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Anoikis patterns via machine learning strategy and experimental verification exhibit distinct prognostic and immune landscapes in melanoma

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Abstract

Background

Anoikis is a cell death programmed to eliminate dysfunctional or damaged cells induced by detachment from the extracellular matrix. Utilizing an anoikis-based risk stratification is anticipated to understand melanoma's prognostic and immune landscapes comprehensively.

Methods

Differential expression genes (DEGs) were analyzed between melanoma and normal skin tissues in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression data sets. Next, least absolute shrinkage and selection operator, support vector machine–recursive feature elimination algorithm, and univariate and multivariate Cox analyses on the 308 DEGs were performed to build the prognostic signature in the TCGA–melanoma data set. Finally, the signature was validated in GSE65904 and GSE22155 data sets. NOTCH3, PIK3R2, and SOD2 were validated in our clinical samples by immunohistochemistry.

Results

The prognostic model for melanoma patients was developed utilizing ten hub anoikis-related genes. The overall survival (OS) of patients in the high-risk subgroup, which was classified by the optimal cutoff value, was remarkably shorter in the TCGA–melanoma, GSE65904, and GSE22155 data sets. Low-risk patients exhibited low immune cell infiltration and high expression of immunophenoscores and immune checkpoints. They also demonstrated increased sensitivity to various drugs, including dasatinib and dabrafenib. NOTCH3, PIK3R2, and SOD2 were notably associated with OS by univariate Cox analysis in the GSE65904 data set. The clinical melanoma samples showed remarkably higher protein expressions of NOTCH3 (P = 0.003) and PIK3R2 (P = 0.009) than the para-melanoma samples, while the SOD2 protein expression remained unchanged.

Conclusions

In this study, we successfully established a prognostic anoikis-connected signature using machine learning. This model may aid in evaluating patient prognosis, clinical characteristics, and immune treatment modalities for melanoma.

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Data availability

This study's data generated or examined are comprehensively presented in this article or the supplementary materials. Without reservation, the author is committed to providing the raw data confirming the outcomes of this article to qualified researchers.

Abbreviations

ML:

Machine learning

GTEx:

Genotype-tissue expression

TCGA:

The cancer genome atlas

SVM–RFE:

Support vector machine–recursive feature elimination

ICPs:

Immune checkpoints

IPSs:

Immunophenoscores

LASSO:

Least absolute shrinkage and selection operator

DEGs:

Differentially expressed genes

DAVID:

Database for annotation, visualization and integrated discovery

TSG101:

Tumor susceptibility gene 101

NQO1:

NAD(P)H dehydrogenase (quinone 1)

EGFR:

Epidermal growth factor receptor

DAPK2:

Death-associated protein kinase 2

CEBPB:

CCAAT enhancer binding protein b

BNIP3:

Bcl-2/E1B 19 kDa interacting protein

ANXA5:

Annexin A5

PIK3R2:

Phosphatidylinositol 3-kinase p85beta

SOD2:

Superoxide dismutase 2

GO:

Gene Ontology

KEGG:

Kyoto encyclopedia of genes and genomes

AUC:

Area under the curve

AJCC:

American Joint Committee on Cancer

ROC:

Receiver operating characteristic

GDSC:

Genomics of drug sensitivity in cancer

IHC:

Immunohistochemistry

HR:

Hazard regression

TME:

Tumor microenvironment

IPSs:

Immunophenoscores

ICPs:

Immune checkpoints

CAF:

Cancer-associated fibroblast

MDSC:

Myeloid derived suppressive cell

M2 TAM:

M2 tumor-associated macrophages

PD-1:

Anti-programmed death 1

CTLA-4:

Anti-cytotoxic T lymphocyte-associated antigen 4

PD-L1:

Programmed death ligand 1

TIM3:

Mucin-domain containing-3

LAG3:

Lymphocyte-activation gene-3

References

  1. Dong Z, Yang J, Li L, Tan L, Shi P, Zhang J, et al. FOXO3a-SIRT6 axis suppresses aerobic glycolysis in melanoma. Int J Oncol. 2020;56(3):728–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Karimkhani C, Reddy BY, Dellavalle RP, Sundararajan S. Novel therapies for unresectable and metastatic melanoma. BMJ. 2017;359: j5174.

    Article  PubMed  Google Scholar 

  3. Altonsy MO, Ganguly A, Amrein M, Surmanowicz P, Li SS, Lauzon GJ, et al. Beta3-tubulin is critical for microtubule dynamics, cell cycle regulation, and spontaneous release of microvesicles in human malignant melanoma cells (A375). Int J Mol Sci. 2020;21(5):1656.

  4. Aris M, Barrio MM. Combining immunotherapy with oncogene-targeted therapy: a new road for melanoma treatment. Front Immunol. 2015;6:46.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Frisch SM, Francis H. Disruption of epithelial cell-matrix interactions induces apoptosis. J Cell Biol. 1994;124(4):619–26.

    Article  CAS  PubMed  Google Scholar 

  6. Han HJ, Sung JY, Kim SH, Yun UJ, Kim H, Jang EJ, et al. Fibronectin regulates anoikis resistance via cell aggregate formation. Cancer Lett. 2021;508:59–72.

    Article  CAS  PubMed  Google Scholar 

  7. Adeshakin FO, Adeshakin AO, Afolabi LO, Yan D, Zhang G, Wan X. Mechanisms for modulating anoikis resistance in cancer and the relevance of metabolic reprogramming. Front Oncol. 2021;11: 626577.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Di Micco R, Krizhanovsky V, Baker D, d'Adda di Fagagna F. Cellular senescence in ageing: from mechanisms to therapeutic opportunities. Nat Rev Mol Cell Biol. 2021;22(2): 75–95

  9. Kakavandi E, Shahbahrami R, Goudarzi H, Eslami G, Faghihloo E. Anoikis resistance and oncoviruses. J Cell Biochem. 2018;119(3):2484–91.

    Article  CAS  PubMed  Google Scholar 

  10. Shao Y, Aplin AE. Akt3-mediated resistance to apoptosis in B-RAF-targeted melanoma cells. Cancer Res. 2010;70(16):6670–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Madajewski B, Boatman MA, Chakrabarti G, Boothman DA, Bey EA. Depleting tumor-NQO1 potentiates anoikis and inhibits growth of NSCLC. Mol Cancer Res. 2016;14(1):14–25.

    Article  CAS  PubMed  Google Scholar 

  12. Du S, Miao J, Zhu Z, Xu E, Shi L, Ai S, et al. NADPH oxidase 4 regulates anoikis resistance of gastric cancer cells through the generation of reactive oxygen species and the induction of EGFR. Cell Death Dis. 2018;9(10):948.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Du S, Yang Z, Lu X, Yousuf S, Zhao M, Li W, et al. Anoikis resistant gastric cancer cells promote angiogenesis and peritoneal metastasis through C/EBPβ-mediated PDGFB autocrine and paracrine signaling. Oncogene. 2021;40(38):5764–79.

    Article  CAS  PubMed  Google Scholar 

  14. Chen J, Gao F, Liu N. L1CAM promotes epithelial to mesenchymal transition and formation of cancer initiating cells in human endometrial cancer. Exp Ther Med. 2018;15(3):2792–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Hsu MY, Yang MH, Schnegg CI, Hwang S, Ryu B, Alani RM. Notch3 signaling-mediated melanoma-endothelial crosstalk regulates melanoma stem-like cell homeostasis and niche morphogenesis. Lab Invest. 2017;97(6):725–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Sharma R, Gogoi G, Saikia S, Sharma A, Kalita DJ, Sarma A, et al. BMP4 enhances anoikis resistance and chemoresistance of breast cancer cells through canonical BMP signaling. J Cell Commun Signal. 2022;16(2):191–205.

    Article  CAS  PubMed  Google Scholar 

  17. Adeshakin FO, Adeshakin AO, Liu Z, Cheng J, Zhang P, Yan D, et al. Targeting oxidative phosphorylation-proteasome activity in extracellular detached cells promotes anoikis and inhibits metastasis. Life (Basel). 2021;12(1):42.

  18. Zhou Y, Wang C, Chen Y, Zhang W, Fu Z, Li J, et al. A novel risk model based on anoikis: predicting prognosis and immune infiltration in cutaneous melanoma. Front Pharmacol. 2022;13:1090857.

    Article  CAS  PubMed  Google Scholar 

  19. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46(1):389–422.

    Article  Google Scholar 

  20. Ma N, Li J, Lv L, Li C, Li K, Wang B. Bioinformatics evaluation of a novel angiogenesis related genes-based signature for predicting prognosis and therapeutic efficacy in patients with gastric cancer. Am J Transl Res. 2022;14(7):4532–48.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Fan W, Wang D, Li G, Xu J, Ren C, Sun Z, et al. A novel chemokine-based signature for prediction of prognosis and therapeutic response in glioma. CNS Neurosci Ther. 2022;28(12):2090–103.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 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(7): e47.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2009.

    Book  Google Scholar 

  24. da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.

    Article  CAS  PubMed  Google Scholar 

  25. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Huang ML, Hung YH, Lee WM, Li RK, Jiang BR. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. ScientificWorldJournal. 2014;2014: 795624.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D, et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med. 2015;21(8):938–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 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.

    Article  PubMed  Google Scholar 

  29. Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021;22(6):bbab260.

  30. Guizhen Z, Weiwei Z, Yun W, Guangying C, Yize Z, Zujiang Y. An anoikis-based signature for predicting prognosis in hepatocellular carcinoma with machine learning. Front Pharmacol. 2022;13:1096472.

    Article  PubMed  Google Scholar 

  31. Sakamoto S, Kyprianou N. Targeting anoikis resistance in prostate cancer metastasis. Mol Aspects Med. 2010;31(2):205–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pierce CJ, Simmons JL, Broit N, Karunarathne D, Ng MF, Boyle GM. BRN2 expression increases anoikis resistance in melanoma. Oncogenesis. 2020;9(7):64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. McCarter MD, Baumgartner J, Escobar GA, Richter D, Lewis K, Robinson W, et al. Immunosuppressive dendritic and regulatory T cells are upregulated in melanoma patients. Ann Surg Oncol. 2007;14(10):2854–60.

    Article  PubMed  Google Scholar 

  34. Galluzzi L, Vitale I, Aaronson SA, Abrams JM, Adam D, Agostinis P, et al. Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018. Cell Death Differ. 2018;25(3):486–541.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Simpson CD, Anyiwe K, Schimmer AD. Anoikis resistance and tumor metastasis. Cancer Lett. 2008;272(2):177–85.

    Article  CAS  PubMed  Google Scholar 

  36. González-Llorente L, Santacatterina F, García-Aguilar A, Nuevo-Tapioles C, González-García S, Tirpakova Z, et al. Overexpression of mitochondrial IF1 prevents metastatic disease of colorectal cancer by enhancing anoikis and tumor infiltration of NK cells. Cancers (Basel). 2019;12(1):22.

  37. Wade CA, Kyprianou N. Profiling prostate cancer therapeutic resistance. Int J Mol Sci. 2018;19(3):904.

  38. Chhabra Y, Weeraratna AT. Fibroblasts in cancer: Unity in heterogeneity. Cell. 2023;186(8):1580–609.

    Article  CAS  PubMed  Google Scholar 

  39. Sarkar M, Nguyen T, Gundre E, Ogunlusi O, El-Sobky M, Giri B, et al. Cancer-associated fibroblasts: the chief architect in the tumor microenvironment. Front Cell Dev Biol. 2023;11:1089068.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Mao X, Xu J, Wang W, Liang C, Hua J, Liu J, et al. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer. 2021;20(1):131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Liu L, Kshirsagar PG, Gautam SK, Gulati M, Wafa EI, Christiansen JC, et al. Nanocarriers for pancreatic cancer imaging, treatments, and immunotherapies. Theranostics. 2022;12(3):1030–60.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wolchok JD, Kluger H, Callahan MK, Postow MA, Rizvi NA, Lesokhin AM, et al. Nivolumab plus ipilimumab in advanced melanoma. N Engl J Med. 2013;369(2):122–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321–30.

    Article  CAS  PubMed  Google Scholar 

  44. Lee YH, Martin-Orozco N, Zheng P, Li J, Zhang P, Tan H, et al. Inhibition of the B7–H3 immune checkpoint limits tumor growth by enhancing cytotoxic lymphocyte function. Cell Res. 2017;27(8):1034–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18(1):248–62.

    Article  CAS  PubMed  Google Scholar 

  46. Angelova M, Charoentong P, Hackl H, Fischer ML, Snajder R, Krogsdam AM, et al. Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol. 2015;16(1):64.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Liu Y, Cai P, Wang N, Zhang Q, Chen F, Shi L, et al. Combined blockade of Tim-3 and MEK inhibitor enhances the efficacy against melanoma. Biochem Biophys Res Commun. 2017;484(2):378–84.

    Article  CAS  PubMed  Google Scholar 

  48. Araujo BDLV, Borch A, Hansen M, Draghi A, Spanggaard I, Rohrberg K, et al. Common phenotypic dynamics of tumor-infiltrating lymphocytes across different histologies upon checkpoint inhibition: impact on clinical outcome. Cytotherapy. 2020;22(4):204–13.

    Article  Google Scholar 

  49. Wu J, Liao X, Yu B, Su B. Dasatinib inhibits primary melanoma cell proliferation through morphology-dependent disruption of Src-ERK signaling. Oncol Lett. 2013;5(2):527–32.

    Article  PubMed  Google Scholar 

  50. Du Four S, Maenhout SK, De Pierre K, Renmans D, Niclou SP, Thielemans K, et al. Axitinib increases the infiltration of immune cells and reduces the suppressive capacity of monocytic MDSCs in an intracranial mouse melanoma model. Oncoimmunology. 2015;4(4): e998107.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Abdel-Wahab O, Klimek VM, Gaskell AA, Viale A, Cheng D, Kim E, et al. Efficacy of intermittent combined RAF and MEK inhibition in a patient with concurrent BRAF- and NRAS-mutant malignancies. Cancer Discov. 2014;4(5):538–45.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ou C, Liu H, Ding Z, Zhou L. Chloroquine promotes gefitinib-induced apoptosis by inhibiting protective autophagy in cutaneous squamous cell carcinoma. Mol Med Rep. 2019;20(6):4855–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Schlegel CR, Georgiou ML, Misterek MB, Stöcker S, Chater ER, Munro CE, et al. DAPK2 regulates oxidative stress in cancer cells by preserving mitochondrial function. Cell Death Dis. 2015;6(3): e1671.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Chua HH, Kameyama T, Mayeda A, and Yeh TH, Cancer-specifically re-spliced TSG101 mRNA promotes invasion and metastasis of nasopharyngeal carcinoma. Int J Mol Sci. 2019;20(3):773.

  55. Sowter HM, Ratcliffe PJ, Watson P, Greenberg AH, Harris AL. HIF-1-dependent regulation of hypoxic induction of the cell death factors BNIP3 and NIX in human tumors. Cancer Res. 2001;61(18):6669–73.

    CAS  PubMed  Google Scholar 

  56. Peng B, Guo C, Guan H, Liu S, Sun MZ. Annexin A5 as a potential marker in tumors. Clin Chim Acta. 2014;427:42–8.

    Article  CAS  PubMed  Google Scholar 

  57. Siegel D, Franklin WA, Ross D. Immunohistochemical detection of NAD(P)H:quinone oxidoreductase in human lung and lung tumors. Clin Cancer Res. 1998;4(9):2065–70.

    CAS  PubMed  Google Scholar 

  58. Schlager JJ, Powis G. Cytosolic NAD(P)H:(quinone-acceptor)oxidoreductase in human normal and tumor tissue: effects of cigarette smoking and alcohol. Int J Cancer. 1990;45(3):403–9.

    Article  CAS  PubMed  Google Scholar 

  59. Yang Y, Zhang Y, Wu Q, Cui X, Lin Z, Liu S, et al. Clinical implications of high NQO1 expression in breast cancers. J Exp Clin Cancer Res. 2014;33(1):14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ma Y, Kong J, Yan G, Ren X, Jin D, Jin T, et al. NQO1 overexpression is associated with poor prognosis in squamous cell carcinoma of the uterine cervix. BMC Cancer. 2014;14:414.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Li Z, Zhang Y, Jin T, Men J, Lin Z, Qi P, et al. NQO1 protein expression predicts poor prognosis of non-small cell lung cancers. BMC Cancer. 2015;15:207.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Park SJ, Zhao H, Spitz MR, Grossman HB, Wu X. An association between NQO1 genetic polymorphism and risk of bladder cancer. Mutat Res. 2003;536(1–2):131–7.

    Article  CAS  PubMed  Google Scholar 

  63. Thapa D, Huang SB, Muñoz AR, Yang X, Bedolla RG, Hung CN, et al. Attenuation of NAD[P]H:quinone oxidoreductase 1 aggravates prostate cancer and tumor cell plasticity through enhanced TGFβ signaling. Commun Biol. 2020;3:12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Du Q, Tan Z, Shi F, Tang M, Xie L, Zhao L, et al. PGC1α/CEBPB/CPT1A axis promotes radiation resistance of nasopharyngeal carcinoma through activating fatty acid oxidation. Cancer Sci. 2019;110(6):2050–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Jerhammar F, Ceder R, Garvin S, Grénman R, Grafström RC, Roberg K. Fibronectin 1 is a potential biomarker for radioresistance in head and neck squamous cell carcinoma. Cancer Biol Ther. 2010;10(12):1244–51.

    Article  CAS  PubMed  Google Scholar 

  66. Guerzoni C, Bardini M, Mariani SA, Ferrari-Amorotti G, Neviani P, Panno ML, et al. Inducible activation of CEBPB, a gene negatively regulated by BCR/ABL, inhibits proliferation and promotes differentiation of BCR/ABL-expressing cells. Blood. 2006;107(10):4080–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Termini L, Fregnani JH, Boccardo E, da Costa WH, Longatto-Filho A, Andreoli MA, et al. SOD2 immunoexpression predicts lymph node metastasis in penile cancer. BMC Clin Pathol. 2015;15:3.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Xing M. Molecular pathogenesis and mechanisms of thyroid cancer. Nat Rev Cancer. 2013;13(3):184–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Vartanian A, Gatsina G, Grigorieva I, Solomko E, Dombrovsky V, Baryshnikov A, et al. The involvement of Notch signaling in melanoma vasculogenic mimicry. Clin Exp Med. 2013;13(3):201–9.

    Article  CAS  PubMed  Google Scholar 

  70. Ivan C, Hu W, Bottsford-Miller J, Zand B, Dalton HJ, Liu T, et al. Epigenetic analysis of the Notch superfamily in high-grade serous ovarian cancer. Gynecol Oncol. 2013;128(3):506–11.

    Article  CAS  PubMed  Google Scholar 

  71. Lai IC, Shih PH, Yao CJ, Yeh CT, Wang-Peng J, Lui TN, et al. Elimination of cancer stem-like cells and potentiation of temozolomide sensitivity by Honokiol in glioblastoma multiforme cells. PLoS ONE. 2015;10(3): e0114830.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Wang J, Cai S, Xiong Q, Weng D, Wang Q, Ma Z. PIK3R2 predicts poor outcomes for patients with melanoma and contributes to the malignant progression via PI3K/AKT/NF-κB axis. Clin Transl Oncol. 2023;25(5):1402–12.

    Article  CAS  PubMed  Google Scholar 

  73. Liu Y, Wang D, Li Z, Li X, Jin M, Jia N, et al. Pan-cancer analysis on the role of PIK3R1 and PIK3R2 in human tumors. Sci Rep. 2022;12(1):5924.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

This research was supported by the National Science Foundation of China [grant number 31870974].

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Authors and Affiliations

  1. Department of Plastic and Reconstructive Surgery, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, No. 301 Middle Yanchang Road, Shanghai, China

    Jinfang Liu, Siyuan Chen & Guangpeng Liu

  2. School of Life Sciences, Northwest University, Xi’an, 710069, China

    Rong Ma

  3. Department of Dermatologic Surgery, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, No. 1278 Baode Road, Shanghai, China

    Yongxian Lai

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  1. Jinfang Liu
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Contributions

Conceptualization, JL; data curation, JL, NN and RM; formal analysis, JL and SZ; funding acquisition, GL and YL; investigation, JL; methodology, JL and QD; software, JL; supervision, GL; validation, JL and SC; visualization, JL; writing—original draft, JL and GL; writing—review and editing, GL and YL.

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Correspondence to Yongxian Lai or Guangpeng Liu.

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12094_2023_3336_MOESM1_ESM.docx

Supplementary data to this article can be found online. Supplementary Table S1. The coefficients assessed by multivariate Cox regression. Supplementary Table S2. Correlations between the expression of NOTCH3 and PIK3R2 and clinical characteristics in melanoma patients. Supplementary Figure S1. Univariate Cox regression, OS, and ROC analysis of melanoma patients in the GSE65904 or GSE22155 datasets. (A)OS analysis of melanoma patients in the GSE22155 dataset. (B)ROC analysis of melanoma patients in the GSE22155 dataset. (C)Univariate Cox regression of melanoma patients in the GSE65904 dataset. (DOCX 266 KB)

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Liu, J., Ma, R., Chen, S. et al. Anoikis patterns via machine learning strategy and experimental verification exhibit distinct prognostic and immune landscapes in melanoma. Clin Transl Oncol 26, 1170–1186 (2024). https://doi.org/10.1007/s12094-023-03336-w

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