Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

Nomogram for Prediction of Hepatocellular Carcinoma Prognosis

Author(s): Shuai Yang, Jiangang Zhang, Jingchun Wang, Yanquan Xu, Huakan Zhao, Juan Lei, Yu Zhou, Yu Chen, Lei Wu, Mingyue Zhou, Dingshan Li, Enwen Wang* and Yongsheng Li*

Volume 17, Issue 8, 2022

Published on: 05 July, 2022

Page: [685 - 697] Pages: 13

DOI: 10.2174/1574893617666220408085955

Price: $65

Abstract

Background: Hepatocellular Carcinoma (HCC) is associated with high mortality rates and requires the identification of new therapeutic targets. We sought to develop a nomogram for reliably predicting HCC prognosis.

Methods: Gene expression was analyzed in R software, while the hub genes were defined as overlapping candidates across five datasets. A prognostic nomogram was constructed using multivariate Cox analysis and evaluated by receiver operating characteristic curve and concordance index analysis. The fractions of tumor microenvironment cells were determined by using xCell. Hypoxia scores were calculated by single-sample gene set enrichment analysis. Statistically, significance and correlation analyses were processed in R.

Results: Tow hub genes were identified, and a prognostic nomogram was established and evaluated in the internal validation dataset (Area Under the Curve [AUC] 0.72, 95% Confidence Interval [CI] 0.63- 0.81) and external cohorts (AUC 0.70, 95% CI 0.55-0.85). The risk scores of the prognostic model were positively and negatively correlated with fractions of the T helper 2 (Th2) cells (R = 0.39, p <0.001) and the hematopoietic stem cells (R = -0.27, p <0.001) and Endothelial Cells (ECs; R = -0.24, p <0.001), respectively. Angiogenesis was more active in the high-risk group, accompanied by increased proliferation of ECs. Furthermore, the significance of Hypoxia-Inducible Factor 1-Alpha (HIF1A) gene-related hypoxia in predicting HCC prognosis was demonstrated.

Conclusion: A robust prognostic nomogram for predicting the prognosis of patients with HCC was developed. The results suggested that Th2 cells, VEGF-related angiogenesis and HIF1A-related hypoxia may be promising therapeutic targets for prolonging the overall survival of HCC patients.

Keywords: Hepatocellular carcinoma, prognosis, nomogram, overall survival, tumor microenvironment, endothelial cells, T helper 2 cells.

Graphical Abstract
[1]
Villanueva A. Hepatocellular carcinoma. N Engl J Med 2019; 380(15): 1450-62.
[http://dx.doi.org/10.1056/NEJMra1713263] [PMID: 30970190]
[2]
Llovet JM, Zucman-Rossi J, Pikarsky E, et al. Hepatocellular carcinoma. Nat Rev Dis Primers 2016; 2(1): 16018.
[http://dx.doi.org/10.1038/nrdp.2016.18] [PMID: 27158749]
[3]
Yang JD, Heimbach JK. New advances in the diagnosis and management of hepatocellular carcinoma. BMJ 2020; 371: m3544.
[http://dx.doi.org/10.1136/bmj.m3544] [PMID: 33106289]
[4]
Bruix J, da Fonseca LG, Reig M. Insights into the success and failure of systemic therapy for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2019; 16(10): 617-30.
[http://dx.doi.org/10.1038/s41575-019-0179-x] [PMID: 31371809]
[5]
Galon J, Bruni D. Tumor immunology and tumor evolution: Intertwined histories. Immunity 2020; 52(1): 55-81.
[http://dx.doi.org/10.1016/j.immuni.2019.12.018] [PMID: 31940273]
[6]
Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer 2019; 19(3): 133-50.
[http://dx.doi.org/10.1038/s41568-019-0116-x] [PMID: 30755690]
[7]
Tzartzeva K, Obi J, Rich NE, et al. Surveillance imaging and alpha fetoprotein for early detection of hepatocellular carcinoma in patients with cirrhosis: A meta-analysis. Gastroenterology 2018; 154(6): 1706-1718.e1.
[http://dx.doi.org/10.1053/j.gastro.2018.01.064] [PMID: 29425931]
[8]
Nault JC, Villanueva A. Biomarkers for hepatobiliary cancers. Hepatology 2020; 73(S1): 115-27.
[http://dx.doi.org/10.1002/hep.31175]
[9]
Nault JC, De Reyniès A, Villanueva A, et al. A hepatocellular carcinoma 5-gene score associated with survival of patients after liver resection. Gastroenterology 2013; 145(1): 176-87.
[http://dx.doi.org/10.1053/j.gastro.2013.03.051] [PMID: 23567350]
[10]
Sia D, Villanueva A, Friedman SL, Llovet JM. Liver cancer cell of origin, molecular class, and effects on patient prognosis. Gastroenterology 2017; 152(4): 745-61.
[http://dx.doi.org/10.1053/j.gastro.2016.11.048] [PMID: 28043904]
[11]
Hoshida Y, Villanueva A, Kobayashi M, et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med 2008; 359(19): 1995-2004.
[http://dx.doi.org/10.1056/NEJMoa0804525] [PMID: 18923165]
[12]
Craig AJ, von Felden J, Garcia-Lezana T, Sarcognato S, Villanueva A. Tumour evolution in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2020; 17(3): 139-52.
[http://dx.doi.org/10.1038/s41575-019-0229-4] [PMID: 31792430]
[13]
Chaisaingmongkol J, Budhu A, Dang H, et al. TIGER-LC Consortium. Common molecular subtypes among asian hepatocellular carcinoma and cholangiocarcinoma. Cancer Cell 2017; 32(1): 57-70.e3.
[http://dx.doi.org/10.1016/j.ccell.2017.05.009] [PMID: 28648284]
[14]
Xue R, Chen L, Zhang C, et al. Genomic and transcriptomic profiling of combined hepatocellular and intrahepatic cholangiocarcinoma reveals distinct molecular subtypes. Cancer Cell 2019; 35(6): 932-947.e8.
[http://dx.doi.org/10.1016/j.ccell.2019.04.007] [PMID: 31130341]
[15]
Uhlen M, Zhang C, Lee S, et al. A pathology atlas of the human cancer transcriptome. Science 2017; 357(6352)eaan2507
[http://dx.doi.org/10.1126/science.aan2507] [PMID: 28818916]
[16]
Ren C, Li M, Du W, et al. Comprehensive bioinformatics analysis reveals hub genes and inflammation state of rheumatoid arthritis. BioMed Res Int 2020; 20206943103
[http://dx.doi.org/10.1155/2020/6943103] [PMID: 32802866]
[17]
Ghedira K, Hamdi Y, El Béji A, Othman H. An integrative computational approach for the prediction of human-plasmodium protein-protein interactions. BioMed Res Int 2020; 20202082540
[http://dx.doi.org/10.1155/2020/2082540] [PMID: 33426052]
[18]
Hu Y, Lu Y, Wang S, Zhang M, Qu X, Niu B. Application of machine learning approaches for the design and study of anticancer drugs. Curr Drug Targets 2019; 20(5): 488-500.
[http://dx.doi.org/10.2174/1389450119666180809122244] [PMID: 30091413]
[19]
Niu B, Liang C, Lu Y, et al. Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks. Genomics 2020; 112(1): 837-47.
[http://dx.doi.org/10.1016/j.ygeno.2019.05.024] [PMID: 31150762]
[20]
Villanueva A, Hoshida Y, Battiston C, et al. Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. Gastroenterology 2011; 140(5): 1501-12.e2.
[http://dx.doi.org/10.1053/j.gastro.2011.02.006] [PMID: 21320499]
[21]
Ganne-Carrié N, Layese R, Bourcier V, et al. ANRS CO12 CirVir Study Group. Nomogram for individualized prediction of hepatocellular carcinoma occurrence in hepatitis C virus cirrhosis (ANRS CO12 CirVir). Hepatology 2016; 64(4): 1136-47.
[http://dx.doi.org/10.1002/hep.28702] [PMID: 27348075]
[22]
Riera-Domingo C, Audigé A, Granja S, et al. Immunity, hypoxia, and metabolism-the ménage à trois of cancer: Implications for immunotherapy. Physiol Rev 2020; 100(1): 1-102.
[http://dx.doi.org/10.1152/physrev.00018.2019] [PMID: 31414610]
[23]
Quail DF, Joyce JA. Microenvironmental regulation of tumor progression and metastasis. Nat Med 2013; 19(11): 1423-37.
[http://dx.doi.org/10.1038/nm.3394] [PMID: 24202395]
[24]
Hanahan D, Coussens LM. Accessories to the crime: Functions of cells recruited to the tumor microenvironment. Cancer Cell 2012; 21(3): 309-22.
[http://dx.doi.org/10.1016/j.ccr.2012.02.022] [PMID: 22439926]
[25]
Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: More than meets the eye. Lancet Oncol 2015; 16(4): e173-80.
[http://dx.doi.org/10.1016/S1470-2045(14)71116-7] [PMID: 25846097]
[26]
Wang Z, Gerstein M, Snyder M. RNA-Seq: A revolutionary tool for transcriptomics. Nat Rev Genet 2009; 10(1): 57-63.
[http://dx.doi.org/10.1038/nrg2484] [PMID: 19015660]
[27]
Candia J, Bayarsaikhan E, Tandon M, et al. The genomic landscape of Mongolian hepatocellular carcinoma. Nat Commun 2020; 11(1): 4383.
[http://dx.doi.org/10.1038/s41467-020-18186-1] [PMID: 32873799]
[28]
Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: An enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 2019; 47(W1)W556-60
[http://dx.doi.org/10.1093/nar/gkz430] [PMID: 31114875]
[29]
Fox J, Weisberg S. An r companion to applied regression. (3rd ed.), New York, USA: SAGE Publications Inc. 2019.
[30]
Aran D, Hu Z, Butte AJ. xCell: Digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 2017; 18(1): 220.
[http://dx.doi.org/10.1186/s13059-017-1349-1] [PMID: 29141660]
[31]
Li T, Fu J, Zeng Z, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res 2020; 48(W1)W509-14
[http://dx.doi.org/10.1093/nar/gkaa407] [PMID: 32442275]
[32]
Buffa FM, Harris AL, West CM, Miller CJ. Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene. Br J Cancer 2010; 102(2): 428-35.
[http://dx.doi.org/10.1038/sj.bjc.6605450] [PMID: 20087356]
[33]
Ye Y, Hu Q, Chen H, et al. Characterization of hypoxia-associated molecular features to aid hypoxia-targeted therapy. Nat Metab 2019; 1(4): 431-44.
[http://dx.doi.org/10.1038/s42255-019-0045-8] [PMID: 31984309]
[34]
Wang HW, Hsieh TH, Huang SY, et al. Forfeited hepatogenesis program and increased embryonic stem cell traits in young hepatocellular carcinoma (HCC) comparing to elderly HCC. BMC Genomics 2013; 14(1): 736.
[http://dx.doi.org/10.1186/1471-2164-14-736] [PMID: 24160375]
[35]
Shimada S, Mogushi K, Akiyama Y, et al. Comprehensive molecular and immunological characterization of hepatocellular carcinoma. EBioMedicine 2019; 40: 457-70.
[http://dx.doi.org/10.1016/j.ebiom.2018.12.058] [PMID: 30598371]
[36]
Wang SM, Ooi LL, Hui KM. Identification and validation of a novel gene signature associated with the recurrence of human hepatocellular carcinoma. Clin Cancer Res 2007; 13(21): 6275-83.
[http://dx.doi.org/10.1158/1078-0432.CCR-06-2236] [PMID: 17975138]
[37]
Schulze K, Imbeaud S, Letouzé E, et al. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat Genet 2015; 47(5): 505-11.
[http://dx.doi.org/10.1038/ng.3252] [PMID: 25822088]
[38]
Nault JC, Martin Y, Caruso S, et al. Clinical impact of genomic diversity from early to advanced hepatocellular carcinoma. Hepatology 2020; 71(1): 164-82.
[http://dx.doi.org/10.1002/hep.30811] [PMID: 31206197]
[39]
Fujiwara N, Friedman SL, Goossens N, Hoshida Y. Risk factors and prevention of hepatocellular carcinoma in the era of precision medicine. J Hepatol 2018; 68(3): 526-49.
[http://dx.doi.org/10.1016/j.jhep.2017.09.016] [PMID: 28989095]
[40]
Meurette O, Mehlen P. Notch signaling in the tumor microenvironment. Cancer Cell 2018; 34(4): 536-48.
[http://dx.doi.org/10.1016/j.ccell.2018.07.009] [PMID: 30146333]
[41]
Bertout JA, Patel SA, Simon MC. The impact of O2 availability on human cancer. Nat Rev Cancer 2008; 8(12): 967-75.
[http://dx.doi.org/10.1038/nrc2540] [PMID: 18987634]
[42]
Schaefer CF, Anthony K, Krupa S, et al. Pid: The pathway interaction database. Nucleic Acids Res 2009; 37 (Suppl. 1): D674-9.
[http://dx.doi.org/10.1093/nar/gkn653] [PMID: 18832364]
[43]
Palazon A, Tyrakis PA, Macias D, et al. An hif-1&#945;/vegf-a axis in cytotoxic t cells regulates tumor progression. Cancer Cell 2017; 32(5): 669-683.e5.
[http://dx.doi.org/10.1016/j.ccell.2017.10.003] [PMID: 29136509]
[44]
Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 2013; 501(7467): 346-54.
[http://dx.doi.org/10.1038/nature12626] [PMID: 24048067]
[45]
Wu T, Dai Y. Tumor microenvironment and therapeutic response. Cancer Lett 2017; 387: 61-8.
[http://dx.doi.org/10.1016/j.canlet.2016.01.043] [PMID: 26845449]
[46]
Hirata E, Sahai E. Tumor microenvironment and differential responses to therapy. Cold Spring Harb Perspect Med 2017; 7(7)a026781
[http://dx.doi.org/10.1101/cshperspect.a026781] [PMID: 28213438]
[47]
Weng L, Du J, Zhou Q, et al. Identification of cyclin B1 and Sec62 as biomarkers for recurrence in patients with HBV-related hepatocellular carcinoma after surgical resection. Mol Cancer 2012; 11(1): 39.
[http://dx.doi.org/10.1186/1476-4598-11-39] [PMID: 22682366]
[48]
Alisi A, Ghidinelli M, Zerbini A, Missale G, Balsano C. Hepatitis C virus and alcohol: Same mitotic targets but different signaling pathways. J Hepatol 2011; 54(5): 956-63.
[http://dx.doi.org/10.1016/j.jhep.2010.08.016] [PMID: 21145809]
[49]
Liu SH, Lin CY, Peng SY, et al. Down-regulation of annexin A10 in hepatocellular carcinoma is associated with vascular invasion, early recurrence, and poor prognosis in synergy with p53 mutation. Am J Pathol 2002; 160(5): 1831-7.
[http://dx.doi.org/10.1016/S0002-9440(10)61129-7] [PMID: 12000734]
[50]
Liu X, Peng D, Cao Y, et al. Upregulated lncrna dlx6-as1 underpins hepatocellular carcinoma progression via the mir-513c/cul4a/anxa10 axis. Cancer Gene Ther 2021; 28: 486-501.
[http://dx.doi.org/10.1038/s41417-020-00233-0]
[51]
Sharma A, Arambula JF, Koo S, et al. Hypoxia-targeted drug delivery. Chem Soc Rev 2019; 48(3): 771-813.
[http://dx.doi.org/10.1039/C8CS00304A] [PMID: 30575832]
[52]
Zhu J, Paul WE. Heterogeneity and plasticity of T helper cells. Cell Res 2010; 20(1): 4-12.
[http://dx.doi.org/10.1038/cr.2009.138] [PMID: 20010916]
[53]
Li S, Liu M, Do MH, et al. Cancer immunotherapy via targeted TGF-β signalling blockade in TH cells. Nature 2020; 587(7832): 121-5.
[http://dx.doi.org/10.1038/s41586-020-2850-3] [PMID: 33087933]
[54]
Liu M, Kuo F, Capistrano KJ, et al. TGF-β suppresses type 2 immunity to cancer. Nature 2020; 587(7832): 115-20.
[http://dx.doi.org/10.1038/s41586-020-2836-1] [PMID: 33087928]
[55]
Lewis DB. Allergy immunotherapy and inhibition of Th2 immune responses: A sufficient strategy? Curr Opin Immunol 2002; 14(5): 644-51.
[http://dx.doi.org/10.1016/S0952-7915(02)00388-6] [PMID: 12183167]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy