Research article Special Issues

Unveiling the link between inflammasomes and skin cutaneous melanoma: Insights into expression patterns and immunotherapy response prediction


  • Received: 04 July 2023 Revised: 15 August 2023 Accepted: 25 September 2023 Published: 01 November 2023
  • Skin cutaneous melanoma (SKCM) is one of the most malignant forms of skin cancer, characterized by its high metastatic potential and low cure rate in advanced stages. Despite advancements in clinical therapies, the overall cure rate for SKCM remains low due to its resistance to conventional treatments. Inflammation is associated with the activation and regulation of inflammatory responses and plays a crucial role in the immune system. It has been implicated in various physiological and pathological processes, including cancer. However, the mechanisms of inflammasome activation in SKCM remain largely unexplored. In this study, we quantified the expression level of six inflammasome-related gene sets using transcriptomic data from SKCM patients. As a result, we found that inflammasome features were closely associated with various clinical characteristics and served as a favorable prognostic factor for patients. A functional enrichment analysis revealed the oncogenic role of inflammasome features in SKCM. Unsupervised clustering was applied to identify immune clusters and inflammatory subtypes, revealing a significant overlap between immune cluster 4 and SKCM subtype 2. The CASP1, GSDMD, NLRP3, IL1B, and IL18 features could predict immune checkpoint blockade therapy response in various SKCM cohorts. In conclusion, our study highlighted the significant association between the inflammasome and cancer treatment. Understanding the role of inflammasome signaling in SKCM pathology can help identify potential therapeutic targets and improve patient prognosis.

    Citation: Yu Sheng, Jing Liu, Miao Zhang, Shuyun Zheng. Unveiling the link between inflammasomes and skin cutaneous melanoma: Insights into expression patterns and immunotherapy response prediction[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 19912-19928. doi: 10.3934/mbe.2023881

    Related Papers:

  • Skin cutaneous melanoma (SKCM) is one of the most malignant forms of skin cancer, characterized by its high metastatic potential and low cure rate in advanced stages. Despite advancements in clinical therapies, the overall cure rate for SKCM remains low due to its resistance to conventional treatments. Inflammation is associated with the activation and regulation of inflammatory responses and plays a crucial role in the immune system. It has been implicated in various physiological and pathological processes, including cancer. However, the mechanisms of inflammasome activation in SKCM remain largely unexplored. In this study, we quantified the expression level of six inflammasome-related gene sets using transcriptomic data from SKCM patients. As a result, we found that inflammasome features were closely associated with various clinical characteristics and served as a favorable prognostic factor for patients. A functional enrichment analysis revealed the oncogenic role of inflammasome features in SKCM. Unsupervised clustering was applied to identify immune clusters and inflammatory subtypes, revealing a significant overlap between immune cluster 4 and SKCM subtype 2. The CASP1, GSDMD, NLRP3, IL1B, and IL18 features could predict immune checkpoint blockade therapy response in various SKCM cohorts. In conclusion, our study highlighted the significant association between the inflammasome and cancer treatment. Understanding the role of inflammasome signaling in SKCM pathology can help identify potential therapeutic targets and improve patient prognosis.



    加载中


    [1] D. Schadendorf, D. E. Fisher, C. Garbe, J. E. Gershenwald, J. Grob, A. Halpern, et al., Melanoma, Nat. Rev. Dis. Primers, 1 (2015), 15003. https://doi.org/10.1038/nrdp.2015.3 doi: 10.1038/nrdp.2015.3
    [2] A. M. M. Eggermont, A. Spatz, C. Robert, Cutaneous melanoma, Lancet, 383 (2014), 816–827. https://doi.org/10.1016/S0140-6736(13)60802-8 doi: 10.1016/S0140-6736(13)60802-8
    [3] G. C. Leonardi, L. Falzone, R. Salemi, A. Zanghi, D. A. Spandidos, J. A. McCubrey, et al., Cutaneous melanoma: From pathogenesis to therapy (Review), Int. J. Oncol., 52 (2018), 1071–1080. https://doi.org/10.3892/ijo.2018.4287 doi: 10.3892/ijo.2018.4287
    [4] D. J. Shah, R. S. Dronca, Latest advances in chemotherapeutic, targeted, and immune approaches in the treatment of metastatic melanoma, Mayo Clin. Proc., 89 (2014), 504–519. https://doi.org/10.1016/j.mayocp.2014.02.002 doi: 10.1016/j.mayocp.2014.02.002
    [5] P. P. Centeno, V. Pavet, R. Marais, The journey from melanocytes to melanoma, Nat. Rev. Cancer, 23 (2023), 372–390. https://doi.org/10.1038/s41568-023-00565-7 doi: 10.1038/s41568-023-00565-7
    [6] F. R. Greten, S. I. Grivennikov, Inflammation and cancer: Triggers, mechanisms, and consequences, Immunity, 51 (2019), 27–41. https://doi.org/10.1016/j.immuni.2019.06.025 doi: 10.1016/j.immuni.2019.06.025
    [7] J. Amin, D. Boche, S. Rakic, What do we know about the inflammasome in humans?, Brain Pathol., 27 (2017), 192–204. https://doi.org/10.1111/bpa.12479 doi: 10.1111/bpa.12479
    [8] F. Martinon, K. Burns, J. Tschopp, The inflammasome: a molecular platform triggering activation of inflammatory caspases and processing of proIL-beta, Mol. Cell, 10 (2002), 417–426. https://doi.org/10.1016/s1097-2765(02)00599-3 doi: 10.1016/s1097-2765(02)00599-3
    [9] T. Strowig, J. Henao-Mejia, E. Elinav, R. Flavell, Inflammasomes in health and disease, Nature, 481 (2012), 278–286. https://doi.org/10.1038/nature10759 doi: 10.1038/nature10759
    [10] R. Karki, S. M. Man, T. Kanneganti, Inflammasomes and Cancer, Cancer Immunol. Res., 5 (2017), 94–99. https://doi.org/10.1158/2326-6066.CIR-16-0269 doi: 10.1158/2326-6066.CIR-16-0269
    [11] V. A. K. Rathinam, S. K. Vanaja, K. A. Fitzgerald, Regulation of inflammasome signaling, Nat. Immunol., 13 (2012), 333–342. https://doi.org/10.1038/ni.2237 doi: 10.1038/ni.2237
    [12] N. Kayagaki, I. B. Stowe, B. L. Lee, K. O'Rourke, K. Anderson, S. Warming, et al., Caspase-11 cleaves gasdermin D for non-canonical inflammasome signalling, Nature, 526 (2015), 666–671. https://doi.org/10.1038/nature15541 doi: 10.1038/nature15541
    [13] J. Shi, Y. Zhao, K. Wang, X. Shi, Y. Wang, H. Huang, et al., Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death, Nature, 526 (2015), 660–665. https://doi.org/10.1038/nature15514 doi: 10.1038/nature15514
    [14] Y. Li, G. Nanayakkara, Y. Sun, X. Li, L. Wang, R. Cueto, et al., Analyses of caspase-1-regulated transcriptomes in various tissues lead to identification of novel IL-1β-, IL-18- and sirtuin-1-independent pathways, J. Hematol. Oncol., 10 (2017), 40. https://doi.org/10.1186/s13045-017-0406-2 doi: 10.1186/s13045-017-0406-2
    [15] C. Jin, R. A. Flavell, Molecular mechanism of NLRP3 inflammasome activation, J. Clin. Immunol., 30 (2010), 628–631. https://doi.org/10.1007/s10875-010-9440-3 doi: 10.1007/s10875-010-9440-3
    [16] L. Chen, L. Wang, N. Tsang, D. M. Ojcius, C. Chen, C. Ouyang, et al., Tumour inflammasome-derived IL-1beta recruits neutrophils and improves local recurrence-free survival in EBV-induced nasopharyngeal carcinoma, EMBO Mol. Med., 4 (2012), 1276–1293. https://doi.org/10.1002/emmm.201201569 doi: 10.1002/emmm.201201569
    [17] M. S. Carlino, J. Larkin, G. V. Long, Immune checkpoint inhibitors in melanoma, Lancet, 398 (2021), 1002–1014. https://doi.org/10.1016/S0140-6736(21)01206-X doi: 10.1016/S0140-6736(21)01206-X
    [18] D. M. Pardoll, The blockade of immune checkpoints in cancer immunotherapy, Nat. Rev. Cancer, 12 (2012), 252–264. https://doi.org/10.1038/nrc3239 doi: 10.1038/nrc3239
    [19] M. A. Curran, W. Montalvo, H. Yagita, J. P. Allison, PD-1 and CTLA-4 combination blockade expands infiltrating T cells and reduces regulatory T and myeloid cells within B16 melanoma tumors, Proc. Natl. Acad. Sci., 107 (2010), 4275–4280. https://doi.org/10.1073/pnas.0915174107 doi: 10.1073/pnas.0915174107
    [20] E. M. Van Allen, D. Miao, B. Schilling, S. A. Shukla, C. Blank, L. Zimmer, et al., Genomic correlates of response to CTLA-4 blockade in metastatic melanoma, Science, 350 (2015), 207–211. https://doi.org/10.1126/science.aad0095 doi: 10.1126/science.aad0095
    [21] C. N. Owen, X. Bai, T. Quah, S. N. Lo, C. Allayous, S. Callaghan, et al., Delayed immune-related adverse events with anti-PD-1-based immunotherapy in melanoma, Ann. Oncol., 32 (2021), 917–925. https://doi.org/10.1016/j.annonc.2021.03.204 doi: 10.1016/j.annonc.2021.03.204
    [22] J. Larkin, V. Chiarion-Sileni, R. Gonzalez, J. Grob, P. Rutkowski, C. D. Lao, et al., Five-year survival with combined nivolumab and ipilimumab in advanced melanoma, New Engl. J. Med., 381 (2019), 1535–1546. https://doi.org/10.1056/NEJMoa1910836 doi: 10.1056/NEJMoa1910836
    [23] J. Larkin, C. D. Lao, W. J. Urba, D. F. McDermott, C. Horak, J. Jiang, et al., Efficacy and safety of nivolumab in patients with braf v600 mutant and braf wild-type advanced melanoma: A pooled analysis of 4 clinical trials, JAMA Oncol., 1 (2015), 433–440. https://doi.org/10.1001/jamaoncol.2015.1184 doi: 10.1001/jamaoncol.2015.1184
    [24] J. Larkin, V. Chiarion-Sileni, R. Gonzalez, J. J. Grob, C. L. Cowey, C. D. Lao, et al., Combined nivolumab and ipilimumab or monotherapy in untreated melanoma, New Engl. J. Med., 373 (2015), 23–34. https://doi.org/10.1056/NEJMoa1504030 doi: 10.1056/NEJMoa1504030
    [25] J. Hou, M. Karin, B. Sun, Targeting cancer-promoting inflammation - have anti-inflammatory therapies come of age?, Nat. Rev. Clin. Oncol., 18 (2021), 261–279. https://doi.org/10.1038/s41571-020-00459-9 doi: 10.1038/s41571-020-00459-9
    [26] B. Theivanthiran, K. S. Evans, N. C. DeVito, M. Plebanek, M. Sturdivant, L. P. Wachsmuth, et al., A tumor-intrinsic PD-L1/NLRP3 inflammasome signaling pathway drives resistance to anti-PD-1 immunotherapy, J. Clin. Invest., 130 (2020), 2570–2586. https://doi.org/10.1172/JCI133055 doi: 10.1172/JCI133055
    [27] C. I. Diakos, K. A. Charles, D. C. McMillan, S. J. Clarke, Cancer-related inflammation and treatment effectiveness, Lancet Oncol., 15 (2014), 493–503. https://doi.org/10.1016/S1470-2045(14)70263-3 doi: 10.1016/S1470-2045(14)70263-3
    [28] M. Ju, J. Bi, Q. Wei, L. Jiang, Q. Guan, M. Zhang, et al., Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer, Briefings Bioinf., 22 (2021), 345. https://doi.org/10.1093/bib/bbaa345 doi: 10.1093/bib/bbaa345
    [29] R. Karki, T. Kanneganti, Diverging inflammasome signals in tumorigenesis and potential targeting, Nat. Rev. Cancer, 19 (2019), 197–214. https://doi.org/10.1038/s41568-019-0123-y doi: 10.1038/s41568-019-0123-y
    [30] Q. Liang, J. Wu, X. Zhao, S. Shen, C. Zhu, T. Liu, et al., Establishment of tumor inflammasome clusters with distinct immunogenomic landscape aids immunotherapy, Theranostics, 11 (2021), 9884–9903. https://doi.org/10.7150/thno.63202 doi: 10.7150/thno.63202
    [31] J. Lonsdale, J. Thomas, M. Salvatore, R. Phillips, E. Lo, S. Shad, et al., The Genotype-Tissue Expression (GTEx) project, Nat. Genet., 45 (2013), 580–585. https://doi.org/10.1038/ng.2653 doi: 10.1038/ng.2653
    [32] M. J. Goldman, B. Craft, M. Hastie, K. Repecka, F. McDade, A. Kamath, et al., Visualizing and interpreting cancer genomics data via the Xena platform, Nat. Biotechnol., 38 (2020), 675–678. https://doi.org/10.1038/s41587-020-0546-8 doi: 10.1038/s41587-020-0546-8
    [33] M. E. Ritchie, B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi, et al., limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res., 43 (2015), 47. https://doi.org/10.1093/nar/gkv007 doi: 10.1093/nar/gkv007
    [34] K. Yoshihara, M. Shahmoradgoli, E. Martinez, R. Vegesna, H. Kim, W. Torres-Garcia, et al., Inferring tumour purity and stromal and immune cell admixture from expression data, Nat. Commun., 4 (2013), 2612. https://doi.org/10.1038/ncomms3612 doi: 10.1038/ncomms3612
    [35] A. Bagaev, N. Kotlov, K. Nomie, V. Svekolkin, A. Gafurov, O. Isaeva, et al., Conserved pan-cancer microenvironment subtypes predict response to immunotherapy, Cancer Cell, 39 (2021), 845–865. https://doi.org/10.1016/j.ccell.2021.04.014 doi: 10.1016/j.ccell.2021.04.014
    [36] M. D. Wilkerson, D. N. Hayes, ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking, Bioinformatics, 26 (2010), 1572–1573. https://doi.org/10.1093/bioinformatics/btq170 doi: 10.1093/bioinformatics/btq170
    [37] Y. Zhou, B. Zhou, L. Pache, M. Chang, A. H. Khodabakhshi, O. Tanaseichuk, et al., Metascape provides a biologist-oriented resource for the analysis of systems-level datasets, Nat. Commun., 10 (2019), 1523. https://doi.org/10.1038/s41467-019-09234-6 doi: 10.1038/s41467-019-09234-6
    [38] F. Sanchez-Vega, M. Mina, J. Armenia, W. K. Chatila, A. Luna, K. C. La, et al., Oncogenic signaling pathways in the cancer genome atlas, Cell, 173 (2018), 321–337. https://doi.org/10.1016/j.cell.2018.03.035 doi: 10.1016/j.cell.2018.03.035
    [39] D. Hanahan, R. A. Weinberg, Hallmarks of cancer: the next generation, Cell, 144 (2011), 646–674. https://doi.org/10.1016/j.cell.2011.02.013 doi: 10.1016/j.cell.2011.02.013
    [40] A. Liberzon, C. Birger, H. Thorvaldsdottir, M. Ghandi, J. P. Mesirov, P. Tamayo, The Molecular Signatures Database (MSigDB) hallmark gene set collection, Cell Syst., 1 (2015), 417–425. https://doi.org/10.1016/j.cels.2015.12.004 doi: 10.1016/j.cels.2015.12.004
    [41] S. Hanzelmann, R. Castelo, J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data, BMC Bioinf., 14 (2013), 7. https://doi.org/10.1186/1471-2105-14-7 doi: 10.1186/1471-2105-14-7
    [42] T. N. Gide, C. Quek, A. M. Menzies, A. T. Tasker, P. Shang, J. Holst, et al., Distinct immune cell populations define response to Anti-PD-1 monotherapy and Anti-PD-1/Anti-CTLA-4 combined therapy, Cancer Cell, 35 (2019), 238-255. https://doi.org/10.1016/j.ccell.2019.01.003 doi: 10.1016/j.ccell.2019.01.003
    [43] T. N. Gide, J. S. Wilmott, R. A. Scolyer, G. V. Long, Primary and acquired resistance to immune checkpoint inhibitors in metastatic melanoma, Clin. Cancer Res., 24 (2018), 1260–1270. https://doi.org/10.1158/1078-0432.CCR-17-2267 doi: 10.1158/1078-0432.CCR-17-2267
    [44] R. Akbani, K. C. Akdemir, B. A. Aksoy, M. Albert, A. Ally, S. B. Amin, et al., Genomic classification of cutaneous melanoma, Cell, 161 (2015), 1681–1696. https://doi.org/10.1016/j.cell.2015.05.044 doi: 10.1016/j.cell.2015.05.044
    [45] T. A. Waldmann, Cytokines in Cancer Immunotherapy, Cold Spring Harbor Perspect. Biol., 10 (2018). https://doi.org/10.1101/cshperspect.a028472 doi: 10.1101/cshperspect.a028472
    [46] M. Shakiba, P. Zumbo, G. Espinosa-Carrasco, L. Menocal, F. Dündar, S. E. Carson, et al., TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion, J. Exp. Med., 219 (2022). https://doi.org/10.1084/jem.20201966 doi: 10.1084/jem.20201966
    [47] R. Kolb, G. Liu, A. M. Janowski, F. S. Sutterwala, W. Zhang, Inflammasomes in cancer: a double-edged sword, Protein Cell, 5 (2014), 12–20. https://doi.org/10.1007/s13238-013-0001-4 doi: 10.1007/s13238-013-0001-4
    [48] M. Okamoto, W. Liu, Y. Luo, A. Tanaka, X. Cai, D. A. Norris, et al., Constitutively active inflammasome in human melanoma cells mediating autoinflammation via caspase-1 processing and secretion of interleukin-1beta, J. Biol. Chem., 285 (2010), 6477–6488. https://doi.org/10.1074/jbc.M109.064907 doi: 10.1074/jbc.M109.064907
    [49] V. A. Rathinam, K. A. Fitzgerald, Inflammasome complexes: Emerging mechanisms and effector functions, Cell, 165 (2016), 792–800. https://doi.org/10.1016/j.cell.2016.03.046 doi: 10.1016/j.cell.2016.03.046
    [50] J. M. Redman, G. T. Gibney, M. B. Atkins, Advances in immunotherapy for melanoma, BMC Med., 14 (2016). https://doi.org/10.1186/s12916-016-0571-0 doi: 10.1186/s12916-016-0571-0
    [51] B. J. Schneider, J. Naidoo, B. D. Santomasso, C. Lacchetti, S. Adkins, M. Anadkat, et al., Management of immune-related adverse events in patients treated with immune checkpoint inhibitor therapy: Asco guideline update, J. Clin. Oncol., 39 (2021), 4073–4126. https://doi.org/10.1200/JCO.21.01440 doi: 10.1200/JCO.21.01440
    [52] D. B. Johnson, C. A. Nebhan, J. J. Moslehi, J. M. Balko, Immune-checkpoint inhibitors: long-term implications of toxicity, Nat. Rev. Clin. Oncol., 19 (2022), 254–267. https://doi.org/10.1038/s41571-022-00600-w doi: 10.1038/s41571-022-00600-w
    [53] B. Theivanthiran, N. Yarla, T. Haykal, Y. V. Nguyen, L. Cao, M. Ferreira, et al., Tumor-intrinsic NLRP3-HSP70-TLR4 axis drives premetastatic niche development and hyperprogression during anti-PD-1 immunotherapy, Sci. Transl. Med., 14 (2022), 7019. https://doi.org/10.1126/scitranslmed.abq7019 doi: 10.1126/scitranslmed.abq7019
    [54] J. J. Havel, D. Chowell, T. A. Chan, The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy, Nat. Rev. Cancer, 19 (2019), 133-150. https://doi.org/10.1038/s41568-019-0116-x doi: 10.1038/s41568-019-0116-x
    [55] G. Dranoff, Cytokines in cancer pathogenesis and cancer therapy, Nat. Rev. Cancer, 4 (2004), 11–22. https://doi.org/10.1038/nrc1252 doi: 10.1038/nrc1252
  • mbe-20-11-881-Supplementary.docx
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(592) PDF downloads(39) Cited by(0)

Article outline

Figures and Tables

Figures(6)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog