基于生物信息学构建胰腺癌坏死性凋亡相关lncRNA的预后风险评分模型

doi:10.16138/j.1673-6087.2025.03.08

摘要/Abstract

摘要：

目的：构建基于坏死性凋亡相关的长链非编码RNA（necroptosis-related long non-coding RNA，NRL）的胰腺癌预后风险模型。 方法：方法：通过TCGA和GTEx数据库获得基因表达数据和临床数据，包括171例正常胰腺组织和178例胰腺癌组织样本。使用LASSO回归及Cox回归分析筛选出与胰腺癌预后相关的NRL来构建预后风险模型。通过受试者工作特征（receiver operating characteristic,ROC）曲线评估模型的预测价值，并在临床蛋白质组肿瘤分析联盟（Clinical Proteomic Tumor Analysis Consortium，CPTAC）数据库中验证。同时进行基因富集分析、免疫浸润分析以及化疗药物的敏感性分析。 结果：共筛选出8个与胰腺癌预后有关的NRL（LINC01559、TMEM161B-AS1、AL157392.3、AC099850.3、AC136475.3、AL162274.2、MIR217HG、UNC5B-AS）用于构建预后风险模型。生存分析提示高风险组患者具有较差的预后(P<0.001)，ROC曲线提示模型的风险预测能力较好。回归分析证实该模型是预测胰腺癌患者预后的独立因素（P<0.05)，同时CPTAC数据集验证该模型的有效性。此外，高、低风险组中信号通路的富集、免疫细胞浸润程度、肿瘤突变负荷水平、免疫检查位点的表达以及对化疗药物的敏感性均存在差异（均P<0.05）。 结论：基于生物信息学筛选出的8个NRL构建的风险预后模型，能够有效预测胰腺癌的预后，并与胰腺癌中免疫细胞浸润水平以及免疫相关治疗药物密切相关。

关键词: 胰腺癌, lncRNA, 坏死性凋亡, 生物信息学, TCGA, 风险模型

Abstract:

Objective To construct a prognostic risk model for pancreatic cancer based on necroptosis-related long non-coding RNA (NRL). Methods The gene expression data and clinical data were from the Cancer Genome Atlas (TCGA) and GTEx databases, including 171 normal pancreas and 178 pancreatic cancer samples. LASSO regression and Cox regression analysis were used to identify NRL associated with pancreatic cancer prognosis to construct the risk model. The predictive value of the model was evaluated using receiver operating characteristic (ROC) curves and validated in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Gene enrichment analysis, immune infiltration analysis, and chemotherapy drug sensitivity analysis were also conducted. Results The eight NRL (LINC01559, TMEM161B-AS1, AL157392.3, AC099850.3, AC136475.3, AL162274.2, MIR217HG, UNC5B-AS) were screened for constructing the NRL risk model. Survival analysis indicated that patients in the high-risk group had poorer prognosis (P<0.001). ROC curves were both >0.6, confirming the accuracy of the model. Regression analysis confirmed that the model was an independent prognostic factor for pancreatic cancer patients (P<0.05), and CPTAC data showed that the effectiveness of this model was good. Additionally, there were significant differences (P<0.05) in pathway enrichment, immune cell infiltration, tumor mutation burden, expression of immune checkpoints, and chemotherapy drug sensitivity between the high risk and low risk groups. Conclusions The risk model constructed based on 8 NRL can effectively predicts the prognosis of pancreatic cancer, and strongly correlated with the level of immune infiltration in pancreatic cancer which may provide new reference for immunotherapy and chemotherapy drug selection.

Key words: Pancreatic cancer, lncRNA, Necroptosis, Bioinformatics, TCGA, Risk model

中图分类号:

R735.9

杨子云, 姚玮艳. 基于生物信息学构建胰腺癌坏死性凋亡相关lncRNA的预后风险评分模型[J]. 内科理论与实践, 2025, 20(03): 232-241.

YANG Ziyun, YAO Weiyan. Construction of necroptosis-related lncRNA risk model of pancreatic cancer based on bioinformatics[J]. Journal of Internal Medicine Concepts & Practice, 2025, 20(03): 232-241.

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特征	例数(n)	单因素分析		多因素分析
特征	例数(n)	风险比(95% CI)	P	风险比 (95% CI)	P
年龄	178	1.028 (1.006 ~ 1.050)	0.012	1.025 (1.003 ~ 1.048)	0.027
性别	178	0.897 (0.588 ~ 1.368)	0.614
女性	80	基准值（Ref）
男性	98	0.897 (0.588 ~ 1.368)	0.614
分级	176	1.377 (1.020 ~ 1.859)	0.037	1.276 (0.943 ~ 1.048)	0.114
1	31	基准值（Ref）
2	95	1.795 (0.898 ~ 3.586)	0.098
3	48	2.286 (1.101 ~ 4.748)	0.027
4	2	1.650 (0.210 ~ 12.995)	0.634
分期	175	1.422 (0.979 ~ 2.066)	0.065
2	147	基准值（Ref）		基准值（Ref）
4	4	0.886 (0.217 ~ 3.620)	0.866	0.920 (0.225 ~ 3.762)	0.907
1	21	0.305 (0.123 ~ 0.761)	0.011	0.348 (0.139 ~ 0.871)	0.054
3	3	0.668 (0.093 ~ 4.807)	0.688	0.704 (0.098 ~ 5.078)	0.728
风险评分	178	1.014 (1.007 ~ 1.021)	< 0.001	1.014 (1.006 ~ 1.022)	< 0.001