|Table of Contents|

Screening and identification of metabolic genes related to prognosis of hepatocellular carcinoma by bioinformatics

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2022 05
Page:
860-869
Research Field:
Publishing date:

Info

Title:
Screening and identification of metabolic genes related to prognosis of hepatocellular carcinoma by bioinformatics
Author(s):
ZHANG Rongjie1; ZHOU Ge2; LI Fuyang1; LIU Jiahui1; CHEN Zexiong1
1.Traditional Chinese Medicine Department,the First Affiliated Hospital of Sun Yatsen University,Guangdong Guangzhou 510030，China;2.Guangdong Second Hospital of Traditional Chinese Medicine,Guangdong Guangzhou 510030，China.
Keywords:
hepatocellular carcinoma; metabolic genes; prognosis model; bioinformatics analysis
PACS:
R735.7
DOI:
10.3969/j.issn.1672-4992.2022.05.022
Abstract:
Objective:To find the potential metabolic genes related to the pathogenesis and prognosis of HCC and to construct a model to predict the prognosis of HCC patients.Methods:All the genes related to metabolic pathway were obtained by GSEA database,and the gene expression data of HCC and normal tissues were downloaded from TCGA database.The differential expression of these metabolic genes(DEGs) in HCC tissue was analyzed.Then the metabolic genes related to prognosis were screened by univariate Cox regression analysis.On this basis,LASSO analysis was used to further screen prognostic genes to construct a prognostic risk model and analyze the prognosis of high and low risk groups.The genes in the prognostic model were analyzed by KEGG pathway enrichment analysis and GO analysis.Univariate Cox analysis and multivariate Cox regression analysis were used to analyze the independent prognosis of the prognostic model,and Nomogram diagram was drawn to evaluate the prognosis of the patients.Finally,the expression of prognostic genes in tumor tissues and normal tissues was compared by GEPIA2 database,and the effect of prognostic genes on survival was analyzed.Finally,the liver cancer samples(GSE14520) in GEO database were used for external verification.Results:A total of 959 metabolism-related genes were obtained.There were 156 genes with significant differences in expression(DEGs),of which 105 genes were up-regulated and 51 genes were down-regulated.Then,through univariate Cox analysis of these DEGs,58 candidate genes related to prognosis were obtained.On this basis,the prognostic model of eleven-gene signature was constructed by LASSO analysis.In the model,the prognosis of the high risk group was worse than that of the low risk group(P＜0.05).The model can be repeated in GSE14520.The main pathways involved in the prognostic model were pyrimidine metabolism,glutathione metabolism,drug metabolism,carbon metabolism,purine metabolism,amino sugar and nucleotide glucose metabolism.Among the 11 genes,survival analysis showed that the high expression of ATIC,ENO1,G6PD,GNPDA1,HEXB,ME1,RRM1,RRM2 and UCK2 and the low expression of CYP2C9 were significantly correlated with poor prognosis.Conclusion:The model of 11-gene signature and Nomogram map established in this study can help clinicians to evaluate the prognosis of patients with liver cancer.

References:

[1] GAO Q,ZHU H,DONG L,et al.Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma［J］.Cell,2019,179(2):561-577.
[2] ZHANG Q,HE Y,LUO N,et al.Landscape and dynamics of single immune cells in hepatocellular carcinoma［J］.Cell,2019,179(4):829-845.
[3] BOSETTI C,TURATI F,LA VECCHIA C.Hepatocellular carcinoma epidemiology［J］.Best Pract Res Clin Gastroenterol,2014,28(5):753-770.
[4] DE MARTEL C,MAUCORT-BOULCH D,PLUMMER M,et al.World-wide relative contribution of hepatitis B and C viruses in hepatocellular carcinoma［J］.Hepatology,2015,62(4):1190-1200.
[5] WANG J,HA J,LOPEZ A,et al.Medicaid and uninsured hepatocellular carcinoma patients have more advanced tumor stage and are less likely to receive treatment［J］.J Clin Gastroenterol,2018,52(5):437-443.
[6] KULIK L,HEIMBACH JK,ZAIEM F,et al.Therapies for patients with hepatocellular carcinoma awaiting liver transplantation:A systematic review and meta-analysis［J］.Hepatology,2018,67(1):381-400.
[7] LIU J,LU J,MA Z,et al.A nomogram based on a three-gene signature derived from AATF coexpressed genes predicts overall survival of hepatocellular carcinoma patients［J］.Biomed Res Int,2020,2020:7310768.
[8] KIM SM,LEEM SH,CHU IS,et al.Sixty-five gene-based risk score classifier predicts overall survival in hepatocellular carcinoma［J］.Hepatology,2012,55(5):1443-1452.
[9] MUIR K,HAZIM A,HE Y,et al.Proteomic and lipidomic signatures of lipid metabolism in NASH-associated hepatocellular carcinoma［J］.Cancer Res,2013,73(15):4722-4731.
[10] POPE R,KIMBROUGH EO,VEMIREDDY LP,et al.Aberrant lipid metabolism as a therapeutic target in liver cancer［J］.Expert Opin Ther Targets,2019,23(6):473-483.
[11] EL-KHOUEIRY AB,SANGROB,YAU T,et al.Nivolumab in patients with advanced hepatocellular carcinoma(CheckMate 040):an open-label,non-comparative,phase 1/2 dose escalation and expansion trial［J］.Lancet,2017,389(10088):2492-2502.
[12] BRUIX J,QIN S,MERLE P,et al.Regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment(RESORCE):a randomised,double-blind,placebo-controlled,phase 3 trial［J］.Lancet,2017,389(10064):56-66.
[13] ABOU-ALFA GK,MEYER T,CHENG AL,et al.Cabozantinib in patients with advanced and progressing hepatocellular carcinoma［J］.N Engl J Med,2018,379(1):54-63.
[14] GUO Y,BAO Y,MA M,et al.Identification of key candidate genes and pathways in colorectal cancer by integrated bioinformatical analysis［J］.Int J Mol Sci,2017,18(4):722.
[15] SUBRAMANIAN A,TAMAYO P,MOOTHA VK,et al.Gene set enrichment analysis:a knowledge-based approach for interpreting genome-wide expression profiles［J］.Proc Natl Acad Sci USA,2005,102(43):15545-15550.
[16] SAUERBREI W,ROYSTON P,BINDER H.Selection of important variables and determination of functional form for continuous predictors in multivariable model building［J］.Stat Med,2007,26(30):5512-5528.
[17] LIANG R,ZHI Y,ZHENG G,et al.Analysis of long non-coding RNAs in glioblastoma for prognosis prediction using weighted gene co-expression network analysis,Cox regression,and L1-LASSO penalization［J］.Onco Targets Ther,2019,12:157-168.
[18] WANG H,ZHANG L,LIU Z,et al.Predicting medication nonadherence risk in a Chinese inflammatory rheumatic disease population:development and assessment of a new predictive nomogram［J］.Patient Prefer Adherence,2018,12:1757-1765.
[19] IASONOS A,SCHRAG D,RAJ GV,et al.How to build and interpret a nomogram for cancer prognosis［J］.J Clin Oncol,2008,26(8):1364-1370.
[20] TANG Z,LI C,KANG B,et al.GEPIA:a web server for cancer and normal gene expression profiling and interactive analyses［J］.Nucleic Acids Res,2017,45(W1):W98-W102.
[21] KANEHISA M,SATO Y,KAWASHIMA M,et al.KEGG as a reference resource for gene and protein annotation［J］.Nucleic Acids Res,2016,44(D1):D457-D462.
[22] KANEHISA M,FURUMICHI M,TANABE M,et al.KEGG:new perspectives on genomes,pathways,diseases and drugs［J］.Nucleic Acids Res,2017,45(D1):D353-D361.
[23] DU Z,ZHOU X,LING Y,et al.agriGO:a GO analysis toolkit for the agricultural community［J］.Nucleic Acids Res,2010,38(Web Server issue):W64-W70.
[24] DING R,QU Y,WU CH,et al.Automatic gene annotation using GO terms from cellular component domain［J］.BMC Med Inform Decis Mak,2018,18(Suppl 5):119.
[25] TAO ZH,WAN JL,ZENG LY,et al.miR-612 suppresses the invasive-metastatic cascade in hepatocellular carcinoma［J］.J Exp Med,2013,210(4):789-803.
[26] FITZMAURICE C,ALLEN C,BARBER RM,et al.Global,regional and national cancer incidence,mortality,years of life lost,years lived with disability and disability-adjusted life-years for 32 cancer groups,1990 to 2015:A systematic analysis for the global burden of disease study［J］.JAMA Oncol,2017,3(4):524-548.
[27] KAUSHIK AC,MEHMOOD A,WEI DQ,et al.Robust biomarker screening using spares learning approach for liver cancer prognosis［J］.Front Bioeng Biotechnol,2020,8:241.
[28] TENNANT DA,DURAN RV,GOTTLIEB E.Targeting metabolic transformation for cancer therapy［J］.Nat Rev Cancer,2010,10(4):267-277.
[29] BUDHU A,FORGUES M,YE QH,et al.Prediction of venous metastases,recurrence,and prognosis in hepatocellular carcinoma based on a unique immune response signature of the liver microenvironment［J］.Cancer Cell,2006,10(2):99-111.
[30] LI W,LU J,MA Z,et al.An integrated model based on a six-gene signature predicts overall survival in patients with hepatocellular carcinoma［J］.Front Genet,2019,10:1323.
[31] IASONOS A,SCHRAG D,RAJ GV,et al.How to build and interpret a nomogram for cancer prognosis［J］.J Clin Oncol,2008,26(8):1364-1370.
[32] LI D,CHENG X,ZHENG W,et al.Glucosamine-6-phosphate isomerase 1 promotes tumor progression and indicates poor prognosis in hepatocellular carcinoma［J］.Cancer Manag Res,2020,12:4923-4935.
[33] SHANG RZ,QU SB,WANG DS.Reprogramming of glucose metabolism in hepatocellular carcinoma:Progress and prospects［J］.World J Gastroenterol,2016,22(45):9933-9943.
[34] JIANG P,DU W,MANCUSO A,et al.Reciprocal regulation of p53 and malic enzymes modulates metabolism and senescence［J］.Nature,2013,493(7434):689-693.
[35] WEN D,LIU D,TANG J,et al.Malic enzyme 1 induces epithelial-mesenchymal transition and indicates poor prognosis in hepatocellular carcinoma［J］.Tumour Biol,2015,36(8):6211-6221.
[36] MARTINEZ-OUTSCHOORN UE,PEIRIS-PAGéS M,PESTELL RG,et al.Cancer metabolism:a therapeutic perspective［J］.Nat Rev Clin Oncol,2017,14(2):113.
[37] STANTON RC.Glucose-6-phosphate dehydrogenase,NADPH,and cell survival［J］.IUBMB Life,2012,64(5):362-369.
[38] KONG DH,LI S,DU ZX,et al.BAG3 elevation inhibits cell proliferation via direct interaction with G6PD in hepatocellular carcinomas［J］.Oncotarget,2016,7(1):700-711.
[39] LU M,LU L,DONG Q,et al.Elevated G6PD expression contributes to migration and invasion of hepatocellular carcinoma cells by inducing epithelial-mesenchymal transition［J］.Acta Biochim Biophys Sin(Shanghai),2018,50(4):370-380.
[40] LI M,JIN C,XU M,et al.Bifunctional enzyme ATIC promotes propagation of hepatocellular carcinoma by regulating AMPK-mTOR-S6 K1 signaling［J］.Cell Commun Signal,2017,15(1):52.
[41] LANE AN,FAN TW.Regulation of mammalian nucleotide metabolism and biosynthesis［J］.Nucleic Acids Res,2015,43(4):2466-2485.
[42] HUANG S,LI J,TAM NL,et al.Uridine-cytidine kinase 2 upregulation predicts poor prognosis of hepatocellular carcinoma and is associated with cancer aggressiveness［J］.Mol Carcinog,2019,58(4):603-615.
[43] ZHOU Q,JIANG H,ZHANG J,et al.Uridine-cytidine kinase 2 promotes metastasis of hepatocellular carcinoma cells via the Stat3 pathway［J］.Cancer Manag Res,2018,10:6339-6355.
[44] LI D,CHENG X,ZHENG W,et al.Glucosamine-6-phosphate isomerase 1 promotes tumor progression and indicates poor prognosis in hepatocellular carcinoma［J］.Cancer Manag Res,2020,12:4923-4935.
[45] QIAO G,XU H,LI C,et al.Granulin a synergizes with cisplatin to inhibit the growth of human hepatocellular carcinoma［J］.Int J Mol Sci,2018,19(10):3060.
[46] LIU X,XU Z,HOU C,et al.Inhibition of hepatitis B virus replication by targeting ribonucleotide reductase M2 protein［J］.Biochem Pharmacol,2016,103:118-128.
[47] TSUNEDOMI R,IIZUKA N,HAMAMOTO Y,et al.Patterns of expression of cytochrome p450 genes in progression of hepatitis C virus-associated hepatocellular carcinoma［J］.Int J Oncol,2005,27(3):661-667.

Memo

Memo:
广东省自然科学基金(编号：2017A030313723,2018A0303130171)；广东省中医药局科研项目(编号：20181055)
Last Update: 2022-01-27