基于衰老相关基因特征的胰腺癌风险分层及预后预测模型

doi:10.19586/j.2095-2341.2024.0027

生物技术进展 ›› 2025, Vol. 15 ›› Issue (1): 158-169.DOI: 10.19586/j.2095-2341.2024.0027

• 研究论文 • 上一篇

基于衰老相关基因特征的胰腺癌风险分层及预后预测模型

李新雄¹(), 洪伟煊², 冯添顺³, 房俊伟², 赵虎², 肖春红², 王梅平²()

^1.厦门大学附属东方医院普通外科，福州 350025
^2.联勤保障部队第九〇〇医院普通外科，福州 350025
^3.厦门大学附属东方医院神经外科，福州 350025

收稿日期:2024-02-20 接受日期:2024-07-02 出版日期:2025-01-25 发布日期:2025-03-07
通讯作者: 王梅平
作者简介:李新雄 E-mail: xx1129552947@163.com；
基金资助:
福建省自然科学基金项目(2021J011262);联勤保障部队第九〇〇医院青年自主创新项目孵化专项(2022QC07)

Pancreatic Cancer Risk Stratification and Prognostic Prediction Model Based on Aging-related Gene Characteristics

Xinxiong LI¹(), Weixuan HONG², Tianshun FENG³, Junwei FANG², Hu ZHAO², Chunhong XIAO², Meiping WANG²()

^1.Department of General Surgery，Dongfang Hospital，Xiamen University，Fuzhou 350025，China
^2.Department of General Surgery，the 900th Hospital of Joint Logistics Support Force，Fuzhou 350025，China
^3.Department of Neurosurgery，Dongfang Hospital，Xiamen University，Fuzhou 350025，China

Received:2024-02-20 Accepted:2024-07-02 Online:2025-01-25 Published:2025-03-07
Contact: Meiping WANG

摘要/Abstract

摘要：

为鉴定出胰腺癌（pancreatic adenocarcinoma，PAAD）患者中有预后意义的衰老相关长链非编码RNA（long noncoding RNAs，lncRNA），基于衰老相关基因（aging-related gene，ARG）构建预后预测模型。从癌症基因组图谱数据库（The Cancer Genome Atlas Database，TCGA）、基因型组织表达数据库（Genotypic Tissue Expression Database，GTEX）获取PAAD和正常样本的转录组信息，相关性分析筛选出ARG中具有显著相关性的lncRNA。通过差异分析、单因素回归、Lasso回归和多因素回归鉴定出目标lncRNA，并构建出PAAD预后风险模型。研究共鉴定出1 109个衰老相关的lncRNA，筛选后最终获得9个lncRNA构建风险评分预后模型，包括AC245041.2、AC244153.1、AC091057.1、MIR3142HG、AL137779.2、AC145207.5、TDRKH-AS1、AC068620.2和AC127024.6。模型的ROC（receiver operating curve）曲线和曲线下AUC（area under the curve）达到0.798。根据风险中值分为高风险组和低风险组，Kaplan-Meier分析两组总体生存期（overall survival，OS）具有显著差异，将风险评分和临床病理特征结合，多因素Cox回归分析风险评分（HR：1.136，95%CI：1.090~1.183）、年龄（HR：1.021，95%CI：1.000~1.042）是预后独立因素，并构建出PAAD预后评估的列线图。进一步，免疫学相关分析揭示了高低风险组存在免疫浸润的差异以及免疫检查点基因的表达差异，模型的预测特征与免疫状态相关。研究基于9个衰老相关lncRNA构建了PAAD预后模型，有助于改善PAAD患者预后管理。

关键词: 胰腺癌, 衰老, lncRNA, 预后, 模型

Abstract:

In order to identify aging-related long noncoding RNAs （lncRNAs） with prognostic significance in patients with pancreatic adenocarcinoma （PAAD）， a prognostic prediction model was constructed based on aging-related genes （ARG）. The transcriptome information of PAAD and normal samples were obtained from The Cancer Genome Atlas Database （TCGA） and Genotypic Tissue Expression Database （GTEX）， and correlation analysis screened out lncRNAs with significant correlations among ARG. The target lncRNA was identified through differential analysis， univariate regression， Lasso regression and multivariate regression， and a PAAD prognostic risk model was constructed. A total of 1 109 aging-related lncRNAs were identified， and 9 lncRNAs were screened to construct a risk score prognostic model， including AC245041.2， AC244153.1， AC091057.1， MIR3142HG， AL137779.2， AC145207.5， TDRKH-AS1， AC068620.2 and AC127024.6. The receiver operating curve （ROC） curve and AUC （the area under the curve） reached 0.798. The patients were divided into high-risk group and low-risk group according to the median risk. Kaplan-Meier analysis showed a significant difference in overall survival （OS） between the two groups. Combining the risk score and clinicopathological characteristics， multivariate Cox regression analysis risk score （HR： 1.136， 95%CI： 1.090~1.183） and age （HR： 1.021， 95%CI： 1.000~1.042） were independent prognostic factors， and a nomogram for PAAD prognostic evaluation was constructed. Furthermore， immunological correlation analysis revealed the differences in immune infiltration and the expression of immune checkpoint genes in the high and low risk groups， and the predictive characteristics of the model were related to immune status. The study constructed a PAAD prognostic model based on 9 aging-related lncRNAs， which may help improve the prognosis management of PAAD patients.

Key words: pancreatic cancer, aging, lncRNA, prognosis, model

中图分类号:

Q354

李新雄, 洪伟煊, 冯添顺, 房俊伟, 赵虎, 肖春红, 王梅平. 基于衰老相关基因特征的胰腺癌风险分层及预后预测模型[J]. 生物技术进展, 2025, 15(1): 158-169.

Xinxiong LI, Weixuan HONG, Tianshun FENG, Junwei FANG, Hu ZHAO, Chunhong XIAO, Meiping WANG. Pancreatic Cancer Risk Stratification and Prognostic Prediction Model Based on Aging-related Gene Characteristics[J]. Current Biotechnology, 2025, 15(1): 158-169.

图/表 7

图1 PAAD中ARG的获取以及衰老相关DEG的GO和KEGG分析A：维恩图显示总共富集 593个ARG；B：火山图显示PAAD转录组中ARG的差异表达，红点代表上调基因，蓝点代表下调基因；C：DEG的GO分析；D：DEG的KEGG分析，PAAD—胰腺癌；HAGR—人类衰老基因组资源；Genecards—人类基因数据库；ARG—衰老相关基因；DEG—差异表达基因。

Fig. 1 Acquisition of ARGs in pancreatic cancer and GO and KEGG analysis of aging-related DEGs

图2 PAAD衰老相关lncRNA预后模型的建立A：由最佳λ确定非零系数的17个预后相关lncRNA的LASSO系数曲线；B：9个目标lncRNA及其系数；C：森林图显示多因素Cox回归分析；D：ARGs和目标lncRNA的共表达网络，红色圆圈代表lncRNA，蓝色菱形代表ARG；E：桑葚图显示了ARG、目标lncRNA与预后风险之间的关系。PAAD—胰腺癌；ARG—衰老相关基因; coef—系数; *，**，***分别表示差异在P<0.05、P<0.01、P<0.001水平上有统计学意义。

Fig. 2 Establishment of aging-related lncRNA prognostic model for PAAD

图3 衰老相关lncRNA在PAAD中的预后模型的特征A：热图显示目标lncRNA的表达；B：预后危险因子在两组中的表达情况；C：预后保护因子在两组中的表达情况；D：不同生存状态PAAD患者的风险评分分布；E：PAAD患者风险评分的分布；F：不同风险评分PAAD患者的生存状况分布；G：预后模型的ROC曲线；H：Kaplan-Meier对两组PAAD患者的生存率分析。PAAD—胰腺癌；ns—无显著性，*，**，***分别表示差异在P<0.05、P<0.01、P<0.001水平上有统计学意义。

Fig. 3 Characteristics of the prognostic model of ARLs in PAAD

图4 风险评分与PAAD临床病理特征的相关性A：PAAD患者风险评分和临床病理特征的分布；B：单因素Cox分析森林图；C：多变量Cox分析森林图；D：风险评分和临床病理特征的ROC曲线；E：模型预测的1年、2年和3年生存率的ROC曲线及PAAD患者风险评分和临床病理特征的分布。

Fig. 4 Correlation of risk score with clinicopathological features of PAAD

图5 PAAD患者预后列线图A：基于风险评分构建用于预测1年、2年和3年生存率；B~D：校准曲线显示1年、2年、3年实际生存期和预测生存期之间的一致性。

Fig. 5 The construction of the prognostic nomogram of PAAD

图6 GSEA分析A：富集程度位列前10的生物学途径；B：富集免疫相关生物学途径。

Fig. 6 GSEA analysis

图7 免疫浸润分析和免疫检查点分析A：ESTIMATE分析；B：CIBERSORT分析；C：热图显示了免疫细胞的比例；D：ssGSEA分析；E：两组之间无差异表达的免疫检查点；F：两组之间免疫检查点的表达显著不同。ns，无统计学差异，*，**，***分别表示差异在P<0.05、P<0.01、P<0.001水平上有统计学意义。

Fig. 7 Immune infiltrate analysis and immune checkpoint analysis

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基于衰老相关基因特征的胰腺癌风险分层及预后预测模型

Pancreatic Cancer Risk Stratification and Prognostic Prediction Model Based on Aging-related Gene Characteristics

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