Faculty of Medicine, University of St Andrews, St Andrews, Scotland, UK.
Faculty of Medicine, Sumy State University, Sumy, Ukraine.
Med Oncol. 2023 Dec 21;41(1):27. doi: 10.1007/s12032-023-02260-x.
Thyroid cancer, a prevalent form of endocrine malignancy, has witnessed a substantial increase in occurrence in recent decades. To gain a comprehensive understanding of thyroid cancer at the single-cell level, this narrative review evaluates the applications of single-cell RNA sequencing (scRNA-seq) in thyroid cancer research. ScRNA-seq has revolutionised the identification and characterisation of distinct cell subpopulations, cell-to-cell communications, and receptor interactions, revealing unprecedented heterogeneity and shedding light on novel biomarkers for therapeutic discovery. These findings aid in the construction of predictive models on disease prognosis and therapeutic efficacy. Altogether, scRNA-seq has deepened our understanding of the tumour microenvironment immunologic insights, informing future studies in the development of effective personalised treatment for patients. Challenges and limitations of scRNA-seq, such as technical biases, financial barriers, and ethical concerns, are discussed. Advancements in computational methods, the advent of artificial intelligence (AI), machine learning (ML), and deep learning (DL), and the importance of single-cell data sharing and collaborative efforts are highlighted. Future directions of scRNA-seq in thyroid cancer research include investigating intra-tumoral heterogeneity, integrating with other omics technologies, exploring the non-coding RNA landscape, and studying rare subtypes. Overall, scRNA-seq has transformed thyroid cancer research and holds immense potential for advancing personalised therapies and improving patient outcomes. Efforts to make this technology more accessible and cost-effective will be crucial to ensuring its widespread utilisation in healthcare.
甲状腺癌是一种常见的内分泌恶性肿瘤，近年来其发病率显著增加。为了在单细胞水平全面了解甲状腺癌，本综述评估了单细胞 RNA 测序（scRNA-seq）在甲状腺癌研究中的应用。scRNA-seq 彻底改变了对不同细胞亚群、细胞间通讯和受体相互作用的识别和描述，揭示了前所未有的异质性，并为治疗发现提供了新的生物标志物。这些发现有助于构建疾病预后和治疗效果的预测模型。总之，scRNA-seq 加深了我们对肿瘤微环境免疫的理解，为未来为患者开发有效个性化治疗的研究提供了信息。讨论了 scRNA-seq 的挑战和局限性，如技术偏差、经济障碍和伦理问题。强调了计算方法的进步、人工智能（AI）、机器学习（ML）和深度学习（DL）的出现、单细胞数据共享和协作努力的重要性。scRNA-seq 在甲状腺癌研究中的未来方向包括研究肿瘤内异质性、与其他组学技术整合、探索非编码 RNA 景观以及研究罕见亚型。总的来说，scRNA-seq 改变了甲状腺癌的研究，为推进个性化治疗和改善患者预后提供了巨大潜力。努力使这项技术更加普及和经济实惠对于确保其在医疗保健中的广泛应用至关重要。
