Construction and validation of prognostic risk model for hepatocellular carcinoma based on biological analysis of palmitoyl-associated enzyme long-chain non-coding RNA
YU Li,, SU Xiandu, ZHANG Min, LI Yahui, WANG Le
Clinical Laboratory, Danzhou People's Hospital, Hainan Province, Danzhou 571700, China
目的·基于癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库筛选棕榈酰化相关长链非编码RNA(long non-coding RNA,lncRNA),构建肝癌预后风险模型。方法·从TCGA数据库中下载获取374例肝癌组织及50例正常组织样本的测序数据和对应的患者临床及预后资料,对肝癌组织与正常组织的差异锌指DHHC结构域(zinc finger aspartate-histidine-histidine-cysteine domain,ZDHHC)蛋白家族构建相关的lncRNA表达谱,并采用单因素回归分析筛选预后相关lncRNA,进一步通过最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归算法构建预测模型;对模型预测的有效性进行验证,并分析模型高、低风险组与免疫功能的关系以及预测免疫治疗应答效果。结果·发现20个肝癌差异表达的ZDHHC,其中有656个lncRNA和差异ZDHHC有相关性(均P<0.05)。单因素COX分析筛选出22个lncRNA与肝癌的预后相关(HR为1.47~13.05,均P<0.05),LASSO回归分析纳入3个lncRNA构建风险模型,即风险模型分数=0.662 6×AC026356.1+0.213 9×AC026401.3+0.405 6×POLH-AS1,模型中高风险组患者的总生存期(overall survival,OS)和无进展生存期(progression-free survival,PFS)明显低于低风险组患者(均P<0.05)。多因素COX回归分析显示,该模型作为风险因素是影响生存期的独立因素(HR=1.375,95%CI为1.208~1.566)。风险模型中高风险和低风险的免疫功能通路有明显差异,且高风险患者对免疫治疗的应答水平更低(P<0.05)。结论·使用基于棕榈酰化相关lncRNA表达的风险模型能够独立预测肝癌患者的生存期,为患者接受免疫治疗提供参考。
关键词:肝细胞癌
;
长链非编码RNA
;
棕榈酰化
;
风险模型
;
免疫治疗
Abstract
Objective ·To explore the effect of screening the expression of long non-coding RNA (lncRNA) related to palmitoylation on prognosis of liver cancer based on The Cancer Genome Atlas (TCGA) database and construct a risk prediction model in liver cancer. Methods ·The sequencing data and the corresponding clinical information of 374 liver cancer tissues and 50 normal tissue samples were downloaded from TCGA database. The differential zinc finger aspartate-histidine-histidine-cysteine domain (ZDHHC) between liver cancer tissues and normal tissues was used to construct the expression profile of lncRNA related to ZDHHC. Furthermore, the prediction model was constructed by LASSO regression algorithm and the validity of the model prediction was verified to analyze the relationship between high-risk and low-risk groups and immune function and to predict the response to immunotherapy. Results ·There were 20 differentially expressed ZDHHCs in hepatocellular carcinoma, among which 656 lncRNAs were correlated with differential ZDHHCs (all P<0.05). Univariate COX analysis showed that 22 lncRNAs were associated with the prognosis of hepatocellular carcinoma (HR 1.47‒13.05, all P<0.05), and LASSO regression analysis included 3 lncRNAs to construct a risk model. The risk score=0.662 6×AC026356.1+0.213 9×AC026401.3+0.405 6×POLH-AS1. In the model, the overall survival (OS) and progression-free survival (PFS) of patients in the high-risk group were significantly lower than those in the low-risk group (all P<0.05). Multivariate COX regression analysis showed that the model as a risk factor was an independent factor affecting survival (HR=1.375, 95%CI 1.208‒1.566). In the risk model, there were significant differences between high-risk and low-risk immune function pathways, and the response level of high-risk patients to immunotherapy was lower (P<0.05). Conclusion ·The use of a risk model based on palm acylation related lncRNA expression can independently predict the survival period of liver cancer patients, providing reference for patients receiving immunotherapy.
Keywords:hepatocellular carcinoma (HCC)
;
long non-coding RNA (lncRNA)
;
palmitoylation
;
risk model
;
immune therapy
YU Li, SU Xiandu, ZHANG Min, LI Yahui, WANG Le. Construction and validation of prognostic risk model for hepatocellular carcinoma based on biological analysis of palmitoyl-associated enzyme long-chain non-coding RNA. Journal of Shanghai Jiao Tong University (Medical Science)[J], 2023, 43(6): 747-754 doi:10.3969/j.issn.1674-8115.2023.06.011
肝细胞癌是原发性肝脏恶性肿瘤,发病率在所有肿瘤中位居第六[1]。肝细胞癌的高发病率和高致死率使它成为全球第四大癌症相关致死疾病[2]。棕榈酰化在肝癌的发生发展中发挥重要作用[3-4],肝癌组织与正常组织差异的锌指DHHC结构域(zinc finger aspartate-histidine-histidine-cysteine domain,ZDHHC)蛋白家族的蛋白酰基转移酶是一个含有保守结构域Asp-His-His-Cys(DHHC)的酶家族,通过将碳十六脂肪酸棕榈酸酯共价连接到底物蛋白特定半胱氨酸残基的硫醇基团来介导棕榈酰化[5]。棕榈酰化酶在蛋白质定位、累积、分泌中稳定发挥重要作用[6-8]。ZDHHC的异常表达参与了各种癌症的发生以及转移,严重影响了癌症患者的治疗和预后[9]。免疫检查点的棕榈酰化,包括程序性死亡蛋白-1(programmed death-1,PD-1)[10]、程序性死亡蛋白配体1(programmed death ligand-1,PD-L1)[11]。靶向PD-L1棕榈酰化使肿瘤细胞对T细胞杀伤敏感,抑制肿瘤生长,而且PD-L1棕榈酰化在稳定PD-L1中发挥重要作用[12]。ZDHHC3的表达升高与乳腺癌中肿瘤生长增强和患者生存期降低相关[13]。ZDHHC9失活提高N-Ras驱动的白血病治疗效果[14],也促进乳腺癌的免疫治疗应答。棕榈酰酶家族抑制剂水解酶结构域的蛋白17(α/β hydrolase domain-containing protein 17,ABHD17)诱导急性髓性白血病细胞中的N-Ras去棕榈酰化,对N-Ras突变的细胞抑制作用显著[15]。长链非编码RNA(long non-coding RNA,lncRNA)在表观遗传调控、细胞周期调控和细胞分化调控等众多生命活动中发挥重要作用,同时也可以调控ZDHHC相关酶的表达[16]。然而,ZDHHC蛋白在肝细胞癌中的表达模式、预后价值和生物学功能尚未系统阐明。本研究利用肝癌数据库对ZDHHC在肝细胞癌中的表达进行了全面的生物信息学分析,旨在为临床医师选择合适的治疗靶点和药物提供更多理论支持。
1 资料与方法
1.1 数据获取
从癌症基因组图谱(The Cancer Genome Atlas,TCGA)下载基因组数据共享(Genomic Data Commons,GDC)网站(https://portal.gdc.cancer.gov/)获取424例肝癌样本的RNA测序数据(包含50例正常组织和374例肝癌样本)和患者的临床资料,删除没有生存时间的样本,删除年龄、性别以及临床分期不全的样本,最终得到370例用于后续分析。
Note: A/B. Expression of ZDHHCs [ZDHHC1‒ZDHHC9 and ZDHHC11‒ZDHHC12 (A), and ZDHHC13‒ZDHHC24 (B)] in hepatocellular carcinomas (n=370) and normal liver tissues (n=50). *P<0.05. C. Kaplan-Meier survival analysis and Log-Rank test were performed on different ZDHHCs. The median of the high-risk and low-risk groups (n=185) was taken as the cutoff. D. ZDHHC-related lncRNA. E. Univariate Cox regression analysis to screen prognostic lncRNAs associated with ZDHHC.
Fig 1
Univariate analysis of associated lncRNAs affecting prognosis in patients with hepatocellular carcinoma
Note: A. LASSO regression analysis. B. Distribution of risk scores in all samples for high-risk and low-risk groups. C. Distribution of follow-up time for all samples. D/E. OS (D) and PFS (E) of patients in high-risk and low-risk groups.
Fig 2
Construction of a risk model for liver cancer patients based on ZDHHC-related lncRNA
Note: A/B. ROC (A) and C-index (B) based on risk score, age, grade, and stage. C/D. Univariate (C) and multivariate (D) COX regression analysis suggested the impact of clinical factors and risk scores on prognosis.
Fig 3
Single factor and multiple regression analysis
Note: A. The nomogram predicted 1-year, 3-year, and 5-year survival for hepatocellular carcinoma patients. B. ROC curves for 1-year, 3-year, and 5-year OS. C. Calibration curve analysis.
Fig 4
Column chart creation and prognostic prediction calibration
Note: A. Correlation between high and low risk groups and immune-related pathways. B. TIDE predicted the response to immunotherapy in both risk groups.*P<0.05.
Fig 5
Analysis of pathways related to immune response in high-risk and low-risk groups
YU Li and SU Xiandu participated in the experimental design. YU Li and ZHANG Min participated in the writing and revision of the paper, and LI Yahui and WANG Le participated in the data collection and analysis. All authors have read the last version of paper and consented for submission.
利益冲突声明
所有作者声明不存在利益冲突。
COMPETING INTERESTS
All authors disclose no relevant conflict of interests.
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