Skip to content
2000
image of Establishing a Ten Disulfidptosis-related Gene Signature for Prognostic Prediction in Skin Cutaneous Melanoma

Abstract

Aim

Disulfidptosis is a new metabolic-related regulated cell death associated with cancer growth. This study aimed to investigate the molecular mechanisms associated with disulfidptosis in skin cutaneous melanoma (SKCM) and establish a disulfidptosis-related gene signature for prognostic prediction in SKCM.

Methods

Disulfidptosis-associated genes were identified from RNA-seq data of SKCM. A risk score signature was developed and validated through univariate Cox and LASSO analyses. Additionally, the immune microenvironment related to the risk score signature was investigated. Finally, a disulfidptosis-related genes-transcription factor -miRNA network was developed, and the expression levels of five disulfidptosis-related genes were initially verified in SKCM cell lines.

Results

A total of 107 disulfidptosis-related differentially expressed genes in SKCM samples were identified. A ten-disulfidptosis-gene signature was established, including , and The SKCM samples were divided into high- and low-risk groups, of which samples in the low-risk group showed better survival performance. The receiver operating characteristic curve analysis confirmed the good potency of the disulfidptosis-related gene prognostic model. Except for , the other nine genes were positively related with T cell CD8+, T cell CD4+ memory activated, T cell gamma delta, NK cell activated, and macrophage M1, and they were all negatively related with NK cell resting, macrophage M0, macrophage M2, and mast cell activated. Finally, we verified downregulated levels of SOCS1 and DEFB1 and upregulated CXCR3, BIN2, and CCL3L3 in A875 and A375.

Conclusion

We successfully established ten disulfidptosis-related genes' prediction prognostic signatures for SKCM patients.

Loading

Article metrics loading...

/content/journals/cchts/10.2174/0113862073307469240528065718
2024-06-14
2025-05-04
Loading full text...

Full text loading...

References

  1. Pega F. Momen N.C. Streicher K.N. Leon-Roux M. Neupane S. Schubauer-Berigan M.K. Schüz J. Baker M. Driscoll T. Guseva Canu I. Kiiver H.M. Li J. Nwanaji-Enwerem J.C. Turner M.C. Viegas S. Villeneuve P.J. Technical Advisory Group on Occupational Burden of Disease Estimation Global, regional and national burdens of non-melanoma skin cancer attributable to occupational exposure to solar ultraviolet radiation for 183 countries, 2000–2019: A systematic analysis from the WHO/ILO joint estimates of the work-related burden of disease and injury. Environ. Int. 2023 181 108226 10.1016/j.envint.2023.108226 37945424
    [Google Scholar]
  2. Arnold M. Singh D. Laversanne M. Vignat J. Vaccarella S. Meheus F. Cust A.E. de Vries E. Whiteman D.C. Bray F. Global burden of cutaneous melanoma in 2020 and projections to 2040. JAMA Dermatol. 2022 158 5 495 503 10.1001/jamadermatol.2022.0160 35353115
    [Google Scholar]
  3. Mollica V. Rizzo A. Marchetti A. Tateo V. Tassinari E. Rosellini M. Massafra R. Santoni M. Massari F. The impact of ECOG performance status on efficacy of immunotherapy and immune-based combinations in cancer patients: The MOUSEION-06 study. Clin. Exp. Med. 2023 23 8 5039 5049 10.1007/s10238‑023‑01159‑1 37535194
    [Google Scholar]
  4. Rizzo A. Nivolumab plus ipilimumab in melanoma brain metastases. Lancet Oncol. 2022 23 2 e52 10.1016/S1470‑2045(22)00010‑9 35114121
    [Google Scholar]
  5. Guven D.C. Sahin T.K. Erul E. Rizzo A. Ricci A.D. Aksoy S. Yalcin S. The association between albumin levels and survival in patients treated with immune checkpoint inhibitors: A systematic review and meta-analysis. Front. Mol. Biosci. 2022 9 1039121 10.3389/fmolb.2022.1039121 36533070
    [Google Scholar]
  6. Rizzo A. Mollica V. Tateo V. Tassinari E. Marchetti A. Rosellini M. De Luca R. Santoni M. Massari F. Hypertransaminasemia in cancer patients receiving immunotherapy and immune-based combinations: The MOUSEION-05 study. Cancer Immunol. Immunother. 2023 72 6 1381 1394 10.1007/s00262‑023‑03366‑x 36695827
    [Google Scholar]
  7. Liu X. Olszewski K. Zhang Y. Lim E.W. Shi J. Zhang X. Zhang J. Lee H. Koppula P. Lei G. Zhuang L. You M.J. Fang B. Li W. Metallo C.M. Poyurovsky M.V. Gan B. Cystine transporter regulation of pentose phosphate pathway dependency and disulfide stress exposes a targetable metabolic vulnerability in cancer. Nat. Cell Biol. 2020 22 4 476 486 10.1038/s41556‑020‑0496‑x 32231310
    [Google Scholar]
  8. Li X.J. Wen R. Wen D.Y. Lin P. Pan D.H. Zhang L.J. He Y. Shi L. Qin Y.Y. Lai Y.H. Lai J.N. Yang J.L. Lai Q.Q. Wang J. Ma J. Yang H. Pang Y.Y. Downregulation of miR‑193a‑3p via targeting cyclin D1 in thyroid cancer. Mol. Med. Rep. 2020 22 3 2199 2218 10.3892/mmr.2020.11310 32705210
    [Google Scholar]
  9. Zhao Y. Wei Y. Fan L. Nie Y. Li J. Zeng R. Li J. Zhan X. Lei L. Kang Z. Li J. Zhang W. Yang Z. Leveraging a disulfidptosis-related signature to predict the prognosis and immunotherapy effectiveness of cutaneous melanoma based on machine learning. Mol. Med. 2023 29 1 145 10.1186/s10020‑023‑00739‑x 37884883
    [Google Scholar]
  10. Satam H. Joshi K. Mangrolia U. Waghoo S. Zaidi G. Rawool S. Thakare R.P. Banday S. Mishra A.K. Das G. Malonia S.K. Next-generation sequencing technology: Current trends and advancements. Biology 2023 12 7 997 10.3390/biology12070997 37508427
    [Google Scholar]
  11. Maghsoudi S. Taghavi Shahraki B. Rameh F. Nazarabi M. Fatahi Y. Akhavan O. Rabiee M. Mostafavi E. Lima E.C. Saeb M.R. Rabiee N. A review on computer‐aided chemogenomics and drug repositioning for rational COVID ‐19 drug discovery. Chem. Biol. Drug Des. 2022 100 5 699 721 10.1111/cbdd.14136 36002440
    [Google Scholar]
  12. Alimirzaei F. Kieslich C.A. Machine learning models for predicting membranolytic anticancer peptides. Computer Aided Chemical Engineering. Kokossis A.C. Georgiadis M.C. Pistikopoulos E. Elsevier 2023 52 2691 2696
    [Google Scholar]
  13. Tabakhi S. Suvon M.N.I. Ahadian P. Lu H. Multimodal learning for multi-omics: A survey. World Sci. Ann. Rev. Artif. Intell. 2023 1 2250004 10.1142/S2811032322500047
    [Google Scholar]
  14. Robert C. Gautheret D. Multi-omics prediction in melanoma immunotherapy: A new brick in the wall. Cancer Cell 2022 40 1 14 16 10.1016/j.ccell.2021.12.008 35016026
    [Google Scholar]
  15. Cirenajwis H. Ekedahl H. Lauss M. Harbst K. Carneiro A. Enoksson J. Rosengren F. Werner-Hartman L. Törngren T. Kvist A. Fredlund E. Bendahl P.O. Jirström K. Lundgren L. Howlin J. Borg Å. Gruvberger-Saal S.K. Saal L.H. Nielsen K. Ringnér M. Tsao H. Olsson H. Ingvar C. Staaf J. Jönsson G. 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 10.18632/oncotarget.3655 25909218
    [Google Scholar]
  16. Cabrita R. Lauss M. Sanna A. Donia M. Skaarup Larsen M. Mitra S. Johansson I. Phung B. Harbst K. Vallon-Christersson J. van Schoiack A. Lövgren K. Warren S. Jirström K. Olsson H. Pietras K. Ingvar C. Isaksson K. Schadendorf D. Schmidt H. Bastholt L. Carneiro A. Wargo J.A. Svane I.M. Jönsson G. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 2020 577 7791 561 565 10.1038/s41586‑019‑1914‑8 31942071
    [Google Scholar]
  17. Barrett T. Troup D.B. Wilhite S.E. Ledoux P. Evangelista C. Kim I.F. Tomashevsky M. Marshall K.A. Phillippy K.H. Sherman P.M. Muertter R.N. Holko M. Ayanbule O. Yefanov A. Soboleva A. NCBI GEO: Archive for functional genomics data sets--10 years on. Nucleic Acids Res. 2011 39 Database D1005 D1010 10.1093/nar/gkq1184 21097893
    [Google Scholar]
  18. Wang T. Guo K. Zhang D. Wang H. Yin J. Cui H. Wu W. Disulfidptosis classification of hepatocellular carcinoma reveals correlation with clinical prognosis and immune profile. Int. Immunopharmacol. 2023 120 110368 10.1016/j.intimp.2023.110368 37247499
    [Google Scholar]
  19. Zhong Z. Wang Y. Yin J. Ni S. Liu W. Geng R. Liu J. Bai J. Yu H. Identification of specific cervical cancer subtypes and prognostic gene sets in tumor and nontumor tissues based on GSVA analysis. J. Oncol. 2022 2022 1 17 10.1155/2022/6951885 36284631
    [Google Scholar]
  20. Camp R.L. Dolled-Filhart M. Rimm D.L. D.L.X-tile: A new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin. Cancer Res. 2004 10 21 7252 7259 10.1158/1078‑0432.CCR‑04‑0713 15534099
    [Google Scholar]
  21. Wang P. Wang Y. Hang B. Zou X. Mao J.H. A novel gene expression-based prognostic scoring system to predict survival in gastric cancer. Oncotarget 2016 7 34 55343 55351 10.18632/oncotarget.10533 27419373
    [Google Scholar]
  22. Langfelder P. Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 2008 9 1 559 10.1186/1471‑2105‑9‑559 19114008
    [Google Scholar]
  23. Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 43 7 e47 10.1093/nar/gkv007 25605792
    [Google Scholar]
  24. Huang D.W. Sherman B.T. Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009 4 1 44 57 10.1038/nprot.2008.211 19131956
    [Google Scholar]
  25. Huang D.W. Sherman B.T. Lempicki R.A. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009 37 1 1 13 10.1093/nar/gkn923 19033363
    [Google Scholar]
  26. Mayr A. Schmid M. Boosting the concordance index for survival data--a unified framework to derive and evaluate biomarker combinations. PLoS One 2014 9 1 e84483 10.1371/journal.pone.0084483 24400093
    [Google Scholar]
  27. 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. USA 2005 102 43 15545 15550 10.1073/pnas.0506580102 16199517
    [Google Scholar]
  28. Chen B. Khodadoust M.S. Liu C.L. Newman A.M. Alizadeh A.A. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol. Biol. 2018 1711 243 259 10.1007/978‑1‑4939‑7493‑1_12 29344893
    [Google Scholar]
  29. Eberly L.E. Correlation and simple linear regression. Methods Mol. Biol. 2007 404 143 164 10.1007/978‑1‑59745‑530‑5_8 18450049
    [Google Scholar]
  30. Han H. Cho J.W. Lee S. Yun A. Kim H. Bae D. Yang S. Kim C.Y. Lee M. Kim E. Lee S. Kang B. Jeong D. Kim Y. Jeon H.N. Jung H. Nam S. Chung M. Kim J.H. Lee I. TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2018 46 D1 D380 D386 10.1093/nar/gkx1013 29087512
    [Google Scholar]
  31. Cui C. Zhong B. Fan R. Cui Q. HMDD v4.0: A database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2023 52 D1 D1327 D1332 37650649
    [Google Scholar]
  32. Barbai T. Fejős Z. Puskas L.G. Tímár J. Rásó E. The importance of microenvironment: The role of CCL8 in metastasis formation of melanoma. Oncotarget 2015 6 30 29111 29128 10.18632/oncotarget.5059 26320180
    [Google Scholar]
  33. Baton F. Deruyffelaere C. Chapin M. Prod’homme T. Charron D. Al-Daccak R. Alcaide-Loridan C. Class II transactivator (CIITA) isoform expression and activity in melanoma. Melanoma Res. 2004 14 6 453 461 10.1097/00008390‑200412000‑00004 15577315
    [Google Scholar]
  34. Kawada K. Sonoshita M. Sakashita H. Takabayashi A. Yamaoka Y. Manabe T. Inaba K. Minato N. Oshima M. Taketo M.M. Pivotal role of CXCR3 in melanoma cell metastasis to lymph nodes. Cancer Res. 2004 64 11 4010 4017 10.1158/0008‑5472.CAN‑03‑1757 15173015
    [Google Scholar]
  35. Liao M. Zeng F. Li Y. Gao Q. Yin M. Deng G. Chen X. A novel predictive model incorporating immune-related gene signatures for overall survival in melanoma patients. Sci. Rep. 2020 10 1 12462 10.1038/s41598‑020‑69330‑2 32719391
    [Google Scholar]
  36. Alderdice M. Craig S.G. Humphries M.P. Gilmore A. Johnston N. Bingham V. Coyle V. Senevirathne S. Longley D.B. Loughrey M.B. McQuaid S. James J.A. Salto-Tellez M. Lawler M. McArt D.G. Evolutionary genetic algorithm identifies IL2RB as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer. NAR Genom. Bioinform. 2021 3 2 lqab016 10.1093/nargab/lqab016 33928242
    [Google Scholar]
  37. Takeshita A. Shinjo K. Naito K. Matsui H. Shigeno K. Nakamura S. Horii T. Maekawa M. Kitamura K. Naoe T. Ohnishi K. Ohno R. P-glycoprotein (P-gp) and multidrug resistance-associated protein 1 (MRP1) are induced by arsenic trioxide (As2O3), but are not the main mechanism of As2O3-resistance in acute promyelocytic leukemia cells. Leukemia 2003 17 3 648 650 10.1038/sj.leu.2402851 12646961
    [Google Scholar]
  38. Ding L. Yang Y. Ge Y. Lu Q. Yan Z. Chen X. Du J. Hafizi S. Xu X. Yao J. Liu J. Cao Z. Weygant N. Inhibition of DCLK1 with DCLK1-IN-1 suppresses renal cell carcinoma invasion and stemness and promotes cytotoxic T-cell-mediated anti-tumor immunity. Cancers 2021 13 22 5729 10.3390/cancers13225729 34830884
    [Google Scholar]
  39. Diao P. Jiang Y. Li Y. Wu X. Li J. Zhou C. Jiang L. Zhang W. Yan E. Zhang P. Ding X. Wu H. Yuan H. Ye J. Song X. Wan L. Wu Y. Jiang H. Wang Y. Cheng J. Immune landscape and subtypes in primary resectable oral squamous cell carcinoma: Prognostic significance and predictive of therapeutic response. J. Immunother. Cancer 2021 9 6 e002434 10.1136/jitc‑2021‑002434 34130988
    [Google Scholar]
  40. Gu C. Gu X. Wang Y. Yao Z. Zhou C. Construction and validation of a novel immunosignature for overall survival in uveal melanoma. Front. Cell Dev. Biol. 2021 9 710558 10.3389/fcell.2021.710558 34552928
    [Google Scholar]
  41. Li S. Zhang R. A novel interactive deep cascade spectral graph convolutional network with multi-relational graphs for disease prediction. Neural Netw. 2024 175 106285 10.1016/j.neunet.2024.106285 38593556
    [Google Scholar]
  42. Hasanbeig M. Pavel L. On synchronous binary log-linear learning and second order Q-learning * *This work was supported by an NSERC grant. IFAC-PapersOnLine 2017 50 1 8987 8992 10.1016/j.ifacol.2017.08.1326
    [Google Scholar]
  43. Faghihinejad F. Zoghifard M. Amiri A.M. Monajem S. Evaluating social and spatial equity in public transport: A case study. Transp. Lett. 2023 15 10 1420 1429 10.1080/19427867.2022.2158541
    [Google Scholar]
  44. Ahadian P. Babaei M. Parand K. Using singular value decomposition in a convolutional neural network to improve brain tumor segmentation accuracy. Int. J. Comput. Sci. Inf. Technol. 2022 14 165 171 10.5121/csit.2022.121717
    [Google Scholar]
  45. Wang Q. Qiao W. Zhang H. Liu B. Li J. Zang C. Mei T. Zheng J. Zhang Y. Nomogram established on account of Lasso-Cox regression for predicting recurrence in patients with early-stage hepatocellular carcinoma. Front. Immunol. 2022 13 1019638 10.3389/fimmu.2022.1019638 36505501
    [Google Scholar]
  46. Xu K. Zhang Y. Yan Z. Wang Y. Li Y. Qiu Q. Du Y. Chen Z. Liu X. Identification of disulfidptosis related subtypes, characterization of tumor microenvironment infiltration, and development of DRG prognostic prediction model in RCC, in which MSH3 is a key gene during disulfidptosis. Front. Immunol. 2023 14 1205250 10.3389/fimmu.2023.1205250 37426643
    [Google Scholar]
  47. Chen H. Yang W. Li Y. Ma L. Ji Z. Leveraging a disulfidptosis-based signature to improve the survival and drug sensitivity of bladder cancer patients. Front. Immunol. 2023 14 1198878 10.3389/fimmu.2023.1198878 37325625
    [Google Scholar]
/content/journals/cchts/10.2174/0113862073307469240528065718
Loading
/content/journals/cchts/10.2174/0113862073307469240528065718
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher's website along with the published article.


  • Article Type:
    Research Article
Keywords: T cell ; disulfidptosis ; prognosis ; RNA-seq ; Molecular mechanism ; Skin cutaneous melanoma
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test