衰老相关分泌表型相关的lncRNAs模型预测肝细胞癌患者预后的构建和验证

doi:10.20047/j.issn1673-7210.2023.25.02

摘要
图/表
参考文献(26)
相关文章 (15)

全文: PDF (2546 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要目的构建衰老相关分泌表型（SASP）相关的可独立预测肝细胞癌（HCC）预后的风险模型，提高HCC的临床疗效。方法在癌症基因组学图谱（TCGA）数据库中获取HCC的测序数据及临床数据并分离出lncRNAs；在GeneCard网站中检索并筛选出相关系数＞7的SASP相关基因，利用R语言“limma”包筛选出差异表达的SASP相关的基因及对应的lncRNAs。利用Cox回归分析和lasso构建SASP相关的lncRNAs预测模型，根据样本的风险评分中位值将HCC患者分为高风险组、低风险组，采用Kaplan-Meier曲线进行生存分析。利用单样本基因集富集分析（ssGSEA）来研究样本的风险评分和免疫状态之间的联系。结果从TCGA数据库中获取了374个肿瘤样本和50个正常组织样本，筛选出6个SASP相关的lncRNAs组成预后模型，计算出各样本的风险评分。生存分析显示，低风险组患者的预后优于高风险组患者的预后。风险模型1年生存受试者操作特征曲线的曲线下面积0.812。单因素和多因素回归分析显示，患者风险评分是HCC预后的独立危险因素（OR＞1，P＜0.05）。ssGSEA结果显示，高风险组免疫检查点特异性靶点和相关通路的表达水平更高。结论本研究成功构建SASP相关的lncRNAs风险模型，风险模型可独立预测HCC患者的预后情况。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	周丁杰　　戈伟　　曹德东

关键词 ：肝细胞癌, 衰老相关分泌表型, LncRNA, 生物标志物, 诊断

Abstract：Objective To construct a senescence-associated secretory phenotype(SASP) related risk model that can independently predict the prognosis of hepatocellular carcinoma (HCC), and to improve the clinical efficacy of HCC. Methods Sequencing and clinical data of HCC were obtained from the cancer genome atlas(TCGA) database, and lncRNA were isolated. SASP related genes with correlation coefficient ＞ seven were searched and screened from the GeneCard website, and genes related to differentially expressed SASP associated lncRNAs were screened using the R language “limma” package. Cox regression analysis and lasso were used to construct the lncRNAs prediction model related to SASP. HCC patients were divided into high-risk group and low-risk group according to the median risk score of the samples. Kaplan-Meier curve was used for survival analysis. Single sample gene set enrichment analysis (ssGSEA) was used to investigate the association between risk scores and immune status in samples. Results A total of 374 tumor samples and 50 normal tissue samples were obtained from the TCGA database. Six lncRNAs associated with SASP were selected form a prognostic model, and the risk scores of each sample were calculated. Survival analysis showed that the prognosis of patients in the low-risk group was better than that in the high-risk group. Area under one-year survival receiver operating characteristic curve of risk model was 0.812. Univariate and multivariate regression analysis showed that patient risk score was an independent risk factor for HCC prognosis (OR ＞ 1, P ＜ 0.05). ssGSEA results showed higher expression levels of immune checkpoint specific targets and associated pathways in the high-risk group. Conclusion In this study, the SASP-related lncRNAs risk model is successfully constructed, and the risk model can independently predict the prognosis of HCC patients.

Key words： Hepatocellular carcinoma Senescence-associated secretory phenotype LNcRNA Biomarker Diagnosis

基金资助:国家自然科学基金青年科学基金资助项目（81700208）。

通讯作者: 戈伟（1960.5-），男，博士，副主任医师；研究方向：肺癌、消化道肿瘤、淋巴瘤。

作者简介: 周丁杰（1997.11-），男，武汉大学第一临床学院2020级肿瘤学专业在读硕士研究生；研究方向：肿瘤衰老与肿瘤代谢。

引用本文:

周丁杰　　戈伟　　曹德东. 衰老相关分泌表型相关的lncRNAs模型预测肝细胞癌患者预后的构建和验证[J]. 中国医药导报, 2023, 20(25): 12-19.
ZHOU Dingjie GE Wei CAO Dedong. Construction and validation of a senescence-associated secretory phenotype associated lncRNAs model to predict prognosis in patients with hepatocellular carcinoma. 中国医药导报, 2023, 20(25): 12-19.

链接本文:

https://www.yiyaodaobao.com.cn/CN/10.20047/j.issn1673-7210.2023.25.02 或 https://www.yiyaodaobao.com.cn/CN/Y2023/V20/I25/12

[1] Yang JD，Hainaut P，Gores GJ，et al. A global view of hepato- cellular carcinoma：trends，risk，prevention and manage- ment [J]. Na Rev Gastroenterol Hepatol，2019，16（10）：589- 604.
[2] Allemani C，Matsuda T，Di Carlo V，et al. Global surveillance of trends in cancer survival 2000-14 （CONCORD-3）：analysis of individual records for 37513025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries [J]. Lancet，2018，391（10125）：1023-1075.
[3] Birch J，Gil J. Senescence and the SASP：many therapeutic avenues [J]. Genes Dev，2020，34（23-24）：1565-1576.
[4] Chambers CR，Ritchie S，Pereira BA，et al. Overcoming the senescence-associated secretory phenotype （SASP）：a complex mechanism of resistance in the treatment of cancer [J]. Mol Oncol，2021，15（12）：3242-3255.
[5] Wang X，Wang J，Tsui YM，et al. RALYL increases hepatocellular carcinoma stemness by sustaining the mRNA stability of TGF-β2 [J]. Nat Commun，2021，12（1）：1518-1534.
[6] Liu H，Zhao H，Sun Y. Tumor microenvironment and cellular senescence：Understanding therapeutic resistance and harnessing strategies [J]. Semin Cancer Biol，2022，86（Pt 3）：769-781.
[7] Tam C，Wong JH，Tsui SKW，et al. LncRNAs with miRNAs in regulation of gastric，liver，and colorectal cancers：updates in recent years [J]. Appl Microbiol Biotechnol，2019，103（12）：4649-4677.
[8] Ahmad S，Abbas M，Ullah MF，et al. Long non-coding RNAs regulated NF-κB signaling in cancer metastasis：Micromanaging by not so small non-coding RNAs [J]. Semin Cancer Biol，2022，85：155-163.
[9] Wang M，Yu F，Chen X，et al. The Underlying Mechanisms of Noncoding RNAs in the Chemoresistance of Hepatocellular Carcinoma [J]. Mol Ther Nucleic Acids，2020，21：13-27.
[10] Chang K，Creighton CJ，Davis C，et al. The Cancer Genome Atlas Pan-Cancer analysis project [J]. Nature Genetics，2013， 45（10）：1113-1120.
[11] Martin FJ，Amode MR，Aneja A，et al. Ensembl 2023 [J]. Nucleic Acids Res，2023，51（D1）：D933-D941.
[12] Safran M，Rosen N，Twik M，et al. The GeneCards Suite [M]//Abugessaisa I，Kasukawa T. Practical Guide to Life Science Databases. Singapore; Springer Nature Singapore，2021：27-56.
[13] Ritchie ME，Phipson B，Wu D，et al. limma powers differential expression analyses for RNA-sequencing and microarray studies [J]. Nucleic Acids Res，2015，43（7）：47-58.
[14] Engebretsen S，Bohlin J. Statistical predictions with glmnet [J]. Clin Epigenetics，2019，11（1）：123-131.
[15] H?覿nzelmann S，Castelo R，Guinney J. GSVA：gene set variation analysis for microarray and RNA-Seq data [J]. BMC Bioinformatics，2013，14（1）：7-14.
[16] Chidambaranathan-Reghupaty S，Fisher PB，Sarkar D. Hepatocellular carcinoma （HCC）：Epidemiology，etiology and molecular classification [J]. Adv Cancer Res，2021，149：1332-1355.
[17] Vogel A，Meyer T，Sapisochin G，et al. Hepatocellular carcinoma [J]. Lancet，2022，400（10360）：1345-1362.
[18] Liu P，Tang Q，Chen M，et al. Hepatocellular Senescence：Immunosurveillance and Future Senescence-Induced Therapy in Hepatocellular Carcinoma [J]. Fronti Oncol，2020， 10：5899-5908.
[19] Ma Y，Li S，Ye S，et al. Hexavalent chromium triggers hepatocytes premature senescence via the GATA4/NF-κB signaling pathway mediated by the DNA damage response [J]. Ecotoxicol Environ Saf，2022，239（11）：36-45.
[20] Huang W，Hickson LJ，Eirin A，et al. Cellular senescence：the good，the bad and the unknown [J]. Nat Rev Nephrol，2022， 18（10）：611-627.
[21] Liu H，Zhao H，Sun Y. Tumor microenvironment and cellular senescence：Understanding therapeutic resistance and harnessing strategies [J]. Semin Cancer Biol，2022， 86：769- 781.
[22] Fang Y，Yang Y，Li N，et al. Emerging role of long noncoding RNAs in recurrent hepatocellular carcinoma [J]. World J Clin Cases，2021，9（32）：9699-9710.
[23] Oh C，Koh D，Jeon HB，et al. The Role of Extracellular Vesicles in Senescence [J]. Mol Cells，2022，45（9）：603-609.
[24] Wirtz TH，Saal A，Bergmann I，et al. Macrophage migration inhibitory factor exerts pro-proliferative and anti-apoptotic effects via CD74 in murine hepatocellular carcinoma [J]. Br J Pharmacol，2021，178（22）：4452-4467.
[25] Zhou D，Wang Y，Hu H，et al. lncRNA MALAT1 promotes HCC metastasis through the peripheral vascular infiltration via miRNA-613：a primary study using contrast ultrasound [J]. World J Surg Oncol，2022，20（1）：203-221.
[26] Lu J，Guo J，Liu J，et al. Long Non-coding RNA MALAT1：A Key Player in Liver Diseases [J]. Front Med （Lausanne），2021，8（6）：734-743.