Login Register Cart 0
Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Comprehensive scRNA-seq Analysis and Identification of CD8_+T Cell Related Gene Markers for Predicting Prognosis and Drug Resistance of Hepatocellular Carcinoma

Author(s): Lu Cao, Muqi Liu, Xiaoqian Ma, Pengfei Rong, Juan Zhang and Wei Wang*

Volume 31, Issue 17, 2024

Published on: 01 November, 2023

Page: [2414 - 2430] Pages: 17

DOI: 10.2174/0109298673274578231030065454

Price: $65

Abstract

Background: Tumor heterogeneity of immune infiltration of cells plays a decisive role in hepatocellular carcinoma (HCC) therapy response and prognosis. This study investigated the effect of different subtypes of CD8+T cells on the HCC tumor microenvironment about its prognosis.

Methods: Single-cell RNA sequencing, transcriptome, and single-nucleotide variant data from LUAD patients were obtained based on the GEO, TCGA, and HCCD18 databases. CD8+ T cells-associated subtypes were identified by consensus clustering analysis, and genes with the highest correlation with prognostic CD8+ T cell subtypes were identified using WGCNA. The ssGSEA and ESTIMATE algorithms were used to calculate pathway enrichment scores and immune cell infiltration levels between different subtypes. Finally, the TIDE algorithm, CYT score, and tumor responsiveness score were utilized to predict patient response to immunotherapy.

Results: We defined 3 CD8+T cell clusters (CD8_0, CD8_1, CD8_2) based on the scRNA- seq dataset (GSE149614). Among, CD8_2 was prognosis-related risk factor with HCC. We screened 30 prognosis genes from CD8_2, and identified 3 molecular subtypes (clust1, clust2, clust3). Clust1 had better survival outcomes, higher gene mutation, and enhanced immune infiltration. Furthermore, we identified a 12 genes signature (including CYP7A1, SPP1, MSC, CXCL8, CXCL1, GCNT3, TMEM45A, SPP2, ME1, TSPAN13, S100A9, and NQO1) with excellent prediction performance for HCC prognosis. In addition, High-score patients with higher immune infiltration benefited less from immunotherapy. The sensitivity of low-score patients to multiple drugs including Parthenolide and Shikonin was significantly higher than that of high-score patients. Moreover, high-score patients had increased oxidative stress pathways scores, and the RiskScore was closely associated with oxidative stress pathways scores. And the nomogram had good clinical utility.

Conclusion: To predict the survival outcome and immunotherapy response for HCC, we developed a 12-gene signature based on the heterogeneity of the CD8+ T cells.

« Previous Next »
[1]
Forner, A.; Reig, M.; Bruix, J. Hepatocellular carcinoma. Lancet, 2018, 391(10127), 1301-1314.
[http://dx.doi.org/10.1016/S0140-6736(18)30010-2] [PMID: 29307467]
[2]
Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality Worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2021, 71(3), 209-249.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[3]
Kulik, L.; El-Serag, H.B. Epidemiology and management of hepatocellular carcinoma. Gastroenterology, 2019, 156(2), 477-491.e1.
[http://dx.doi.org/10.1053/j.gastro.2018.08.065] [PMID: 30367835]
[4]
Wang, L.; Park, H.; Chhim, S.; Ding, Y.; Jiang, W.; Queen, C.; Kim, K.J. A novel monoclonal antibody to fibroblast growth factor 2 effectively inhibits growth of hepatocellular carcinoma xenografts. Mol. Cancer Ther., 2012, 11(4), 864-872.
[http://dx.doi.org/10.1158/1535-7163.MCT-11-0813] [PMID: 22351746]
[5]
Radvanyi, L.G. Tumor-infiltrating lymphocyte therapy. Cancer J., 2015, 21(6), 450-464.
[http://dx.doi.org/10.1097/PPO.0000000000000162] [PMID: 26588676]
[6]
Rosenberg, S.A.; Spiess, P.; Lafreniere, R. A new approach to the adoptive immunotherapy of cancer with tumor-infiltrating lymphocytes. Science, 1986, 233(4770), 1318-1321.
[http://dx.doi.org/10.1126/science.3489291] [PMID: 3489291]
[7]
Harlin, H.; Meng, Y.; Peterson, A.C.; Zha, Y.; Tretiakova, M.; Slingluff, C.; McKee, M.; Gajewski, T.F. Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. Cancer Res., 2009, 69(7), 3077-3085.
[http://dx.doi.org/10.1158/0008-5472.CAN-08-2281] [PMID: 19293190]
[8]
Ji, R.R.; Chasalow, S.D.; Wang, L.; Hamid, O.; Schmidt, H.; Cogswell, J.; Alaparthy, S.; Berman, D.; Jure-Kunkel, M.; Siemers, N.O.; Jackson, J.R.; Shahabi, V. An immune-active tumor microenvironment favors clinical response to ipilimumab. Cancer Immunol. Immunother., 2012, 61(7), 1019-1031.
[http://dx.doi.org/10.1007/s00262-011-1172-6] [PMID: 22146893]
[9]
Hutter, C.; Zenklusen, J.C. The cancer genome atlas: Creating lasting value beyond its data. Cell, 2018, 173(2), 283-285.
[http://dx.doi.org/10.1016/j.cell.2018.03.042] [PMID: 29625045]
[10]
Toro-Domínguez, D.; Martorell-Marugán, J.; López-Domínguez, R.; García-Moreno, A.; González-Rumayor, V.; Alarcón-Riquelme, M.E.; Carmona-Sáez, P. ImaGEO: Integrative gene expression meta-analysis from GEO database. Bioinformatics, 2019, 35(5), 880-882.
[http://dx.doi.org/10.1093/bioinformatics/bty721] [PMID: 30137226]
[11]
Lian, Q.; Wang, S.; Zhang, G.; Wang, D.; Luo, G.; Tang, J.; Chen, L.; Gu, J. HCCDB: A database of hepatocellular carcinoma expression atlas. Genomics Proteom. Bioinform., 2018, 16(4), 269-275.
[http://dx.doi.org/10.1016/j.gpb.2018.07.003] [PMID: 30266410]
[12]
Langfelder, P.; Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform., 2008, 9(1), 559.
[http://dx.doi.org/10.1186/1471-2105-9-559] [PMID: 19114008]
[13]
Horvath, S.; Zhang, B.; Carlson, M.; Lu, K.V.; Zhu, S.; Felciano, R.M.; Laurance, M.F.; Zhao, W.; Qi, S.; Chen, Z.; Lee, Y.; Scheck, A.C.; Liau, L.M.; Wu, H.; Geschwind, D.H.; Febbo, P.G.; Kornblum, H.I.; Cloughesy, T.F.; Nelson, S.F.; Mischel, P.S. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc. Natl. Acad. Sci., 2006, 103(46), 17402-17407.
[http://dx.doi.org/10.1073/pnas.0608396103] [PMID: 17090670]
[14]
Wilkerson, M.D.; Hayes, D.N. ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking. Bioinformatics, 2010, 26(12), 1572-1573.
[http://dx.doi.org/10.1093/bioinformatics/btq170] [PMID: 20427518]
[15]
Yang, P.; Chen, W.; Xu, H.; Yang, J.; Jiang, J.; Jiang, Y.; Xu, G. Correlation of CCL8 expression with immune cell infiltration of skin cutaneous melanoma: Potential as a prognostic indicator and therapeutic pathway. Cancer Cell Int., 2021, 21(1), 635.
[http://dx.doi.org/10.1186/s12935-021-02350-8] [PMID: 34844613]
[16]
Charoentong, P.; Finotello, F.; Angelova, M.; Mayer, C.; Efremova, M.; Rieder, D.; Hackl, H.; Trajanoski, Z. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep., 2017, 18(1), 248-262.
[http://dx.doi.org/10.1016/j.celrep.2016.12.019] [PMID: 28052254]
[17]
Engebretsen, S.; Bohlin, J. Statistical predictions with glmnet. Clin. Epigenetics, 2019, 11(1), 123.
[http://dx.doi.org/10.1186/s13148-019-0730-1] [PMID: 31443682]
[18]
Pan, X.; Jin, X.; Wang, J.; Hu, Q.; Dai, B. Placenta inflammation is closely associated with gestational diabetes mellitus. Am. J. Transl. Res., 2021, 13(5), 4068-4079.
[PMID: 34149999]
[19]
Yan, D.; Li, C.; Zhou, Y.; Yan, X.; Zhi, W.; Qian, H.; Han, Y. Exploration of combinational therapeutic strategies for HCC based on TCGA HCC database. Oncologie, 2022, 24(1), 101-111.
[http://dx.doi.org/10.32604/oncologie.2022.020357]
[20]
Speiser, D.E.; Ho, P.C.; Verdeil, G. Regulatory circuits of T cell function in cancer. Nat. Rev. Immunol., 2016, 16(10), 599-611.
[http://dx.doi.org/10.1038/nri.2016.80] [PMID: 27526640]
[21]
Thommen, D.S.; Schumacher, T.N. T cell dysfunction in cancer. Cancer Cell, 2018, 33(4), 547-562.
[http://dx.doi.org/10.1016/j.ccell.2018.03.012] [PMID: 29634943]
[22]
Poureau, P.G.; Metges, J.P. Fundamentals of digestive cancers immunology, especially gastric and hepatocellular carcinomasfondamentaux de l’immunologie des cancers digestifs (gastriques et hépatocellulaires). Oncologie, 2021, 23(1), 47-59.
[http://dx.doi.org/10.32604/Oncologie.2021.15525]
[23]
Hashimoto, M.; Kamphorst, A.O.; Im, S.J.; Kissick, H.T.; Pillai, R.N.; Ramalingam, S.S.; Araki, K.; Ahmed, R. CD8 T cell exhaustion in chronic infection and cancer: Opportunities for interventions. Annu. Rev. Med., 2018, 69(1), 301-318.
[http://dx.doi.org/10.1146/annurev-med-012017-043208] [PMID: 29414259]
[24]
He, Q.F.; Xu, Y.; Li, J.; Huang, Z.M.; Li, X.H.; Wang, X. CD8+ T-cell exhaustion in cancer: Mechanisms and new area for cancer immunotherapy. Brief. Funct. Genomics, 2019, 18(2), 99-106.
[http://dx.doi.org/10.1093/bfgp/ely006] [PMID: 29554204]
[25]
Wherry, E.J. T cell exhaustion. Nat. Immunol., 2011, 12(6), 492-499.
[http://dx.doi.org/10.1038/ni.2035] [PMID: 21739672]
[26]
Wang, X.; Lu, X.J.; Sun, B. The pros and cons of dying tumour cells in adaptive immune responses. Nat. Rev. Immunol., 2017, 17(9), 591.
[http://dx.doi.org/10.1038/nri.2017.87] [PMID: 28757605]
[27]
Flecken, T.; Schmidt, N.; Hild, S.; Gostick, E.; Drognitz, O.; Zeiser, R.; Schemmer, P.; Bruns, H.; Eiermann, T.; Price, D.A.; Blum, H.E.; Neumann-Haefelin, C.; Thimme, R. Immunodominance and functional alterations of tumor-associated antigen-specific CD8 + T-cell responses in hepatocellular carcinoma. Hepatology, 2014, 59(4), 1415-1426.
[http://dx.doi.org/10.1002/hep.26731] [PMID: 24002931]
[28]
Wang, L.; Ma, X.; Chen, Y.; Zhang, J.; Zhang, J.; Wang, W.; Chen, S. MiR-145-5p suppresses hepatocellular carcinoma progression by targeting ABHD17C. Oncologie, 2022, 24(4), 897-912.
[http://dx.doi.org/10.32604/oncologie.2022.025693]
[29]
Wu, C.; Zhou, Y.; Wang, M.; Dai, G.; Liu, X.; Lai, L.; Tang, S. Bioinformatics analysis explores potential hub genes in nonalcoholic fatty liver disease. Front. Genet., 2021, 12, 772487.
[http://dx.doi.org/10.3389/fgene.2021.772487] [PMID: 34777484]
[30]
Casagrande, V.; Mauriello, A.; Bischetti, S.; Mavilio, M.; Federici, M.; Menghini, R. Hepatocyte specific TIMP3 expression prevents diet dependent fatty liver disease and hepatocellular carcinoma. Sci. Rep., 2017, 7(1), 6747.
[http://dx.doi.org/10.1038/s41598-017-06439-x] [PMID: 28751722]
[31]
Liu, L.; Zhang, R.; Deng, J.; Dai, X.; Zhu, X.; Fu, Q.; Zhang, H.; Tong, Z.; Zhao, P.; Fang, W.; Zheng, Y.; Bao, X. Construction of TME and Identification of crosstalk between malignant cells and macrophages by SPP1 in hepatocellular carcinoma. Cancer Immunol. Immunother., 2022, 71(1), 121-136.
[http://dx.doi.org/10.1007/s00262-021-02967-8] [PMID: 34028567]
[32]
Zhu, J.; Zhou, Y.; Wang, L.; Hao, J.; Chen, R.; Liu, L.; Li, J. CXCL5/CXCL8 is a promising potential prognostic and tumor microenvironment-related cluster in hepatocellular carcinoma. J. Gastrointest. Oncol., 2020, 11(6), 1364-1380.
[http://dx.doi.org/10.21037/jgo-20-556] [PMID: 33457007]
[33]
He, Q.; Huang, W.; Liu, D.; Zhang, T.; Wang, Y.; Ji, X.; Xie, M.; Sun, M.; Tian, D.; Liu, M.; Xia, L. Homeobox B5 promotes metastasis and poor prognosis in Hepatocellular Carcinoma, via FGFR4 and CXCL1 upregulation. Theranostics, 2021, 11(12), 5759-5777.
[http://dx.doi.org/10.7150/thno.57659] [PMID: 33897880]
[34]
Liu, T.; Zhang, S.; Chen, J.; Jiang, K.; Zhang, Q.; Guo, K.; Liu, Y. The transcriptional profiling of glycogenes associated with hepatocellular carcinoma metastasis. PLoS One, 2014, 9(9), e107941.
[http://dx.doi.org/10.1371/journal.pone.0107941] [PMID: 25232831]
[35]
Xu, D.; Wang, Y.; Wu, J.; Lin, S.; Chen, Y.; Zheng, J. Identification and clinical validation of EMT-associated prognostic features based on hepatocellular carcinoma. Cancer Cell Int., 2021, 21(1), 621.
[http://dx.doi.org/10.1186/s12935-021-02326-8] [PMID: 34819088]
[36]
Zhang, X.; Kang, C.; Li, N.; Liu, X.; Zhang, J.; Gao, F.; Dai, L. Identification of special key genes for alcohol-related hepatocellular carcinoma through bioinformatic analysis. PeerJ, 2019, 7, e6375.
[http://dx.doi.org/10.7717/peerj.6375] [PMID: 30755830]
[37]
Wen, D.; Liu, D.; Tang, J.; Dong, L.; Liu, Y.; Tao, Z.; Wan, J.; Gao, D.; Wang, L.; Sun, H.; Fan, J.; Wu, W. Malic enzyme 1 induces epithelial–mesenchymal transition and indicates poor prognosis in hepatocellular carcinoma. Tumour Biol., 2015, 36(8), 6211-6221.
[http://dx.doi.org/10.1007/s13277-015-3306-5] [PMID: 25753478]
[38]
Liao, J.; Li, J.Z.; Xu, J.; Xu, Y.; Wen, W.P.; Zheng, L.; Li, L. High S100A9+ cell density predicts a poor prognosis in hepatocellular carcinoma patients after curative resection. Aging, 2021, 13(12), 16367-16380.
[http://dx.doi.org/10.18632/aging.203162] [PMID: 34157683]
[39]
Zhong, C.; Niu, Y.; Liu, W.; Yuan, Y.; Li, K.; Shi, Y.; Qiu, Z.; Li, K.; Lin, Z.; Huang, Z.; Zuo, D.; Yang, Z.; Liao, Y.; Zhang, Y.; Wang, C.; Qiu, J.; He, W.; Yuan, Y.; Li, B. S100A9 derived from chemoembolization-induced hypoxia governs mitochondrial function in hepatocellular carcinoma progression. Adv. Sci., 2022, 9(30), 2202206.
[http://dx.doi.org/10.1002/advs.202202206] [PMID: 36041055]
[40]
Yang, Y.; Zheng, J.; Wang, M.; Zhang, J.; Tian, T.; Wang, Z.; Yuan, S.; Liu, L.; Zhu, P.; Gu, F.; Fu, S.; Shan, Y.; Pan, Z.; Zhou, W. Retracted: NQO1 promotes an aggressive phenotype in hepatocellular carcinoma via amplifying ERK-NRF2 signaling. Cancer Sci., 2021, 112(2), 641-654.
[http://dx.doi.org/10.1111/cas.14744] [PMID: 33222332]
[41]
Sharma, A.; Rajappa, M.; Saxena, A.; Sharma, M. Antioxidant status in advanced cervical cancer patients undergoing neoadjuvant chemoradiation. Br. J. Biomed. Sci., 2007, 64(1), 23-27.
[http://dx.doi.org/10.1080/09674845.2007.11732751] [PMID: 17444415]
[42]
Jelic, M.; Mandic, A.; Maricic, S.; Srdjenovic, B. Oxidative stress and its role in cancer. J. Cancer Res. Ther., 2021, 17(1), 22-28.
[http://dx.doi.org/10.4103/jcrt.JCRT_862_16] [PMID: 33723127]
[43]
Gabbia, D.; Cannella, L.; De Martin, S. The role of oxidative stress in NAFLD-NASH-HCC transition-focus on nadph oxidases. Biomedicines, 2021, 9(6), 687.
[http://dx.doi.org/10.3390/biomedicines9060687] [PMID: 34204571]
[44]
Yang, Y.; Karsli-Uzunbas, G.; Poillet-Perez, L.; Sawant, A.; Hu, Z.S.; Zhao, Y.; Moore, D.; Hu, W.; White, E. Autophagy promotes mammalian survival by suppressing oxidative stress and p53. Genes Dev., 2020, 34(9-10), 688-700.
[http://dx.doi.org/10.1101/gad.335570.119] [PMID: 32193353]

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