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Multi-omics advances in understanding cancer drug resistance

  • Xuechao Liu
  • , Kunpeng Wei
  • , Enyu Lin
  • , Yi Li
  • , Pengzhen Zhuang
  • , Yanbing Zhou*
  • , Guilai Zuo*
  • , Zhaojian Niu*
  • *Tämän työn vastaava kirjoittaja

    Tutkimustuotos: LehtiartikkeliKatsausartikkelivertaisarvioitu

    1 Sitaatiot (Scopus)
    32 Lataukset (Pure)

    Abstrakti

    Cancer drug resistance presents a significant challenge in modern oncology, necessitating detailed exploration of its underlying mechanisms and the development of effective counterstrategies. This review aims to systematically evaluate the most recent applications and advancements of multi-omics technologies in elucidating the mechanisms of cancer drug resistance. Multi-omics includes genomics, transcriptomics, proteomics, microbiomics, metabolomics, and epigenomics. Notably, emerging methodologies such as single-cell and spatial omics have been instrumental in revealing the biological characteristics and resistance mechanisms of tumor cells across various layers and dimensions. The review highlights resistance mechanisms uncovered through the combined application of multi-omics, including gene mutations and epigenetic modifications, reprogramming of signaling pathways, drug efflux and cytoskeletal reorganization, and DNA repair mechanisms. It also explores novel mechanisms in the tumor immune microenvironment (TIME), metabolic reprogramming, and microbiome interactions. The review assesses the benefits of integrating multi-omics data, the application of these technologies in identifying key genes and pathways, and their role in personalized treatment strategies. It provides a comprehensive understanding of the dynamic changes and heterogeneity in cancer drug resistance to aid precision treatment strategies. Additionally, the article offers insights into the future directions of multi-omics technologies in oncology drug resistance research and discusses the primary challenges ahead. We aim to provide novel perspectives and directions for innovation and optimization in cancer treatment, ultimately enhancing patient prognosis and quality of life in oncology.

    AlkuperäiskieliEnglanti
    Artikkeli100119
    JulkaisuBiomedical Technology
    Vuosikerta12
    DOI - pysyväislinkit
    TilaJulkaistu - jouluk. 2025
    OKM-julkaisutyyppiA2 Katsausartikkeli tiedejulkaisuussa (artikkeli)

    Rahoitus

    This work was supported by the National Science Foundation of China (Grant Nos. 82002528),the Shandong Provincial Natural Science Foundation (Grant Nos. ZR2021MH380; ZR2024MH091) and Outstanding Young Talent Training Project of Qingdao Municipal Health Commission.

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