Single-cell transcriptomic analysis uncovers the origin and intratumoral heterogeneity of parotid pleomorphic adenoma

The cytoplasmic sensor, the AIM2 inflammasome: A precise therapeutic target in vascular and metabolic diseases

Cardio-cerebrovascular diseases encompass pathological changes in the heart, brain and vascular system, which pose a great threat to health and well-being worldwide. Moreover, metabolic diseases contribute to and exacerbate the impact of vascular diseases. Inflammation is a complex process that protects against noxious stimuli but is also dysregulated in numerous so-called inflammatory diseases, one of which is atherosclerosis. Inflammation involves multiple organ systems and a complex cascade of molecular and cellular events. Numerous studies have shown that inflammation plays a vital role in cardio-cerebrovascular diseases and metabolic diseases. The absent in melanoma 2 (AIM2) inflammasome detects and is subsequently activated by double-stranded DNA in damaged cells and pathogens. With the assistance of the mature effector molecule caspase-1, the AIM2 inflammasome performs crucial biological functions that underpin its involvement in cardio-cerebrovascular diseases and related metabolic diseases: The production of interleukin-1 beta (IL-1β), interleukin-18 (IL-18) and N-terminal pore-forming Gasdermin D fragment (GSDMD-N) mediates a series of inflammatory responses and programmed cell death (pyroptosis and PANoptosis). Currently, several agents have been reported to inhibit the activity of the AIM2 inflammasome and have the potential to be evaluated for use in clinical settings. In this review, we systemically elucidate the assembly, biological functions, regulation and mechanisms of the AIM2 inflammasome in cardio-cerebrovascular diseases and related metabolic diseases and outline the inhibitory agents of the AIM2 inflammasome as potential therapeutic drugs.

An ultrasound-based ensemble machine learning model for the preoperative classification of pleomorphic adenoma and Warthin tumor in the parotid gland

OBJECTIVES

The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT.

METHODS

A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong's test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model.

RESULTS

The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774-0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720-0.932) and clinical (0.814; 95% CI, 0.682-0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively.

CONCLUSIONS

The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland.

CLINICAL RELEVANCE STATEMENT

This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application.

KEY POINTS

• Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy. • Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy. • The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.

Single-cell transcriptomics reveals cell atlas and identifies cycling tumor cells responsible for recurrence in ameloblastoma

Ameloblastoma is a benign tumor characterized by locally invasive phenotypes, leading to facial bone destruction and a high recurrence rate. However, the mechanisms governing tumor initiation and recurrence are poorly understood. Here, we uncovered cellular landscapes and mechanisms that underlie tumor recurrence in ameloblastoma at single-cell resolution. Our results revealed that ameloblastoma exhibits five tumor subpopulations varying with respect to immune response (IR), bone remodeling (BR), tooth development (TD), epithelial development (ED), and cell cycle (CC) signatures. Of note, we found that CC ameloblastoma cells were endowed with stemness and contributed to tumor recurrence, which was dominated by the EZH2-mediated program. Targeting EZH2 effectively eliminated CC ameloblastoma cells and inhibited tumor growth in ameloblastoma patient-derived organoids. These data described the tumor subpopulation and clarified the identity, function, and regulatory mechanism of CC ameloblastoma cells, providing a potential therapeutic target for ameloblastoma.

CD36 as a double-edged sword in cancer

The membrane protein CD36 is a lipid transporter, scavenger receptor, and receptor for the antiangiogenic protein thrombospondin 1 (TSP1). CD36 is expressed by cancer cells and by many associated cells including various cancer-infiltrating immune cell types. Thereby, CD36 plays critical roles in cancer, and it has been reported to affect cancer growth, metastasis, angiogenesis, and drug resistance. However, these roles are partly contradictory, as CD36 has been both reported to promote and inhibit cancer progression. Moreover, the mechanisms are also partly contradictory, because CD36 has been shown to exert opposite cellular effects such as cell division, senescence and cell death. This review provides an overview of the diverse effects of CD36 on tumor progression, aiming to shed light on its diverse pro- and anti-cancer roles, and the implications for therapeutic targeting.

Ligand-dependent CD36 functions in cancer progression, metastasis, immune response, and drug resistance

CD36, a multifunctional glycoprotein, has been shown to play critical roles in tumor initiation, progression, metastasis, immune response, and drug resistance. CD36 serves as a receptor for a wide range of ligands, including lipid-related ligands (e.g., long-chain fatty acid (LCFA), oxidized low-density lipoprotein (oxLDL), and oxidized phospholipids), as well as protein-related ligands (e.g., thrombospondins, amyloid proteins, collagens I and IV). CD36 is overexpressed in various cancers and may act as an independent prognostic marker. While it was initially identified as a mediator of anti-angiogenesis through its interaction with thrombospondin-1 (TSP1), recent research has highlighted its role in promoting tumor growth, metastasis, drug resistance, and immune suppression. The varied impact of CD36 on cancer is likely ligand-dependent. Therefore, we focus specifically on the ligand-dependent role of CD36 in cancer to provide a critical review of recent advances, perspectives, and challenges.