基于铁死亡相关LncRNA构建肝细胞癌预后模型的研究

doi:10.19586/j.2095-2341.2022.0121

生物技术进展 ›› 2023, Vol. 13 ›› Issue (3): 473-481.DOI: 10.19586/j.2095-2341.2022.0121

• 研究论文 • 上一篇

基于铁死亡相关LncRNA构建肝细胞癌预后模型的研究

王彦容(), 郭元彪()

西南医科大学附属医院，四川泸州 646000

收稿日期:2022-07-04 接受日期:2023-03-16 出版日期:2023-05-25 发布日期:2023-06-12
通讯作者: 郭元彪
作者简介:王彦容 E-mail: xuetuyan@foxmail.com；
基金资助:
四川省科技计划项目(2020YFS0492)

Construction of a Prognostic Signature for Hepatocellular Carcinoma Based on Ferroptosis-related LncRNAs

Yanrong WANG(), Yuanbiao GUO()

Affiliated Hospital of Southwest Medical University，Sichuan Luzhou 646000，China

Received:2022-07-04 Accepted:2023-03-16 Online:2023-05-25 Published:2023-06-12
Contact: Yuanbiao GUO

摘要/Abstract

摘要：

为了探讨铁死亡相关长链非编码RNA(long non-coding RNAs, lncRNA)在预测肝细胞癌(hepatocellular carcinoma, HCC)患者临床预后方面的价值，基于铁死亡相关lncRNA构建了预后风险模型，用于评估HCC患者的生存及预后状况。从癌症基因组图谱（the cancer genome atlas, TCGA）数据库中收集HCC患者的转录组及临床数据，通过R软件包“LIMMA”鉴定出HCC与正常组织之间差异表达的lncRNA和mRNA，从FerrDB数据库中收集铁死亡相关mRNA。将上述mRNA取交集后得到差异表达的铁死亡相关mRNA，通过GO和KEGG分析阐明这些差异表达的mRNA在HCC中的作用。通过Pearson相关性分析得到了与铁死亡相关mRNA具有显著相关性的差异lncRNA。将这些lncRNA纳入Cox回归分析构建铁死亡相关lncRNA的预后风险模型。运用Kaplan-Meier（KM）生存分析、决策曲线分析(decision curve analysis, DCA)、受试者工作特征（receiver operating characteristic, ROC）曲线分析及独立预后分析评估该预后风险模型的准确性。研究共鉴定出17个铁死亡相关lncRNA(ZFPM2-AS1、AC012073.1、AL031985.3、AC026401.3、POLH-AS1、SNHG21、LINC00205、LINC00942、AP001469.3、AL139384.1、AC145207.8、AC090772.3、AL603839.3、SNHG10、AC099850.1、MKLN1-AS和AL928654.1)，它们是HCC潜在的预后生物标志物。通过这些铁死亡相关lncRNA构建的预后模型，其曲线下面积(area under curve, AUC)达到0.801。根据模型计算的风险评分将患者分为高风险组和低风险组，Kaplan-Meier曲线分析结果表明高风险组的总生存期较短。基因集富集分析(gene set enrichment analysis, GSEA)结果表明，免疫和肿瘤相关通路在高危人群中被激活。多因素独立预后分析表明，该预后风险模型是HCC的独立预后因素(HR：1.229，95CI：1.171~1.290)。研究结果提示基于17个铁死亡相关lncRNA构建的HCC预后风险模型是预测HCC患者预后的可靠工具。

关键词: 铁死亡, 肝细胞癌, 长链非编码RNA, 预后模型, 生物信息

Abstract:

In order to explore the value of ferroptosis-related lncRNAs in predicting the clinical prognosis of HCC patients， the study constructed a prognostic risk model based on ferroptosis-related long non-coding RNAs （lncRNA） to evaluate the survival and prognosis of patients with hepatocellular carcinoma （HCC）. Transcriptomic and clinical data of HCC patients were collected from the cancer genome atlas （TCGA） database. Differentially expressed lncRNAs and mRNAs between HCC and normal tissues were identified by the R software package “LIMMA”. Ferroptosis-related mRNAs were collected from the FerrDB database. The differentially expressed ferroptosis-related mRNAs were obtained by intersecting the above mRNAs， and the roles of these differentially expressed mRNAs in HCC were clarified by GO and KEGG analysis. Differential lncRNAs with significant correlation with ferroptosis-related mRNA were obtained by Pearson correlation analysis. These lncRNAs were included in Cox regression analysis to construct a prognostic risk model of ferroptosis-related lncRNAs. Kaplan-Meier （KM） survival analysis， decision curve analysis （DCA）， receiver operating characteristic （ROC） curve analysis and independent prognostic analysis were used to evaluate the accuracy of the prognostic risk model. Results showed that， a total of 17 ferroptosis-related lncRNAs were identified （ZFPM2-AS1， AC012073.1， AL031985.3， AC026401.3， POLH-AS1， SNHG21， LINC00205， LINC00942， AP001469.3， AL139384.1， AC145207.8， AC090772.3， AL603839.3， SNHG10， AC099850.1， MKLN1-AS and AL928654.1）， they were potential prognostic biomarkers for HCC. The area under the curve （AUC） of the prognostic model constructed by these ferroptosis-related lncRNAs reached 0.801. The patients were divided into high-risk group and low-risk group according to the risk score calculated by the model， and the results of Kaplan-Meier curve analysis showed that the high-risk group had a shorter overall survival. Gene set enrichment analysis （GSEA） demonstrated that immune and tumor-related pathways were activated in high-risk populations. Multivariate independent prognostic analysis showed that this prognostic risk model was an independent prognostic factor for HCC （HR： 1.229， 95CI： 1.171-1.290）. The results suggested that the HCC prognostic risk model based on 17 ferroptosis-related lncRNAs was a reliable tool for predicting the prognosis of HCC patients.

Key words: ferroptosis, hepatocellular carcinoma, long non-coding RNAs, prognostic signature, bioinformation

中图分类号:

R735.7

王彦容, 郭元彪. 基于铁死亡相关LncRNA构建肝细胞癌预后模型的研究[J]. 生物技术进展, 2023, 13(3): 473-481.

Yanrong WANG, Yuanbiao GUO. Construction of a Prognostic Signature for Hepatocellular Carcinoma Based on Ferroptosis-related LncRNAs[J]. Current Biotechnology, 2023, 13(3): 473-481.

图/表 6

图1 差异表达基因分析结果A：差异表达基因的GO分析；B：差异表达基因的KEGG分析

Fig. 1 Analysis of differentially expressed genes

图2 lncRNA预后模型对临床结局的预测A：Kaplan-Meier曲线结果；B：风险曲线图和生存状态图；C：受试者工作特征曲线；D：决策曲线

Fig. 2 Prediction of clinical outcomes by lncRNA prognostic risk model

图3 lncRNA在高风险和低风险组中的表达情况A：受试者工作特征曲线预测患者第1、3、5年的生存状态；B：17种铁死亡相关lncRNA的热图

Fig. 3 Expression of lncRNA in high-risk and low-risk groups

图4 多种方式验证lncRNA预后模型对预后的预测能力A：单因素Cox分析；B：多因素Cox分析；C：纳入预后变量建立的诺莫图；D：lncRNA和mRNA之间的网络关系。N—淋巴结转移状况；M—远处转移状；T—原发肿瘤的大小和范围。

Fig. 4 Multiple validations of the prognostic ability of the lncRNA prognostic model

图5 17个lncRNA和临床病理学特征的热图注：N—淋巴结转移状况；M—远处转移状；T—原发肿瘤的大小和范围。

Fig. 5 Heatmap of 17 lncRNAs and clinicopathological features

图6 铁死亡相关lncRNA的基因富集分析

Fig. 6 Gene enrichment analysis of ferroptosis related lncRNA

参考文献 32

1	TOHME S, YAZDANI H O, RAHMAN A, et al.. The use of machine learning to create a risk score to predict survival in patients with hepatocellular carcinoma: a TCGA cohort analysis[J/OL]. Can. J. Gastroenterol. Hepatol., 2021:5212953[2022-11-15]. .
2	SAYINER M, GOLABI P, YOUNOSSI Z M. Disease burden of hepatocellular carcinoma: a global perspective[J]. Dig. Dis. Sci., 2019, 64(4): 910-917.
3	SUNG H, FERLAY J, SIEGEL R L, et al.. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J. Clin., 2021, 71(3): 209-249.
4	FUJIWARA N, FRIEDMAN S L, GOOSSENS N, et al.. Risk factors and prevention of hepatocellular carcinoma in the era of precision medicine[J]. J. Hepatol., 2018, 68(3): 526-549.
5	FAMULARO S, CERESOLI M, GIANI A, et al.. Is it just a matter of surgical extension to achieve the cure of hepatocarcinoma? a meta-analysis of propensity-matched and randomized studies for anatomic bersus parenchyma-sparing liver resection[J]. J. Gastroin. Surg., 2021,25(1): 94-103.
6	AKADA K, KOYAMA N, TANIGUCHI S, et al.. Database analysis of patients with hepatocellular carcinoma and treatment flow in early and advanced stages[J/OL]. Pharmacol. Res. Perspect., 2019, 7(4): e00486[2022-11-15]. .
7	DIXON S J, LEMBERG K M, LAMPRECHT M R, et al.. Ferroptosis: an iron-dependent form of nonapoptotic cell death[J]. Cell, 2012, 149(5): 1060-1072.
8	WANG Y, WEI Z, PAN K, et al.. The function and mechanism of ferroptosis in cancer[J]. Apoptosis, 2020, 25(11-12): 786-798.
9	SHEN Z, LIU T, LI Y, et al.. Fenton-reaction-acceleratable magnetic nanoparticles for ferroptosis therapy of orthotopic brain tumors[J]. ACS Nano., 2018, 12(11): 11355-11365.
10	SUN X, NIU X, CHEN R, et al.. Metallothionein-1G facilitates sorafenib resistance through inhibition of ferroptosis[J]. Hepatology, 2016, 64(2): 488-500.
11	BAO H, SU H. Long noncoding RNAs act as novel biomarkers for hepatocellular carcinoma: progress and prospects[J/OL]. Biomed. Res. Int., 2017: 6049480[2022-11-15]. .
12	薛姗,唐铎,赵子杰,等.长链非编码RNA LINC00885在人食管癌细胞中的作用研究[J].生物技术进展,2022,12(3):419-426.
13	武丽娜,彭小忠,鲁重美.长链非编码RNA ZFAS1在结直肠癌中的表达及生物学功能[J].生物技术进展,2022,12(3):427-435.
14	MAI H, ZHOU B, LIU L, et al.. Molecular pattern of lncRNAs in hepatocellular carcinoma[J/OL]. J. Exp. Clin. Cancer Res., 2019, 38(1): 198[2022-11-15]. .
15	TAN C, CAO J, CHEN L, et al.. Noncoding RNAs serve as diagnosis and prognosis biomarkers for hepatocellular carcinoma[J]. Clin. Chem., 2019, 65(7): 905-915.
16	JIANG X, STOCKWELL B R, CONRAD M. Ferroptosis: mechanisms, biology and role in disease[J]. Nat. Rev. Mol. Cell Biol., 2021, 22(4): 266-282.
17	GAO R, KALATHUR R K R, COTO-LLERENA M, et al.. YAP/TAZ and ATF4 drive resistance to sorafenib in hepatocellular carcinoma by preventing ferroptosis[J/OL]. EMBO Mol. Med, 2021,13: e14351[2022-11-15]. .
18	VANDERBORGHT B, LEFERE S, VLIERBERGHE H V, et al.. The angiopoietin/Tie2 pathway in hepatocellular carcinoma[J/OL]. Cells, 2020, 9(11): 9112382 [2022-11-15]. .
19	JASZAI J, SCHMIDT M H H. Trends and challenges in tumor anti-angiogenic therapies[J/OL]. Cells, 2019, 8(9): 8091102 [2022-11-15]. .
20	LEE S H, GOLINSKA M, GRIFFITHS J R. HIF-1-independent mechanisms regulating metabolic adaptation in hypoxic cancer cells[J/OL]. Cells, 2021, 10(9): 10092371[2022-11-15].
21	KORBECKI J, SIMINSKA D, GASSOWSKA-DOBROWOLSKA M, et al.. Chronic and cycling hypoxia: drivers of cancer chronic inflammation through HIF-1 and NF-kappaB activation: a review of the molecular mechanisms[J/OL]. Int. J. Mol. Sci., 2021, 22(19): 221910701[2022-11-15]. .
22	MANUELLI V, PECORARI C, FILOMENI G, et al.. Regulation of redox signaling in HIF-1-dependent tumor angiogenesis[J/OL]. FEBS J., 2021, 289(18): 5413-5425.
23	YANG Q, WANG L, LIU J, et al.. Targeting the complex I and Ⅲ of mitochondrial electron transport chain as a potentially viable option in liver cancer management[J/OL]. Cell Death Discov., 2021, 7(1): 293[2022-11-15]. .
24	KIM T S, LEE M, PARK M, et al.. Metformin and dichloroacetate suppress proliferation of liver cancer cells by inhibiting mTOR complex 1[J/OL]. Int. J. Mol. Sci., 2021, 22(18): 221810027 [2022-11-15]. .
25	DAI T, LI J, LU X, et al.. Prognostic role and potential mechanisms of the ferroptosis-related metabolic gene signature in hepatocellular carcinoma[J]. Pharm. Pers. Med., 2021, 14: 927-945.
26	BAI Y, QI W, LIU L, et al.. Identification of seven-gene hypoxia signature for predicting overall survival of hepatocellular carcinoma[J/OL]. Front. Genet., 2021, 12: 637418[2022-11-15]. .
27	FENG R, LI J, XUAN W, et al.. An autophagy-related gene-based prognostic risk signature for hepatocellular carcinoma: construction and validation[J/OL]. Comput. Math. Methods Med., 2021: 5770228[2022-11-15]. .
28	TIAN D, YU Y, ZHANG L, et al.. A five-gene-based prognostic signature for hepatocellular carcinoma[J/OL]. Front. Med.(Lausanne), 2021, 8: 681388[2022-11-15]. .
29	ZHANG H, XIA P, MA W, et al.. Development and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma[J]. J. Clin. Transl. Hepatol., 2021, 9(5): 635-646.
30	GUO C, ZHOU W, YI P, et al.. Long non-coding RNA muskelin 1 antisense RNA (MKLN1-AS) is a potential diagnostic and prognostic biomarker and therapeutic target for hepatocellular carcinoma[J/OL]. Exp. Mol. Pathol., 2021,120: 104638[2022-11-15]. .
31	HE H, WANG Y, YE P, et al.. Long noncoding RNA ZFPM2-AS1 acts as a miRNA sponge and promotes cell invasion through regulation of miR-139/GDF10 in hepatocellular carcinoma[J/OL]. J. Exp. Clin. Cancer Res., 2020, 39: 159[2022-11-15]. .
32	XU Z, PENG B, LIANG Q, et al.. Construction of a ferroptosis-related nine-lncRNA signature for predicting prognosis and immune response in hepatocellular carcinoma[J/OL]. Front. Immunol., 2021, 12: 719175[2022-11-15]. .

[1]	胡文冉, 郝晓燕, 赵准, 邵武奎, 高升旗, 李建平, 黄全生. 棉花CAD基因家族的生物信息学分析[J]. 生物技术进展, 2023, 13(3): 412-424.
[2]	曹静钰, 刘承梅, 祁晨旭, 杜开颜, 陈蒙, 侯思伟. Nrf2在脊髓损伤后铁死亡的研究进展[J]. 生物技术进展, 2023, 13(2): 240-246.
[3]	李冠群, 梁超杰, 夏峰. 基于甲基化组学数据建立并验证肝细胞癌预后评价模型研究[J]. 生物技术进展, 2023, 13(2): 282-290.
[4]	朱兴墅, 黄军杰, 翁才明, 刘旺武, 陈永元, 陈惠燕, 赵虎, 王瑜, 林承志. 基于生物信息学分析林奇综合征相关结直肠癌关键Hub基因的筛选与功能富集分析[J]. 生物技术进展, 2023, 13(2): 291-297.
[5]	李凤梅, 汤雪媛, 焦高洋, 齐玉亭, 杜献婷, 鲍倩倩, 张梦, 徐小博, 徐鑫, 张艳芳. 金银花花形基因的发掘及生物信息学分析[J]. 生物技术进展, 2023, 13(1): 90-101.
[6]	杜开颜, 祁晨旭, 曹静钰, 陈蒙, 高静, 刘承梅. 铁死亡参与脊髓损伤调控的研究进展[J]. 生物技术进展, 2022, 12(6): 869-874.
[7]	于敏, 王敏, 魏延焕, 刘毅毅. SARS-CoV-2病毒感染潜在关键分子生物标志物及免疫浸润特征分析[J]. 生物技术进展, 2022, 12(5): 760-768.
[8]	薛姗, 唐铎, 赵子杰, 洪启浩, 王谦, 刘紫佳, 周志祥. 长链非编码RNA LINC00885在人食管癌细胞中的作用研究[J]. 生物技术进展, 2022, 12(3): 419-426.
[9]	武丽娜, 彭小忠, 鲁重美. 长链非编码RNA ZFAS1在结直肠癌中的表达及生物学功能[J]. 生物技术进展, 2022, 12(3): 427-435.
[10]	雍嘉欣, 韦权峰, 刘尚辉. 基于TCGA和GEO数据库的胃癌lncRNA筛选与ceRNA网络构建[J]. 生物技术进展, 2022, 12(1): 149-157.
[11]	金童, 陈铖. 铁死亡与肾脏疾病相关性的研究进展[J]. 生物技术进展, 2022, 12(1): 68-74.
[12]	许广平,张志勇,任忠宏,张志伟. 黑鲷ELO基因编码蛋白结构与功能的生物信息学分析[J]. 生物技术进展, 2021, 11(3): 344-352.
[13]	许杰,王可飞,魏晓晶,龚莉欣,焦阳,邱录贵,郝牧. 基于生物信息学分析SCHIP1在急性髓系白血病中的表达及其临床意义[J]. 生物技术进展, 2020, 10(4): 417-425.
[14]	张凤丽,杨雅麟,夏锐,高辰辰,李栋,冉超,张震,张洪玲,周志刚. 吉陶单极虫可分泌蛋白酶基因的克隆与生物信息学分析[J]. 生物技术进展, 2019, 9(4): 375-383.
[15]	徐长禄,赵艳红,马艺戈,王冰蕊,王鼎,郭青,佟静媛,高洁,李亚朴,刘金花,石莉红. 利用生物信息学方法解析自噬相关基因在人体红细胞终末分化各阶段的动态表达[J]. 生物技术进展, 2019, 9(3): 271-276.

基于铁死亡相关LncRNA构建肝细胞癌预后模型的研究

Construction of a Prognostic Signature for Hepatocellular Carcinoma Based on Ferroptosis-related LncRNAs

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 32

相关文章 15

编辑推荐

Metrics

本文评价