遗传 ›› 2025, Vol. 47 ›› Issue (4): 456-475.doi: 10.16288/j.yczz.24-208
何锐(
), 郑秀娟, 王宁宁, 李旭颖, 李明其, 辇士晶, 王克维()
收稿日期:2024-09-09
修回日期:2024-11-29
出版日期:2025-04-20
发布日期:2024-12-03
通讯作者:
王克维，教授，博士生导师，研究方向：肝损伤、骨关节炎、细胞凋亡。E-mail: keweiwang0718@163.com作者简介:何锐，硕士研究生，专业方向：流行病与卫生统计学。E-mail: herui55555@163.com
基金资助:
Rui He(), Xiujuan Zheng, Ningning Wang, Xuying Li, Mingqi Li, Shijing Nian, Kewei Wang()
Received:2024-09-09
Revised:2024-11-29
Published:2025-04-20
Online:2024-12-03
Supported by:摘要：
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.
Rui He, Xiujuan Zheng, Ningning Wang, Xuying Li, Mingqi Li, Shijing Nian, Kewei Wang. Identification of PANoptosis-related lncRNAs in hepatocellular carcinoma based on bioinformatics and construction of a prognostic model[J]. Hereditas(Beijing), 2025, 47(4): 456-475.
表1
PANoptosis相关基因集的构建"
| 细胞死亡类型 | 基因 |
|---|---|
| 细胞焦亡 (Pyroptosis) | NLRC4、NLRP1、MEFV、PYCARD、GSDMD、CASP1、CASP5、NLRP3、AIM2、CASP4、CASP12、BAX、IRF1、GSDMB、GSDMC、GSDMA、NLRP6、ELAVL1 |
| 细胞凋亡 (Apoptosis) | CASP10、CASP8、CASP2、APAF1、ZBP1、CASP9、CASP6、CASP3、CASP7、TNFRSF1A、DCN、RNF31、TNFAIP3、MAP3K7、PSTPIP2、ADAR、DIABLO、NLRP9、NAIP、CYLD、STAT1、JAK2、IFNGR1、JAK1、TRADD、TBK1 |
| 坏死性凋亡 (Necroptosis) | RIPK3、RIPK1、MLKL、FADD、FAS、TNF、RBCK1 |
图1
PANoptosis相关lncRNAs的识别 A：热图显示GSE138178、GSE101728和GSE58043数据集中肿瘤组和非肿瘤组之间最为显著的50个重叠差异表达基因。B：分组热图显示了TCGA数据集中肿瘤组和非肿瘤组之间最为显著的50个差异表达基因。C：韦恩图揭示了TCGA-LIHC、GEO和PANoptosis相关基因的交集结果。D：集合图显示了3个基因集之间的交互情况。"
表2
TCGA队列中PANoptosis相关lncRNAs的单因素和多因素Cox回归分析"
| 基因 | 单变量 | 多变量 | ||||
|---|---|---|---|---|---|---|
| HR | 95%CI | P.adj | HR | 95%CI | P.adj | |
| AC091057.1 | 1.18 | 1.06~1.32 | 0.007 | 1.05 | 0.87~1.26 | 0.894 |
| FAM99A | 0.95 | 0.90~0.99 | 0.029 | 0.98 | 0.92~1.05 | 0.894 |
| LINC01703 | 1.18 | 1.06~1.32 | 0.007 | 1.01 | 0.88~1.16 | 0.937 |
| LINC02580 | 0.85 | 0.74~0.96 | 0.014 | 0.93 | 0.79~1.09 | 0.829 |
| AL445524.1 | 1.22 | 1.07~1.39 | 0.007 | 1.13 | 0.95~1.35 | 0.765 |
| LINC01093 | 0.93 | 0.88~0.99 | 0.034 | 0.98 | 0.89~1.07 | 0.894 |
| LINC02499 | 0.91 | 0.85~0.98 | 0.014 | 0.95 | 0.87~1.04 | 0.765 |
| LINC02475 | 1.12 | 1.04~1.20 | 0.007 | 1.08 | 0.99~1.18 | 0.765 |
| AC109322.1 | 1.20 | 1.05~1.38 | 0.012 | 0.97 | 0.80~1.19 | 0.937 |
| AL365361.1 | 0.91 | 0.82~1.00 | 0.049 | 0.94 | 0.82~1.06 | 0.765 |
| AL133338.1 | 1.18 | 1.05~1.33 | 0.012 | 1.11 | 0.96~1.30 | 0.765 |
| FBXL19-AS1 | 1.15 | 1.02~1.3 | 0.029 | 0.99 | 0.84~1.17 | 0.937 |
| AC007773.1 | 1.21 | 1.07~1.36 | 0.007 | 1.03 | 0.85~1.23 | 0.937 |
| AC010864.1 | 1.30 | 1.10~1.55 | 0.007 | 1.13 | 0.90~1.41 | 0.765 |
| AC102953.2 | 1.20 | 1.04~1.38 | 0.014 | 1.00 | 0.84~1.20 | 0.983 |
| AC079174.2 | 1.21 | 1.05~1.40 | 0.012 | 0.93 | 0.75~1.15 | 0.894 |
| AC023593.1 | 1.30 | 1.14~1.49 | <0.001 | 1.13 | 0.92~1.39 | 0.765 |
图2
预后lncRNAs和肝癌亚型与PANoptosis潜在的关系 A：桑葚图显示了预后lncRNAs与PANoptosis潜在的关系。分支的宽度代表着预后lncRNAs与PANoptosis基因的相关性强弱。B：相关性热图显示了预后lncRNAs与PANoptosis基因的相关性。C：箱式图显示了Cluster 1亚型和Cluster 2亚型中PANoptosis相关基因的表达水平。*代表P≤0.05，**代表P≤0.01，***代表P≤0.001，****代表P≤0.0001, ns代表无统计学意义。"
图3
一致性聚类分析基于预后PANoptosis相关lncRNAs将肝癌患者分为两个亚型 A：矩阵热图揭示了一致性分类情况。矩阵的行和列表示的都是样本，当k=2时，其区分度较高。B：一致性累积分布函数(Cumulative Distribution Function，CDF)图展示了k取不同数值时的累积分布函数。C：三角洲面积图(Delta Area Plot)展示了k和k-1相比CDF曲线下面积的相对变化情况。D：聚类共识图展示了不同k值下每个分类的聚类共时值(cluster-consensus value)。该值越高代表稳定性越高。E：分组热图显示了两个亚型患者临床病理特征的关系。F：Kaplan-Meier生存曲线显示两个亚型患者的生存率情况。"
图4
预后lncRNAs在两个肝细胞癌亚型中的表达情况 A~Q：预后lncRNAs在两个肝细胞癌亚型中表达情况。除了FAM99A、LINC02580、LINC01093、LINC02499和AL365361.1外，其余lncRNAs均在Cluster 2中高表达，并且结果均具有统计学意义。"
图5
功能富集分析和免疫浸润分析揭示两个肝细胞癌亚型的功能富集和免疫浸润情况 A：气泡图揭示了GO分析的结果。生物过程(BP)、细胞成分(CC)、分子功能(MF)类别均展示了富集最为明显的10个功能。B：柱形图揭示了KEGG分析的结果。图中揭示了前10条重要的途径。C：热图揭示了每个样本在不同类型免疫细胞中的富集得分。D：分组箱式图揭示了不同类型免疫细胞在Cluster 1亚型和Cluster 2亚型中的丰度水平。*代表P≤0.05，**代表P≤0.01，***代表P≤0.001，****代表P≤0.0001, ns代表无统计学意义。"
图6
PANoptosis相关lncRNAs预后风险特征的构建 A：Lasso-Cox降维分析进一步筛选预后lncRNA。B：将风险分数与M分期、T分期相结合构建列线图。列线图可以预测肝细胞癌患者1年、3年、5年的生存概率。C：ROC曲线证实了列线图的高精度。D：校准曲线证实了列线图的高精度。"
图7
训练集和验证集检验特征模型的可靠性 A~C：训练集的ROC曲线、校准曲线和Kaplan-Meier生存曲线说明该特征模型具有较高的准确性。D~F：验证集的ROC曲线、校准曲线和Kaplan-Meier生存曲线说明该特征模型具有较高的准确性。"
图8
高低风险组的功能富集分析和免疫浸润分析 A~B：气泡图和柱形图分别揭示了GO分析和KEGG分析的结果。C：分组热图显示了每个样本在不同类型免疫细胞中的富集得分。D：分组箱式图揭示了不同类型免疫细胞在高低风险组中的丰度水平。E：相关性热图揭示了免疫细胞与免疫细胞的相关性。F：相关性热图揭示了特征lncRNAs与免疫细胞的相关性。*代表P≤0.05，**代表P≤0.01，***代表P≤0.001，****代表P≤0.0001, ns代表无统计学意义。"
图9
药物敏感性分析确定对高风险组敏感的抗癌药物 A~I：比较高低风险组对抗癌药物的敏感性。*代表P≤0.05，**代表P≤0.01，***代表P≤0.001，****代表P≤0.0001。"
图10
单因素和多因素COX分析确定风险分数是肝细胞癌的独立预后因素 A：热图揭示高低风险组与临床病理特征的关系。B：K-M生存曲线揭示风险分数对肝细胞癌患者预后的影响。C：多因素COX分析揭示风险分数是肝细胞癌的独立预后因素。*代表P≤0.05，**代表P≤0.01，***代表P≤0.001，****代表P≤0.0001。"
表3
风险分数与临床特征的单因素和多因素COX分析"
| 变量 | 单变量 | 多变量 | ||||
|---|---|---|---|---|---|---|
| HR | 95%CI | P.adj | HR | 95%CI | P.adj | |
| Age | 1.27 | 0.90~1.80 | 0.267 | 1.07 | 0.75~1.54 | 0.757 |
| Gender | 1.23 | 0.86~1.75 | 0.332 | 1.06 | 0.74~1.52 | 0.757 |
| M_stage | 1.27 | 1.06~1.53 | 0.023 | 1.27 | 0.99~1.63 | 0.168 |
| N_stage | 1.23 | 1.03~1.48 | 0.043 | 1.09 | 0.86~1.39 | 0.720 |
| Stage | 1.79 | 1.42~2.27 | <0.001 | 1.16 | 0.79~1.70 | 0.720 |
| T_stage | 2.24 | 1.63~3.08 | <0.001 | 2.11 | 1.33~3.37 | 0.009 |
| Treatment | 1.17 | 0.70~1.97 | 0.589 | 1.49 | 0.90~2.47 | 0.266 |
| Treatment_type | 1.10 | 0.78~1.55 | 0.589 | 1.09 | 0.76~1.55 | 0.757 |
| RiskScore | 3.28 | 2.26~4.75 | <0.001 | 3.65 | 2.49~5.35 | <0.001 |
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