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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

An Immune-Related Gene Signature Predicting Prognosis and Immunotherapy Response in Hepatocellular Carcinoma

Author(s): Feng Zhang, Jialiang Cai, Keshu Hu, Wenfeng Liu, Shenxin Lu, Bei Tang, Miao Li, Weizhong Wu, Zhenggang Ren* and Xin Yin*

Volume 25, Issue 13, 2022

Published on: 12 April, 2022

Page: [2203 - 2216] Pages: 14

DOI: 10.2174/1386207325666220304115006

Price: $65

Abstract

Background: Hepatocellular carcinoma (HCC) is inflammation-associated cancer with high incidence and poor prognosis. In the last decade, immunotherapy has become an important strategy for managing HCC.

Objective: This study aimed to establish an immune-related gene signature for predicting prognosis and immunotherapy response in HCC.

Methods: We identified immune-related differentially expressed genes (IRDEGs) based on The Cancer Genome Atlas (TCGA) database and the Immunology Database and Analysis Portal (ImmPort) database. The weighted gene co-expression network analysis (WGCNA) and Cox proportional hazard model were utilized to determine hub immune-related genes (IRGs). The TIDE tool and R package pRRophetic were used to assess the correlation between the immune-related gene signature and the clinical responses to immunotherapy and chemotherapy.

Results: By using WGCNA combined with Cox proportional hazard model, PRC1, TOP2A, TPX2, and ANLN were identified as hub IRGs. The prognostic value of the newly developed gene signature (IRGPI) was demonstrated in both the TCGA database and the Gene Expression Omnibus (GEO) database. The TIDE tool showed that the high- and low-IRGPI groups presented significantly different tumor immune microenvironment and immunotherapy responses. Furthermore, the high-IRGPI group also had significantly lower chemoresistance to cisplatin than the low-IRGPI group.

Conclusion: The IRGPI is a tool for predicting prognosis as well as responsiveness to immunotherapy and chemotherapy in HCC.

Keywords: Hepatocellular carcinoma, immune-related gene, immunotherapy, prognosis, tumor microenvironment, weighted gene co-expression network analysis.

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[1]
Benson, A.B.; D’Angelica, M.I.; Abbott, D.E.; Abrams, T.A.; Alberts, S.R.; Saenz, D.A.; Are, C.; Brown, D.B.; Chang, D.T.; Covey, A.M. NCCN guidelines insights: Hepatobiliary cancers, Version 1.2017. J. Natl. Compr. Canc. Netw., 2017, 15(5), 563-573.
[http://dx.doi.org/10.6004/jnccn.2017.0059]
[2]
Galle, P.R.; Forner, A.; Llovet, J.M.; Mazzaferro, V.; Piscaglia, F.; Raoul, J.L.; Schirmacher, P.; Vilgrain, V. EASL clinical practice guidelines: Management of hepatocellular carcinoma. J. Hepatol., 2018, 69(1), 182-236.
[http://dx.doi.org/10.1016/j.jhep.2018.03.019] [PMID: 29628281]
[3]
Vogel, A.; Saborowski, A. Current strategies for the treatment of intermediate and advanced hepatocellular carcinoma. Cancer Treat. Rev., 2020, 82, 101946.
[http://dx.doi.org/10.1016/j.ctrv.2019.101946] [PMID: 31830641]
[4]
Langfelder, P.; Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9, 559.
[http://dx.doi.org/10.1186/1471-2105-9-559] [PMID: 19114008]
[5]
Gu, Y.; Li, J.; Guo, D.; Chen, B.; Liu, P.; Xiao, Y.; Yang, K.; Liu, Z.; Liu, Q. Identification of 13 key genes correlated with progression and prognosis in hepatocellular carcinoma by weighted gene co-expression network analysis. Front. Genet., 2020, 11, 153.
[http://dx.doi.org/10.3389/fgene.2020.00153] [PMID: 32180800]
[6]
Bhattacharya, S.; Andorf, S.; Gomes, L.; Dunn, P.; Schaefer, H.; Pontius, J.; Berger, P.; Desborough, V.; Smith, T.; Campbell, J.; Thomson, E.; Monteiro, R.; Guimaraes, P.; Walters, B.; Wiser, J.; Butte, A.J. ImmPort: Disseminating data to the public for the future of immunology. Immunol. Res., 2014, 58(2-3), 234-239.
[http://dx.doi.org/10.1007/s12026-014-8516-1] [PMID: 24791905]
[7]
Harris, M.A.; Clark, J.I.; Ireland, A.; Lomax, J.; Ashburner, M.; Collins, R.; Eilbeck, K.; Lewis, S.; Mungall, C.; Richter, J. The Gene Ontology (GO) project in 2006. Nucleic Acids Res., 2006, 34, D322-D326.
[http://dx.doi.org/10.1093/nar/gkj021]
[8]
Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res., 2017, 45(D1), D353-D361.
[http://dx.doi.org/10.1093/nar/gkw1092] [PMID: 27899662]
[9]
Yi, Y.; Zhao, Y.; Li, C.; Zhang, L.; Huang, H.; Li, Y.; Liu, L.; Hou, P.; Cui, T.; Tan, P.; Hu, Y.; Zhang, T.; Huang, Y.; Li, X.; Yu, J.; Wang, D. RAID v2.0: An updated resource of RNA-associated interactions across organisms. Nucleic Acids Res., 2017, 45(D1), D115-D118.
[http://dx.doi.org/10.1093/nar/gkw1052] [PMID: 27899615]
[10]
Han, H.; Cho, J.W.; Lee, S.; Yun, A.; Kim, H.; Bae, D.; Yang, S.; Kim, C.Y.; Lee, M.; Kim, E.; Lee, S.; Kang, B.; Jeong, D.; Kim, Y.; Jeon, H.N.; Jung, H.; Nam, S.; Chung, M.; Kim, J.H.; Lee, I. TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res., 2018, 46(D1), D380-D386.
[http://dx.doi.org/10.1093/nar/gkx1013] [PMID: 29087512]
[11]
Newman, A.M.; Liu, C.L.; Green, M.R.; Gentles, A.J.; Feng, W.; Xu, Y.; Hoang, C.D.; Diehn, M.; Alizadeh, A.A. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods, 2015, 12(5), 453-457.
[http://dx.doi.org/10.1038/nmeth.3337] [PMID: 25822800]
[12]
Brinkman, E.K.; Chen, T.; Amendola, M.; van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res., 2014, 42(22), e168.
[http://dx.doi.org/10.1093/nar/gku936] [PMID: 25300484]
[13]
Geeleher, P.; Cox, N.; Huang, R.S. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One, 2014, 9(9), e107468.
[http://dx.doi.org/10.1371/journal.pone.0107468] [PMID: 25229481]
[14]
Tahmasebi Birgani, M.; Carloni, V. Tumor Microenvironment, a Paradigm in hepatocellular carcinoma progression and therapy. Int. J. Mol. Sci., 2017, 18(2), 405.
[http://dx.doi.org/10.3390/ijms18020405] [PMID: 28216578]
[15]
Chen, J.; Rajasekaran, M.; Xia, H.; Zhang, X.; Kong, S.N.; Sekar, K.; Seshachalam, V.P.; Deivasigamani, A.; Goh, B.K.; Ooi, L.L.; Hong, W.; Hui, K.M. The microtubule-associated protein PRC1 promotes early recurrence of hepatocellular carcinoma in association with the Wnt/β-catenin signalling pathway. Gut, 2016, 65(9), 1522-1534.
[http://dx.doi.org/10.1136/gutjnl-2015-310625] [PMID: 26941395]
[16]
Wang, Y.; Shi, F.; Xing, G.H.; Xie, P.; Zhao, N.; Yin, Y.F.; Sun, S.Y.; He, J.; Wang, Y.; Xuan, S.Y. Protein regulator of cytokinesis PRC1 confers chemoresistance and predicts an unfavorable postoperative survival of hepatocellular carcinoma patients. J. Cancer, 2017, 8(5), 801-808.
[http://dx.doi.org/10.7150/jca.17640] [PMID: 28382142]
[17]
de Resende, M.F.; Vieira, S.; Chinen, L.T.; Chiappelli, F.; da Fonseca, F.P.; Guimarães, G.C.; Soares, F.A.; Neves, I.; Pagotty, S.; Pellionisz, P.A.; Barkhordarian, A.; Brant, X.; Rocha, R.M. Prognostication of prostate cancer based on TOP2A protein and gene assessment: TOP2A in prostate cancer. J. Transl. Med., 2013, 11, 36.
[http://dx.doi.org/10.1186/1479-5876-11-36] [PMID: 23398928]
[18]
Panvichian, R.; Tantiwetrueangdet, A.; Angkathunyakul, N.; Leelaudomlipi, S. TOP2A amplification and overexpression in hepatocellular carcinoma tissues. BioMed Res. Int., 2015, 2015, 381602.
[http://dx.doi.org/10.1155/2015/381602] [PMID: 25695068]
[19]
Wong, N.; Yeo, W.; Wong, W.L.; Wong, N.L.; Chan, K.Y.; Mo, F.K.; Koh, J.; Chan, S.L.; Chan, A.T.; Lai, P.B.; Ching, A.K.; Tong, J.H.; Ng, H.K.; Johnson, P.J.; To, K.F. TOP2A overexpression in hepatocellular carcinoma correlates with early age onset, shorter patients survival and chemoresistance. Int. J. Cancer, 2009, 124(3), 644-652.
[http://dx.doi.org/10.1002/ijc.23968] [PMID: 19003983]
[20]
Huang, Y.; Guo, W.; Kan, H. TPX2 is a prognostic marker and contributes to growth and metastasis of human hepatocellular carcinoma. Int. J. Mol. Sci., 2014, 15(10), 18148-18161.
[http://dx.doi.org/10.3390/ijms151018148] [PMID: 25302620]
[21]
Liang, B.; Jia, C.; Huang, Y.; He, H.; Li, J.; Liao, H.; Liu, X.; Liu, X.; Bai, X.; Yang, D. TPX2 level correlates with hepatocellular carcinoma cell proliferation, apoptosis, and EMT. Dig. Dis. Sci., 2015, 60(8), 2360-2372.
[http://dx.doi.org/10.1007/s10620-015-3730-9] [PMID: 26025609]
[22]
Huang, D.H.; Jian, J.; Li, S.; Zhang, Y.; Liu, L.Z. TPX2 silencing exerts anti-tumor effects on hepatocellular carcinoma by regulating the PI3K/AKT signaling pathway. Int. J. Mol. Med., 2019, 44(6), 2113-2122.
[http://dx.doi.org/10.3892/ijmm.2019.4371] [PMID: 31638175]
[23]
Aref, A.M.; Hoa, N.T.; Ge, L.; Agrawal, A.; Dacosta-Iyer, M.; Lambrecht, N.; Ouyang, Y.; Cornforth, A.N.; Jadus, M.R. HCA519/TPX2: A potential T-cell tumor-associated antigen for human hepatocellular carcinoma. OncoTargets Ther., 2014, 7, 1061-1070.
[http://dx.doi.org/10.2147/OTT.S61442] [PMID: 24966688]
[24]
Hickson, G.R.; O’Farrell, P.H. Anillin: A pivotal organizer of the cytokinetic machinery. Biochem. Soc. Trans., 2008, 36(Pt 3), 439-441.
[http://dx.doi.org/10.1042/BST0360439] [PMID: 18481976]
[25]
Zhou, W.; Wang, Z.; Shen, N.; Pi, W.; Jiang, W.; Huang, J.; Hu, Y.; Li, X.; Sun, L. Knockdown of ANLN by lentivirus inhibits cell growth and migration in human breast cancer. Mol. Cell. Biochem., 2015, 398(1-2), 11-19.
[http://dx.doi.org/10.1007/s11010-014-2200-6] [PMID: 25223638]
[26]
Zhang, S.; Nguyen, L.H.; Zhou, K.; Tu, H.C.; Sehgal, A.; Nassour, I.; Li, L.; Gopal, P.; Goodman, J.; Singal, A.G.; Yopp, A.; Zhang, Y.; Siegwart, D.J.; Zhu, H. Knockdown of anillin actin binding protein blocks cytokinesis in hepatocytes and reduces liver tumor development in mice without affecting regeneration. Gastroenterology, 2018, 154(5), 1421-1434.
[http://dx.doi.org/10.1053/j.gastro.2017.12.013] [PMID: 29274368]
[27]
Lian, Y.F.; Huang, Y.L.; Wang, J.L.; Deng, M.H.; Xia, T.L.; Zeng, M.S.; Chen, M.S.; Wang, H.B.; Huang, Y.H. Anillin is required for tumor growth and regulated by miR-15a/miR-16-1 in HBV-related hepatocellular carcinoma. Aging (Albany NY), 2018, 10(8), 1884-1901.
[http://dx.doi.org/10.18632/aging.101510] [PMID: 30103211]
[28]
Bronte, V.; Brandau, S.; Chen, S.H.; Colombo, M.P.; Frey, A.B.; Greten, T.F.; Mandruzzato, S.; Murray, P.J.; Ochoa, A.; Ostrand-Rosenberg, S.; Rodriguez, P.C.; Sica, A.; Umansky, V.; Vonderheide, R.H.; Gabrilovich, D.I. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat. Commun., 2016, 7, 12150.
[http://dx.doi.org/10.1038/ncomms12150] [PMID: 27381735]
[29]
Ilkovitch, D.; Lopez, D.M. The liver is a site for tumor-induced myeloid-derived suppressor cell accumulation and immunosuppression. Cancer Res., 2009, 69(13), 5514-5521.
[http://dx.doi.org/10.1158/0008-5472.CAN-08-4625] [PMID: 19549903]
[30]
(a)Gao, X.H.; Tian, L.; Wu, J.; Ma, X.L.; Zhang, C.Y.; Zhou, Y.; Sun, Y.F.; Hu, B.; Qiu, S.J.; Zhou, J. Circulating CD14 HLA-DR myeloid-derived suppressor cells predicted early recurrence of hepatocellular carcinoma after surgery. Hepatol. Res., 2017, 47(10), 1061-1071.
[http://dx.doi.org/10.1111/hepr.12831]
(b)Mizukoshi, E.; Yamashita, T.; Arai, K.; Terashima, T.; Kitahara, M.; Nakagawa, H.; Iida, N.; Fushimi, K.; Kaneko, S. Myeloid-derived suppressor cells correlate with patient outcomes in hepatic arterial infusion chemotherapy for hepatocellular carcinoma. Cancer Immunol. Immunother., 2016, 65(6), 715-725.
[31]
Weston, C.J.; Zimmermann, H.W.; Adams, D.H. The role of myeloid-derived cells in the progression of liver disease. Front. Immunol., 2019, 10, 893.
[http://dx.doi.org/10.3389/fimmu.2019.00893] [PMID: 31068952]
[32]
Ishii, G.; Ochiai, A.; Neri, S. Phenotypic and functional heterogeneity of cancer-associated fibroblast within the tumor microenvironment. Adv. Drug Deliv. Rev., 2016, 99(Pt B), 186-196.
[http://dx.doi.org/10.1016/j.addr.2015.07.007] [PMID: 26278673]
[33]
(a)Costa, A.; Kieffer, Y.; Scholer-Dahirel, A.; Pelon, F.; Bourachot, B.; Cardon, M.; Sirven, P.; Magagna, I.; Fuhrmann, L.; Bernard, C. Fibroblast heterogeneity and immunosuppressive environment in human breast cancer. Cancer Cell, 2018, 33(3), 463-479.
[http://dx.doi.org/10.1016/j.ccell.2018.01.011]
(b)Shintani, Y.; Fujiwara, A.; Kimura, T.; Kawamura, T.; Funaki, S.; Minami, M.; Okumura, M. IL-6 secreted from cancer-associated fibroblasts mediates chemoresistance in NSCLC by increasing epithelial-mesenchymal transition signaling. J. Thorac. Oncol., 2016, 11(9), 1482-1492.
[34]
Yin, Z.; Dong, C.; Jiang, K.; Xu, Z.; Li, R.; Guo, K.; Shao, S.; Wang, L. Heterogeneity of cancer-associated fibroblasts and roles in the progression, prognosis, and therapy of hepatocellular carcinoma. J. Hematol. Oncol., 2019, 12(1), 101.
[http://dx.doi.org/10.1186/s13045-019-0782-x] [PMID: 31547836]
[35]
(a)Zeng, D.; Li, M.; Zhou, R.; Zhang, J.; Sun, H.; Shi, M.; Bin, J.; Liao, Y.; Rao, J.; Liao, W. Tumor microenvironment characterization in gastric cancer identifies prognostic and immunotherapeutically relevant gene signatures. Cancer Immunol. Res., 2019, 7(5), 737-750.
[http://dx.doi.org/10.1158/2326-6066.CIR-18-0436]
(b)Bi, G.; Chen, Z.; Yang, X.; Liang, J.; Hu, Z.; Bian, Y.; Sui, Q.; Li, R.; Zhan, C.; Fan, H. Identification and validation of tumor environment phenotypes in lung adenocarcinoma by integrative genome-scale analysis. Cancer Immunol. Immunother., 2020, 69(7), 1293-1305.

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