肝细胞癌相关的核编码线粒体基因及临床信息的综合预后模型

doi:10.3969/j.issn.1674-8115.2024.01.001

上海交通大学学报（医学版） ›› 2024, Vol. 44 ›› Issue (1): 1-12.doi: 10.3969/j.issn.1674-8115.2024.01.001

• 论著 · 基础研究 •

肝细胞癌相关的核编码线粒体基因及临床信息的综合预后模型

克德尔亚·艾山江¹^,²(), 傅怡², 赖冬林²^,³, 邬海龙²^,⁴(), 龚伟¹^,⁵()

^1.上海交通大学医学院附属新华医院普外科，上海 200092
^2.上海健康医学院协同科研中心，上海 201318
^3.南昌大学江西医学院第一附属医院，南昌 330006
^4.上海健康医学院药学院，上海 201318
^5.上海市胆道疾病研究重点实验室，上海交通大学医学院胆道疾病研究所，上海市胆道疾病研究中心，上海 200092

收稿日期:2023-09-18 接受日期:2023-12-06 出版日期:2024-01-28 发布日期:2024-02-28
通讯作者: 邬海龙,龚伟 E-mail:kadirya95@163.com;wuhl@sumhs.edu.cn;gongwei@xinhuamed.com.cn
作者简介:克德尔亚·艾山江（1995—），女，维吾尔族，硕士生；电子信箱：kadirya95@163.com。
基金资助:
国家自然科学基金(31870905);上海市卫生健康委员会基金(201940352);上海市科学技术委员会基金(22ZR1428100);上海交通大学医学院“双百人”项目(20151001);上海交通大学医学院附属新华医院临床研究项目(21XHDB10);上海市胆道疾病重点实验室研究基金(17DZ2260200)

An integrated prognostic model of nuclear-encoded mitochondrial gene signature and clinical information for hepatocellular carcinoma

Aishanjiang Kedeerya¹^,²(), FU Yi², LAI Donglin²^,³, WU Hailong²^,⁴(), GONG Wei¹^,⁵()

^1.Department of General Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
^2.Collaborative Innovation Center for Biomedicine, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
^3.The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
^4.School of Pharmacy, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
^5.Shanghai Key Laboratory of Biliary Tract Disease Research, Research Institute of Biliary Tract Disease, Shanghai Jiao Tong University School of Medicine, Shanghai Research Center of Biliary Tract Disease, Shanghai 200092, China

Received:2023-09-18 Accepted:2023-12-06 Online:2024-01-28 Published:2024-02-28
Contact: WU Hailong,GONG Wei E-mail:kadirya95@163.com;wuhl@sumhs.edu.cn;gongwei@xinhuamed.com.cn
Supported by:
National Natural Science Foundation of China(31870905);Scientific Program of Shanghai Municipal Health Commission(201940352);General Project of Shanghai Municipal Commission of Science and Technology(22ZR1428100);"Two-hundred Talents" Program of Shanghai Jiao Tong University School of Medicine(20151001);Xinhua Hospital Funded Clinical Research(21XHDB10);Shanghai Key Laboratory of Biliary Tract Disease Research Foundation(17DZ2260200)

摘要/Abstract

摘要：

目的·建立一个基于线粒体基因和临床信息的肝细胞癌（hepatocellular carcinoma，HCC）总生存率（overall survival，OS）的预后模型。方法·从癌症基因组图谱（The Cancer Genome Atlas，TCGA）下载369例HCC患者和50例肝脏正常对照的基因表达谱和临床数据。核编码的线粒体基因（nuclear encoded mitochondrial gene，NEMG）从MitoCarta3.0数据库获得。使用“DEseq2”R包和单变量Cox分析选择与HCC患者OS相关并参与氧化磷酸化、三羧酸循环和细胞凋亡通路的NEMG［（泛素细胞色素C还原酶铰链蛋白（ubiquinol cytochrome C reductase hinge protein，UQCRH）、腺苷三磷酸柠檬酸裂解酶（ATP citrate lyase，ACLY）、磷酸烯醇式丙酮酸羧激酶2（phosphoenolpyruvate carboxykinase 2，PCK2）、Bcl-2同源拮抗剂1（Bcl-2 homologous antagonist/killer 1，BAK1）、Bcl-2相关X蛋白（Bcl-2-associated X protein，BAX）和Bcl-2/腺病毒E1B相互作用蛋白3样（Bcl-2/adenovirus E1B interacting protein 3-like，BNIP3L）］。应用多变量Cox回归来确定HCC OS的独立危险因素。建立一个基于独立危险因素（6个NEMG风险特征和TNM分期）的综合预后模型和预后列线图，计算中位预后评分。以中位预后评分作为分界点，将HCC患者分为高风险组和低风险组。进行Kaplan-Meier生存曲线分析，并进行对数秩检验来评估低风险组和高风险组患者OS的差异。使用“timeROC”软件包计算受试者操作特征（receiver operating characteristic，ROC）曲线下面积（area under the curve，AUC）。用基因表达数据库（Gene Expression Omnibus，GEO）下载HCC队列（GSE14520）验证综合预后模型对1、3、5年OS的预测性能。通过实时荧光定量聚合酶链反应（real-time quantitative polymerase chain reaction，qPCR）在来自上海交通大学医学院附属新华医院的34例HCC临床样本中验证6-NEMG的相对表达水平。结果·ROC分析结果显示，与仅6-NEMG风险特征（1、3、5年AUC分别为0.77、0.66、0.65）或仅TNM分期（1、3、5年AUC分别为0.66、0.67、0.63）相比，该综合预后模型对1年（AUC，0.78）、3年（AUC，0.73）和5年（AUC，0.69）HCC OS显示出更好的预测性能。Kaplan-Meier生存曲线分析结果显示高风险组患者的OS明显比低风险组差（P=0.001）。此外，在GEO外部队列中发现该预后模型的预测性能较好（1、3、5年AUC分别为0.67、0.66、0.74），高、低风险组患者的预后差异有统计学意义（P=0.001），与TCGA数据的结果一致。在临床HCC队列中，与癌旁肝脏组织相比，除BNIP3L外，其他5个NEMG在肿瘤组织的表达水平上调或者下调。相关性分析显示，在GSE14520与临床HCC队列中预后评分与HCC肿瘤的大小和数量呈正相关。结论·构建并验证了一个将6-NEMG风险特征与TNM分期相结合的HCC预后预测模型。该模型可能有助于HCC患者的预后预测。

关键词: 核编码线粒体基因, 肝细胞癌, 癌症基因组图谱（TCGA）, 基因表达数据库（GEO）, 总生存率

Abstract:

Objective ·To establish a prognostic model for the overall survival (OS) of hepatocellular carcinoma (HCC) based on mitochondrial genes and clinical information. Methods ·The gene expression and the clinical data of 369 HCC patients and 50 controls with normal liver were downloaded from The Cancer Genome Atlas (TCGA) database. The nuclear-encoded mitochondrial genes (NEMGs) were obtained from the MitoCarta3.0 database. The "DESeq2" R package and univariate Cox analysis were used to select NEMGs [ubiquinol cytochrome C reductase hinge protein (UQCRH),ATP citrate lyase (ACLY),phosphoenolpyruvate carboxykinase 2 (PCK2), Bcl-2 homologous antagonist/killer1 (BAK1), Bcl-2-associated X protein (BAX) andBcl-2/adenovirus E1B interacting protein 3-like (BNIP3L)] in HCC that were associated with OS of HCC and participated in dysregulation of oxidative phosphorylation, tricarboxylic acid cycle and cell apoptosis. Multivariate Cox analysis was applied to select independent risk factors for OS of HCC. A comprehensive prognostic model and a prognostic nomogram with 6-NEMG risk characteristics and TNM staging were established. By using the median of prognostic scores as a cut-off, HCC patients were classified into low-risk and high-risk group. Kaplan-Meier survival curve analysis was conducted and log-rank test was performed to evaluate the survival rates between the low-risk and high-risk group. The area under the curve (AUC) values of receiver operating characteristic (ROC) curve were calculated via using the "timeROC" package. The prognostic model for HCC was validated by using the GEO HCC cohort (GSE14520) for 1, 3 and 5 years. Finally, the relative expression level of 6-NEMG was validated in 34 clinical samples of HCC from Xinhua Hospital, Shanghai Jiao Tong University School of Medicine by using real-time quantitative polymerase chain reaction (qPCR) method. Results ·Compared to 6-NEMG risk signature only (AUCs for 1, 3 and 5 years were 0.77, 0.66 and 0.65, respectively) or TNM stage only (AUCs for 1, 3 and 5 years were 0.66, 0.67 and 0.63, respectively), ROC curve analysis showed that this integrated prognostic model displayed better predictive performance for 1-year (AUC, 0.78), 3-year (AUC, 0.73) and 5-year (AUC, 0.69) OS of HCC. The Kaplan-Meier survival curve analysis showed that the OS of HCC patients in the high-risk group was significantly worse than that in the low-risk group (P=0.001). In addition, predictive performance of the prognostic model (AUC for 1, 3 and 5 years is 0.67, 0.66 and 0.74, respectively) and prognostic differences between the high-risk and low-risk group (P=0.001) were further validated in GEO (GSE14520) external cohort, and these results were consistent with the TCGA data. In addition to BNIP3L, dysregulation of five other NEMGs in the clinical HCC cohort was validated. The correlation analysis in GSE14520 and HCC clinical cohort showed a positive correlation between prognosis score and the size and number of tumors. Conclusion ·A new prognostic model that combines 6-NEMG risk characteristics with TNM staging for predicting OS in HCC patients was constructed and validated. This model may help improve the prognosis prediction of HCC patients.

Key words: nuclear encoded mitochondrial gene, hepatocellular carcinoma, TCGA, GEO, overall survival

中图分类号:

R657.3

克德尔亚·艾山江, 傅怡, 赖冬林, 邬海龙, 龚伟. 肝细胞癌相关的核编码线粒体基因及临床信息的综合预后模型[J]. 上海交通大学学报（医学版）, 2024, 44(1): 1-12.

Aishanjiang Kedeerya, FU Yi, LAI Donglin, WU Hailong, GONG Wei. An integrated prognostic model of nuclear-encoded mitochondrial gene signature and clinical information for hepatocellular carcinoma[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(1): 1-12.

图/表 7

表1 qPCR引物序列

Tab 1 Primer sequences for qPCR

Primer	Forward sequence (5′→3′)	Reverse sequence (5′→3′)
UQCRH	GCTCTGTGATGAGCGTGTATCC	GTTGTTAAAGAGTTTGTGGGCCAC
ACLY	GCTCTGCCTATGACAGCACCAT	GTCCGATGATGGTCACTCCCTT
PCK2	TAGTGCCTGTGGCAAGACCAAC	GAAGCCGTTCTCAGGGTTGATG
BAK1	TTACCGCCATCAGCAGGAACAG	GGAACTCTGAGTCATAGCGTCG
BAX	TCAGGATGCGTCCACCAAGAAG	TGTGTCCACGGCGGCAATCATC
BNIP3L	TGTGGAAATGCACACCAGCAGG	CTACTGGACCAGTCTGATACCC
18S	GGAGAGGGAGCCTGAGAAACG	TTACAGGGCCTCGAAAGAGTCC

图1 预后相关NEMG的识别和基于6-NEMG的风险特征模型的构建Note： A. DEGs between HCC and adjacent paired normal tissues. Red dots represent significant upregulation and blue dots represent significant downregulation of DEGs in HCC tissues. B. Heatmap of 264 deNEMGs in TCGA cohort. N—adjacent paired normal tissues; T—HCC tumor tissues. C. Venn diagram showing differently expressed deNEMGs between tumor and adjacent tissues. D. Univariate Cox regression analysis of 6 significant prognostic NEMGs associated with OXPHOS, TCA cycle and cell apoptosis. E. Multivariate Cox analysis of 6-NEMG risk signature.

Fig 1 Identification of prognosis-related NEMGs and construction of a 6-NEMG-based risk signature

图2 6-NEMG风险特征与TNM分期相结合的预后模型的构建Note： A/B. Univariate (A) and multivariate (B) Cox analysis of risk score and clinicopathological features for OS of HCC. C. ROC analysis for the prognostic model at 1-, 3- and 5-year survival in the TCGA-LIHC cohort. D. Kaplan-Meier survival analysis of OS in the high- and low-risk group in the TCGA-LIHC cohort.

Fig 2 Construction of a prognostic model integrating the 6-NEMG risk signature with TNM stage

表2 不同风险组患者的基本特征

Tab 2 Baseline characteristics of the patients in different risk groups

Feature	TCGA (n=339)			GEO (n=219)
Feature	High-risk group	Low-risk group	P value	High-risk group	Low-risk group	P value
Age/n(%)			0.681			0.969
≤50 years	36 (21.30)	39 (22.94)		53 (11.93)	58 (52.73)
>50 years	133 (78.73)	131 (77.06)		56 (88.07)	52 (47.27)
Gender/n(%)			0.254			0.156
Male	107 (63.31)	124 (72.94)		96 (88.07)	93 (84.55)
Female	62 (36.69)	46 (27.06)		13 (11.93)	17 (15.45)
Stage/n(%)			0.000			0.000
Ⅰ+Ⅱ	89 (52.66)	165 (97.06)		65 (59.63)	105 (95.45)
Ⅲ+Ⅳ	80 (47.34)	5 (2.94)		44 (40.37)	5 (4.55)
AFP/n(%)			0.031			0.045
≤20 ng·mL^-1 (TCGA) or ≤300 ng·mL^-1 (GEO)	118 (69.82)	111 (65.29)		57 (52.29)	61 (55.45)
>20 ng·mL^-1 (TCGA) or >300 ng·mL^-1 (GEO)	51 (30.18)	59 (34.71)		52 (47.71)	49 (44.55)
Event/n(%)
Survival	92 (54.44)	131 (77.06)		57 (52.29)	78 (70.91)
Death	77 (45.56)	39 (22.94)		52 (47.71)	32 (29.09)

图3 基于TCGA队列的HCC预后预测模型列线图的建立Note： A?C. ROC analysis of the prognostic score, risk score and TNM stage for 1-year OS (A), 3-year OS (B) and 5-year OS (C). D. Nomogram constructed by the prognostic score to predict OS of HCC. E. Calibration curves of the prognostic score-derived nomogram for 1-year, 3-year and 5-year OS of HCC.

Fig 3 Establishment of the prognostic score-derived nomogram based on TCGA cohort

图4 GEO和临床HCC队列中预后模型的验证Note： A. ROC analysis of the prognostic model for OS of HCC in GSE14520. B. Kaplan-Meier analysis of OS in HCC patients of GSE14520 which were stratified into high- and low-risk groups by this prognostic model. C. Correlation analysis between clinicopathological features and prognostic score in HCC patients of GSE14520. The correlation coefficient in red indicates P<0.05. D. Correlation analysis between clinicopathological features and prognostic score in 34-pair HCC and matched normal tissues. The correlation coefficient in red indicates P<0.05. E. Gene expression levels of the 6 NEMGs involved in this prognostic model in 34-pair HCC tumor tissues (T) and matched normal tissues (N).

Fig 4 Validation of the prognostic model in GEO and clinical HCC cohort

表3 临床队列中HCC患者的临床病理特征

Tab 3 Clinicopathological features of HCC patients in the clinical cohort

Feature	Total/n(%)
Age
≤50 year	8 (23.53)
>50 year	26 (76.47)
Gender
Male	24 (70.59)
Female	10 (29.41)
TNM stage
Ⅰ	15 (44.12)
Ⅱ	5 (14.71)
Ⅲ	11 (32.35)
Ⅳ	3 (8.82)
AFP
≤20 ng·mL^-1	21 (61.76)
>20 ng·mL^-1	13 (38.24)
Size
≤5 cm	20 (58.82)
>5 cm	14 (41.18)
Tumor number
=1	22 (64.71)
>1	12 (35.29)
HBsAg
-	7 (20.59)
+	27 (79.41)
GPT
≤40 U·L^-1	25 (73.53)
>40 U·L^-1	9 (26.47)
Cirrhosis
-	16 (40.06)
+	18 (59.94)

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肝细胞癌相关的核编码线粒体基因及临床信息的综合预后模型

An integrated prognostic model of nuclear-encoded mitochondrial gene signature and clinical information for hepatocellular carcinoma

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