis reveals two distinct subtypes of HCC patients: Cluster 1 and Cluster 2. Compared with the Cluster 2 subtype, Cluster 1 shows a better prognosis and higher levels of immune infiltration. We then perform a Lasso-Cox regression analysis of PANoptosis-related lncRNAs to construct a risk assessment model for predicting the prognosis of HCC patients. Kaplan-Meier analysis indicates that patients in the low-risk group have higher survival rates, while ROC (receiver operating characteristic curve) and calibration curves demonstrate the model’s good predictive performance. These findings provide deeper insights into the critical role of PANoptosis-related lncRNAs in developing HCC, offering potential biomarkers and therapeutic targets for future HCC treatment.
Key words： PANoptosis; apoptosis; pyroptosis; necroptosis; lncRNA; hepatocellular carcinoma
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