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

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.

Impact of skip mediastinal lymph node metastasis on outcomes after resection for primary lung cancer.

OBJECTIVES: Patients with non-small cell lung cancer and nodal disease are a heterogeneous group with varied patterns of disease. The aim of this study was to assess long-term outcomes of patients with skip N2 disease in comparison to those with N1 or non-skip N2 disease. MATERIALS AND METHODS: A retrospective review of 445 patients undergoing anatomical lung resection for primary lung cancer between 2012 and 2019 with post-operative histological confirmation of nodal disease was undertaken. Log rank analysis was used to assess differences in estimated median overall survival according to nodal status. Multivariable Cox regression analysis was performed to determine whether skip N2 disease was independently associated with overall survival. RESULTS: Mean patient age was 67.0 years (standard deviation ± 9.2 years) and 48.1% (n = 214) were male. In total, 20.7% (n = 92) of patients had N1 disease, 32.1% (n = 143) had skip N2 disease and 47.2% (n = 210) had non-skip N2 disease. Post-operative upstaging took place in 33.0% (n = 147) of patients. Median follow-up time was 35 months (interquartile range 14-68 months). Skip N2 patients had significantly longer estimated median overall survival in comparison to their non-skip N2 counterparts (47 months vs 28 months, log rank analysis p = 0.029) and non-skip N2 disease remained independently associated with reduced overall survival after multivariable analysis (hazard ratio 1.421, 95% confidence interval 1.060-1.907, p = 0.019). CONCLUSION: Skip N2 disease is a positive prognostic factor for patients with N2 lung cancer, suggesting that lung cancer staging guidelines should consider separating N2 disease into additional subgroups in order to improve prognostic accuracy.