基于免疫相关基因的肝细胞癌预后模型的构建和验证

doi:10.16098/j.issn.0529-1356.2024.03.009

解剖学报 ›› 2024, Vol. 55 ›› Issue (3): 319-328.doi: 10.16098/j.issn.0529-1356.2024.03.009

基于免疫相关基因的肝细胞癌预后模型的构建和验证

陈冬冬¹ 楼金金² 黄燕燕¹ 周璐¹ 李世波³ 续力云^1*

1.温州医科大学附属舟山医院细胞分子生物学实验室; 2.温州医科大学研究生培养基地（舟山医院）;3.温州医科大学附属舟山医院传染病科，浙江舟山 316000

收稿日期:2023-03-17 修回日期:2023-09-10 出版日期:2024-06-06 发布日期:2024-06-11
通讯作者: 续力云 E-mail:xuliyunhappy@126.com

Construction and validation of a prognostic model of hepatocellular carcinoma based on immune-related genes

CHEN Don-dong1 LOU Jin-jin2 HUANG Yan-yan1 ZHOU Lu1 LI Shi-bo3 XU Li-yun1*

1.Department of Cell Molecular Biology Laboratory, Zhoushan Hospital, Wenzhou Medical University; 2.Zhoushan Hospital，Postgraduate Training Base Alliance of Wenzhou Medical University; 3. Department of Infectious Diseases, Zhoushan Hospital, Wenzhou Medical University， Zhejiang Zhoushan 316000, China

Received:2023-03-17 Revised:2023-09-10 Online:2024-06-06 Published:2024-06-11
Contact: XU Li-yun E-mail:xuliyunhappy@126.com

摘要/Abstract

摘要：

目的构建一种基于免疫相关基因的肝细胞癌（LIHC）预后模型。方法在UCSC Xena数据库和癌症基因组图谱(TCGA)数据库中下载肝癌和正常组织样本数据。对LIHC样本和癌旁/正常样本的基因数据进行差异分析。对差异表达基因（DEGs）进行富集分析。使用TCGA队列中的肝癌样本进行Kaplan-Meier生存分析获得生存与免疫相关的差异表达基因，再采用LASSO Cox 和多因素Cox 回归分析构建基因风险预后模型。从高通量基因表达（GEO）数据库中获取数据用于外部验证。利用CellMiner数据库研究枢纽基因对常用抗肿癌药物的敏感性。结果富集分析结果表明，差异表达基因主要与分解代谢相关。通过差异分析和Kaplan-Meier生存分析获得25个生存与免疫相关的差异表达基因。再基于LASSO Cox和多因素Cox回归分析结果，获得5个枢纽基因（FYN、CSF3R、HLA-G、FOS和BIRC5），并构建列线图。训练队列和验证队列的一致性指数(C-index)值分别为0.739和0.625。根据枢纽基因与抗肿瘤药物的敏感性结果，选出12种抗肿瘤药物用于后续实验。结论该模型能够有效预测LIHC患者的预后，为LIHC的免疫治疗提供了新的思路。

关键词: 肝细胞癌, 免疫浸润, 列线图, 免疫检查点基因, 药敏试验, 富集分析, Cox回归分析

Abstract:

Objective To construct a prognostic model for liver hepatocellular carcinoma(LIHC) based on immune-related genes. Methods LIHC and normal tissue samples were downloaded from the UCSC Xena database and The Cancer Genome Atlas (TCGA) database. Differential analysis was performed on the gene data of LIHC samples and adjacent/normal samples. Enrichment analysis was conducted on differentially expressed genes. Kaplan-Meier survival analysis was performed on liver cancer samples from the TCGA cohort to obtain survival- and immune-related differentially expressed genes. LASSO Cox and multivariate Cox regression analysis were used to identify hub genes and construct a gene risk prognostic model. Data from a high-throughput gene expression (GEO) database was obtained for external validation. The sensitivity of hub genes to common anticancer drugs was investigated using the CellMiner database. ResultsEnrichment analysis result indicated that differentially expressed genes were mainly associated with metabolic pathways. Through differential analysis and Kaplan-Meier survival analysis, 25 survival- and immune-related differentially expressed genes were obtained. Based on the result of LASSO Cox and multivariate Cox regression analysis, five hub genes (FYN, CSF3R, HLA-G, FOS, BIRC5) were identified and a nomogram was constructed. The concordance index(C-index) value for the training cohort and validation cohort were 0.739 and 0.625, respectively. Based on the sensitivity of hub genes to anticancer drugs, 12 types of anticancer drugs were selected for subsequent experiments. Conclusion This model can effectively predict the prognosis of LIHC patients and provide a new insights for immune therapy in LIHC.

Key words: Liver hepatocellular carcinoma, Immune infiltration, Nomogram;Immune check point gene, Drug sensitivity test, Enrichment analysis, Cox regression analysis

中图分类号:

R730.1

陈冬冬楼金金黄燕燕周璐李世波续力云. 基于免疫相关基因的肝细胞癌预后模型的构建和验证[J]. 解剖学报, 2024, 55(3): 319-328.

CHEN Don-dong LOU Jin-jin HUANG Yan-yan ZHOU Lu LI Shi-bo XU Li-yun. Construction and validation of a prognostic model of hepatocellular carcinoma based on immune-related genes [J]. Acta Anatomica Sinica, 2024, 55(3): 319-328.

参考文献

［1］ Hu X, Zhu H, Zhang X, et al. Comprehensive analysis of pancancer reveals potential of ASF1B as a prognostic and immunological biomarker ［J］. Cancer Med, 2021,10(19):6897-6916.

［2］ Barry KC, Hsu J, Broz ML, et al. A natural killer-dendritic cell axis defines checkpoint therapy-responsive tumor microenvironments ［J］. Nat Med, 2018,24(8):1178-1191.

［3］ Long J, Lin J, Wang A, et al. PD-1/PD-L blockade in gastrointestinal cancers: lessons learned and the road toward precision immunotherapy ［J］. J Hematol Oncol, 2017,10(1):1-21.

［4］ Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation ［J］. Cell, 2011,144(5):646-674.

［5］ Hinshaw DC, Shevde LA. The tumor microenvironment innately modulates cancer progression ［J］. Cancer Res, 2019,79(18):4557-4566.

［6］ Bare DJ, Lauder JM, Wilkie MB, et al. p59fyn in rat brain is localized in developing axonal tracts and subpopulations of adult neurons and glia ［J］. Oncogene, 1993,8(6):1429-1436.

［7］ Beekman R, Touw IP. G-CSF and its receptor in myeloid malignancy ［J］. Blood, 2010,115(25):5131-5136.

［8］ Guan ChJ, Yu HJ, Zhang XD, et al. Expression and clinical significance of phosphoglycerate kinase 1 in hepatocellular carcinoma based on bioinformatics methods ［J］. Acta Anatomica Sinica,2022,53(6):744-753.(in Chinese)

管成剑,于华婧,张小东,等.基于癌症多组学数据库深度解析磷酸甘油激酶1在肝细胞癌中的表达及临床意义［J］.解剖学报,2022,53(6):744-753.

［9］ Carosella ED, Moreau P, Lemaoult J, et al. HLA-G: from biology to clinical benefits ［J］. Trends Immunol, 2008,29(3):125-132.

［10］ Chang CC, Campoli M, Ferrone S. HLA class I antigen expression in malignant cells: why does it not always correlate with CTL-mediated lysis ［J］? Curr Opin Immunol, 2004,16(5):644-650.

［11］ Eferl R, Wagner EF. AP-1: a double-edged sword in tumorigenesis ［J］. Nat Rev Cancer, 2003,3(11):859-868.

［12］ Wu P, Xiao Y, Guo T, et al. Identifying miRNA-mRNA pairs and novel miRNAs from hepatocelluar carcinoma miRNomes and TCGA database ［J］. J Cancer, 2019,10(11):2552-2559.

[1]	孙雷王星宇谢水华. 老年骨质疏松性胸腰椎压缩性骨折患者PKP术后再发骨折的风险分析及列线图预测模型的构建[J]. 解剖学报, 2024, 55(1): 98-104.
[2]	韩毅蔡力冯枭 . 脊柱骨质疏松性压缩性骨折经皮穿刺椎体成形后骨水泥渗漏风险分析及列线图预测模型的构建 [J]. 解剖学报, 2023, 54(6): 710-715.
[3]	AN Shao-guang MA Jun-jie ZANG Hao-xuan LU Jin. 爪蟾驱动蛋白样蛋白2靶蛋白在肝细胞癌的表达及临床预后的意义[J]. 解剖学报, 2023, 54(4): 434-444.
[4]	于华婧魏路阳刘姗姗管成剑张忠涛. MLLT1超伸长复合亚基在肝细胞癌发生发展中的作用及其临床意义[J]. 解剖学报, 2023, 54(4): 425-433.
[5]	管成剑于华婧张小东饶全张伟涛刘坤吴鸿伟汪栋张忠涛郭伟. 基于癌症多组学数据库深度解析磷酸甘油激酶1在肝细胞癌中的表达及临床意义[J]. 解剖学报, 2022, 53(6): 744-753.
[6]	王万里任伟南赵千石贞玉厉永强. 一种新型肾透明细胞癌焦亡相关基因预后风险模型的建立和验证[J]. 解剖学报, 2022, 53(5): 620-627.
[7]	谭玉靓戴姿薇唐标. 磷脂酰肌醇蛋白聚糖3和泛素D基因在肝细胞癌中的异常表达及其相互作用[J]. 解剖学报, 2021, 52(5): 744-750.
[8]	陈小龙万亚锋黄平. 基于质粒的蛋白激酶B1特异性小干扰RNA和P53共表达协同抑制肝细胞癌细胞的增殖、迁移和侵袭[J]. 解剖学报, 2021, 52(2): 251-257.
[9]	刘敏曾云祝珊珊巫佳翠高洪泉伊雪. 蛋白激酶CβⅡ诱导上皮-间质转化及血管新生促进肝癌发展[J]. 解剖学报, 2020, 51(6): 912-918.
[10]	马战姜钟翔姜政. PHLDA2对肝癌细胞的增殖、侵袭、迁移和凋亡的影响[J]. 解剖学报, 2020, 51(3): 398-404.
[11]	王明丽韩大正晁玮霞刘瑞敏赵云岗张天齐义军*. 二维电泳和质谱技术鉴定肝细胞癌相关N-连接糖蛋白[J]. 解剖学报, 2015, 46(4): 495-502.
[12]	陆海霞;危丹明;冯震博. EZH2在肝细胞癌中的表达及临床意义[J]. , 2011, 42(1): 75-79.
[13]	郑洁;董承伟;蒋吉英;张皓云 ;魏德全;丁洁. 肝细胞核因子-1α和肝细胞核因子-4α在人肝细胞癌中的表达及意义[J]. , 2009, 40(6): 933-937.
[14]	苏荣健;李贞;程留芳;李宏丹;魏嘉;包翠芬. Grp78表达对人肝细胞癌细胞外信号调节激酶1/2活性和表达的影响[J]. , 2009, 40(6): 928-932.

基于免疫相关基因的肝细胞癌预后模型的构建和验证

Construction and validation of a prognostic model of hepatocellular carcinoma based on immune-related genes

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 14

编辑推荐

Metrics

本文评价