Application of Multigene Model in Prognosis Prediction of Hepatocellular Carcinoma

doi:10.13560/j.cnki.biotech.bull.1985.2019-1254

Abstract

Abstract: Predicting the types of hepatocellular carcinoma（HCC）while using little molecular information may provide patients with more personalized therapy. Here,known HCC prognosis-related pathways were investigated,and 41 dominant genes were identified. Based on these genes,a risk prediction model was constructed using machine learning and validated with 4 HCC datasets. The results revealed that the model was proven to efficiently divide HCC patients into 2 subgroups with significantly different prognosis：average log rank P-values of cross-validation within TCGA dataset was 0.03,and the log rank P-values of validation on other external datasets were 0.000 38,0.002 1 and 0.01,respectively. After analyzing the HCC subgroups with bioinformatics pipeline,the prognosis of HCC significantly was related to signal pathways such as cell cycle,and 12 potential biomarkers of HCC were screened. In sum,the 41-gene based stratification model may robustly and accurately predict prognosis among HCC patients.

Key words: hepatocellular carcinoma, prognosis, biomarker

WEI Zhi-han, FA Bo-tao, YU Zhang-sheng. Application of Multigene Model in Prognosis Prediction of Hepatocellular Carcinoma[J]. Biotechnology Bulletin, 2020, 36(5): 183-192.

References

[1] Zhou M, Wang H, Zeng X, et al.Mortality, morbidity, and risk factors in China and its provinces, 1990-2017:a systematic analysis for the Global Burden of Disease Study 2017[J]. The Lancet, 2019, 394(10204):1145-1158.
[2] Chaudhary K, Poirion OB, Lu L, et al.Deep learning-based multi-omics integration robustly predicts survival in liver cancer[J]. Clinical Cancer Research, 2018, 24(6):1248-1259.
[3] Zhu RX, Seto WK, Lai CL, et al.Epidemiology of hepatocellular carcinoma in the Asia-Pacific region[J]. Gut and Liver, 2016, 10(3):332-339.
[4] Fan G, Tu Y, Chen C, et al.DNA methylation biomarkers for hepatocellular carcinoma[J]. Cancer Cell International, 2018, 18(1):140.
[5] Montironi C, Montal R, Llovet JM.New drugs effective in the systemic treatment of hepatocellular carcinoma[J]. Clinical Liver Disease, 2019, 14(2):56-61.
[6] Bruix J, Gores GJ, Mazzaferro V.Hepatocellular carcinoma:clinical frontiers and perspectives[J]. Gut, 2014;63(5):844-855.
[7] Wang X, Gao J, Zhou B, et al.Identification of prognostic markers for hepatocellular carcinoma based on miRNA expression profiles[J]. Life Sciences, 2019, 232:116596.
[8] Wang Y, Ruan Z, Yu S, et al.A four-methylated mRNA signature-based risk score system predicts survival in patients with hepatocellular carcinoma[J]. Aging, 2019, 11(1):160-173.
[9] Long J, Zhang L, Wan X, et al.A four-gene-based prognostic model predicts overall survival in patients with hepatocellular carcinoma[J]. J Cell Mol Med, 2018, 22(12):5928-5938.
[10] Zheng Y, Liu Y, Zhao S, et al.Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma[J]. Cancer Manage Res, 2018, 10:6079-6096.
[11] Liu GM, Zeng HD, Zhang CY, et al.Identification of a six-gene signature predicting overall survival for hepatocellular carcinoma[J]. Cancer Cell International, 2019, 19(1):138.
[12] Wang Z, Teng D, Li Y, et al.A six-gene-based prognostic signature for hepatocellular carcinoma overall survival prediction[J]. Life Sciences, 2018, 203(24):83-91.
[13] Liu G, Dong C, Liu L.Integrated multiple “-omics” data reveal subtypes of hepatocellular carcinoma[J]. PLoS One, 2016, 11(11):e0165457.
[14] Fa B, Luo C, Tang Z, et al.Pathway-based biomarker identification with crosstalk analysis for robust prognosis prediction in hepatocellular carcinoma[J]. EBioMedicine, 2019, 44:250-260.
[15] Wei L, Jin Z, Yang S, et al.TCGA-assembler 2:software pipeline for retrieval and processing of TCGA/CPTAC data[J]. Bioinformatics, 2018, 34(9):1615-1617.
[16] Fujimoto A, Furuta M, et al.Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer[J]. Nature Genetics, 2016, 48(5):500-509.
[17] Roessler S, Jia HL, Budhu A, et al.A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients[J]. Cancer Research, 2010, 70(24):10202-10212.
[18] Villa E, Critelli R, et al.Neoangiogenesis-related genes are hallmarks of fast-growing hepatocellular carcinomas and worst survival. Results from a prospective study[J]. Gut, 2016, 65(5):861-869.
[19] Therneau TM.A package for survival analysis in S. version 2. 38[J]. 2015.
[20] Schröder MS, Culhane AC, et al.survcomp:An R/Bioconductor package for performance assessment and comparison of survival models[J]. Bioinformatics, 2011, 27(22):3206-3208.
[21] Harrell FE, Lee KL, Mark DB.Multivariable prognostic models:issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors[J]. Statistics in Medicine, 1996, 15(4):361-387.
[22] Love MI, Huber W, Anders S.Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[J]. Genome Biology, 2014, 15(12):550.
[23] Ritchie ME, Phipson B, Wu D, et al.limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Research, 2015, 43(7):e47.
[24] Benjamini Y, Hochberg Y.Controlling the false discovery rate:a practical and powerful approach to multiple testing[J]. JRS Soc:Series B Methodol, 1995, 57(1):289-300.
[25] Yu G, Wang LG, Han Y, et al.ClusterProfiler:an R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5):284-287.
[26] Szklarczyk D, Gable AL, Lyon D, et al.STRING v11:protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets[J]. Nucleic Acids Research, 2019, 47(D1):D607-D613.
[27] Su G, Morris JH, Demchak B, et al. Biological network exploration with Cytoscape 3[J]. Curr Protoc Bioinformatics, 2014;47:8. 13. 1-8. 24.
[28] Scardoni G, Tosadori G, Faizan M, et al.Biological network analysis with CentiScaPe:Centralities and experimental dataset integration[J]. F1000Research, 2015, 3:139.
[29] Bader GD, Hogue CWV.An automated method for finding molecular complexes in large protein interaction networks[J]. BMC Bioinformatics, 2003, 4(1):2.
[30] Bindea G, Mlecnik B, et al.ClueGO:a Cytoscape plug-in to deci-pher functionally grouped gene ontology and pathway annotation networks[J]. Bioinformatics, 2009, 25(8):1091-1093.
[31] Bindea G, Galon J, Mlecnik B.CluePedia Cytoscape plugin:pathway insights using integrated experimental and in silico data[J]. Bioinformatics, 2013, 29(5):661-663.
[32] Bisteau X, Caldez M, Kaldis P.The Complex relationship between liver cancer and the cell cycle:a story of multiple regulations[J]. Cancers, 2014, 6(1):79-111.
[33] 陈珂, 丁艳平, 王建林, 等. p53参与代谢调控的研究进展[J]. 生物技术通报, 2016, 32(11):52-58.
[34] Zhuang L, Yang Z, Meng Z.Upregulation of BUB1B, CCNB1, CDC7, CDC20, and MCM3 in tumor tissues predicted worse overall survival and disease-free survival in hepatocellular carcinoma patients[J]. Biomed Res Int, 2018:7897346.
[35] Li N, Li L, Chen Y.The Identification of core gene expression signature in hepatocellular carcinoma[J]. Oxidative Medicine and Cellular Longevity, 2018:3478305.
[36] Yang WX, Pan YY, You CG.CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 may be potential therapeutic targets for hepatocellular carcinoma using integrated bioinformatic analysis[J]. Biomed Res Int, 2019:1245072.
[37] Liu ZK, Zhang RY, Yong YL, et al.Identification of crucial genes based on expression profiles of hepatocellular carcinomas by bioinformatics analysis[J]. Peer J, 2019, 7(8):e7436.
[38] Liu Z, Li J, Chen J, et al.MCM family in HCC:MCM6 indicates
adverse tumor features and poor outcomes and promotes S/G2 cell cycle progression[J]. BMC Cancer, 2018, 18(1):200.
[39] Liu J, Li W, Zhang J, et al.Identification of key genes and long non-coding RNA associated ceRNA networks in hepatocellular carcinoma[J]. Peer J, 2019:e8021.