AccScience Publishing / EJMO / Online First / DOI: 10.36922/EJMO025220218
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ORIGINAL RESEARCH ARTICLE

Machine learning-based screening of anoikis-related genes in melanoma diagnosis and their clinicopathological significance

Tengfei Wang1 ,  Jinhcao Zhu2*
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1 Department of Pathology, Funan County People’s Hospital, Fuyang City, Anhui, China
2 Department of Pathology, The Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
EJMO, 025220218 https://doi.org/10.36922/EJMO025220218
Received: 26 May 2025 | Revised: 26 June 2025 | Accepted: 9 July 2025 | Published online: 12 August 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/ )
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Abstract

Introduction: Cutaneous melanoma, a highly aggressive malignant tumor, continues to pose significant challenges in early diagnosis and prognosis assessment. Although anoikis-related genes (ARGs) play crucial roles in tumor progression, systematic molecular diagnostic biomarkers remain lacking.

Objective: This study aims to elucidate the molecular mechanisms and diagnostic value of ARGs in cutaneous melanoma using advanced bioinformatics and machine learning approaches.

Methods: Multiple melanoma datasets from The Cancer Genome Atlas and GEO databases (GSE3189, GSE15605, GSE19234, GSE65904, and GSE66839) were integrated to analyze ARG expression patterns. Differential expression analysis identified candidate genes significantly associated with cutaneous skin melanoma. A total of 113 machine learning models were employed to screen and validate candidate genes, with receiver operating characteristic curves evaluating diagnostic value. Core genes were identified through protein-protein interaction (PPI) networks. The immune infiltration correlations of core genes were analyzed, and their tissue-level expression was validated using immunohistochemistry.

Results: Twenty-two ARGs were significantly enriched in cancer signaling pathways, including phosphoinositide 3-kinase-protein kinase B, hypoxia-inducible factor 1, and programmed death-ligand 1. Nine candidate genes were identified through differential and intersection analysis, and eight model genes were further screened using machine learning. Seven genes exhibited high diagnostic efficacy in both training and external validation sets (area under the curve [AUC] >0.7). Survival analysis revealed that high expression of carcinoembryonic antigen-related cell adhesion molecule (CEACAM)5, CEACAM6, epidermal growth factor receptor (EGFR), stratifin (SFN), and Polo-like kinase 1 (PLK1) correlated with poor prognosis. PPI analysis confirmed these as core regulatory factors, and immune infiltration analysis revealed strong associations with dendritic cells, T cells, and macrophages. The five-gene combination yielded excellent diagnostic performance, with AUCs of 0.969 (training) and 0.971 (validation). Immunohistochemistry confirmed their elevated expression in melanoma tissues.

Conclusion: This study identified five key ARGs, CEACAM5, CEACAM6, EGFR, SFN, and PLK1, as significantly upregulated in cutaneous melanoma and associated with poor prognosis. Their combined diagnostic power suggests strong potential as clinical pathological biomarkers.

Keywords
Machine learning
Immunohistochemistry
Biomarkers
Melanoma
Anoikis
Funding
None.
Conflict of interest
The authors declare that they have no conflicts of interest.
References
  1. Davis LE, Shalin SC, Tackett AJ. Current State of melanoma diagnosis and treatment. Cancer Biol Ther. 2019;20(11):1366-1379. doi: 10.1080/15384047.2019.1640032

 

  1. Nurla LA, Wafi G, Tatar R, et al. Recent-onset melanoma and the implications of the excessive use of tanning devices-case report and review of the literature. Medicina (Kaunas). 2024;60(1):187. doi: 10.3390/medicina60010187

 

  1. Apalla Z, Lallas A, Sotiriou E, Lazaridou E, Ioannides D. Epidemiological trends in skin cancer. Dermatol Pract Concept. 2017;7(2):1-6. doi: 10.5826/dpc.0702a01

 

  1. Liu-Smith F, Ziogas A. Age-dependent interaction between sex and geographic ultraviolet index in melanoma risk. J Am Acad Dermatol. 2020;82(5):1102-1108.e3. doi: 10.1016/j.jaad.2017.11.049

 

  1. Alhazmi R, Tong S, Darwish S, et al. Bis-cinnamamide derivatives as APE/Ref-1 inhibitors for the treatment of human melanoma. Molecules. 2022;27(9):2672. doi: 10.3390/molecules27092672

 

  1. Huang YJ, Gao Y, Wang CJ, et al. Hydroxyurea regulates the development and survival of B16 melanoma cells by upregulating MiR-7013-3p. Int J Med Sci. 2021;18(8):1877-1885. doi: 10.7150/ijms.52177

 

  1. Tumeh PC, Harview CL, Yearley JH, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515(7528):568-571. doi: 10.1038/nature13954

 

  1. Ashrafizadeh M, Mohammadinejad R, Tavakol S, Ahmadi Z, Roomiani S, Katebi M. Autophagy, anoikis, ferroptosis, necroptosis, and endoplasmic reticulum stress: Potential applications in melanoma therapy. J Cell Physiol. 2019;234(11):19471-19479. doi: 10.1002/jcp.28740

 

  1. Yu LG. Cancer cell resistance to anoikis: MUC1 glycosylation comes to play. Cell Death Dis. 2017;8(7):e2962. doi: 10.1038/cddis.2017.363

 

  1. Frisch SM, Screaton RA. Anoikis mechanisms. Curr Opin Cell Biol. 2001;13(5):555-562. doi: 10.1016/s0955-0674(00)00251-9

 

  1. Raeisi M, Zehtabi M, Velaei K, Fayyazpour P, Aghaei N, Mehdizadeh A. Anoikis in cancer: The role of lipid signaling. Cell Biol Int. 2022;46(11):1717-1728. doi: 10.1002/cbin.11896

 

  1. Jiang G, Song C, Wang X, et al. The multi-omics analysis identifies a novel cuproptosis-anoikis-related gene signature in prognosis and immune infiltration characterization of lung adenocarcinoma. Heliyon. 2023;9(3):e14091. doi: 10.1016/j.heliyon.2023.e14091

 

  1. Juan Z, Dake C, Tanaka K, Shuixiang H. EGFL7 as a novel therapeutic candidate regulates cell invasion and anoikis in colorectal cancer through PI3K/AKT signaling pathway. Int J Clin Oncol. 2021;26(6):1099-1108.doi: 10.1007/s10147-021-01888-x

 

  1. Yuan H, Xu R, Li S, et al. The malignant transformation of viral hepatitis to hepatocellular carcinoma: Mechanisms and interventions. MedComm (2020). 2025;6(3):e70121. doi: 10.1002/mco2.70121

 

  1. Zhu Z, Fang C, Xu H, et al. Anoikis resistance in diffuse glioma: The potential therapeutic targets in the future. Front Oncol. 2022;12:976557. doi: 10.3389/fonc.2022.976557

 

  1. Wang J, Luo Z, Lin L, et al. Anoikis-associated lung cancer metastasis: Mechanisms and therapies. Cancers (Basel). 2022;14(19):4791. doi: 10.3390/cancers14194791

 

  1. Paoli P, Giannoni E, Chiarugi P. Anoikis molecular pathways and its role in cancer progression. Biochim Biophys Acta. 2013;1833(12):3481-3498. doi: 10.1016/j.bbamcr.2013.06.026

 

  1. Neuendorf HM, Simmons JL, Boyle GM. Therapeutic targeting of anoikis resistance in cutaneous melanoma metastasis. Front Cell Dev Biol. 2023;11:1183328. doi: 10.3389/fcell.2023.1183328

 

  1. Simpson CD, Anyiwe K, Schimmer AD. Anoikis resistance and tumor metastasis. Cancer Lett. 2008;272(2):177-185. doi: 10.1016/j.canlet.2008.05.029

 

  1. Song J, Liu Y, Liu F, et al. The 14-3-3σ protein promotes HCC anoikis resistance by inhibiting EGFR degradation and thereby activating the EGFR-dependent ERK1/2 signaling pathway. Theranostics. 2021;11(3):996-1015. doi: 10.7150/thno.51646

 

  1. Yu L, Wang X, Du Y, Zhang X, Ling Y. FASN knockdown inhibited anoikis resistance of gastric cancer cells via P-ERK1/2/Bcl-xL pathway. Gastroenterol Res Pract. 2021;2021:6674204. doi: 10.1155/2021/6674204

 

  1. 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. doi: 10.3389/fonc.2021.626577

 

  1. Helbig D. Hemato-oncological diseases as risk factor for recurrence or metastasis of pleomorphic dermal sarcoma. Front Oncol. 2022;12:873771. doi: 10.3389/fonc.2022.873771

 

  1. Piepkorn MW, Barnhill RL, Elder DE, et al. The MPATH-Dx reporting schema for melanocytic proliferations and melanoma. J Am Acad Dermatol. 2014;70(1):131-141. doi: 10.1016/j.jaad.2013.07.027

 

  1. Elmore JG, Barnhill RL, Elder DE, et al. Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: Observer accuracy and reproducibility study. BMJ. 2017;357:j2813. doi: 10.1136/bmj.j2813

 

  1. Gershenwald JE, Scolyer RA, Hess KR, et al. Melanoma staging: Evidence-based changes in the American joint committee on cancer eighth edition cancer staging manual. CA A Cancer J Clin. 2017;67(6):472-492. doi: 10.3322/caac.21409

 

  1. Lee KH, Suh HY, Lee MW, Lee WJ, Chang SE. Prognostic significance of epidermal growth factor receptor expression in distant metastatic melanoma from primary cutaneous melanoma. Ann Dermatol. 2021;33(5):432-439. doi: 10.5021/ad.2021.33.5.432

 

  1. Strebhardt K. Multifaceted polo-like kinases: Drug targets and antitargets for cancer therapy. Nat Rev Drug Discov. 2010;9(8):643-660. doi: 10.1038/nrd3184

 

  1. Moore XTR, Gheghiani L, Fu Z. The role of polo-like kinase 1 in regulating the forkhead box family transcription factors. Cells. 2023;12(9):1344. doi: 10.3390/cells12091344

 

  1. Hammarström S. The carcinoembryonic antigen (CEA) family: Structures, suggested functions and expression in normal and malignant tissues. Semin Cancer Biol. 1999;9(2):67-81. doi: 10.1006/scbi.1998.0119

 

  1. Beauchemin N, Arabzadeh A. Carcinoembryonic antigen-related cell adhesion molecules (CEACAMs) in cancer progression and metastasis. Cancer Metastasis Rev. 2013;32(3-4):643-671. doi: 10.1007/s10555-013-9444-6

 

  1. Northrop-Albrecht EJ, Kim Y, Taylor WR, Majumder S, Kisiel JB, Lucien F. The proteomic landscape of stool-derived extracellular vesicles in patients with pre-cancerous lesions and colorectal cancer. Commun Biol. 2025;8(1):228. doi: 10.1038/s42003-025-07652-5

 

  1. Lee H, Jang Y, Park S, et al. Development and evaluation of a CEACAM6-targeting theranostic nanomedicine for photoacoustic-based diagnosis and chemotherapy of metastatic cancer. Theranostics. 2018;8(15):4247-4261. doi: 10.7150/thno.25131

 

  1. Dai Y, Zhang X, Ou Y, et al. Anoikis resistance--protagonists of breast cancer cells survive and metastasize after ECM detachment. Cell Commun Signal. 2023;21(1):190. doi: 10.1186/s12964-023-01183-4

 

  1. Que Z, Luo B, Yu P, et al. Polyphyllin VII induces CTC anoikis to inhibit lung cancer metastasis through EGFR pathway regulation. Int J Biol Sci. 2023;19(16):5204-5217. doi: 10.7150/ijbs.83682

 

  1. Nathanson KL, Martin AM, Wubbenhorst B, et al. Tumor genetic analyses of patients with metastatic melanoma treated with the BRAF inhibitor dabrafenib (GSK2118436). Clin Cancer Res. 2013;19(17):4868-4878. doi: 10.1158/1078-0432.Ccr-13-0827

 

  1. Liu X, Erikson RL. Polo-like kinase (Plk)1 depletion induces apoptosis in cancer cells. Proceed Natl Acad Sci United States Am. 2003;100(10):5789-5794. doi: 10.1073/pnas.1031523100

 

  1. Cholewa BD, Liu X, Ahmad N. The role of polo-like kinase 1 in carcinogenesis: Cause or consequence? Cancer Res. 2013;73(23):6848-6855. doi: 10.1158/0008-5472.Can-13-2197

 

  1. Hermeking H. The 14-3-3 cancer connection. Nat Rev Cancer. 2003;3(12):931-943. doi: 10.1038/nrc1230

 

  1. Benzinger A, Muster N, Koch HB, Yates JR 3rd, Hermeking H. Targeted proteomic analysis of 14-3-3 sigma, a p53 effector commonly silenced in cancer. Mol Cell Proteomics. 2005;4(6):785-795. doi: 10.1074/mcp.M500021-MCP200

 

  1. Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013;14(10):1014-1022. doi: 10.1038/ni.2703

 

  1. Sayitoglu EC, Luca BA, Boss AP, et al. AML/T cell interactomics uncover correlates of patient outcomes and the key role of ICAM1 in T cell killing of AML. Leukemia. 2024;38(6):1246-1255. doi: 10.1038/s41375-024-02255-1

 

  1. Li Y, Gao Y, Chu W, Lv J, Li Z, Shi T. Differences in and verification of genetic alterations in chemotherapy and immunotherapy for metastatic melanoma. Aging (Albany NY). 2021;13(20):23672-23688. doi: 10.18632/aging.203640

 

  1. Saliba E, Bhawan J. Aberrant expression of immunohistochemical markers in malignant melanoma: A review. Dermatopathology (Basel). 2021;8(3):359-370. doi: 10.3390/dermatopathology8030040

 

  1. Azorin P, Bonin F, Tariq Z, et al. Kindlin-1 modulates the EGFR pathway and predicts sensitivity to EGFR inhibitors across cancer types. Clin Transl Med. 2022;12(4):e813. doi: 10.1002/ctm2.813

 

  1. Sinha N, Shen X, Haag J, et al. Onvansertib exhibits anti-proliferative and anti-invasive effects in endometrial cancer. Front Pharmacol. 2025;16:1545038. doi: 10.3389/fphar.2025.1545038
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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