Login Register Cart 0
Generic placeholder image

Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

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

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

A Novel Glycolysis-Related Gene Signature Predicts Prognosis For Cutaneous Melanoma

Author(s): Lianghui Zhang, Yi Chen, Yiwen Wang, Feifei Kong and Lingjun Zhu*

Volume 26, Issue 5, 2023

Published on: 17 August, 2022

Page: [965 - 978] Pages: 14

DOI: 10.2174/1386207325666220520105634

Price: $65

Abstract

Background: There exists a lack of effective tools predicting prognosis for cutaneous melanoma patients. Glycolysis plays an essential role in the carcinogenesis process.

Objective: We intended to construct a new prognosis model for cutaneous melanoma.

Methods: Based on the data from the TCGA database, we conducted a univariate Cox regression analysis and identified prognostic glycolysis-related genes (GRGs). Meanwhile, the GSE15605 dataset was used to identify differentially expressed genes (DEGs). The intersection of prognostic GRGs and DEGs was extracted for the subsequent multivariate Cox regression analysis.

Results: A prognostic signature containing ten GRGs was built, and the TCGA cohort was classified into high and low risk subgroups based on the risk score of each patient. K-M analysis manifested that the overall survival of the high-risk group was statistically worse than that of the lowrisk group. Further study indicated that the risk-score could be used as an independent prognostic factor that effectively predicted the clinical prognosis in patients of different ages, genders, and stages. GO and KEGG enrichment analysis showed DEGs between high and low risk groups were enriched in immune-related functions and pathways. In addition, a significant difference existed between high and low risk groups in infiltration pattern of immune cells and expression levels of inhibitory immune checkpoint genes.

Conclusion: A new glycolysis-related gene signature was established for identifying cutaneous melanoma patients with poor prognoses and formulating individualized treatment.

Keywords: : glycolysis, cutaneous melanoma, gene signature, prognosis, inhibitory immune checkpoint

« Previous Next »
[1]
Fischer, G.M.; Vashisht Gopal, Y.N.; McQuade, J.L.; Peng, W.; DeBerardinis, R.J.; Davies, M.A. Metabolic strategies of melanoma cells: Mechanisms, interactions with the tumor microenvironment, and therapeutic implications. Pigment Cell Melanoma Res., 2018, 31(1), 11-30.
[http://dx.doi.org/10.1111/pcmr.12661] [PMID: 29049843]
[2]
Che, G.; Huang, B.; Xie, Z.; Zhao, J.; Yan, Y.; Wu, J.; Sun, H.; Ma, H. Trends in incidence and survival in patients with melanoma, 1974-2013. Am. J. Cancer Res., 2019, 9(7), 1396-1414.
[PMID: 31392077]
[3]
Bello, D.M.; Panageas, K.S.; Hollmann, T.; Shoushtari, A.N.; Momtaz, P.; Chapman, P.B.; Postow, M.A.; Callahan, M.K.; Wolchok, J.D.; Brady, M.S.; Coit, D.G.; Ariyan, C.E. Survival outcomes after metastasectomy in melanoma patients categorized by response to checkpoint blockade. Ann. Surg. Oncol., 2020, 27(4), 1180-1188.
[http://dx.doi.org/10.1245/s10434-019-08099-9] [PMID: 31848819]
[4]
Ogata, D.; Namikawa, K.; Takahashi, A.; Yamazaki, N. A review of the AJCC melanoma staging system in the TNM classification(eighth edition). In: Jpn. J. Clin. Oncol; , 2021, 51, pp, p. (5)671-674.
[5]
Liu, Y.; Jing, R.; Xu, J.; Liu, K.; Xue, J.; Wen, Z.; Li, M. Comparative analysis of oncogenes identified by microarray and RNA-sequencing as biomarkers for clinical prognosis. Biomarkers Med., 2015, 9(11), 1067-1078.
[http://dx.doi.org/10.2217/bmm.15.97] [PMID: 26501374]
[6]
Feng, J.; Li, J.; Wu, L.; Yu, Q.; Ji, J.; Wu, J.; Dai, W.; Guo, C. Emerging roles and the regulation of aerobic glycolysis in hepatocellular carcinoma. J. Exp. Clin. Cancer Res., 2020, 39(1), 126.
[http://dx.doi.org/10.1186/s13046-020-01629-4] [PMID: 32631382]
[7]
Nie, H.; Ju, H.; Fan, J.; Shi, X.; Cheng, Y.; Cang, X.; Zheng, Z.; Duan, X.; Yi, W. O-GlcNAcylation of PGK1 coordinates glycolysis and TCA cycle to promote tumor growth. Nat. Commun., 2020, 11(1), 36.
[http://dx.doi.org/10.1038/s41467-019-13601-8] [PMID: 31911580]
[8]
Nájera, L.; Alonso-Juarranz, M.; Garrido, M.; Ballestín, C.; Moya, L.; Martínez-Díaz, M.; Carrillo, R.; Juarranz, A.; Rojo, F.; Cuezva, J.M.; Rodríguez-Peralto, J.L. Prognostic implications of markers of the metabolic phenotype in human cutaneous melanoma. Br. J. Dermatol., 2019, 181(1), 114-127.
[http://dx.doi.org/10.1111/bjd.17513] [PMID: 30537064]
[9]
Koch, A.; Ebert, E.V.; Seitz, T.; Dietrich, P.; Berneburg, M.; Bosserhoff, A.; Hellerbrand, C. Characterization of glycolysis-related gene expression in malignant melanoma. Pathol. Res. Pract., 2020, 216(1), 152752.
[http://dx.doi.org/10.1016/j.prp.2019.152752] [PMID: 31791701]
[10]
Shen, S.; Faouzi, S.; Souquere, S.; Roy, S.; Routier, E.; Libenciuc, C.; André, F.; Pierron, G.; Scoazec, J.Y.; Robert, C. Melanoma persister cells are tolerant to braf/mek inhibitors via ACOX1-mediated fatty acid oxidation. Cell Rep., 2020, 33(8), 108421.
[http://dx.doi.org/10.1016/j.celrep.2020.108421] [PMID: 33238129]
[11]
Nath, K.; Roman, J.; Nelson, D.S.; Guo, L.; Lee, S.C.; Orlovskiy, S.; Muriuki, K.; Heitjan, D.F.; Pickup, S.; Leeper, D.B.; Blair, I.A.; Putt, M.E.; Glickson, J.D. Effect of differences in metabolic activity of melanoma models on response to lonidamine plus doxorubicin. Sci. Rep., 2018, 8(1), 14654.
[http://dx.doi.org/10.1038/s41598-018-33019-4] [PMID: 30279592]
[12]
Abildgaard, C.; Rizza, S.; Christiansen, H.; Schmidt, S.; Dahl, C.; Abdul-Al, A.; Christensen, A.; Filomeni, G.; Guldberg, P. Screening of metabolic modulators identifies new strategies to target metabolic reprogramming in melanoma. Sci. Rep., 2021, 11(1), 4390.
[http://dx.doi.org/10.1038/s41598-021-83796-8] [PMID: 33623106]
[13]
Tang, J.; Luo, Y.; Wu, G. A glycolysis-related gene expression signature in predicting recurrence of breast cancer. Aging (Albany NY), 2020, 12(24), 24983-24994.
[http://dx.doi.org/10.18632/aging.103806] [PMID: 33201835]
[14]
Jiang, L.; Zhao, L.; Bi, J.; Guan, Q.; Qi, A.; Wei, Q.; He, M.; Wei, M.; Zhao, L. Glycolysis gene expression profilings screen for prognostic risk signature of hepatocellular carcinoma. Aging (Albany NY), 2019, 11(23), 10861-10882.
[http://dx.doi.org/10.18632/aging.102489] [PMID: 31790363]
[15]
Wang, J.; Kong, P.F.; Wang, H.Y.; Song, D.; Wu, W.Q.; Zhou, H.C.; Weng, H.Y.; Li, M.; Kong, X.; Meng, B.; Chen, Z.K.; Chen, J.J.; Li, C.Y.; Shao, J.Y. Identification of a gene-related risk signature in melanoma patients using bioinformatic profiling. J. Oncol., 2020, 2020, 7526204.
[http://dx.doi.org/10.1155/2020/7526204] [PMID: 32411243]
[16]
Jiang, J.; Liu, C.; Xu, G.; Liang, T.; Yu, C.; Liao, S.; Zhang, Z.; Lu, Z.; Wang, Z.; Chen, J.; Chen, T.; Li, H.; Zhan, X. Identification of hub genes associated with melanoma development by comprehensive bioinformatics analysis. Front. Oncol., 2021, 11, 621430.
[http://dx.doi.org/10.3389/fonc.2021.621430] [PMID: 33912448]
[17]
Bartrons, R.; Simon-Molas, H.; Rodríguez-García, A.; Castaño, E.; Navarro-Sabaté, À.; Manzano, A.; Martinez-Outschoorn, U.E. Fructose 2,6-bisphosphate in cancer cell metabolism. Front. Oncol., 2018, 8, 331.
[http://dx.doi.org/10.3389/fonc.2018.00331] [PMID: 30234009]
[18]
Zhang, C.; Gou, X.; He, W.; Yang, H.; Yin, H. A glycolysis-based 4-mRNA signature correlates with the prognosis and cell cycle process in patients with bladder cancer. Cancer Cell Int., 2020, 20(1), 177.
[http://dx.doi.org/10.1186/s12935-020-01255-2] [PMID: 32467671]
[19]
Noda, M.; Okayama, H.; Kofunato, Y.; Chida, S.; Saito, K.; Tada, T.; Ashizawa, M.; Nakajima, T.; Aoto, K.; Kikuchi, T.; Sakamoto, W.; Endo, H.; Fujita, S.; Saito, M.; Momma, T.; Ohki, S.; Kono, K. Prognostic role of FUT8 expression in relation to p53 status in stage II and III colorectal cancer. PLoS One, 2018, 13(7), e0200315.
[http://dx.doi.org/10.1371/journal.pone.0200315] [PMID: 29975776]
[20]
Scott, E.; Munkley, J. Glycans as biomarkers in prostate cancer. Int. J. Mol. Sci., 2019, 20(6), E1389.
[http://dx.doi.org/10.3390/ijms20061389] [PMID: 30893936]
[21]
Agrawal, P.; Fontanals-Cirera, B.; Sokolova, E.; Jacob, S.; Vaiana, C.A.; Argibay, D.; Davalos, V.; McDermott, M.; Nayak, S.; Darvishian, F.; Castillo, M.; Ueberheide, B.; Osman, I.; Fenyö, D.; Mahal, L.K.; Hernando, E. A systems biology approach identifies FUT8 as a driver of melanoma metastasis. Cancer Cell, 2017, 31(6), 804-819.e7.
[http://dx.doi.org/10.1016/j.ccell.2017.05.007] [PMID: 28609658]
[22]
Hirata, H.; Sugimachi, K.; Komatsu, H.; Ueda, M.; Masuda, T.; Uchi, R.; Sakimura, S.; Nambara, S.; Saito, T.; Shinden, Y.; Iguchi, T.; Eguchi, H.; Ito, S.; Terashima, K.; Sakamoto, K.; Hirakawa, M.; Honda, H.; Mimori, K. Decreased expression of fructose-1,6-bisphosphatase associates with glucose metabolism and tumor progression in hepatocellular carcinoma. Cancer Res., 2016, 76(11), 3265-3276.
[http://dx.doi.org/10.1158/0008-5472.CAN-15-2601] [PMID: 27197151]
[23]
Cheng, Y.; Gao, X.H.; Li, X.J.; Cao, Q.H.; Zhao, D.D.; Zhou, J.R.; Wu, H.X.; Wang, Y.; You, L.J.; Yang, H.B.; He, Y.L.; Li, Y.R.; Bian, J.S.; Zhu, Q.Y.; Birnbaumer, L.; Yang, Y. Depression promotes prostate cancer invasion and metastasis via a sympathetic-cAMP-FAK signaling pathway. Oncogene, 2018, 37(22), 2953-2966.
[http://dx.doi.org/10.1038/s41388-018-0177-4] [PMID: 29515233]
[24]
Müller, B.; Bovet, M.; Yin, Y.; Stichel, D.; Malz, M.; González-Vallinas, M.; Middleton, A.; Ehemann, V.; Schmitt, J.; Muley, T.; Meister, M.; Herpel, E.; Singer, S.; Warth, A.; Schirmacher, P.; Drasdo, D.; Matthäus, F.; Breuhahn, K. Concomitant expression of far upstream element (FUSE) binding protein (FBP) interacting repressor (FIR) and its splice variants induce migration and invasion of non-small cell lung cancer (NSCLC) cells. J. Pathol., 2015, 237(3), 390-401.
[http://dx.doi.org/10.1002/path.4588] [PMID: 26177862]
[25]
Liu, J.; Li, S.; Feng, G.; Meng, H.; Nie, S.; Sun, R.; Yang, J.; Cheng, W. Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma. Cancer Cell Int., 2020, 20(1), 183.
[http://dx.doi.org/10.1186/s12935-020-01264-1] [PMID: 32489319]
[26]
Xu, F.; Xu, H.; Li, Z.; Huang, Y.; Huang, X.; Li, Y.; Zheng, X.; Chen, Y.; Lin, L. Glycolysis-based genes are potential biomarkers in thyroid cancer. Front. Oncol., 2021, 11, 534838.
[http://dx.doi.org/10.3389/fonc.2021.534838] [PMID: 33981593]
[27]
Wang, L.; Zhou, W.; Zhong, Y.; Huo, Y.; Fan, P.; Zhan, S.; Xiao, J.; Jin, X.; Gou, S.; Yin, T.; Wu, H.; Liu, T. Overexpression of G protein-coupled receptor GPR87 promotes pancreatic cancer aggressiveness and activates NF-κB signaling pathway. Mol. Cancer, 2017, 16(1), 61.
[http://dx.doi.org/10.1186/s12943-017-0627-6] [PMID: 28288630]
[28]
Yasui, H.; Nishinaga, Y.; Taki, S.; Takahashi, K.; Isobe, Y.; Shimizu, M.; Koike, C.; Taki, T.; Sakamoto, A.; Katsumi, K.; Ishii, K.; Sato, K. Near-infrared photoimmunotherapy targeting GPR87: Development of a humanised anti-GPR87 mAb and therapeutic efficacy on a lung cancer mouse model. EBioMedicine, 2021, 67, 103372.
[http://dx.doi.org/10.1016/j.ebiom.2021.103372] [PMID: 33993055]
[29]
Marcuzzi, E.; Angioni, R.; Molon, B.; Calì, B. Chemokines and chemokine receptors: orchestrating tumor metastasization. Int. J. Mol. Sci., 2018, 20(1), E96.
[http://dx.doi.org/10.3390/ijms20010096] [PMID: 30591657]
[30]
Zhang, L.; Chen, J.; Cheng, T.; Yang, H.; Li, H.; Pan, C. Identification of the key genes and characterizations of Tumor Immune Microenvironment in Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). J. Cancer, 2020, 11(17), 4965-4979.
[http://dx.doi.org/10.7150/jca.42531] [PMID: 32742444]
[31]
Cao, M.; Yan, H.; Han, X.; Weng, L.; Wei, Q.; Sun, X.; Lu, W.; Wei, Q.; Ye, J.; Cai, X.; Hu, C.; Yin, X.; Cao, P. Ginseng-derived nanoparticles alter macrophage polarization to inhibit melanoma growth. J. Immunother. Cancer, 2019, 7(1), 326.
[http://dx.doi.org/10.1186/s40425-019-0817-4] [PMID: 31775862]
[32]
Liu, R.; Yang, F.; Yin, J.Y.; Liu, Y.Z.; Zhang, W.; Zhou, H.H. Influence of tumor immune infiltration on immune checkpoint inhibitor therapeutic efficacy: A computational retrospective study. Front. Immunol., 2021, 12, 685370.
[http://dx.doi.org/10.3389/fimmu.2021.685370] [PMID: 34220837]
[33]
Taggart, D.; Andreou, T.; Scott, K.J.; Williams, J.; Rippaus, N.; Brownlie, R.J.; Ilett, E.J.; Salmond, R.J.; Melcher, A.; Lorger, M. Anti-PD-1/anti-CTLA-4 efficacy in melanoma brain metastases depends on extracranial disease and augmentation of CD8+ T cell trafficking. Proc. Natl. Acad. Sci. USA, 2018, 115(7), E1540-E1549.
[http://dx.doi.org/10.1073/pnas.1714089115] [PMID: 29386395]
[34]
Jenkins, R.W.; Fisher, D.E. 2020 and Beyond. J. Invest. Dermatol., 2021, 141(1), 23-31.
[http://dx.doi.org/10.1016/j.jid.2020.03.943] [PMID: 32268150]
[35]
Hugo, W.; Zaretsky, J.M.; Sun, L.; Song, C.; Moreno, B.H.; Hu-Lieskovan, S.; Berent-Maoz, B.; Pang, J.; Chmielowski, B.; Cherry, G.; Seja, E.; Lomeli, S.; Kong, X.; Kelley, M.C.; Sosman, J.A.; Johnson, D.B.; Ribas, A.; Lo, R.S. Genomic and transcriptomic features of response to Anti-PD-1 therapy in metastatic melanoma. Cell, 2016, 165(1), 35-44.
[http://dx.doi.org/10.1016/j.cell.2016.02.065] [PMID: 26997480]
[36]
Jiang, Z.; Liu, Z.; Li, M.; Chen, C.; Wang, X. Increased glycolysis correlates with elevated immune activity in tumor immune microenvironment. EBioMedicine, 2019, 42, 431-442.
[http://dx.doi.org/10.1016/j.ebiom.2019.03.068] [PMID: 30935888]
[37]
Najjar, Y.G.; Menk, A.V.; Sander, C.; Rao, U.; Karunamurthy, A.; Bhatia, R.; Zhai, S.; Kirkwood, J.M.; Delgoffe, G.M. Tumor cell oxidative metabolism as a barrier to PD-1 blockade immunotherapy in melanoma. JCI Insight, 2019, 4(5), 124989.
[http://dx.doi.org/10.1172/jci.insight.124989] [PMID: 30721155]
[38]
Seban, R.D.; Moya-Plana, A.; Antonios, L.; Yeh, R.; Marabelle, A.; Deutsch, E.; Schwartz, L.H.; Gómez, R.G.H.; Saenger, Y.; Robert, C.; Ammari, S.; Dercle, L. Prognostic 18F-FDG PET biomarkers in metastatic mucosal and cutaneous melanoma treated with immune checkpoint inhibitors targeting PD-1 and CTLA-4. Eur. J. Nucl. Med. Mol. Imaging, 2020, 47(10), 2301-2312.
[http://dx.doi.org/10.1007/s00259-020-04757-3] [PMID: 32206839]
[39]
Cascone, T.; McKenzie, J.A.; Mbofung, R.M.; Punt, S.; Wang, Z.; Xu, C.; Williams, L.J.; Wang, Z.; Bristow, C.A.; Carugo, A.; Peoples, M.D.; Li, L.; Karpinets, T.; Huang, L.; Malu, S.; Creasy, C.; Leahey, S.E.; Chen, J.; Chen, Y.; Pelicano, H.; Bernatchez, C.; Gopal, Y.N.V.; Heffernan, T.P.; Hu, J.; Wang, J.; Amaria, R.N.; Garraway, L.A.; Huang, P.; Yang, P.; Wistuba, I.I.; Woodman, S.E.; Roszik, J.; Davis, R.E.; Davies, M.A.; Heymach, J.V.; Hwu, P.; Peng, W. Increased tumor glycolysis characterizes immune resistance to adoptive T cell therapy. Cell Metab., 2018, 27(5), 977-987.e4.
[http://dx.doi.org/10.1016/j.cmet.2018.02.024] [PMID: 29628419]
[40]
Burugu, S.; Dancsok, A.R.; Nielsen, T.O. Emerging targets in cancer immunotherapy. Semin. Cancer Biol., 2018, 52(Pt 2), 39-52.
[http://dx.doi.org/10.1016/j.semcancer.2017.10.001] [PMID: 28987965]
[41]
Di Micco, R.; Krizhanovsky, V.; Baker, D.; d’Adda di Fagagna, F. Cellular senescence in ageing: From mechanisms to therapeutic opportunities. Nat. Rev. Mol. Cell Biol., 2021, 22(2), 75-95.
[http://dx.doi.org/10.1038/s41580-020-00314-w] [PMID: 33328614]
[42]
Garcia-Peterson, L.M.; Wilking-Busch, M.J.; Ndiaye, M.A.; Philippe, C.G.A.; Setaluri, V.; Ahmad, N. Sirtuins in skin and skin cancers. Skin Pharmacol. Physiol., 2017, 30(4), 216-224.
[http://dx.doi.org/10.1159/000477417] [PMID: 28704830]
[43]
Ye, X.; Li, M.; Hou, T.; Gao, T.; Zhu, W.G.; Yang, Y. Sirtuins in glucose and lipid metabolism. Oncotarget, 2017, 8(1), 1845-1859.
[http://dx.doi.org/10.18632/oncotarget.12157] [PMID: 27659520]
[44]
Pinho, A.V.; Mawson, A.; Gill, A.; Arshi, M.; Warmerdam, M.; Giry-Laterriere, M.; Eling, N.; Lie, T.; Kuster, E.; Camargo, S.; Biankin, A.V.; Wu, J.; Rooman, I. Sirtuin 1 stimulates the proliferation and the expression of glycolysis genes in pancreatic neoplastic lesions. Oncotarget, 2016, 7(46), 74768-74778.
[http://dx.doi.org/10.18632/oncotarget.11013] [PMID: 27494892]
[45]
Chen, J.; Cao, L.; Li, Z.; Li, Y. SIRT1 promotes GLUT1 expression and bladder cancer progression via regulation of glucose uptake. Hum. Cell, 2019, 32(2), 193-201.
[http://dx.doi.org/10.1007/s13577-019-00237-5] [PMID: 30868406]

Rights & Permissions Print Cite
Article Metrics
3
© 2024 Bentham Science Publishers | Privacy Policy