Identification and targeting of centrosome amplification related signature genes for prognosis and therapy in skin cutaneous melanoma

Authors

  • Wendong Chen The first affiliated hospital of Anhui Medical University
  • Dawei Wang The First Affiliated Hospital of Anhui Medical University
  • Zhenyu Wu The First Affiliated Hospital of Anhui Medical University
  • Shengrong Cheng The First Affiliated Hospital of Anhui Medical University
  • Hailin Yao The First Affiliated Hospital of Anhui Medical University
  • Fei Zhu The First Affiliated Hospital of Anhui Medical University

DOI:

https://doi.org/10.71321/xhgab094

Keywords:

SKCM, centrosome amplification, immune infiltration, prognosis

Abstract

Background: Skin cutaneous melanoma (SKCM) is a highly aggressive cancer with significant mortality, necessitating novel prognostic markers and therapeutic strategies. Centrosome amplification (CA), a hallmark of genomic instability, contributes to cancer progression, but its role in SKCM remains unclear.

Methods: Transcriptomic data from SKCM patients were analyzed to identify differentially expressed genes (DEGs) between SKCM and normal tissues. Centrosome amplification-related genes (CA-RGs) were selected based on centrosomal functions. Prognostic CA-RGs were identified using Cox regression and LASSO analyses, resulting in a CA-RG-based risk model. Single-cell RNA sequencing (scRNA-seq) was employed to investigate cellular mechanisms, and immune infiltration analyses were conducted to assess CA-RGs’ impacts on the tumor microenvironment.

Results: Four CA-RGs (CDK2, KAT2B, NUBP1, CEP120) were identified as prognostic markers. A risk model effectively stratified patients by survival outcomes and was validated in external datasets. Immune infiltration analysis showed that low-risk patients had higher immune and stromal scores, with increased CD8+ T cells and M1 macrophages. ScRNA-seq analysis revealed interactions among fibroblasts, keratinocytes, and malignant cells, indicating CDK2 and KAT2B may promote tumor progression through intercellular signaling.

Conclusions: This study identifies novel CA-RGs and establishes a robust risk model for SKCM prognosis. Insights into the immune microenvironment and intercellular interactions provide a foundation for targeted therapies, including immunotherapy, offering potential strategies for improving SKCM management.

References

[1] Xu Y, Lou L, Wang Y, Miao Q, Jin K, Chen M, et al. Epidemiological Study of Uveal Melanoma from US Surveillance, Epidemiology, and End Results Program (2010–2015). Journal of Ophthalmology 2020;2020:1–8. https://doi.org/10.1155/2020/3614039.

[2] Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA A Cancer J Clinicians 2023;73:17–48. https://doi.org/10.3322/caac.21763.

[3] Shain AH, Bastian BC. From melanocytes to melanomas. Nat Rev Cancer 2016;16:345–58. https://doi.org/10.1038/nrc.2016.37.

[4] Willsmore ZN, Coumbe BGT, Crescioli S, Reci S, Gupta A, Harris RJ, et al. Combined anti‐PD‐1 and anti‐CTLA‐4 checkpoint blockade: Treatment of melanoma and immune mechanisms of action. Eur J Immunol 2021;51:544–56. https://doi.org/10.1002/eji.202048747.

[5] Vinogradova T, Paul R, Grimaldi AD, Loncarek J, Miller PM, Yampolsky D, et al. Concerted effort of centrosomal and Golgi-derived microtubules is required for proper Golgi complex assembly but not for maintenance. MBoC 2012;23:820–33. https://doi.org/10.1091/mbc.E11-06-0550.

[6] Zhao JZ, Ye Q, Wang L, Lee SC. Centrosome amplification in cancer and cancer-associated human diseases. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 2021;1876:188566. https://doi.org/10.1016/j.bbcan.2021.188566.

[7] Mittal K, Kaur J, Sharma S, Sharma N, Wei G, Choudhary I, et al. Hypoxia Drives Centrosome Amplification in Cancer Cells via HIF1α-dependent Induction of Polo-Like Kinase 4. Mol Cancer Res 2022;20:596–606. https://doi.org/10.1158/1541-7786.MCR-20-0798.

[8] Denu RA, Shabbir M, Nihal M, Singh CK, Longley BJ, Burkard ME, et al. Centriole Overduplication is the Predominant Mechanism Leading to Centrosome Amplification in Melanoma. Mol Cancer Res 2018;16:517–27. https://doi.org/10.1158/1541-7786.MCR-17-0197.

[9] Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015;43:e47–e47. https://doi.org/10.1093/nar/gkv007.

[10] Villanueva RAM, Chen ZJ. ggplot2: Elegant Graphics for Data Analysis (2nd ed.). Measurement: Interdisciplinary Research and Perspectives 2019;17:160–7. https://doi.org/10.1080/15366367.2019.1565254.

[11] Kolde R. pheatmap: Pretty Heatmaps 2019.

[12] Yan [aut L, cre. ggvenn: Draw Venn Diagram by “ggplot2” 2023.

[13] Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS: A Journal of Integrative Biology 2012;16:284–7. https://doi.org/10.1089/omi.2011.0118.

[14] Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics 2015;31:2912–4. https://doi.org/10.1093/bioinformatics/btv300.

[15] Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res 2003;13:2498–504. https://doi.org/10.1101/gr.1239303.

[16] Therneau TM, until 2009) TL (original S->R port and R maintainer, Elizabeth A, Cynthia C. survival: Survival Analysis 2024.

[17] Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 2010;33:1–22.

[18] Gordon M, Lumley T. forestplot: Advanced Forest Plot Using “grid” Graphics 2023.

[19] Pawar A, Chowdhury OR, Salvi O. A narrative review of survival analysis in oncology using R. Cancer Research, Statistics, and Treatment 2022;5:554–61. https://doi.org/10.4103/crst.crst_230_22.

[20] Li L, Greene T, Hu B. A simple method to estimate the time-dependent receiver operating characteristic curve and the area under the curve with right censored data. Stat Methods Med Res 2018;27:2264–78. https://doi.org/10.1177/0962280216680239.

[21] Lu X, Wang Y, Jiang L, Gao J, Zhu Y, Hu W, et al. A Pre-operative Nomogram for Prediction of Lymph Node Metastasis in Bladder Urothelial Carcinoma. Front Oncol 2019;9. https://doi.org/10.3389/fonc.2019.00488.

[22] Jr FEH. rms: Regression Modeling Strategies 2024.

[23] Blanche P, Dartigues J, Jacqmin‐Gadda H. Estimating and comparing time‐dependent areas under receiver operating characteristic curves for censored event times with competing risks. Statistics in Medicine 2013;32:5381–97. https://doi.org/10.1002/sim.5958.

[24] Li R, Chen Y, Yang B, Li Z, Wang S, He J, et al. Integrated bioinformatics analysis and experimental validation identified CDCA families as prognostic biomarkers and sensitive indicators for rapamycin treatment of glioma. PLoS ONE 2024;19:e0295346. https://doi.org/10.1371/journal.pone.0295346.

[25] Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 2013;14:7. https://doi.org/10.1186/1471-2105-14-7.

[26] Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 2017;18:220. https://doi.org/10.1186/s13059-017-1349-1.

[27] Kassambara A. ggpubr: “ggplot2” Based Publication Ready Plots 2023.

[28] Wei T, Simko V, Levy M, Xie Y, Jin Y, Zemla J, et al. corrplot: Visualization of a Correlation Matrix 2024.

[29] Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 2013;4:2612. https://doi.org/10.1038/ncomms3612.

[30] Geeleher P, Cox N, Huang RS. pRRophetic: An R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels. PLoS ONE 2014;9:e107468. https://doi.org/10.1371/journal.pone.0107468.

[31] Zhang G, Wang Y, Chen B, Guo L, Cao L, Ren C, et al. Characterization of frequently mutated cancer genes in Chinese breast tumors: a comparison of Chinese and TCGA cohorts. Ann Transl Med 2019;7:179–179. https://doi.org/10.21037/atm.2019.04.23.

[32] Mayakonda A, Lin D-C, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 2018;28:1747–56. https://doi.org/10.1101/gr.239244.118.

[33] Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, et al. Integrated analysis of multimodal single-cell data. Cell 2021;184:3573-3587.e29. https://doi.org/10.1016/j.cell.2021.04.048.

[34] Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan C-H, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 2021;12:1088. https://doi.org/10.1038/s41467-021-21246-9.

[35] Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 2014;32:381–6. https://doi.org/10.1038/nbt.2859.

[36] Tadesse S, Anshabo AT, Portman N, Lim E, Tilley W, Caldon CE, et al. Targeting CDK2 in cancer: challenges and opportunities for therapy. Drug Discovery Today 2020;25:406–13. https://doi.org/10.1016/j.drudis.2019.12.001.

[37] Matsumoto Y, Hayashi K, Nishida E. Cyclin-dependent kinase 2 (Cdk2) is required for centrosome duplication in mammalian cells. Current Biology 1999;9:429–32. https://doi.org/10.1016/s0960-9822(99)80191-2.

[38] Matsuura I, Denissova NG, Wang G, He D, Long J, Liu F. Cyclin-dependent kinases regulate the antiproliferative function of Smads. Nature 2004;430:226–31. https://doi.org/10.1038/nature02650.

[39] Qiu X, Li Y, Zhang Z. Crosstalk between oxidative phosphorylation and immune escape in cancer: a new concept of therapeutic targets selection. Cell Oncol 2023;46:847–65. https://doi.org/10.1007/s13402-023-00801-0.

[40] Liu X, Wang L, Zhao K, Thompson PR, Hwang Y, Marmorstein R, et al. The structural basis of protein acetylation by the p300/CBP transcriptional coactivator. Nature 2008;451:846–50. https://doi.org/10.1038/nature06546.

[41] Wu Y, Wang X, Xu F, Zhang L, Wang T, Fu X, et al. The regulation of acetylation and stability of HMGA2 via the HBXIP-activated Akt–PCAF pathway in promotion of esophageal squamous cell carcinoma growth. Nucleic Acids Res 2020;48:4858–76. https://doi.org/10.1093/nar/gkaa232.

[42] Hu H, Zhu W, Qin J, Chen M, Gong L, Li L, et al. Acetylation of PGK1 promotes liver cancer cell proliferation and tumorigenesis. Hepatology 2017;65:515–28. https://doi.org/10.1002/hep.28887.

[43] Jia Y-L, Xu M, Dou C-W, Liu Z-K, Xue Y-M, Yao B-W, et al. P300/CBP-associated factor (PCAF) inhibits the growth of hepatocellular carcinoma by promoting cell autophagy. Cell Death Dis 2016;7:e2400–e2400. https://doi.org/10.1038/cddis.2016.247.

[44] Li Y-H, Li Y-X, Li M, Song S, Ge Y, Jin J, et al. The Ras-ERK1/2 signaling pathway regulates H3K9ac through PCAF to promote the development of pancreatic cancer. Life Sciences 2020;256:117936. https://doi.org/10.1016/j.lfs.2020.117936.

[45] Fan X, Barshop WD, Vashisht AA, Pandey V, Leal S, Rayatpisheh S, et al. Iron-regulated assembly of the cytosolic iron–sulfur cluster biogenesis machinery. Journal of Biological Chemistry 2022;298:102094. https://doi.org/10.1016/j.jbc.2022.102094.

[46] Christodoulou A, Lederer CW, Surrey T, Vernos I, Santama N. Motor protein KIFC5A interacts with Nubp1 and Nubp2, and is implicated in the regulation of centrosome duplication. J Cell Sci 2006;119:2035–47. https://doi.org/10.1242/jcs.02922.

[47] Fukasawa K. Centrosome amplification, chromosome instability and cancer development. Cancer Letters 2005;230:6–19. https://doi.org/10.1016/j.canlet.2004.12.028.

[48] Tsai J-J, Hsu W-B, Liu J-H, Chang C-W, Tang TK. CEP120 interacts with C2CD3 and Talpid3 and is required for centriole appendage assembly and ciliogenesis. Sci Rep 2019;9:6037. https://doi.org/10.1038/s41598-019-42577-0.

[49] Sharma A, Gerard SF, Olieric N, Steinmetz MO. Cep120 promotes microtubule formation through a unique tubulin binding C2 domain. Journal of Structural Biology 2018;203:62–70. https://doi.org/10.1016/j.jsb.2018.01.009.

[50] Hanahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation. Cell 2011;144:646–74. https://doi.org/10.1016/j.cell.2011.02.013.

[51] Jorgovanovic D, Song M, Wang L, Zhang Y. Roles of IFN-γ in tumor progression and regression: a review. Biomark Res 2020;8:49. https://doi.org/10.1186/s40364-020-00228-x.

[52] Cheng H, Wang Z, Fu L, Xu T. Macrophage Polarization in the Development and Progression of Ovarian Cancers: An Overview. Front Oncol 2019;9:421. https://doi.org/10.3389/fonc.2019.00421.

[53] St. Paul M, Ohashi PS. The Roles of CD8+ T Cell Subsets in Antitumor Immunity. Trends in Cell Biology 2020;30:695–704. https://doi.org/10.1016/j.tcb.2020.06.003.

[54] Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJM, Robert L, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014;515:568–71. https://doi.org/10.1038/nature13954.

[55] Weber JS, D’Angelo SP, Minor D, Hodi FS, Gutzmer R, Neyns B, et al. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. The Lancet Oncology 2015;16:375–84. https://doi.org/10.1016/S1470-2045(15)70076-8.

Type

Research Article

Published

2025-05-14

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/ repositories and accession number(s) can be found in the article

Issue

Section

Cell Cycle and Proliferation

How to Cite

Chen, W., Wang, D., Wu, Z., Cheng, S. ., & Zhu, F. (2025). Identification and targeting of centrosome amplification related signature genes for prognosis and therapy in skin cutaneous melanoma. Cell Conflux, 1, e163. https://doi.org/10.71321/xhgab094