{
  "abstract": "Cancer stem cells (CSCs) are a small population of cancer cells that endow unique traits of self-renewal, differentiation, tumorigenicity, quiescence and therapeutic resistance, ultimately leading to cancer relapse and poor prognosis. Conventional chemo- and radiotherapies effectively destroy rapidly dividing cancer cells; however, CSCs are largely refractory to these treatments due to special characteristics that include metabolic plasticity, low reactive oxygen species (ROS) levels, apoptosis inhibition and modified DNA repair systems, effectively hijacking the tumour microenvironment and evading host immune surveillance. This review summarises the potential CSC diagnostic methods that can be used in routine clinical investigation for tailoring personalised treatment plans both pre- and post-surgery. In this way, a CSC gene signature, reactivity and load can be estimated and, by using artificial intelligence and machine learning algorithms in a personalised therapeutic regimen, the most effective treatment response can be predicted. Furthermore, we propose incorporation of CSC-targeted therapies in conjugation with conventional chemo- and radiotherapies to eradicate CSCs, thereby eliminating the risk of relapse and therapy failure in cancer patients. This review also describes multiple potential molecular targets that may be used to target CSCs and emphasises the need for further evaluation, in order that they may be applied clinically.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Oral Pathology, National University of Medical Sciences NUMS, Rawalpindi 46000, Pakistan",
    "Department of Oral Biology, The Dental College of Georgia, Augusta University, Augusta, GA 30912, USA",
    "Georgia Cancer Center, Augusta University, Augusta, GA 30912, USA",
    "Clinical Biochemistry Department, Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia",
    "Centre for Oral Immunobiology and Regenerative Medicine, Institute of Dentistry, Faculty of Medicine and Dentistry, Queen Mary University of London, Turner Street, London E1 2AD, UK"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Saima Usman",
      "orcid": "0000-0002-2679-5628"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "William Andrew Yeudall",
      "orcid": "0000-0002-5279-6333"
    },
    {
      "affs": [
        3
      ],
      "name": "Ahmad Jamal"
    },
    {
      "affs": [
        4
      ],
      "name": "Muy-Teck Teh",
      "orcid": "0000-0002-7725-8355"
    },
    {
      "affs": [
        4
      ],
      "name": "Ahmad Waseem",
      "orcid": "0000-0002-7941-266X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6q86468r3x62p.bib",
  "cited_by": 0,
  "doi": "10.3390/cells15211978",
  "fulltext_url": null,
  "id": "6q86468r3x62p",
  "issns": [
    "2073-4409"
  ],
  "landing_url": "https://www.mdpi.com/2073-4409/15/21/1978",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/6q86468r3x62p.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:55Z",
      "native_id": "10.3390/cells15211978",
      "source": "crossref",
      "url": "https://www.mdpi.com/2073-4409/15/21/1978"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "issn:2073-4409",
      "subfield": "cell-biology"
    }
  ],
  "title": "Cancer Stem Cells as Central Nodes in Therapeutic Strategies",
  "title_text": "Cancer Stem Cells as Central Nodes in Therapeutic Strategies",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:20:53Z",
  "url": "https://research.draconitic.com/paper/6q86468r3x62p",
  "venue": "Cells",
  "versions": []
}