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Combinatorial Chemistry & High Throughput Screening

Author(s): Hui Ma*, Zhongchen Li, Rongxin Chen and Zhenggang Ren

DOI: 10.2174/0113862073257308231026073951

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Development of a Combined Oxidative Stress and Endoplasmic Reticulum Stress-Related Prognostic Signature for Hepatocellular Carcinoma

Page: [2850 - 2860] Pages: 11

  • * (Excluding Mailing and Handling)

Abstract

Background: Oxidative stress and endoplasmic reticulum stress are important components of the cellular stress process, which plays a critical role in tumor initiation and progression.

Methods: First, the correlation between oxidative stress and endoplasmic reticulum stress was detected in 68 human hepatocellular carcinoma (HCC) tissue microarray samples by immunohistochemistry. Differentially expressed oxidative stress- and endoplasmic reticulum stressrelated genes (OESGs) then were screened in HCC. Next, an OESGs prognostic signature was constructed for HCC in the training cohort (TCGA-LIHC from The Cancer Genome Atlas), by least absolute shrinkage and selection operator Cox and stepwise Cox regression analyses, and was verified in the external cohort (GSE14520 from the Gene Expression Omnibus). The MCP counter was employed to evaluate immune cell infiltration. The C-index was used to evaluate the predictive power of prognostic signature. Finally, a prognostic nomogram model was constructed to predict the survival probability of patients with HCC based on the results of Cox regression analysis.

Results: We demonstrated a positive correlation between oxidative stress and endoplasmic reticulum stress in human HCC samples. We then identified five OESGs as a prognostic signature consisting of IL18RAP, ECT2, PPARGC1A, STC2, and NQO1 for HCC. Related risk scores correlated with tumor stage, grade, and response to transcatheter arterial chemoembolization therapy, and the higher risk score group had less T cells, CD8+ T cells, cytotoxic lymphocytes and natural killer cell infiltration. The C-index of our OESGs prognostic signature was superior to four previously published signatures. Furthermore, we developed a nomogram based on the OESGs prognostic signature and clinical parameters for patients with HCC that is an effective quantitative analysis tool to predict patient survival.

Conclusion: The OESGs signature showed excellent performance in predicting survival and therapeutic responses for patients with HCC.

[1]
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]
[2]
Llovet, J.M.; Kelley, R.K.; Villanueva, A.; Singal, A.G.; Pikarsky, E.; Roayaie, S.; Lencioni, R.; Koike, K.; Zucman-Rossi, J.; Finn, R.S. Hepatocellular carcinoma. Nat. Rev. Dis. Primers, 2021, 7(1), 6.
[http://dx.doi.org/10.1038/s41572-020-00240-3] [PMID: 33479224]
[3]
Ally, A.; Balasundaram, M.; Carlsen, R.; Chuah, E.; Clarke, A.; Dhalla, N.; Holt, R.A.; Jones, S.J.M.; Lee, D.; Ma, Y.; Marra, M.A.; Mayo, M.; Moore, R.A.; Mungall, A.J.; Schein, J.E.; Sipahimalani, P.; Tam, A.; Thiessen, N.; Cheung, D.; Wong, T.; Brooks, D.; Robertson, A.G.; Bowlby, R.; Mungall, K.; Sadeghi, S.; Xi, L.; Covington, K.; Shinbrot, E.; Wheeler, D.A.; Gibbs, R.A.; Donehower, L.A.; Wang, L.; Bowen, J.; Gastier-Foster, J.M.; Gerken, M.; Helsel, C.; Leraas, K.M.; Lichtenberg, T.M.; Ramirez, N.C.; Wise, L.; Zmuda, E.; Gabriel, S.B.; Meyerson, M.; Cibulskis, C.; Murray, B.A.; Shih, J.; Beroukhim, R.; Cherniack, A.D.; Schumacher, S.E.; Saksena, G.; Pedamallu, C.S.; Chin, L.; Getz, G.; Noble, M.; Zhang, H.; Heiman, D.; Cho, J.; Gehlenborg, N.; Saksena, G.; Voet, D.; Lin, P.; Frazer, S.; Defreitas, T.; Meier, S.; Lawrence, M.; Kim, J.; Creighton, C.J.; Muzny, D.; Doddapaneni, H.V.; Hu, J.; Wang, M.; Morton, D.; Korchina, V.; Han, Y.; Dinh, H.; Lewis, L.; Bellair, M.; Liu, X.; Santibanez, J.; Glenn, R.; Lee, S.; Hale, W.; Parker, J.S.; Wilkerson, M.D.; Hayes, D.N.; Reynolds, S.M.; Shmulevich, I.; Zhang, W.; Liu, Y.; Iype, L.; Makhlouf, H.; Torbenson, M.S.; Kakar, S.; Yeh, M.M.; Jain, D.; Kleiner, D.E.; Jain, D.; Dhanasekaran, R.; El-Serag, H.B.; Yim, S.Y.; Weinstein, J.N.; Mishra, L.; Zhang, J.; Akbani, R.; Ling, S.; Ju, Z.; Su, X.; Hegde, A.M.; Mills, G.B.; Lu, Y.; Chen, J.; Lee, J-S.; Sohn, B.H.; Shim, J.J.; Tong, P.; Aburatani, H.; Yamamoto, S.; Tatsuno, K.; Li, W.; Xia, Z.; Stransky, N.; Seiser, E.; Innocenti, F.; Gao, J.; Kundra, R.; Zhang, H.; Heins, Z.; Ochoa, A.; Sander, C.; Ladanyi, M.; Shen, R.; Arora, A.; Sanchez-Vega, F.; Schultz, N.; Kasaian, K.; Radenbaugh, A.; Bissig, K-D.; Moore, D.D.; Totoki, Y.; Nakamura, H.; Shibata, T.; Yau, C.; Graim, K.; Stuart, J.; Haussler, D.; Slagle, B.L.; Ojesina, A.I.; Katsonis, P.; Koire, A.; Lichtarge, O.; Hsu, T-K.; Ferguson, M.L.; Demchok, J.A.; Felau, I.; Sheth, M.; Tarnuzzer, R.; Wang, Z.; Yang, L.; Zenklusen, J.C.; Zhang, J.; Hutter, C.M.; Sofia, H.J.; Verhaak, R.G.W.; Zheng, S.; Lang, F.; Chudamani, S.; Liu, J.; Lolla, L.; Wu, Y.; Naresh, R.; Pihl, T.; Sun, C.; Wan, Y.; Benz, C.; Perou, A.H.; Thorne, L.B.; Boice, L.; Huang, M.; Rathmell, W.K.; Noushmehr, H.; Saggioro, F.P.; Tirapelli, D.P.C.; Junior, C.G.C.; Mente, E.D.; Silva, O.C., Jr; Trevisan, F.A.; Kang, K.J.; Ahn, K.S.; Giama, N.H.; Moser, C.D.; Giordano, T.J.; Vinco, M.; Welling, T.H.; Crain, D.; Curley, E.; Gardner, J.; Mallery, D.; Morris, S.; Paulauskis, J.; Penny, R.; Shelton, C.; Shelton, T.; Kelley, R.; Park, J-W.; Chandan, V.S.; Roberts, L.R.; Bathe, O.F.; Hagedorn, C.H.; Auman, J.T.; O’Brien, D.R.; Kocher, J-P.A.; Jones, C.D.; Mieczkowski, P.A.; Perou, C.M.; Skelly, T.; Tan, D.; Veluvolu, U.; Balu, S.; Bodenheimer, T.; Hoyle, A.P.; Jefferys, S.R.; Meng, S.; Mose, L.E.; Shi, Y.; Simons, J.V.; Soloway, M.G.; Roach, J.; Hoadley, K.A.; Baylin, S.B.; Shen, H.; Hinoue, T.; Bootwalla, M.S.; Van Den Berg, D.J.; Weisenberger, D.J.; Lai, P.H.; Holbrook, A.; Berrios, M.; Laird, P.W. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell, 2017, 169(7), 1327-1341.e23.
[http://dx.doi.org/10.1016/j.cell.2017.05.046] [PMID: 28622513]
[4]
Zhu, A.X.; Abbas, A.R.; de Galarreta, M.R.; Guan, Y.; Lu, S.; Koeppen, H.; Zhang, W.; Hsu, C.H.; He, A.R.; Ryoo, B.Y.; Yau, T.; Kaseb, A.O.; Burgoyne, A.M.; Dayyani, F.; Spahn, J.; Verret, W.; Finn, R.S.; Toh, H.C.; Lujambio, A.; Wang, Y. Molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma. Nat. Med., 2022, 28(8), 1599-1611.
[http://dx.doi.org/10.1038/s41591-022-01868-2] [PMID: 35739268]
[5]
Wang, Z.; Li, Z.; Ye, Y.; Xie, L.; Li, W. Oxidative stress and liver cancer: Etiology and therapeutic targets. Oxid. Med. Cell. Longev., 2016, 2016, 1-10.
[http://dx.doi.org/10.1155/2016/7891574] [PMID: 27957239]
[6]
Cheung, E.C.; Vousden, K.H. The role of ROS in tumour development and progression. Nat. Rev. Cancer, 2022, 22(5), 280-297.
[http://dx.doi.org/10.1038/s41568-021-00435-0] [PMID: 35102280]
[7]
Chen, X.; Cubillos-Ruiz, J.R. Endoplasmic reticulum stress signals in the tumour and its microenvironment. Nat. Rev. Cancer, 2021, 21(2), 71-88.
[http://dx.doi.org/10.1038/s41568-020-00312-2] [PMID: 33214692]
[8]
Pavlović, N.; Heindryckx, F. Targeting ER stress in the hepatic tumor microenvironment. FEBS J., 2022, 289(22), 7163-7176.
[http://dx.doi.org/10.1111/febs.16145] [PMID: 34331743]
[9]
Lin, Y.; Jiang, M.; Chen, W.; Zhao, T.; Wei, Y. Cancer and ER stress: Mutual crosstalk between autophagy, oxidative stress and inflammatory response. Biomed. Pharmacother., 2019, 118, 109249.
[http://dx.doi.org/10.1016/j.biopha.2019.109249] [PMID: 31351428]
[10]
Xiong, S.; Chng, W.J.; Zhou, J. Crosstalk between endoplasmic reticulum stress and oxidative stress: A dynamic duo in multiple myeloma. Cell. Mol. Life Sci., 2021, 78(8), 3883-3906.
[http://dx.doi.org/10.1007/s00018-021-03756-3] [PMID: 33599798]
[11]
Guo, Y.; Yang, J.; Gao, H.; Tian, X.; Zhang, X.; Kan, Q. Development and verification of a combined immune- and metabolism-related prognostic signature for hepatocellular carcinoma. Front. Immunol., 2022, 13, 927635.
[http://dx.doi.org/10.3389/fimmu.2022.927635] [PMID: 35874741]
[12]
Ma, H.; Kang, Z.; Foo, T.K.; Shen, Z.; Xia, B. Disrupted BRCA1‐PALB2 interaction induces tumor immunosuppression and T‐lymphocyte infiltration in HCC through cGAS‐STING pathway. Hepatology, 2023, 77(1), 33-47.
[http://dx.doi.org/10.1002/hep.32335] [PMID: 35006619]
[13]
John, T.; Liu, G.; Tsao, M-S. Overview of molecular testing in non-small-cell lung cancer: Mutational analysis, gene copy number, protein expression and other biomarkers of EGFR for the prediction of response to tyrosine kinase inhibitors. Oncogene, 2009, 28(S1)(Suppl. 1), S14-S23.
[http://dx.doi.org/10.1038/onc.2009.197] [PMID: 19680292]
[14]
Roessler, S.; Jia, H.L.; Budhu, A.; Forgues, M.; Ye, Q.H.; Lee, J.S.; Thorgeirsson, S.S.; Sun, Z.; Tang, Z.Y.; Qin, L.X.; Wang, X.W. A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients. Cancer Res., 2010, 70(24), 10202-10212.
[http://dx.doi.org/10.1158/0008-5472.CAN-10-2607] [PMID: 21159642]
[15]
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. USA, 2005, 102(43), 15545-15550.
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[16]
Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 2015, 43(7), e47.
[http://dx.doi.org/10.1093/nar/gkv007] [PMID: 25605792]
[17]
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]
[18]
Rizvi, A.A.; Karaesmen, E.; Morgan, M.; Preus, L.; Wang, J.; Sovic, M.; Hahn, T.; Sucheston-Campbell, L.E. gwasurvivr: An R package for genome-wide survival analysis. Bioinformatics, 2019, 35(11), 1968-1970.
[http://dx.doi.org/10.1093/bioinformatics/bty920] [PMID: 30395168]
[19]
Friedman, J.; Hastie, T.; Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw., 2010, 33(1), 1-22.
[http://dx.doi.org/10.18637/jss.v033.i01] [PMID: 20808728]
[20]
Becht, E.; Giraldo, N.A.; Lacroix, L.; Buttard, B.; Elarouci, N.; Petitprez, F.; Selves, J.; Laurent-Puig, P.; Sautès-Fridman, C.; Fridman, W.H.; de Reyniès, A. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol., 2016, 17(1), 218.
[http://dx.doi.org/10.1186/s13059-016-1070-5] [PMID: 27765066]
[21]
Cabrita, R.; Lauss, M.; Sanna, A.; Donia, M.; Skaarup Larsen, M.; Mitra, S.; Johansson, I.; Phung, B.; Harbst, K.; Vallon-Christersson, J.; van Schoiack, A.; Lövgren, K.; Warren, S.; Jirström, K.; Olsson, H.; Pietras, K.; Ingvar, C.; Isaksson, K.; Schadendorf, D.; Schmidt, H.; Bastholt, L.; Carneiro, A.; Wargo, J.A.; Svane, I.M.; Jönsson, G. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature, 2020, 577(7791), 561-565.
[http://dx.doi.org/10.1038/s41586-019-1914-8] [PMID: 31942071]
[22]
Wakiyama, H.; Masuda, T.; Motomura, Y.; Hu, Q.; Tobo, T.; Eguchi, H.; Sakamoto, K.; Hirakawa, M.; Honda, H.; Mimori, K. Cytolytic activity (CYT) score is a prognostic biomarker reflecting host immune status in hepatocellular carcinoma (HCC). Anticancer Res., 2018, 38(12), 6631-6638.
[http://dx.doi.org/10.21873/anticanres.13030] [PMID: 30504371]
[23]
Lee, J.S. The mutational landscape of hepatocellular carcinoma. Clin. Mol. Hepatol., 2015, 21(3), 220-229.
[http://dx.doi.org/10.3350/cmh.2015.21.3.220] [PMID: 26523267]
[24]
Fu, X.W.; Song, C.Q. Identification and validation of pyroptosis-related gene signature to predict prognosis and reveal immune infiltration in hepatocellular carcinoma. Front. Cell Dev. Biol., 2021, 9, 748039.
[http://dx.doi.org/10.3389/fcell.2021.748039] [PMID: 34820376]
[25]
Zheng, Y.; Liu, Y.; Zhao, S.; Zheng, Z.; Shen, C.; An, L.; Yuan, Y. Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma. Cancer Manag. Res., 2018, 10, 6079-6096.
[http://dx.doi.org/10.2147/CMAR.S181396] [PMID: 30538557]
[26]
Jin, S.; Cao, J.; Kong, L.B. Identification and validation in a novel quantification system of the glutamine metabolism patterns for the prediction of prognosis and therapy response in hepatocellular carcinoma. J. Gastrointest. Oncol., 2022, 13(5), 2505-2521.
[http://dx.doi.org/10.21037/jgo-22-895] [PMID: 36388696]
[27]
Xiang, X.H.; Yang, L.; Zhang, X.; Ma, X.H.; Miao, R.C.; Gu, J.X.; Fu, Y.N.; Yao, Q.; Zhang, J.Y.; Liu, C.; Lin, T.; Qu, K. Seven-senescence-associated gene signature predicts overall survival for Asian patients with hepatocellular carcinoma. World J. Gastroenterol., 2019, 25(14), 1715-1728.
[http://dx.doi.org/10.3748/wjg.v25.i14.1715] [PMID: 31011256]
[28]
Cao, M.Q.; You, A.B.; Cui, W.; Zhang, S.; Guo, Z.G.; Chen, L.; Zhu, X.D.; Zhang, W.; Zhu, X.L.; Guo, H.; Deng, D.J.; Sun, H.C.; Zhang, T. Cross talk between oxidative stress and hypoxia via thioredoxin and HIF‐2α drives metastasis of hepatocellular carcinoma. FASEB J., 2020, 34(4), 5892-5905.
[http://dx.doi.org/10.1096/fj.202000082R] [PMID: 32157720]
[29]
Zhu, Y.; Liu, W.; Wang, Z.; Wang, Y.; Tan, C.; Pan, Z.; Wang, A.; Liu, J.; Sun, G. ARHGEF2/EDN1 pathway participates in ER stress-related drug resistance of hepatocellular carcinoma by promoting angiogenesis and malignant proliferation. Cell Death Dis., 2022, 13(7), 652.
[http://dx.doi.org/10.1038/s41419-022-05099-8] [PMID: 35896520]
[30]
Hoseini, Z.; Sepahvand, F.; Rashidi, B.; Sahebkar, A.; Masoudifar, A.; Mirzaei, H. NLRP3 inflammasome: Its regulation and involvement in atherosclerosis. J. Cell. Physiol., 2018, 233(3), 2116-2132.
[http://dx.doi.org/10.1002/jcp.25930] [PMID: 28345767]
[31]
Wang, T.; Chen, B.; Meng, T.; Liu, Z.; Wu, W. Identification and immunoprofiling of key prognostic genes in the tumor microenvironment of hepatocellular carcinoma. Bioengineered, 2021, 12(1), 1555-1575.
[http://dx.doi.org/10.1080/21655979.2021.1918538] [PMID: 33955820]
[32]
Srougi, M.C.; Burridge, K. The nuclear guanine nucleotide exchange factors Ect2 and Net1 regulate RhoB-mediated cell death after DNA damage. PLoS One, 2011, 6(2), e17108.
[http://dx.doi.org/10.1371/journal.pone.0017108] [PMID: 21373644]
[33]
Xu, D.; Wang, Y.; Wu, J.; Zhang, Z.; Chen, J.; Xie, M.; Tang, R.; Chen, C.; Chen, L.; Lin, S.; Luo, X.; Zheng, J. ECT2 overexpression promotes the polarization of tumor-associated macrophages in hepatocellular carcinoma via the ECT2/PLK1/PTEN pathway. Cell Death Dis., 2021, 12(2), 162.
[http://dx.doi.org/10.1038/s41419-021-03450-z] [PMID: 33558466]
[34]
Aisyah, R.; Sadewa, A.H.; Patria, S.Y.; Wahab, A. The PPARGC1A Is the gene responsible for thrifty metabolism related metabolic diseases: A scoping review. Genes, 2022, 13(10), 1894.
[http://dx.doi.org/10.3390/genes13101894] [PMID: 36292779]
[35]
Zuo, Q.; He, J.; Zhang, S.; Wang, H.; Jin, G.; Jin, H.; Cheng, Z.; Tao, X.; Yu, C.; Li, B.; Yang, C.; Wang, S.; Lv, Y.; Zhao, F.; Yao, M.; Cong, W.; Wang, C.; Qin, W. PPARγ coactivator-1α suppresses metastasis of hepatocellular carcinoma by inhibiting Warburg effect by PPARγ-dependent WNT/β-Catenin/pyruvate dehydrogenase kinase isozyme 1 axis. Hepatology, 2021, 73(2), 644-660.
[http://dx.doi.org/10.1002/hep.31280] [PMID: 32298475]
[36]
Qie, S.; Sang, N. Stanniocalcin 2 (STC2): A universal tumour biomarker and a potential therapeutical target. J. Exp. Clin. Cancer Res., 2022, 41(1), 161.
[http://dx.doi.org/10.1186/s13046-022-02370-w] [PMID: 35501821]
[37]
Wu, Z.; Cheng, H.; Liu, J.; Zhang, S.; Zhang, M.; Liu, F.; Li, Y.; Huang, Q.; Jiang, Y.; Chen, S.; Lv, L.; Li, D.; Zeng, J.Z. The oncogenic and diagnostic potential of stanniocalcin 2 in hepatocellular carcinoma. J. Hepatocell. Carcinoma, 2022, 9, 141-155.
[http://dx.doi.org/10.2147/JHC.S351882] [PMID: 35300206]
[38]
Ross, D.; Siegel, D. The diverse functionality of NQO1 and its roles in redox control. Redox Biol., 2021, 41, 101950.
[http://dx.doi.org/10.1016/j.redox.2021.101950] [PMID: 33774477]
[39]
Shimokawa, M.; Yoshizumi, T.; Itoh, S.; Iseda, N.; Sakata, K.; Yugawa, K.; Toshima, T.; Harada, N.; Ikegami, T.; Mori, M. Modulation of Nqo1 activity intercepts anoikis resistance and reduces metastatic potential of hepatocellular carcinoma. Cancer Sci., 2020, 111(4), 1228-1240.
[http://dx.doi.org/10.1111/cas.14320] [PMID: 31968140]
[40]
Chevet, E.; Hetz, C.; Samali, A. Endoplasmic reticulum stress-activated cell reprogramming in oncogenesis. Cancer Discov., 2015, 5(6), 586-597.
[http://dx.doi.org/10.1158/2159-8290.CD-14-1490] [PMID: 25977222]
[41]
Mandula, J.K.; Chang, S.; Mohamed, E.; Jimenez, R.; Sierra-Mondragon, R.A.; Chang, D.C.; Obermayer, A.N.; Moran-Segura, C.M.; Das, S.; Vazquez-Martinez, J.A.; Prieto, K.; Chen, A.; Smalley, K.S.M.; Czerniecki, B.; Forsyth, P.; Koya, R.C.; Ruffell, B.; Cubillos-Ruiz, J.R.; Munn, D.H.; Shaw, T.I.; Conejo-Garcia, J.R.; Rodriguez, P.C. Ablation of the endoplasmic reticulum stress kinase PERK induces paraptosis and type I interferon to promote anti-tumor T cell responses. Cancer Cell, 2022, 40(10), 1145-1160.e9.
[http://dx.doi.org/10.1016/j.ccell.2022.08.016] [PMID: 36150390]