生物信息学在肝细胞癌风险预测中的应用

方国旭; 张清华; 黄永迎; 王建民; 刘景丰

doi:10.3969/j.issn.1001-5256.2022.01.002

生物信息学在肝细胞癌风险预测中的应用

DOI: 10.3969/j.issn.1001-5256.2022.01.002

方国旭^{1a, 1b},
张清华²,
黄永迎^3a,
王建民^3b,
刘景丰^{1b, 3c, ,}

1a.
福建医科大学孟超肝胆医院肝胆胰外, 福州 350025
1b.
福建医科大学孟超肝胆医院东南肝胆健康大数据研究所, 福州 350025
2.
福建医科大学研究生院, 福州 350108
3a.
福建省肿瘤医院流行病研究室, 福州 350014
3b.
福建省肿瘤医院肿瘤医学创新中心, 福州 350014
3c.
福建省肿瘤医院肝胆胰肿瘤外科, 福州 350014

利益冲突声明：所有作者均声明不存在利益冲突。

作者贡献声明：方国旭负责撰写论文；张清华、黄永迎和王建民负责修改论文；刘景丰负责拟定写作思路，指导撰写文章并最后定稿。

详细信息

通信作者:
刘景丰，drjingfeng@126.com

计量
- 文章访问数: 711
- HTML全文浏览量: 167
- PDF下载量: 135
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-30
- 录用日期: 2021-12-06
- 出版日期: 2022-01-20

Application of bioinformatics in predicting the risk of hepatocellular carcinoma

Guoxu FANG^{1a, 1b},
Qinghua ZHANG²,
Yongying HUANG^3a,
Jianmin WANG^3b,
Jingfeng LIU^{1b, 3c
, ,}

1a.
Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China
1b.
Big Data Institute of Southeast Hepatobiliary Health Information, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China
2.
Graduate School of Fujian Medical University, Fuzhou 350108, China
3a.
Department of Epidemics, Fujian Provincial Tumor Hospital, Fuzhou 350014, China
3b.
Innovation Center for Cancer Research, Fujian Provincial Tumor Hospital, Fuzhou 350014, China
3c.
Department of Hepatopancreatobiliary Surgery, Fujian Provincial Tumor Hospital, Fuzhou 350014, China

摘要

摘要: 生物信息学是一门交叉科学, 通过综合运用数学、计算机科学和生物学的各种工具, 来阐明和理解大量生物数据所包含的生物学意义。随着基因组学测序技术的不断发展, 产生大量的生物学数据资源, 从大数据中挖掘所蕴藏的生物学意义, 已成为了当前亟待解决的主要任务之一。本文主要归纳总结基于特征基因的肝癌风险预测模型, 为肝癌的早期检测、预后和治疗方案的优化提供新观点。
- 癌, 肝细胞 /
- 生物信息学 /
- 预测
Abstract: Bioinformatics is an interdisciplinary science that combines the tools of mathematics, computer science, and biology to clarify and explore the biological implications of large amounts of biological data. With the continuous development of genome sequencing technology, a large number of biological data has been generated, and mining of the biological significance contained in big data has become one of the main tasks that need to be solved urgently. This article summarizes the risk prediction models for hepatocellular carcinoma (HCC) based on feature genes, so as to provide new perspectives for early identification, prognosis, and treatment optimization of HCC.
- Carcinoma, Hepatocellular /
- Bioinformatics /
- Forecasting

HTML全文

参考文献(40)

[1]	BRAY F, FERLAY J, SOERJOMATARAM I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countrie[J]. CA Cancer J Clin, 2018 68(6): 394-424. DOI: 10.3322/caac.21492.
[2]	de STEFANO F, CHACON E, TURCIOS L, et al. Novel biomarkers in hepatocellular carcinoma[J]. Dig Liver Dis, 2018, 50(11): 1115-1123. DOI: 10.1016/j.dld.2018.08.019.
[3]	VOGELSTEIN B, PAPADOPOULOS N, VELCULESCU VE, et al. Cancer genome landscapes[J]. Science, 2013, 339(6127): 1546-1558. DOI: 10.1126/science.1235122.
[4]	TOMCZAK K, CZERWIŃSKA P, WIZNEROWICZ M. The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge[J]. Contemp Oncol (Pozn), 2015, 19(1A): A68-A77. DOI: 10.5114/wo.2014.47136.
[5]	DAVIS S, MELTZER PS. GEOquery: A bridge between the Gene Expression Omnibus (GEO) and BioConductor[J]. Bioinformatics, 2007, 23(14): 1846-1847. DOI: 10.1093/bioinformatics/btm254.
[6]	GALON J, BRUNI D. Tumor immunology and tumor evolution: Intertwined histories[J]. Immunity, 2020, 52(1): 55-81. DOI: 10.1016/j.immuni.2019.12.018.
[7]	MELERO I, BERMAN DM, AZNAR MA, et al. Evolving synergistic combinations of targeted immunotherapies to combat cancer[J]. Nat Rev Cancer, 2015, 15(8): 457-472. DOI: 10.1038/nrc3973.
[8]	CHEN W, OU M, TANG D, et al. Identification and validation of immune-related gene prognostic signature for hepatocellular carcinoma[J]. J Immunol Res, 2020, 2020: 5494858. DOI: 10.1155/2020/5494858.
[9]	FENG X, MU S, MA Y, et al. Development and verification of an immune-related gene pairs prognostic signature in hepatocellular carcinoma[J]. Front Mol Biosci, 2021, 8: 715728. DOI: 10.3389/fmolb.2021.715728.
[10]	HU B, YANG XB, SANG XT. Molecular subtypes based on immune-related genes predict the prognosis for hepatocellular carcinoma patients[J]. Int Immunopharmacol, 2021, 90: 107164. DOI: 10.1016/j.intimp.2020.107164.
[11]	HUANG R, CHEN Z, LI W, et al. Immune system-associated genes increase malignant progression and can be used to predict clinical outcome in patients with hepatocellular carcinoma[J]. Int J Oncol, 2020, 56(5): 1199-1211. DOI: 10.3892/ijo.2020.4998.
[12]	WANG K, CHEN X, JIN C, et al. A novel immune-related genes prognosis biomarker for hepatocellular carcinoma[J]. Aging (Albany NY), 2020, 13(1): 675-693. DOI: 10.18632/aging.202173.
[13]	HANAHAN D, WEINBERG RA. Hallmarks of cancer: The next generation[J]. Cell, 2011, 144(5): 646-674. DOI: 10.1016/j.cell.2011.02.013.
[14]	FENG J, LI J, WU L, et al. Emerging roles and the regulation of aerobic glycolysis in hepatocellular carcinoma[J]. J Exp Clin Cancer Res, 2020, 39(1): 126. DOI: 10.1186/s13046-020-01629-4.
[15]	CHEN Z, ZOU Y, ZHANG Y, et al. A novel prognostic signature based on four glycolysis-related genes predicts survival and clinical risk of hepatocellular carcinoma[J]. J Clin Lab Anal, 2021, 35(11): e24005. DOI: 10.1002/jcla.24005.
[16]	WANG M, JIANG F, WEI K, et al. Identification of novel gene signature associated with cell glycolysis to predict survival in hepatocellular carcinoma patients[J]. J Oncol, 2021, 2021: 5564525. DOI: 10.1155/2021/5564525.
[17]	ZHOU W, ZHANG S, CAI Z, et al. A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma[J]. PeerJ, 2020, 8: e9944. DOI: 10.7717/peerj.9944.
[18]	SANGINETO M, VILLANI R, CAVALLONE F, et al. Lipid metabolism in development and progression of hepatocellular carcinoma[J]. Cancers (Basel), 2020, 12(6): 1419. DOI: 10.3390/cancers12061419.
[19]	WANG W, ZHANG C, YU Q, et al. Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients[J]. BMC Gastroenterol, 2021, 21(1): 68. DOI: 10.1186/s12876-021-01638-3.
[20]	TSUN ZY, POSSEMATO R. Amino acid management in cancer[J]. Semin Cell Dev Biol, 2015, 43: 22-32. DOI: 10.1016/j.semcdb.2015.08.002.
[21]	LUKEY MJ, KATT WP, CERIONE RA. Targeting amino acid metabolism for cancer therapy[J]. Drug Discov Today, 2017, 22(5): 796-804. DOI: 10.1016/j.drudis.2016.12.003.
[22]	LEE DY, KIM EH. Therapeutic effects of amino acids in liver diseases: Current studies and future perspectives[J]. J Cancer Prev, 2019, 24(2): 72-78. DOI: 10.15430/JCP.2019.24.2.72.
[23]	DEJONG CH, van de POLL MC, SOETERS PB, et al. Aromatic amino acid metabolism during liver failure[J]. J Nutr, 2007, 137(6 Suppl 1): 1579S-1585S; discussion 1597S-1598S. DOI: 10.1093/jn/137.6.1579S.
[24]	ZHAO Y, ZHANG J, WANG S, et al. Identification and validation of a nine-gene amino acid metabolism-related risk signature in HCC[J]. Front Cell Dev Biol, 2021, 9: 731790. DOI: 10.3389/fcell.2021.731790.
[25]	WU J, WANG Y, JIANG R, et al. Ferroptosis in liver disease: New insights into disease mechanisms[J]. Cell Death Discov, 7(1): 276. DOI: 10.1038/s41420-021-00660-4.
[26]	LIANG JY, WANG DS, LIN HC, et al. A novel ferroptosis-related gene signature for overall survival prediction in patients with hepatocellular[J]. Int J Biol Sci, 2020, 16(13): 2430-2441. DOI: 10.7150/ijbs.45050.
[27]	WAN S, LEI Y, LI M, et al. A prognostic model for hepatocellular carcinoma patients based on signature ferroptosis-related genes[J]. Hepatol Int, 2021. DOI: 10.1007/s12072-021-10248-w.[Epub ahead of print]
[28]	CHEN ZA, TIAN H, YAO DM, et al. Identification of a ferroptosis-related signature model including mRNAs and lncRNAs for predicting prognosis and immune activity in hepatocellular carcinoma[J]. Front Oncol, 2021, 11: 738477. DOI: 10.3389/fonc.2021.738477.
[29]	LIANG J, ZHI Y, DENG W, et al. Development and validation of ferroptosis-related lncRNAs signature for hepatocellular carcinoma[J]. Peer J, 2021, 9: e11627. DOI: 10.7717/peerj.11627.
[30]	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]. Front Immunol, 2021, 12: 719175. DOI: 10.3389/fimmu.2021.719175.
[31]	LEVY JMM, TOWERS CG, THORBURN A. Targeting autophagy in cancer[J]. Nat Rev Cancer, 2017, 17(9): 528-542. DOI: 10.1038/nrc.2017.53.
[32]	AMARAVADI RK, KIMMELMAN AC, DEBNATH J. Targeting autophagy in cancer: Recent advances and future directions[J]. Cancer Discov, 2019, 9(9): 1167-1181. DOI: 10.1158/2159-8290.CD-19-0292.
[33]	HUO X, QI J, HUANG K, et al. Identification of an autophagy-related gene signature that can improve prognosis of hepatocellular carcinoma patients[J]. BMC Cancer, 2020, 20(1): 771. DOI: 10.1186/s12885-020-07277-3.
[34]	YANG W, NIU L, ZHAO X, et al. Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma[J]. Am J Transl Res, 2020, 12(10): 6705-6722.
[35]	CAO J, WU L, LEI X, et al. A signature of 13 autophagy related gene pairs predicts prognosis in hepatocellular carcinoma[J]. Bioengineered, 2021, 12(1): 697-707. DOI: 10.1080/21655979.2021.1880084.
[36]	WU H, LIU T, QI J, et al. Four autophagy-related lncRNAs predict the prognosis of HCC through coexpression and ceRNA mechanism[J]. Biomed Res Int, 2020, 2020: 3801748. DOI: 10.1155/2020/3801748.
[37]	YANG S, ZHOU Y, ZHANG X, et al. The prognostic value of an autophagy-related lncRNA signature in hepatocellular carcinoma[J]. BMC Bioinformatics, 2021, 22(1): 217. DOI: 10.1186/s12859-021-04123-6.
[38]	CHEN XY, ZHANG J, ZHU JS. The role of m6A RNA methylation in human cancer[J]. Mol Cancer, 2019, 18(1): 103. DOI: 10.1186/s12943-019-1033-z.
[39]	ZHANG L, QIAO Y, HUANG J, et al. Expression pattern and prognostic value of key regulators for m6A RNA modification in hepatocellular carcinoma[J]. Front Med (Lausanne), 2020, 7: 556. DOI: 10.3389/fmed.2020.00556.
[40]	XU Y, HE X, DENG J, et al. Comprehensive analysis of the immune infiltrates and PD-L1 of m6A RNA methylation regulators in hepatocellular carcinoma[J]. Front Cell Dev Biol, 2021, 9: 681745. DOI: 10.3389/fcell.2021.681745.