Download PDF
Open Access
CC BY 4.0 · Brazilian Journal of Oncology 2025; 21: s00451810447
DOI: 10.1055/s-0045-1810447
Original Article
Clinical Oncology

MultiClass Machine Learning-based Identification of Anoikis-related Genes Across Three Adult T-cell Leukemia/Lymphoma Subtypes

Authors

  • Mohadeseh Zarei Ghobadi 

    1   Department of Virology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

  • Elaheh Afsaneh 

    2   Department of Physics, University of Isfahan, Hezar Jarib, Isfahan, Iran.

Funding The authors declare that they did not receive funding from agencies in the public, private or non-profit sectors to conduct the present study.
Further Information
  Permissions and Reprints All articles of this category
Preview

Abstract

Introduction

Adult T-cell leukemia/lymphoma (ATLL) is a type of cancer that originates from T-cells infected with the human T-cell lymphotropic virus type 1 (HTLV-1). Anoikis is a programmed cell death process that occurs when the contact between cells or the extracellular matrix is lost. We aim to identify the specific anoikis-related classifier genes for three ATLL subtypes, which could provide valuable insights into the molecular mechanisms underlying the disease's progression and potential targets for intervention.

Materials and Methods

We conducted an analysis of differentially expressed anoikis genes (DEAGs) for identifying those associated with each subtype. Subsequently, we utilized multiclass support vector machine and logistic regression algorithms to recognize specific classifier anoikis-related genes distinguishing each ATLL subtype.

Results

The results revealed the activation of several cancer- and anoikis-related pathways. Moreover, specific potential biomarkers were pointed out for each ATLL subtype: acute, with S100A9 and MAOA; chronic, with IL10, CDH1, and CYP3A4; and smoldering with BCL2L1 and SNAI2. These anoikis-related genes play a role in regulating cell adhesion and survival signaling which are crucial for maintaining normal cellular homeostasis.

Conclusion

The findings not only contribute to our understanding of ATLL progression but also offer potential targets for developing more effective therapeutic strategies and improving treatment outcomes for patients with different subtypes.

Keywords

human T-cell lymphotropic virus type 1 - adult T-cell leukemia/lymphoma - asymptomatic carriers - anoikis - multiclass machine learning - biomarker

Authors' Contributions

MZG: bioinformatics, statistical analysis, data interpretation, writing of the manuscript. EA: investigation, writing of the manuscript. All authors approved the final manuscript.


Availability of Data and Materials

All data generated or analyzed during this study are included in this published article and its [Supplementary information Files].


Supplementary Material



Publication History

Received: 18 January 2025

Accepted: 05 June 2025

Article published online:
27 August 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit (https://creativecommons.org/licenses/by/4.0/)

Thieme Revinter Publicações Ltda.
Rua Rego Freitas, 175, loja 1, República, São Paulo, SP, CEP 01220-010, Brazil

Bibliographical Record
Mohadeseh Zarei Ghobadi, Elaheh Afsaneh. MultiClass Machine Learning-based Identification of Anoikis-related Genes Across Three Adult T-cell Leukemia/Lymphoma Subtypes. Brazilian Journal of Oncology 2025; 21: s00451810447.
DOI: 10.1055/s-0045-1810447
 

  • References

  • 1 Wang J, Luo Z, Lin L, Sui Z, Yu L, Xu C. et al. Anoikis-associated lung cancer metastasis: mechanisms and therapies. Cancers (Basel) 2022; 14 (19) 4791
    Search in Google Scholar
  • 2 Fard FS, Jalilzadeh N, Mehdizadeh A, Sajjadian F, Velaei K. Understanding and targeting anoikis in metastasis for cancer therapies. Cell Biol Int 2023; 47 (04) 683-698
    Search in Google Scholar
  • 3 Kim YN, Koo KH, Sung JY, Yun UJ, Kim H. Anoikis resistance: an essential prerequisite for tumor metastasis. Int J Cell Biol 2012; 2012: 306879
    Search in Google Scholar
  • 4 Ghobadi MZ, Afsaneh E, Emamzadeh R, Soroush M. Potential miRNA-gene interactions determining progression of various ATLL cancer subtypes after infection by HTLV-1 oncovirus. BMC Med Genomics 2023; 16 (01) 62
    Search in Google Scholar
  • 5 Ghobadi MZ, Afsaneh E, Emamzadeh R. Gene biomarkers and classifiers for various subtypes of HTLV-1-caused ATLL cancer identified by a combination of differential gene co–expression and support vector machine algorithms. Med Microbiol Immunol 2023; 212 (04) 263-270
    Search in Google Scholar
  • 6 Ghobadi MZ, Emamzadeh R, Afsaneh E. Exploration of mRNAs and miRNA classifiers for various ATLL cancer subtypes using machine learning. BMC Cancer 2022; 22 (01) 433
    Search in Google Scholar
  • 7 Taylor GP, Matsuoka M. Natural history of adult T-cell leukemia/lymphoma and approaches to therapy. Oncogene 2005; 24 (39) 6047-6057
    Search in Google Scholar
  • 8 Shafiee A, Seighali N, Taherzadeh-Ghahfarokhi N, Mardi S, Shojaeian S, Shadabi S. et al. Zidovudine and Interferon Alfa based regimens for the treatment of adult T-cell leukemia/lymphoma (ATLL): a systematic review and meta-analysis. Virol J 2023; 20 (01) 118
    Search in Google Scholar
  • 9 Fujikawa D, Nakagawa S, Hori M, Kurokawa N, Soejima A, Nakano K. et al. Polycomb-dependent epigenetic landscape in adult T-cell leukemia. Blood 2016; 127 (14) 1790-1802
    Search in Google Scholar
  • 10 Tattermusch S, Skinner JA, Chaussabel D, Banchereau J, Berry MP, McNab FW. et al. Systems biology approaches reveal a specific interferon-inducible signature in HTLV-1 associated myelopathy. PLoS Pathog 2012; 8 (01) e1002480
    Search in Google Scholar
  • 11 Del Moral P, Nowaczyk S, Pashami S. Why is multiclass classification hard?. IEEE Access 2022; 10: 80448-80462
    Search in Google Scholar
  • 12 Ghobadi MZ, Emamzadeh R. Integration of gene co-expression analysis and multi-class SVM specifies the functional players involved in determining the fate of HTLV-1 infection toward the development of cancer (ATLL) or neurological disorder (HAM/TSP). PLoS One 2022; 17 (01) e0262739
    Search in Google Scholar
  • 13 Ghobadi MZ, Emamzadeh R, Teymoori-Rad M, Afsaneh E. Exploration of blood-derived coding and non-coding RNA diagnostic immunological panels for COVID-19 through a co-expressed-based machine learning procedure. Front Immunol 2022; 13: 1001070
    Search in Google Scholar
  • 14 Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi MZ. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetol Metab Syndr 2022; 14 (01) 196
    Search in Google Scholar
  • 15 Matsui H. Variable and boundary selection for functional data via multiclass logistic regression modeling. Comput Stat Data Anal 2014; 78: 176-185
    Search in Google Scholar
  • 16 Foell D, Wittkowski H, Vogl T, Roth J. S100 proteins expressed in phagocytes: a novel group of damage-associated molecular pattern molecules. J Leukoc Biol 2007; 81 (01) 28-37
    Search in Google Scholar
  • 17 Bergenfelz C, Gaber A, Allaoui R, Mehmeti M, Jirström K, Leanderson T, Leandersson K. S100A9 expressed in ER(-)PgR(-) breast cancers induces inflammatory cytokines and is associated with an impaired overall survival. Br J Cancer 2015; 113 (08) 1234-1243
    Search in Google Scholar
  • 18 Shabani F, Farasat A, Mahdavi M, Gheibi N. Calprotectin (S100A8/S100A9): a key protein between inflammation and cancer. Inflamm Res 2018; 67 (10) 801-812
    Search in Google Scholar
  • 19 Li Y, Kong F, Jin C, Hu H, Shao Q, Liu J. et al. The expression of S100A8/S100A9 is inducible and regulated by the Hippo/YAP pathway in squamous cell carcinomas. BMC Cancer 2019; 19 (01) 597
    Search in Google Scholar
  • 20 Kerkhoff C, Voss A, Scholzen TE, Averill MM, Zänker KS, Bornfeldt KE. Novel insights into the role of S100A8/A9 in skin biology. Exp Dermatol 2012; 21 (11) 822-826
    Search in Google Scholar
  • 21 Godar SC, Fite PJ, McFarlin KM, Bortolato M. The role of monoamine oxidase A in aggression: Current translational developments and future challenges. Prog Neuropsychopharmacol Biol Psychiatry 2016; 69: 90-100
    Search in Google Scholar
  • 22 Li J, Yang XM, Wang YH, Feng MX, Liu XJ, Zhang YL. et al. Monoamine oxidase A suppresses hepatocellular carcinoma metastasis by inhibiting the adrenergic system and its transactivation of EGFR signaling. J Hepatol 2014; 60 (06) 1225-1234
    Search in Google Scholar
  • 23 Li J, Pu T, Yin L, Li Q, Liao CP, Wu BJ. MAOA-mediated reprogramming of stromal fibroblasts promotes prostate tumorigenesis and cancer stemness. Oncogene 2020; 39 (16) 3305-3321
    Search in Google Scholar
  • 24 Sawada L, Nagano Y, Hasegawa A, Kanai H, Nogami K, Ito S. et al. IL-10-mediated signals act as a switch for lymphoproliferation in Human T-cell leukemia virus type-1 infection by activating the STAT3 and IRF4 pathways. PLoS Pathog 2017; 13 (09) e1006597
    Search in Google Scholar
  • 25 Mori N, Gill PS, Mougdil T, Murakami S, Eto S, Prager D. Interleukin-10 gene expression in adult T-cell leukemia. Blood 1996; 88 (03) 1035-1045
    Search in Google Scholar
  • 26 Dai Y, Zhang X, Ou Y, Zou L, Zhang D, Yang Q. et al. Anoikis resistance–protagonists of breast cancer cells survive and metastasize after ECM detachment. Cell Commun Signal 2023; 21 (01) 190
    Search in Google Scholar
  • 27 Tian D, Hu Z. CYP3A4-mediated pharmacokinetic interactions in cancer therapy. Curr Drug Metab 2014; 15 (08) 808-817
    Search in Google Scholar
  • 28 Loo LSW, Soetedjo AAP, Lau HH, Ng NHJ, Ghosh S, Nguyen L. et al. BCL-xL/BCL2L1 is a critical anti-apoptotic protein that promotes the survival of differentiating pancreatic cells from human pluripotent stem cells. Cell Death Dis 2020; 11 (05) 378
    Search in Google Scholar
  • 29 Nicot C, Mahieux R, Takemoto S, Franchini G. Bcl-X(L) is up-regulated by HTLV-I and HTLV-II in vitro and in ex vivo ATLL samples. Blood 2000; 96 (01) 275-281
    Search in Google Scholar
  • 30 Wang Y, Shi J, Chai K, Ying X, Zhou BP. The Role of Snail in EMT and Tumorigenesis. Curr Cancer Drug Targets 2013; 13 (09) 963-972
    Search in Google Scholar
  • 31 Esposito S, Russo MV, Airoldi I, Tupone MG, Sorrentino C, Barbarito G. et al. SNAI2/Slug gene is silenced in prostate cancer and regulates neuroendocrine differentiation, metastasis-suppressor and pluripotency gene expression. Oncotarget 2015; 6 (19) 17121-17134
    Search in Google Scholar
  • 32 Sundararajan V, Tan M, Tan TZ, Ye J, Thiery JP, Huang RY. SNAI1 recruits HDAC1 to suppress SNAI2 transcription during epithelial to mesenchymal transition. Sci Rep 2019; 9 (01) 8295
    Search in Google Scholar
  • 33 Ghobadi MZ, Afsaneh E.. Machine learning-driven discovery of anoikis-related biomarkers in Adult T-Cell Leukemia/Lymphoma subtypes. Advances in Biomarker Sciences and Technology 2025; 7: 180-8
    Search in Google Scholar