Note! Please note that this article is currently in the "Article in Press" stage and is not the final "Version of record". While it has been accepted, copy-edited, and formatted, however, it is still undergoing proofreading and corrections by the authors. Therefore, the text may still change before the final publication. Although "Articles in Press" may not have all bibliographic details available, the DOI and the year of online publication can still be used to cite them. The article title, DOI, publication year, and author(s) should all be included in the citation format. Once the final "Version of record" becomes available the "Article in Press" will be replaced by that.
Abstract
Background: Metastasis is a major cause of death in UM, highlighting the need to use highly specific and sensitive prognostic markers to identify patients with a risk of developing metastasis.
Aims: The aim of this study was to improve the current precision treatment for patients with metastatic uveal melanoma (UM).
Objective: The objective of this work was to investigate the heterogeneity between primary human UM and metastatic UM at the single-cell level and to discover potential molecules regulating UM metastasis.
Methods: Seurat R toolkit was employed to analyze single-cell sequencing data of UM and to identify differentially expressed genes (DEGs) between primary and metastatic UM. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were performed on the DEGs from the bulk RNA-seq cohort to develop a prognostic model. Based on the model, patients were divided into high and low groups. The correlations among the risk score, immune indicators, immune checkpoint blockade (ICB) therapy, and anti-tumor drug therapy were analyzed.
Results: Cell types in primary UM and metastatic UM tumors include B/plasma cells, endothelial cells, melanocytes, monocytes/macrophages, photoreceptor cells, and T cells. Among 157 DEGs between the two tumor types, S100A4, PDE4B, CHCHD10, NSG1, and C4orf48 were selected to construct a prognostic model. The model could accurately and independently predict response to ICB treatment and sensitivity to antineoplastic drugs for UM patients as well as their immune infiltration levels, risk of death, and metastasis possibility.
Conclusions: This study analyzed the tumor ecosystem of primary and metastatic UM, providing a metastasis-related model that could be used to evaluate the prognosis, risk of metastasis, immunotherapy, and efficacy of antineoplastic drug treatment of UM.
[1]
Smit, K.N.; Jager, M.J.; de Klein, A.; Kiliҫ, E. Uveal melanoma: Towards a molecular understanding. Prog. Retin. Eye Res., 2020, 75, 100800.
[http://dx.doi.org/10.1016/j.preteyeres.2019.100800] [PMID: 31563544]
[http://dx.doi.org/10.1016/j.preteyeres.2019.100800] [PMID: 31563544]
[2]
Sajan, A.; Fordyce, S.; Sideris, A.; Liou, C.; Toor, Z.; Filtes, J.; Krishnasamy, V.; Ahmad, N.; Reis, S.; Brejt, S.; Baig, A.; Khan, S.; Caplan, M.; Sperling, D.; Weintraub, J. Minimally invasive treatment options for hepatic uveal melanoma metastases. Diagnostics, 2023, 13(11), 1836.
[http://dx.doi.org/10.3390/diagnostics13111836] [PMID: 37296688]
[http://dx.doi.org/10.3390/diagnostics13111836] [PMID: 37296688]
[3]
Banou, L.; Tsani, Z.; Arvanitogiannis, K.; Pavlaki, M.; Dastiridou, A.; Androudi, S. Radiotherapy in uveal melanoma: A review of ocular complications. Curr. Oncol., 2023, 30(7), 6374-6396.
[http://dx.doi.org/10.3390/curroncol30070470] [PMID: 37504330]
[http://dx.doi.org/10.3390/curroncol30070470] [PMID: 37504330]
[4]
Costanzo, R.; Parmar, V.; Marrone, S.; Gerardo, I.D.; Federico, N.G.; Emmanuele, U.G.; Scalia, G. Differential diagnosis between primary intracranial melanoma and cerebral cavernoma in Crohn’s disease: A case report and literature review. Oncologie, 2022, 24(4), 937-942.
[http://dx.doi.org/10.32604/oncologie.2022.027155]
[http://dx.doi.org/10.32604/oncologie.2022.027155]
[5]
Chattopadhyay, C.; Kim, D.W.; Gombos, D.S.; Oba, J.; Qin, Y.; Williams, M.D.; Esmaeli, B.; Grimm, E.A.; Wargo, J.A.; Woodman, S.E.; Patel, S.P. Uveal melanoma: From diagnosis to treatment and the science in between. Cancer, 2016, 122(15), 2299-2312.
[http://dx.doi.org/10.1002/cncr.29727] [PMID: 26991400]
[http://dx.doi.org/10.1002/cncr.29727] [PMID: 26991400]
[6]
Pašalić, D.; Nikuševa-Martić, T.; Sekovanić, A.; Kaštelan, S. Genetic and epigenetic features of uveal melanoma - an overview and clinical implications. Int. J. Mol. Sci., 2023, 24(16), 12807.
[http://dx.doi.org/10.3390/ijms241612807] [PMID: 37628989]
[http://dx.doi.org/10.3390/ijms241612807] [PMID: 37628989]
[7]
Rantala, E.S.; Hernberg, M.M.; Piperno-Neumann, S.; Grossniklaus, H.E.; Kivelä, T.T. Metastatic uveal melanoma: The final frontier. Prog. Retin. Eye Res., 2022, 90, 101041.
[http://dx.doi.org/10.1016/j.preteyeres.2022.101041] [PMID: 34999237]
[http://dx.doi.org/10.1016/j.preteyeres.2022.101041] [PMID: 34999237]
[8]
Jager, M.J.; Shields, C.L.; Cebulla, C.M.; Abdel-Rahman, M.H.; Grossniklaus, H.E.; Stern, M.H.; Carvajal, R.D.; Belfort, R.N.; Jia, R.; Shields, J.A.; Damato, B.E. Author correction: Uveal melanoma. Nat. Rev. Dis. Primers, 2022, 8(1), 4.
[http://dx.doi.org/10.1038/s41572-022-00339-9] [PMID: 35039549]
[http://dx.doi.org/10.1038/s41572-022-00339-9] [PMID: 35039549]
[9]
Kaštelan, S.; Mrazovac Zimak, D.; Ivanković, M.; Marković, I.; Gverović Antunica, A. Liver metastasis in uveal melanoma - treatment options and clinical outcome. Front. Biosci.-Landmark, 2022, 27(2), 072.
[http://dx.doi.org/10.31083/j.fbl2702072] [PMID: 35227015]
[http://dx.doi.org/10.31083/j.fbl2702072] [PMID: 35227015]
[10]
Wang, M.M.; Chen, C.; Lynn, M.N.; Figueiredo, C.R.; Tan, W.J.; Lim, T.S.; Coupland, S.E.; Chan, A.S.Y. Applying single-cell technology in uveal melanomas: Current trends and perspectives for improving uveal melanoma metastasis surveillance and tumor profiling. Front. Mol. Biosci., 2021, 7, 611584.
[http://dx.doi.org/10.3389/fmolb.2020.611584] [PMID: 33585560]
[http://dx.doi.org/10.3389/fmolb.2020.611584] [PMID: 33585560]
[11]
Chen, Y.N.; Wang, Y.N.; Chen, M.X.; Zhang, K.; Chen, R.T.; Fang, R.; Wang, H.; Zhang, H.H.; Huang, Y.N.; Feng, Y.; Luo, J.T.; Lan, Y.J.; Liu, Y.M.; Li, Y.; Wei, W.B. Machine learning models for outcome prediction of Chinese uveal melanoma patients: A 15-year follow-up study. Cancer Commun., 2022, 42(3), 273-276.
[http://dx.doi.org/10.1002/cac2.12253] [PMID: 35001563]
[http://dx.doi.org/10.1002/cac2.12253] [PMID: 35001563]
[12]
Wang, Y.; Xie, M.; Lin, F.; Sheng, X.; Zhao, X.; Zhu, X.; Wang, Y.; Lu, B.; Chen, J.; Zhang, T.; Wan, X.; Liu, W.; Sun, X. Nomogram of uveal melanoma as prediction model of metastasis risk. Heliyon, 2023, 9(8), e18956.
[http://dx.doi.org/10.1016/j.heliyon.2023.e18956] [PMID: 37609406]
[http://dx.doi.org/10.1016/j.heliyon.2023.e18956] [PMID: 37609406]
[13]
He, L.; Mou, P.; Yang, C.; Huang, C.; Shen, Y.; Zhang, J.; Wei, R. Single-cell sequencing in primary intraocular tumors: Understanding heterogeneity, the microenvironment, and drug resistance. Front. Immunol., 2023, 14, 1194590.
[http://dx.doi.org/10.3389/fimmu.2023.1194590] [PMID: 37359513]
[http://dx.doi.org/10.3389/fimmu.2023.1194590] [PMID: 37359513]
[14]
Butler, A.; Hoffman, P.; Smibert, P.; Papalexi, E.; Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol., 2018, 36(5), 411-420.
[http://dx.doi.org/10.1038/nbt.4096] [PMID: 29608179]
[http://dx.doi.org/10.1038/nbt.4096] [PMID: 29608179]
[15]
Zhang, X.; Lan, Y.; Xu, J.; Quan, F.; Zhao, E.; Deng, C.; Luo, T.; Xu, L.; Liao, G.; Yan, M.; Ping, Y.; Li, F.; Shi, A.; Bai, J.; Zhao, T.; Li, X.; Xiao, Y. CellMarker: A manually curated resource of cell markers in human and mouse. Nucleic Acids Res., 2019, 47(D1), D721-D728.
[http://dx.doi.org/10.1093/nar/gky900] [PMID: 30289549]
[http://dx.doi.org/10.1093/nar/gky900] [PMID: 30289549]
[16]
Durante, M.A.; Rodriguez, D.A.; Kurtenbach, S.; Kuznetsov, J.N.; Sanchez, M.I.; Decatur, C.L.; Snyder, H.; Feun, L.G.; Livingstone, A.S.; Harbour, J.W. Single-cell analysis reveals new evolutionary complexity in uveal melanoma. Nat. Commun., 2020, 11(1), 496.
[http://dx.doi.org/10.1038/s41467-019-14256-1] [PMID: 31980621]
[http://dx.doi.org/10.1038/s41467-019-14256-1] [PMID: 31980621]
[17]
Yoshihara, K.; Shahmoradgoli, M.; Martínez, E.; Vegesna, R.; Kim, H.; Torres-Garcia, W.; Treviño, V.; Shen, H.; Laird, P.W.; Levine, D.A.; Carter, S.L.; Getz, G.; Stemke-Hale, K.; Mills, G.B.; Verhaak, R.G.W. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun., 2013, 4(1), 2612.
[http://dx.doi.org/10.1038/ncomms3612] [PMID: 24113773]
[http://dx.doi.org/10.1038/ncomms3612] [PMID: 24113773]
[18]
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]
[http://dx.doi.org/10.1186/s13059-016-1070-5] [PMID: 27765066]
[19]
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]
[http://dx.doi.org/10.1016/j.celrep.2016.12.019] [PMID: 28052254]
[20]
Hu, F.F.; Liu, C.J.; Liu, L.L.; Zhang, Q.; Guo, A.Y. Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response. Brief. Bioinform., 2021, 22(3), bbaa176.
[http://dx.doi.org/10.1093/bib/bbaa176] [PMID: 32814346]
[http://dx.doi.org/10.1093/bib/bbaa176] [PMID: 32814346]
[21]
Jiang, P.; Gu, S.; Pan, D.; Fu, J.; Sahu, A.; Hu, X.; Li, Z.; Traugh, N.; Bu, X.; Li, B.; Liu, J.; Freeman, G.J.; Brown, M.A.; Wucherpfennig, K.W.; Liu, X.S. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med., 2018, 24(10), 1550-1558.
[http://dx.doi.org/10.1038/s41591-018-0136-1] [PMID: 30127393]
[http://dx.doi.org/10.1038/s41591-018-0136-1] [PMID: 30127393]
[22]
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]
[http://dx.doi.org/10.1371/journal.pone.0107468] [PMID: 25229481]
[23]
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]
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[24]
Brănişteanu, D.E.; Porumb-Andrese, E.; Porumb, V.; Stărică, A.; Moraru, A.D.; Nicolescu, A.C.; Zemba, M.; Brănişteanu, C.I.; Brănişteanu, G.; Brănişteanu, D.C. New treatment horizons in uveal and cutaneous melanoma. Life, 2023, 13(8), 1666.
[http://dx.doi.org/10.3390/life13081666] [PMID: 37629523]
[http://dx.doi.org/10.3390/life13081666] [PMID: 37629523]
[25]
Bustamante, P.; Piquet, L.; Landreville, S.; Burnier, J.V. Uveal melanoma pathobiology: Metastasis to the liver. Semin. Cancer Biol., 2021, 71, 65-85.
[http://dx.doi.org/10.1016/j.semcancer.2020.05.003] [PMID: 32450140]
[http://dx.doi.org/10.1016/j.semcancer.2020.05.003] [PMID: 32450140]
[26]
Koseoglu, N.D.; Corrêa, Z.M.; Liu, T.Y.A. Artificial intelligence for ocular oncology. Curr. Opin. Ophthalmol., 2023, 34(5), 437-440.
[http://dx.doi.org/10.1097/ICU.0000000000000982] [PMID: 37326226]
[http://dx.doi.org/10.1097/ICU.0000000000000982] [PMID: 37326226]
[27]
Krishna, Y.; McCarthy, C.; Kalirai, H.; Coupland, S.E. Inflammatory cell infiltrates in advanced metastatic uveal melanoma. Hum. Pathol., 2017, 66, 159-166.
[http://dx.doi.org/10.1016/j.humpath.2017.06.005] [PMID: 28655639]
[http://dx.doi.org/10.1016/j.humpath.2017.06.005] [PMID: 28655639]
[28]
Doak, G.R.; Schwertfeger, K.L.; Wood, D.K. Distant relations: Macrophage functions in the metastatic niche. Trends Cancer, 2018, 4(6), 445-459.
[http://dx.doi.org/10.1016/j.trecan.2018.03.011] [PMID: 29860988]
[http://dx.doi.org/10.1016/j.trecan.2018.03.011] [PMID: 29860988]
[29]
Qian, B.Z. Inflammation fires up cancer metastasis. Semin. Cancer Biol., 2017, 47, 170-176.
[http://dx.doi.org/10.1016/j.semcancer.2017.08.006] [PMID: 28838845]
[http://dx.doi.org/10.1016/j.semcancer.2017.08.006] [PMID: 28838845]
[30]
Gu, Y.; Liu, Y.; Fu, L.; Zhai, L.; Zhu, J.; Han, Y.; Jiang, Y.; Zhang, Y.; Zhang, P.; Jiang, Z.; Zhang, X.; Cao, X. Tumor-educated B cells selectively promote breast cancer lymph node metastasis by HSPA4-targeting IgG. Nat. Med., 2019, 25(2), 312-322.
[http://dx.doi.org/10.1038/s41591-018-0309-y] [PMID: 30643287]
[http://dx.doi.org/10.1038/s41591-018-0309-y] [PMID: 30643287]
[31]
Preuss, S.F.; Grieshober, D.; Augustin, H.G. Systemic reprogramming of endothelial cell signaling in metastasis and cachexia. Physiology, 2023, 38(4), 0.
[http://dx.doi.org/10.1152/physiol.00001.2023]
[http://dx.doi.org/10.1152/physiol.00001.2023]
[32]
Dahlmann, M.; Kobelt, D.; Walther, W.; Mudduluru, G.; Stein, U. S100A4 in cancer metastasis: Wnt signaling-driven interventions for metastasis restriction. Cancers, 2016, 8(6), 59.
[http://dx.doi.org/10.3390/cancers8060059] [PMID: 27331819]
[http://dx.doi.org/10.3390/cancers8060059] [PMID: 27331819]
[33]
Su, Y.; Ding, J.; Yang, F.; He, C.; Xu, Y.; Zhu, X.; Zhou, H.; Li, H. The regulatory role of PDE4B in the progression of inflammatory function study. Front. Pharmacol., 2022, 13, 982130.
[http://dx.doi.org/10.3389/fphar.2022.982130] [PMID: 36278172]
[http://dx.doi.org/10.3389/fphar.2022.982130] [PMID: 36278172]
[34]
Zheng, Q.; Zhang, L.; Tu, M.; Yin, X.; Cai, L.; Zhang, S.; Yu, L.; Pan, X.; Huang, Y. Development of a panel of autoantibody against NSG1 with CEA, CYFRA21-1, and SCC-Ag for the diagnosis of esophageal squamous cell carcinoma. Clin. Chim. Acta, 2021, 520, 126-132.
[http://dx.doi.org/10.1016/j.cca.2021.06.013] [PMID: 34119530]
[http://dx.doi.org/10.1016/j.cca.2021.06.013] [PMID: 34119530]
[35]
Saura, C.; Oliveira, M.; Feng, Y.H.; Dai, M.S.; Chen, S.W.; Hurvitz, S.A.; Kim, S.B.; Moy, B.; Delaloge, S.; Gradishar, W.; Masuda, N.; Palacova, M.; Trudeau, M.E.; Mattson, J.; Yap, Y.S.; Hou, M.F.; De Laurentiis, M.; Yeh, Y.M.; Chang, H.T.; Yau, T.; Wildiers, H.; Haley, B.; Fagnani, D.; Lu, Y.S.; Crown, J.; Lin, J.; Takahashi, M.; Takano, T.; Yamaguchi, M.; Fujii, T.; Yao, B.; Bebchuk, J.; Keyvanjah, K.; Bryce, R.; Brufsky, A. Neratinib plus capecitabine versus lapatinib plus capecitabine in HER2-positive metastatic breast cancer previously treated with ≥ 2 HER2-directed regimens: Phase III NALA trial. J. Clin. Oncol., 2020, 38(27), 3138-3149.
[http://dx.doi.org/10.1200/JCO.20.00147] [PMID: 32678716]
[http://dx.doi.org/10.1200/JCO.20.00147] [PMID: 32678716]
[36]
Rodrigues, L.L.V.; Moura, Y.B.F.; Viana, J.V.S.; Oliveira, L.R.M.; Praxedes, É.A.; Vieira Neto, J.B.; Sales, S.L.A.; Silva, H.V.R.; Luciano, M.C.S.; Pessoa, C.; Pereira, A.F. Full confluency, serum starvation, and roscovitine for inducing arrest in the G0/G1 phase of the cell cycle in puma skin-derived fibroblast lines. Anim. Reprod., 2023, 20(1), e20230017.
[http://dx.doi.org/10.1590/1984-3143-ar2023-0017] [PMID: 37101424]
[http://dx.doi.org/10.1590/1984-3143-ar2023-0017] [PMID: 37101424]