AccScience Publishing / EJMO / Online First / DOI: 10.36922/ejmo.8574
Submit to EJMO
Cite this article
5
Download
95
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ORIGINAL RESEARCH ARTICLE

Development of a novel risk model for stratifying skin cutaneous melanoma patients based on prognostic senescence-related genes

Yiting Feng1 ,  Lanlan Liu2,3,4,5* ,  Yunjin Xie2,3,4,5* ,  Mingzhu Yin2,3,4,5*
Show Less
1 Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan Province, China
2 Clinical Research Center, Medical Pathology Center, Cancer Early Detection and Treatment Center, and Translational Medicine Research Center, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, China
3 Chongqing Technical Innovation Center for Quality Evaluation and Identification of Authentic Medicinal Herbs, Chongqing, China
4 Chongqing University Three Gorges Hospital and Academy for Advanced Interdisciplinary Technology, CQU - Ferenc Krausz Nobel Laureate Scientific Workstation, Chongqing, China
5 School of Medicine, Chongqing University, Chongqing, China
EJMO, 8574 https://doi.org/10.36922/ejmo.8574
Submitted: 17 January 2025 | Revised: 4 March 2025 | Accepted: 26 March 2025 | Published: 9 April 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
Cite
XML
Abstract

Skin cutaneous melanoma (SKCM) is a highly lethal skin carcinoma. Cellular senescence has a dual effect on tumor progression. This study investigates whether senescence-related genes can guide patient stratification by examining their association with survival outcomes in SKCM. Univariate and multivariate Cox regression analyses were performed to identify prognostic senescence-related genes. Based on these genes, samples were classified into two subtypes using consensus unsupervised clustering. Kaplan-Meier survival analysis was conducted, with statistical significance assessed via the log-rank test. Immune infiltration patterns were assessed using CIBERSORT. Finally, a prognostic risk model was constructed using Lasso regression following by multivariate Cox regression. Based on the prognostic senescence-related genes, samples were classified into two subtypes with distinct survival outcomes and immune profiles. Cluster 2, linked to improved survival and enriched in cytokine-cytokine receptor interactions, showed higher infiltration of activated natural killer (NK) cells, CD8+ T cells as well as CD4+ memory T cells, along with enhanced immune pathway activation compared to cluster 1. Subsequently, a risk model was constructed based on the identified key genes and validated using both internal and external datasets. Stratification of patients by the median risk score showed that the low-risk cohort had a significantly better prognosis, with a favorable immune microenvironment enriched in CD8+ T cells, anti-tumor M1 macrophages, and activated NK cells. Senescence-related gene expression effectively stratified SKCM patients into subtypes with distinct survival outcomes and immune microenvironment profiles, offering new insights for the development of personalized medicine.

Keywords
Skin cutaneous melanoma
Cellular senescence
Immune microenvironment
Risk model
High-throughput RNA sequencing
Funding
This work was supported in part by General project of Chongqing Joint Fund of Science and Technology (Grant No. CSTB2024NSCQ-LMX0016) [Y.X.]; Chongqing Wanzhou PhD “through train” Research Project (Grant No. Wzstc20230402) [M.Y.].
Conflict of interest
Mingzhu Yin is an Editor in Chief of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
References
  1. Sun Y, Shen Y, Liu Q, et al. Global trends in melanoma burden: A comprehensive analysis from the global burden of disease study, 1990-2021. J Am Acad Dermatol. 2025;92(1):100-107. doi: 10.1016/j.jaad.2024.09.035

 

  1. Arnold M, Singh D, Laversanne M, et al. Global burden of cutaneous melanoma in 2020 and projections to 2040. JAMA Dermatol. 2022;158(5):495-503. doi: 10.1001/jamadermatol.2022.0160

 

  1. Zeng H, Zheng R, Sun K, et al. Cancer survival statistics in China 2019-2021: A multicenter, population-based study. J Natl Cancer Cent. 2024;4(3):203-213. doi: 10.1016/j.jncc.2024.06.005

 

  1. Kalaora S, Nagler A, Wargo JA, Samuels Y. Mechanisms of immune activation and regulation: Lessons from melanoma. Nat Rev Cancer. 2022;22(4):195-207. doi: 10.1038/s41568-022-00442-9

 

  1. Long GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma. Lancet. 2023;402(10400):485-502. doi: 10.1016/S0140-6736(23)00821-8

 

  1. Chesney J, Lewis KD, Kluger H, et al. Efficacy and safety of lifileucel, a one-time autologous tumor-infiltrating lymphocyte (TIL) cell therapy, in patients with advanced melanoma after progression on immune checkpoint inhibitors and targeted therapies: Pooled analysis of consecutive cohorts of the C-144-01 study. J Immunother Cancer. 2022;10(12):e005755. doi: 10.1136/jitc-2022-005755

 

  1. De Visser KE, Joyce JA. The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell. 2023;41(3):374-403. doi: 10.1016/j.ccell.2023.02.016

 

  1. Bochenek HF, Pyne DB, McKune AJ, Neale RE, Anforth RM, Goldsmith CD. Investigating the impact of exercise on T and NK cells in skin cancer: A systematic review. Exerc Immunol Rev. 2024;30:14-25.

 

  1. Hibler W, Merlino G, Yu Y. CAR NK cell therapy for the treatment of metastatic melanoma: Potential & prospects. Cells. 2023;12(23):2750. doi: 10.3390/cells12232750

 

  1. Marzagalli M, Ebelt ND, Manuel ER. Unraveling the crosstalk between melanoma and immune cells in the tumor microenvironment. Semin Cancer Biol. 2019;59:236-250. doi: 10.1016/j.semcancer.2019.08.002

 

  1. Timar J, Ladanyi A. Molecular pathology of skin melanoma: Epidemiology, differential diagnostics, prognosis and therapy prediction. Int J Mol Sci. 2022;23(10):5384. doi: 10.3390/ijms23105384

 

  1. Wang X, Xiong H, Liang D, Chen Z, Li X, Zhang K. The role of SRGN in the survival and immune infiltrates of skin cutaneous melanoma (SKCM) and SKCM-metastasis patients. BMC Cancer. 2020;20(1):378. doi: 10.1186/s12885-020-06849-7

 

  1. Liang Z, Pan L, Shi J, Zhang L. C1QA, C1QB, and GZMB are novel prognostic biomarkers of skin cutaneous melanoma relating tumor microenvironment. Sci Rep. 2022;12(1):20460. doi: 10.1038/s41598-022-24353-9f

 

  1. Huang B, Han W, Sheng ZF, Shen GL. Identification of immune-related biomarkers associated with tumorigenesis and prognosis in cutaneous melanoma patients. Cancer Cell Int. 2020;20:195. doi: 10.1186/s12935-020-01271-2

 

  1. Xu Y, Chen Y, Jiang W, et al. Identification of fatty acid metabolism-related molecular subtype biomarkers and their correlation with immune checkpoints in cutaneous melanoma. Front Immunol. 2022;13:967277. doi: 10.3389/fimmu.2022.967277

 

  1. Tomczak K, Czerwinska P, Wiznerowicz M. The cancer genome atlas (TCGA): An immeasurable source of knowledge. Contemp Oncol (Pozn). 2015;19(1A):A68-A77. doi: 10.5114/wo.2014.47136

 

  1. Cirenajwis H, Ekedahl H, Lauss M, et al. Molecular stratification of metastatic melanoma using gene expression profiling: Prediction of survival outcome and benefit from molecular targeted therapy. Oncotarget. 2015;6(14):12297-12309. doi: 10.18632/oncotarget.3655

 

  1. Yu X, Cong P, Wei W, Zhou Y, Bao Z, Hou H. Construction of prognostic risk model of patients with skin cutaneous melanoma based on TCGA-SKCM methylation cohort. Comput Math Methods Med. 2022;2022:4261329. doi: 10.1155/2022/4261329

 

  1. Lin X, Hessenow R, Yang S, Ma D, Yang S. A seven-immune-genes risk model predicts the survival and suitable treatments for patients with skin cutaneous melanoma. Heliyon. 2023;9(9):e20234. doi: 10.1016/j.heliyon.2023.e20234

 

  1. Hanahan D. Hallmarks of cancer: New dimensions. Cancer Discov. 2022;12(1):31-46. doi: 10.1158/2159-8290.CD-21-1059

 

  1. Hernandez-Segura A, Nehme J, Demaria M. Hallmarks of cellular senescence. Trends Cell Biol. 2018;28(6):436-453. doi: 10.1016/j.tcb.2018.02.001

 

  1. Herranz N, Gil J. Mechanisms and functions of cellular senescence. J Clin Invest. 2018;128(4):1238-1246. doi: 10.1172/JCI95148

 

  1. Kowald A, Passos JF, Kirkwood TBL. On the evolution of cellular senescence. Aging Cell. 2020;19(12):e13270. doi: 10.1111/acel.13270

 

  1. Marin I, Boix O, Garcia-Garijo A, et al. Cellular senescence is immunogenic and promotes antitumor immunity. Cancer Discov. 2023;13(2):410-431. doi: 10.1158/2159-8290.CD-22-0523

 

  1. Aging Atlas Consortium. Aging atlas: A Multi-omics database for aging biology. Nucleic Acids Res. 2021;49(D1):D825-D830. doi: 10.1093/nar/gkaa894

 

  1. Saul D, Kosinsky RL, Atkinson EJ, et al. A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues. Nat Commun. 2022;13(1):4827. doi: 10.1038/s41467-022-32552-1

 

  1. Avelar RA, Ortega JG, Tacutu R, et al. A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome Biol. 2020;21(1):91. doi: 10.1186/s13059-020-01990-9

 

  1. Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453-457. doi: 10.1038/nmeth.3337

 

  1. Jacquelot N, Duong CPM, Belz GT, Zitvogel L. Targeting chemokine and chemokine receptors in melanoma and other cancers. Front Immunol. 2018;9:2480. doi: 10.3389/fimmu.2018.02480

 

  1. Cesati M, Scatozza F, D’Arcangelo D, et al. Investigating serum and tissue expression identified a cytokine/ chemokine signature as a highly effective melanoma marker. Cancers (Basel). 2020;12(12):3680. doi: 10.3390/cancers12123680

 

  1. Ziogas DC, Theocharopoulos C, Koutouratsas T, Haanen J, Gogas H. Mechanisms of resistance to immune checkpoint inhibitors in melanoma: What we have to overcome? Cancer Treat Rev. 2023;113:102499. doi: 10.1016/j.ctrv.2022.102499

 

  1. Nirmal AJ, Maliga Z, Vallius T, et al. The spatial landscape of progression and immune editing in primary melanoma at single-cell resolution. Cancer Discov. 2022;12(6):1518-1541. doi: 10.1158/2159-8290.CD-21-1357

 

  1. Gaydosik AM, Tabib T, Geskin LJ, et al. Single-cell lymphocyte heterogeneity in advanced cutaneous t-cell lymphoma skin tumors. Clin Cancer Res. 2019;25(14):4443-4454. doi: 10.1158/1078-0432.CCR-19-0148

 

  1. Mitra A, Andrews MC, Roh W, et al. Spatially resolved analyses link genomic and immune diversity and reveal unfavorable neutrophil activation in melanoma. Nat Commun. 2020;11(1):1839. doi: 10.1038/s41467-020-15538-9

 

  1. Bogunovic D, O’Neill DW, Belitskaya-Levy I, et al. Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. Proc Natl Acad Sci U S A. 2009;106(48):20429-20434. doi: 10.1073/pnas.0905139106

 

  1. Homann L, Rentschler M, Brenner E, Bohm K, Rocken M, Wieder T. IFN-γ gamma and TNF induce senescence and a distinct senescence-associated secretory phenotype in melanoma. Cells. 2022;11(9):1514. doi: 10.3390/cells11091514

 

  1. Safi M, Jin C, Aldanakh A, et al. Immune checkpoint inhibitor (ICI) genes and aging in malignant melanoma patients: A clinicogenomic TCGA study. BMC Cancer. 2022;22(1):978. doi: 10.1186/s12885-022-09860-2

 

  1. Fukuda K. Networks of CD8+ T cell response activation in melanoma and vitiligo. Front Immunol. 2022;13:866703. doi: 10.3389/fimmu.2022.866703

 

  1. Zeng N, Guo C, Wang Y, et al. Characterization of aging-related genes to predict prognosis and evaluate the tumor immune microenvironment in malignant melanoma. J Oncol. 2022;2022:1271378. doi: 10.1155/2022/1271378

 

  1. Liang X, Lin X, Lin Z, Lin W, Peng Z, Wei S. Genes associated with cellular senescence favor melanoma prognosis by stimulating immune responses in tumor microenvironment. Comput Biol Med. 2023;158:106850. doi: 10.1016/j.compbiomed.2023.106850
Share
Back to top
Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept