基于癌症基因组图谱数据库双硫死亡相关LncRNA构建肝细胞癌预后模型及其效能分析

doi:10.3969/j.issn.1672-5069.2025.01.033

摘要/Abstract

摘要： 目的鉴定与双硫死亡相关长链非编码RNA(DRLs)相关的肝细胞癌(HCC)预后特征并构建预后模型。方法从癌症基因组图谱(TCGA)数据库中获取HCC数据,基于单因素COX回归、套索回归(LASSO)和多因素COX回归筛选DRLs,构建HCC预后模型并验证模型效能。基于风险评分中位值划分HCC高风险组与低风险组。基于DRLs特征,通过聚类分析识别HCC分子亚型。基于不同的风险分组和分子亚型聚类进行生存分析。结果共鉴定出3002个DRLs,经单因素COX回归筛选得到345个与预后相关的DRLs(P＜0.05);LASSO回归进一步将DRLs数量筛选至7个,最后经多因素COX回归筛选获得3个参与模型构建的DRLs;风险评分公式如下:风险评分=0.9478×AC026412.3表达量+0.5511×RNF216P1表达量+0.5367×TMCC1-AS1表达量;高风险组总生存期(OS)显著低于低风险组(P＜0.05);聚类分析将HCC样本分为3个分子亚型,即聚类1(C1)、聚类2(C2)和聚类3(C3);生存分析显示C2类HCC患者预后最好,C1次之,C3最差(P＜0.001)。结论应用3个DRLs构建的HCC预后模型可为患者的个性化管理及治疗提供指导依据。

关键词: 肝细胞癌, 长链非编码RNA, 预后模型, 双硫死亡, 癌症基因组图谱数据库

Abstract: Objective The aim of this study was to identify characterization of disulfidptosis-related long non-coding RNAs (DRLs) and investigate their prognostic features in patients with hepatocellular carcinoma (HCC). Methods Materials of patients with HCC were retrieve from cancer genome atlas database (TCGA), and feature of DRLs was analyzed by univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression. We establish an HCC prognostic model, and the model's performance was validated. The HCC patients were divided into high-risk and low-risk groups based on the median of the risk score. Molecular subtypes of HCC were identified through cluster analysis based on DRLs characteristics. Survival analysis was conducted based on different risk groups and clustering of molecular subtypes. Results A total of 3002 DRLs were identified, among which 345 DRLs were found to be related to prognosis by univariate COX regression(P＜0.05); further selection by LASSO regression reduced the number of DRLs to 7, and finally, 3 DRLs were selected by multivariate COX regression to be believed to participate in the model construction; the risk score was calculated as follows: risk score=0.9478 × AC026412.3 expression level + 0.5511 × RNF216P1 expression level + 0.5367 × TMCC1-AS1 expression level; the overall survival (OS) in the high-risk group was significantly lower than that in the low-risk group(P＜0.05); the cluster analysis categorized HCC samples into three molecular subtypes: e.g., cluster 1(C1), cluster 2(C2), and cluster 3(C3); survival analysis indicated that patients in group C2 had the best prognosis, followed by group C1, and patients in group C3 had the worst prognosis(P＜0.001). Conclusion The HCC prognostic model based on 3 DRLs could provide guidance for personalized management and treatment in patients with HCC.

Key words: Hepatoma, Long non-coding RNA, Prognostic model, Disulfidptosis, Cancer genome atlas database

牛日雨, 王益杰, 王欣, 李成忠. 基于癌症基因组图谱数据库双硫死亡相关LncRNA构建肝细胞癌预后模型及其效能分析[J]. 实用肝脏病杂志, 2025, 28(1): 128-131.

Niu Riyu, Wang Yijie, Wang Xin, et al. Disulfidptosis-related LncRNA for constructing prognostic model of patients with hepatocellular carcinoma based on TCGA database[J]. Journal of Practical Hepatology, 2025, 28(1): 128-131.

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