Generic placeholder image

Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

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

Research Article

Exploration of Key Genes Combining with Immune Infiltration Level and Tumor Mutational Burden in Hepatocellular Carcinoma

Author(s): Jing Chen, Lu Zhang, Cui-Hua Lu* and Chen-Zhou Xu*

Volume 27, Issue 14, 2024

Published on: 11 January, 2024

Page: [2110 - 2124] Pages: 15

DOI: 10.2174/0113862073239916231023053142

Price: $65

Abstract

Background: Hepatocellular carcinoma (HCC) is a lethal malignancy due to its heterogeneity and aggressive behavior. Recently, somatic mutations and tumor cell interactions with the surrounding tumor immune microenvironment (TIME) have been reported to participate in HCC carcinogenesis and predict HCC progression. In this study, we aimed to investigate the association between tumor mutational burden (TMB) and TIME in HCC. Additionally, we sought to identify differentially expressed genes (DEGs) associated with HCC prognosis and progression.

Methods: The expression, clinical, and mutational data were downloaded from the cancer genome atlas (TCGA) database. The immune infiltration levels and TMB levels of the HCC samples were estimated and the samples were divided into immune cluster (ICR)-1 and 2 based on immune infiltration score and high and low TMB groups based on TMB score. Thereafter, differential gene expression analysis was conducted to identify the DEGs in the ICR1/2 and high/low TMB groups, and the intersecting DEGs were selected. Thereafter, Cox regression analysis was performed on 89 significant DEGs, among which 19 were associated with prognosis. These 19 DEGs were then used to construct a prognostic model based on their expression levels and regression coefficients. Thereafter, we analyzed the DEGs in mutant and wildtype TP53 HCC samples and identified high BCL10 and TRAF3 expression in the mutant TP53 samples. BCL10 and TRAF3 expression was detected by real-time quantitative reverse transcription PCR and immunohistochemistry, and their clinical correlation, biological function, and immune infiltration levels were analyzed by chi-square analyses, Gene Set Enrichment Analysis (GSEA), and “ssGSEA”, respectively.

Results: The results of our study revealed that immune infiltration level was correlated with TMB and that they synergistically predicted poor prognosis of HCC patients. DEGs enriched in immune-related pathways could serve as indicators of immunotherapy response in HCC. Among these DEGs, BCL10 and TRAF3 were highly expressed in HCC tissues, especially in the mutant TP53 group, and they co-operatively exhibited immunological function, thereby affecting HCC progression and prognosis.

Conclusion: In this study, we identified BCL10 and TRAF3 as potential prognostic indicators in HCC patients. Additionally, we found that BCL10 and TRAF3 influence TMB and TIME in HCC patients and can be used for the development of immune-based therapies for improving the long-term survival of HCC patients.

Keywords: Hepatocellular carcinoma (HCC), tumor mutational burden (TMB), tumor immune infiltration, tumor immune microenvironment (TIME), prognosis, differentially expressed genes (DEGs).

Graphical Abstract
[1]
Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2018, 68(6), 394-424.
[http://dx.doi.org/10.3322/caac.21492] [PMID: 30207593]
[2]
Fouad, Y.; Lazarus, J.V.; Negro, F.; Peck-Radosavljevic, M.; Sarin, S.K.; Ferenci, P.; Esmat, G.; Ghazinian, H.; Nakajima, A.; Silva, M.; Lee, S.; Colombo, M. MAFLD considerations as a part of the global hepatitis C elimination effort: An international perspective. Aliment. Pharmacol. Ther., 2021, 53(10), 1080-1089.
[http://dx.doi.org/10.1111/apt.16346] [PMID: 33751604]
[3]
Singal, A.G.; Lampertico, P.; Nahon, P. Epidemiology and surveillance for hepatocellular carcinoma: New trends. J. Hepatol., 2020, 72(2), 250-261.
[http://dx.doi.org/10.1016/j.jhep.2019.08.025] [PMID: 31954490]
[4]
Takeda, A.; Sanuki, N.; Tsurugai, Y.; Iwabuchi, S.; Matsunaga, K.; Ebinuma, H.; Imajo, K.; Aoki, Y.; Saito, H.; Kunieda, E. Phase 2 study of stereotactic body radiotherapy and optional transarterial chemoembolization for solitary hepatocellular carcinoma not amenable to resection and radiofrequency ablation. Cancer, 2016, 122(13), 2041-2049.
[http://dx.doi.org/10.1002/cncr.30008] [PMID: 27062278]
[5]
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]
[6]
Rebouissou, S.; Franconi, A.; Calderaro, J.; Letouzé, E.; Imbeaud, S.; Pilati, C.; Nault, J.C.; Couchy, G.; Laurent, A.; Balabaud, C.; Bioulac-Sage, P.; Zucman-Rossi, J. Genotype-phenotype correlation of CTNNB1 mutations reveals different ß-catenin activity associated with liver tumor progression. Hepatology, 2016, 64(6), 2047-2061.
[http://dx.doi.org/10.1002/hep.28638] [PMID: 27177928]
[7]
Yarchoan, M.; Hopkins, A.; Jaffee, E.M. Tumor mutational burden and response rate to PD-1 inhibition. N. Engl. J. Med., 2017, 377(25), 2500-2501.
[http://dx.doi.org/10.1056/NEJMc1713444] [PMID: 29262275]
[8]
Chan, T.A.; Yarchoan, M.; Jaffee, E.; Swanton, C.; Quezada, S.A.; Stenzinger, A.; Peters, S. Development of tumor mutation burden as an immunotherapy biomarker: Utility for the oncology clinic. Ann. Oncol., 2019, 30(1), 44-56.
[http://dx.doi.org/10.1093/annonc/mdy495] [PMID: 30395155]
[9]
Panda, A; Betigeri, A; Subramanian, K; Ross, J; Pavlick, D; Ali, S; Markowski, P. Identifying a clinically applicable mutational burden threshold as a potential biomarker of response to immune checkpoint therapy in solid tumors. JCO Precis. Oncol., 2017, 2017, PO.17.00146.
[http://dx.doi.org/ 10.1200/PO.17.00146]
[10]
Cao, D.; Xu, H.; Xu, X.; Guo, T.; Ge, W. High tumor mutation burden predicts better efficacy of immunotherapy: A pooled analysis of 103078 cancer patients. OncoImmunology, 2019, 8(9), e1629258.
[http://dx.doi.org/10.1080/2162402X.2019.1629258] [PMID: 31428527]
[11]
Wu, H.X.; Wang, Z.X.; Zhao, Q.; Chen, D.L.; He, M.M.; Yang, L.P.; Wang, Y.N.; Jin, Y.; Ren, C.; Luo, H.Y.; Wang, Z.Q.; Wang, F. Tumor mutational and indel burden: A systematic pan-cancer evaluation as prognostic biomarkers. Ann. Transl. Med., 2019, 7(22), 640.
[http://dx.doi.org/10.21037/atm.2019.10.116] [PMID: 31930041]
[12]
Kazdal, D; Endris, V; Allgäuer, M; Kriegsmann, M; Leichsenring, J; Volckmar, A; Harms, A Spatial and temporal heterogeneity of panel-based tumor mutational burden in pulmonary adenocarcinoma: Separating biology from technical artifacts. J. Thorac. Oncol., 2019, 14, 1935-1947.
[13]
Hughes, R.M.; Simons, B.W.; Khan, H.; Miller, R.; Kugler, V.; Torquato, S.; Theodros, D.; Haffner, M.C.; Lotan, T.; Huang, J.; Davicioni, E.; An, S.S.; Riddle, R.C.; Thorek, D.L.J.; Garraway, I.P.; Fertig, E.J.; Isaacs, J.T.; Brennen, W.N.; Park, B.H.; Hurley, P.J. Asporin restricts mesenchymal stromal cell differentiation, alters the tumor microenvironment, and drives metastatic progression. Cancer Res., 2019, 79(14), 3636-3650.
[http://dx.doi.org/10.1158/0008-5472.CAN-18-2931] [PMID: 31123087]
[14]
Peng, Y.; Liu, C.; Li, M.; Li, W.; Zhang, M.; Jiang, X.; Chang, Y.; Liu, L.; Wang, F.; Zhao, Q. Identification of a prognostic and therapeutic immune signature associated with hepatocellular carcinoma. Cancer Cell Int., 2021, 21(1), 98.
[http://dx.doi.org/10.1186/s12935-021-01792-4] [PMID: 33568167]
[15]
Li, X.; Wenes, M.; Romero, P.; Huang, S.C.C.; Fendt, S.M.; Ho, P.C. Navigating metabolic pathways to enhance antitumour immunity and immunotherapy. Nat. Rev. Clin. Oncol., 2019, 16(7), 425-441.
[http://dx.doi.org/10.1038/s41571-019-0203-7] [PMID: 30914826]
[16]
Ribas, A.; Wolchok, J.D. Cancer immunotherapy using checkpoint blockade. Science, 2018, 359(6382), 1350-1355.
[http://dx.doi.org/10.1126/science.aar4060] [PMID: 29567705]
[17]
High TMB Predicts Immunotherapy Benefit. High TMB Predicts Immunotherapy Benefit. Cancer Discov., 2018, 8(6), 668.
[http://dx.doi.org/10.1158/2159-8290.CD-NB2018-048] [PMID: 29661758]
[18]
Colaprico, A.; Silva, T.C.; Olsen, C.; Garofano, L.; Cava, C.; Garolini, D.; Sabedot, T.S.; Malta, T.M.; Pagnotta, S.M.; Castiglioni, I.; Ceccarelli, M.; Bontempi, G.; Noushmehr, H. TCGAbiolinks: An R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res., 2016, 44(8), e71.
[http://dx.doi.org/10.1093/nar/gkv1507] [PMID: 26704973]
[19]
Mayakonda, A.; Lin, D.C.; Assenov, Y.; Plass, C.; Koeffler, H.P. Maftools: Efficient and comprehensive analysis of somatic variants in cancer. Genome Res., 2018, 28(11), 1747-1756.
[http://dx.doi.org/10.1101/gr.239244.118] [PMID: 30341162]
[20]
Yi, M.; Nissley, D.V.; McCormick, F.; Stephens, R.M. ssGSEA score-based Ras dependency indexes derived from gene expression data reveal potential Ras addiction mechanisms with possible clinical implications. Sci. Rep., 2020, 10(1), 10258.
[http://dx.doi.org/10.1038/s41598-020-66986-8] [PMID: 32581224]
[21]
Chen, B.; Khodadoust, M.S.; Liu, C.L.; Newman, A.M.; Alizadeh, A.A. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol. Biol., 2018, 1711, 243-259.
[http://dx.doi.org/10.1007/978-1-4939-7493-1_12] [PMID: 29344893]
[22]
Rasero, J.; Diez, I.; Cortes, J.M.; Marinazzo, D.; Stramaglia, S. Connectome sorting by consensus clustering increases separability in group neuroimaging studies. Netw. Neurosci., 2019, 3(2), 325-343.
[http://dx.doi.org/10.1162/netn_a_00074] [PMID: 30793085]
[23]
Chalmers, Z.R.; Connelly, C.F.; Fabrizio, D.; Gay, L.; Ali, S.M.; Ennis, R.; Schrock, A.; Campbell, B.; Shlien, A.; Chmielecki, J.; Huang, F.; He, Y.; Sun, J.; Tabori, U.; Kennedy, M.; Lieber, D.S.; Roels, S.; White, J.; Otto, G.A.; Ross, J.S.; Garraway, L.; Miller, V.A.; Stephens, P.J.; Frampton, G.M. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med., 2017, 9(1), 34.
[http://dx.doi.org/10.1186/s13073-017-0424-2] [PMID: 28420421]
[24]
Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol., 2014, 15(12), 550.
[http://dx.doi.org/10.1186/s13059-014-0550-8] [PMID: 25516281]
[25]
Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res., 2015, 43(Database issue), D1049-D1056.
[PMID: 25428369]
[26]
He, Y.; Jin, Y.J.; Zhang, Y.H.; Meng, H.X.; Zhao, B.S.; Jiang, Y.; Zhu, J.W.; Liang, G.Y.; Kong, D.; Jin, X.M. Ubiquitin-specific peptidase 22 overexpression may promote cancer progression and poor prognosis in human gastric carcinoma. Transl. Res., 2015, 165(3), 407-416.
[http://dx.doi.org/10.1016/j.trsl.2014.09.005] [PMID: 25445209]
[27]
Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; Mesirov, J.P. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. , 2005, 102(43), 15545-15550.
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[28]
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]
[29]
Sing, T.; Sander, O.; Beerenwinkel, N.; Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics, 2005, 21(20), 3940-3941.
[http://dx.doi.org/10.1093/bioinformatics/bti623] [PMID: 16096348]
[30]
Park, S.Y. Nomogram: An analogue tool to deliver digital knowledge. J. Thorac. Cardiovasc. Surg., 2018, 155(4), 1793.
[http://dx.doi.org/10.1016/j.jtcvs.2017.12.107] [PMID: 29370910]
[31]
Chen, Y.Y.; Zhang, X.N.; Xu, C.Z.; Zhou, D.H.; Chen, J.; Liu, Z.X. sun, Y.; Huang, W.; Qu, L.S. MCCC2 promotes HCC development by supporting leucine oncogenic function. Cancer Cell Int., 2021, 21(1), 22.
[http://dx.doi.org/10.1186/s12935-020-01722-w] [PMID: 33407468]
[32]
Ito, K.; Murphy, D. Application of ggplot2 to Pharmacometric Graphics. CPT Pharmacometrics Syst. Pharmacol., 2013, 2(10), 79.
[http://dx.doi.org/10.1038/psp.2013.56] [PMID: 24132163]
[33]
Zhou, Y.; Ding, J.; Qin, Z.; Wang, Y.; Zhang, J.; Jia, K.; Wang, Y.; Zhou, H.; Wang, F.; Jing, X. Predicting the survival rate of patients with hepatocellular carcinoma after thermal ablation by nomograms. Ann. Transl. Med., 2020, 8(18), 1159.
[http://dx.doi.org/10.21037/atm-20-6116] [PMID: 33241008]
[34]
Wu, Q.; Zhou, L.; Lv, D.; Zhu, X.; Tang, H. Exosome-mediated communication in the tumor microenvironment contributes to hepatocellular carcinoma development and progression. J. Hematol. Oncol., 2019, 12(1), 53.
[http://dx.doi.org/10.1186/s13045-019-0739-0] [PMID: 31142326]
[35]
Ilkhani, K.; Bastami, M.; Delgir, S.; Safi, A.; Talebian, S.; Alivand, M.R. The engaged role of tumor microenvironment in cancer metabolism: Focusing on cancer-associated fibroblast and exosome mediators. Anticancer. Agents Med. Chem., 2021, 21(2), 254-266.
[http://dx.doi.org/10.2174/18715206MTA53ODY5y] [PMID: 32914721]
[36]
Greten, T.F.; Lai, C.W.; Li, G.; Staveley-O’Carroll, K.F. Targeted and immune-based therapies for hepatocellular carcinoma. Gastroenterology, 2019, 156(2), 510-524.
[http://dx.doi.org/10.1053/j.gastro.2018.09.051] [PMID: 30287171]
[37]
Chan, K.K.; Bass, A.R. Autoimmune complications of immunotherapy: pathophysiology and management. BMJ, 2020, 369, m736.
[http://dx.doi.org/10.1136/bmj.m736] [PMID: 32253223]
[38]
Schulze, K.; Nault, J.C.; Villanueva, A. Genetic profiling of hepatocellular carcinoma using next-generation sequencing. J. Hepatol., 2016, 65(5), 1031-1042.
[http://dx.doi.org/10.1016/j.jhep.2016.05.035] [PMID: 27262756]
[39]
Lin, C.; Yuan, G.; Hu, Z.; Zeng, Y.; Qiu, X.; Yu, H.; He, S. Bioinformatics analysis of the interactions among lncRNA, miRNA and mRNA expression, genetic mutations and epigenetic modifications in hepatocellular carcinoma. Mol. Med. Rep., 2019, 19(2), 1356-1364.
[PMID: 30535497]
[40]
Kawai-Kitahata, F.; Asahina, Y.; Tanaka, S.; Kakinuma, S.; Murakawa, M.; Nitta, S.; Watanabe, T.; Otani, S.; Taniguchi, M.; Goto, F.; Nagata, H.; Kaneko, S.; Tasaka-Fujita, M.; Nishimura-Sakurai, Y.; Azuma, S.; Itsui, Y.; Nakagawa, M.; Tanabe, M.; Takano, S.; Fukasawa, M.; Sakamoto, M.; Maekawa, S.; Enomoto, N.; Watanabe, M. Comprehensive analyses of mutations and hepatitis B virus integration in hepatocellular carcinoma with clinicopathological features. J. Gastroenterol., 2016, 51(5), 473-486.
[http://dx.doi.org/10.1007/s00535-015-1126-4] [PMID: 26553052]
[41]
Hainaut, P.; Pfeifer, G.P. Somatic TP53 mutations in the era of genome sequencing. Cold Spring Harb. Perspect. Med., 2016, 6(11), a026179.
[http://dx.doi.org/10.1101/cshperspect.a026179] [PMID: 27503997]
[42]
Lim, Y.P.; Lim, T.T.; Chan, Y.L.; Song, A.C.M.; Yeo, B.H.; Vojtesek, B.; Coomber, D.; Rajagopal, G.; Lane, D. The p53 knowledgebase: An integrated information resource for p53 research. Oncogene, 2007, 26(11), 1517-1521.
[http://dx.doi.org/10.1038/sj.onc.1209952] [PMID: 16953220]
[43]
Yang, C.; Huang, X.; Li, Y.; Chen, J.; Lv, Y.; Dai, S. Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology. Brief. Bioinform., 2020, 22(3), bbaa164.
[PMID: 32789496]
[44]
Harding, J.J.; Nandakumar, S.; Armenia, J.; Khalil, D.N.; Albano, M.; Ly, M.; Shia, J.; Hechtman, J.F.; Kundra, R.; El Dika, I.; Do, R.K.; Sun, Y.; Kingham, T.P.; D’Angelica, M.I.; Berger, M.F.; Hyman, D.M.; Jarnagin, W.; Klimstra, D.S.; Janjigian, Y.Y.; Solit, D.B.; Schultz, N.; Abou-Alfa, G.K. Prospective genotyping of hepatocellular carcinoma: Clinical implications of next-generation sequencing for matching patients to targeted and immune therapies. Clin. Cancer Res., 2019, 25(7), 2116-2126.
[http://dx.doi.org/10.1158/1078-0432.CCR-18-2293] [PMID: 30373752]
[45]
Zheng, C.; Zheng, L.; Yoo, J.K.; Guo, H.; Zhang, Y.; Guo, X.; Kang, B.; Hu, R.; Huang, J.Y.; Zhang, Q.; Liu, Z.; Dong, M.; Hu, X.; Ouyang, W.; Peng, J.; Zhang, Z. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell, 2017, 169(7), 1342-1356.e16.
[http://dx.doi.org/10.1016/j.cell.2017.05.035] [PMID: 28622514]
[46]
Turvey, S.E.; Durandy, A.; Fischer, A.; Fung, S.Y.; Geha, R.S.; Gewies, A.; Giese, T.; Greil, J.; Keller, B.; McKinnon, M.L.; Neven, B.; Rozmus, J.; Ruland, J.; Snow, A.L.; Stepensky, P.; Warnatz, K. The CARD11-BCL10-MALT1 (CBM) signalosome complex: Stepping into the limelight of human primary immunodeficiency. J. Allergy Clin. Immunol., 2014, 134(2), 276-284.
[http://dx.doi.org/10.1016/j.jaci.2014.06.015] [PMID: 25087226]
[47]
Kuo, S.H.; Chen, L.T.; Yeh, K.H.; Wu, M.S.; Hsu, H.C.; Yeh, P.Y.; Mao, T.L.; Chen, C.L.; Doong, S.L.; Lin, J.T.; Cheng, A.L. Nuclear expression of BCL10 or nuclear factor kappa B predicts Helicobacter pylori-independent status of early-stage, high-grade gastric mucosa-associated lymphoid tissue lymphomas. J. Clin. Oncol., 2004, 22(17), 3491-3497.
[http://dx.doi.org/10.1200/JCO.2004.10.087] [PMID: 15337797]
[48]
Aronchik, I.; Bjeldanes, L.F.; Firestone, G.L. Direct inhibition of elastase activity by indole-3-carbinol triggers a CD40-TRAF regulatory cascade that disrupts NF-kappaB transcriptional activity in human breast cancer cells. Cancer Res., 2010, 70(12), 4961-4971.
[http://dx.doi.org/10.1158/0008-5472.CAN-09-3349] [PMID: 20530686]
[49]
Lin, W.W.; Yi, Z.; Stunz, L.L.; Maine, C.J.; Sherman, L.A.; Bishop, G.A. The adaptor protein TRAF3 inhibits interleukin-6 receptor signaling in B cells to limit plasma cell development. Sci. Signal., 2015, 8(392), ra88.
[http://dx.doi.org/10.1126/scisignal.aaa5157] [PMID: 26329582]
[50]
Braggio, E.; Keats, J.J.; Leleu, X.; Van Wier, S.; Jimenez-Zepeda, V.H.; Valdez, R.; Schop, R.F.J.; Price-Troska, T.; Henderson, K.; Sacco, A.; Azab, F.; Greipp, P.; Gertz, M.; Hayman, S.; Rajkumar, S.V.; Carpten, J.; Chesi, M.; Barrett, M.; Stewart, A.K.; Dogan, A.; Bergsagel, P.L.; Ghobrial, I.M.; Fonseca, R. Identification of copy number abnormalities and inactivating mutations in two negative regulators of nuclear factor-kappaB signaling pathways in Waldenstrom’s macroglobulinemia. Cancer Res., 2009, 69(8), 3579-3588.
[http://dx.doi.org/10.1158/0008-5472.CAN-08-3701] [PMID: 19351844]

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