基于生物学分析构建及验证棕榈酰化相关酶长链非编码RNA的肝癌预后风险模型

doi:10.3969/j.issn.1674-8115.2023.06.011

摘要/Abstract

摘要：

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.

Key words: hepatocellular carcinoma (HCC), long non-coding RNA (lncRNA), palmitoylation, risk model, immune therapy

中图分类号:

R730.7

于莉, 苏显都, 张敏, 李雅慧, 王乐. 基于生物学分析构建及验证棕榈酰化相关酶长链非编码RNA的肝癌预后风险模型[J]. 上海交通大学学报（医学版）, 2023, 43(6): 747-754.

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[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(6): 747-754.

图/表 5

图1 影响肝癌患者预后的相关lncRNA单因素分析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

图2 构建基于ZDHHC相关lncRNA的肝癌患者风险模型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

图3 单因素和多因素回归分析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

图4 列线图创建与预后预测校准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

图5 高、低风险组免疫应答相关通路分析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

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