Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review

Abstract

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 identifcation 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 fndings aid in the construction of predictive models on disease prognosis and therapeutic efcacy. Altogether, scRNA-seq has deepened our understanding of the tumour microenvironment immunologic insights, informing future studies in the development of efective personalised treatment for patients. Challenges and limitations of scRNA-seq, such as technical biases, fnancial barriers, and ethical concerns, are discussed. Advancements in computational methods, the advent of artifcial intelligence (AI), machine learning (ML), and deep learning (DL), and the importance of single-cell data sharing and collaborative eforts 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. Eforts to make this technology more accessible and cost-efective will be crucial to ensuring its widespread utilisation in healthcare.

Tumour heterogeneity · Medical oncology