Analysis of the key ligand receptor CADM1_CADM1 in the regulation of thyroid cancer based on scRNA-seq and bulk RNA-seq data

Targeting SIGLEC15 as an emerging immunotherapy for anaplastic thyroid cancer

Anaplastic thyroid carcinoma (ATC) is the most aggressive subtype of thyroid cancer with few effective therapies. Though immunotherapies such as targeting PD-1/PD-L1 axis have benefited patients with solid tumor, the druggable immune checkpoints are quite limited in ATC. In our study, we focused on the anti-tumor potential of sialic acid-binding Ig-like lectins (Siglecs) in ATC. Through screening by integrating microarray datasets including 216 thyroid-cancer tissues and single-cell RNA-sequencing, SIGLEC family members CD33, SIGLEC1, SIGLEC10 and SIGLEC15 were significantly overexpressed in ATC, among which SIGLEC15 increased highest and mainly expressed on cancer cells. SIGLEC15high ATC cells are characterized by high expression of serine protease PRSS23 and cancer stem cell marker CD44. Compared with SIGLEC15low cancer cells, SIGLEC15high ATC cells exhibited higher interaction frequency with tumor microenvironment cells. Further study showed that SIGLEC15high cancer cells mainly interacted with T cells by immunosuppressive signals such as MIF-TNFRSF14 and CXCL12-CXCR4. Notably, treatment of anti-SIGLEC15 antibody profoundly increased the cytotoxic ability of CD8+ T cells in a co-culture model and zebrafish-derived ATC xenografts. Consistently, administration of anti-SIGLEC15 antibody significantly inhibited tumor growth and prolonged mouse survival in an immunocompetent model of murine ATC, which was associated with increase of M1/M2, natural killer (NK) cells and CD8+ T cells, and decrease of myeloid-derived suppressor cells (MDSCs). SIGLEC15 inhibited T cell activation by reducing NFAT1, NFAT2, and NF-κB signals. Blocking SIGLEC15 increased the secretion of IFN-γ and IL-2 in vitro and in vivo. In conclusion, our finding demonstrates that SIGLEC15 is an emerging and promising target for immunotherapy in ATC.

Integration of scRNA and bulk RNA-sequence to construct the 5-gene molecular prognostic model based on the heterogeneity of thyroid carcinoma endothelial cell

Thyroid cancer (TC) is a kind of cancer with high heterogeneity, which leads to significant difference in prognosis. The prognostic molecular processes are not well understood. Cancer cells and tumor microenvironment (TME) cells jointly determine the heterogeneity. However, quite a little attention was paid to cells in the TME in the past years. In this study, we not only reveal that endothelial cells (ECs) are strongly associated with the progress of papillary thyroid cancer (PTC) using single-cell RNA-seq (scRNA-seq) data downloaded from Gene Expression Omnibus (GEO) and WGCNA, but also screen 5 crucial genes of ECs: CLDN5, ABCG2, NOTCH4, PLAT, and TMEM47. Furthermore, the 5-gene molecular prognostic model is constructed, which can predict how well a patient will do on PD-L1 blockade immunotherapy for TC and evaluate prognosis. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis demonstrates that PLAT is decreased in TC and the increase of PLAT can restrain the migratory capacity of TC cells. Meanwhile, in TC cells, PLAT suppresses VEGFa/VEGFR2-mediated human umbilical vascular endothelial cell (HUVEC) proliferation and tube formation. Totally, we construct the 5-gene molecular prognostic model from the perspective of EC and provide a new idea for immunotherapy of TC.

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.