The role of γδ T cells, innate-like lymphocytes with unrestrained MHC, in various malignancies has recently been extensively studied. However, there is limited research regarding γδ T cells in ovarian cancer (OC) patients. Here, we investigated the distribution patterns of γδ T cells and their main subsets in peripheral blood and tumor tissues among OC patients, benign ovarian tumor (BOT) patients, and age-matched healthy controls (HC) by flow cytometry, as well as the expression levels of IFN-γ and IL-17A secreted from γδ T cells. Immunohistochemical staining was utilized to detect the numbers of γδ T cells and their main subsets in different types of ovarian tumor tissues. Additionally, we also investigated chemotaxis effects on γδ T cells, as well as their cytotoxic activity and proliferation. We found that the percentages of γδ T cells and Vδ1 T cells were significantly higher in OC tissues than BOT tissues and normal (N) ovarian tissues, while there were no obvious differences in peripheral blood. Meanwhile, higher numbers of γδ T cells and Vδ1 T cells were observed in OC tissues, and were positively related to advanced clinicopathological features of OC patients. Further, the levels of IFN-γ secreted by γδ T cells were relatively lower, while IL-17A was expressed at a high level in both the peripheral blood and tissues of OC patients. Chemotaxis assay revealed that supernatants derived from OC tissues possessed a stronger capacity to attract and recruit γδ T cells. However, γδ T cells sorted from OC tissues showed weakened cytotoxic activity against ovarian cancer cells, and γδ T cells cocultured with OC tissue supernatants could effectively inhibit the proliferative activity of naïve CD4+ T cells. These data suggested that γδ T cells might have critical roles in OC progression and potential utilization in treatment approaches or prognosis prediction.

Background and Objectives: Regulatory T cells (Tregs) are usually enriched in ovarian cancer (OC), and their immunosuppressive function plays a key role in tumorigenesis and progression. We mainly explored the phenotypical characterization of Treg-related markers on αβ and γδ T cell subsets in patients with OC. Materials and Methods: Thirty-six untreated patients with OC at the Women’s Hospital of Nanjing Medical University from September 2019 to August 2021 were enrolled. Phenotypical characterization of Tregs-related markers were detected by flow cytometry (FCM). Enzyme-linked immunosorbent assay was used to detect the levels of carbohydrate antigen (CA125) and transforming growth factor β (TGF-β). The level of human epididymis protein 4 (HE4) was detected by electrochemiluminescence immunoassay. Results: Circulating CD4⁺ Tregs, CD8⁺ Tregs, and CD3⁺γδ T cell subpopulations from OC patients have elevated Foxp3, CD25, CD122, Vδ1, and reduced CD28 expression compared to benign ovarian tumor (BOT) patients and healthy controls (HC). The upregulation of Foxp3 and Vδ1 and the downregulation of CD28 were highly specific for maintaining the immunosuppression function of CD4⁺ Tregs, CD3⁺γδ T cells, and CD8⁺ Tregs in OC patients. These Treg subpopulations were able to discriminate OC from BOT and HC. The levels of CA125, HE4, and TGF-β were increased in OC patients. A significant positive correlation between Treg subpopulations and CA125, HE4, and TGF-β was revealed. Conclusions: Proportions of CD4⁺ Tregs, CD8+ Tregs, and CD3⁺γδ T cell subsets were significantly increased in OC patients and were positively correlated with FIGO stage/metastasis status, CA125, HE4, and TGF-β. These indicators have the potential to be used as immunosurveillance biomarkers for OC.

Purpose: To investigate the role of mammalian target of rapamycin (mTOR) signal in Toll‐like receptor (TLR) 8‐mediated regulation of glucose metabolism and its effect on reversing immunosuppression in CD4⁺ regulatory T‐cells (Tregs) in ovarian cancer (OC).Methods: Fluorescence‐activated cell sorting was used to detect the expression levels of mTOR⁺ and 4E‐BP1⁺ cells in CD4⁺ Tregs. The prognosis and immune infiltration analysis of mTOR mRNA in OC were performed using the TIMER and Kaplan–Meier plotter database. Furthermore, real‐time polymerase chain reaction (RT‐PCR) and western blot (WB) were used to detect expression levels of glucose metabolism‐related genes and proteins in CD4⁺ Tregs. Glucose uptake and glycolysis levels were detected by colorimetry, while the effects of CD4⁺ Tregs on the proliferation of CD4⁺ T‐effector cells (Teffs) were evaluated by carboxyfluorescein diacetate succinimidyl ester (CFSE).Results: mTOR expression in CD4⁺ Tregs was significantly higher in patients with OC compared with controls and in CD4⁺ Tregs than in CD4⁺ Teffs in OC. Additionally, the expression level of mTOR mRNA was related to prognosis and immune infiltration levels in patients with OC. Blocking the mTOR signal resulted in downregulation of glucose metabolism in CD4⁺ Tregs. Simultaneous inhibition of the mTOR signal while activation of the TLR8 signal had a coordinated inhibitory effect on glucose metabolism and the immunosuppressive function of CD4⁺ Tregs. Furthermore, the mTOR signal played an essential role in TLR8‐mediated reversal of immunosuppressive function in CD4⁺ Tregs.Conclusion: These findings imply that activation of the TLR8 signal inhibits glucose metabolism in CD4⁺ Tregs by downregulating mTOR signaling, thereby reversing the immunosuppressive function of these cells in an OC cell growth environment.

Kawasaki disease (KD) patients could develop coronary artery lesions (CALs) which threatens children’s life. We aimed to develop and validate an artificial intelligence model that can predict CALs risk in KD patients. A total of 506 KD patients were included at Children’s Hospital of Fudan University. Seven predictive features were identified for model building. Among different machine learning (ML) models tested, Multi-Layer Perceptron Classifier (MLPC), Random Forest (RF) and Extra Tree (ET) demonstrated optimal performance. These were finally chosen for time-across validation. Among three of them, MLPC stands out with its highest accuracy. Besides, Mendelian randomization (MR) analysis also provided genetic evidence. Among seven predictive features, two of them were identified as causal associations with CALs. They are activated partial thromboplastin time (APTT) and red cell distribution width (RDW). The causal mechanism reinforced the biological plausibility of the model. ML-based prediction models, combined with genetic validation through MR, offer a reliable approach for early CALs risk stratification in KD patients. This strategy may facilitate timely clinical interventions.

Background and Objectives: This study aimed to investigate the diagnostic value of immunological biomarkers in children with asthmatic bronchitis and asthma and to develop a machine learning (ML) model for rapid differential diagnosis of these two diseases. Materials and Methods: Immunological biomarkers in peripheral blood were detected using flow cytometry and immunoturbidimetry. The importance of characteristic variables was ranked and screened using random forest and extra trees algorithms. Models were constructed and tested using the Scikit-learn ML library. K-fold cross-validation and Brier scores were used to evaluate and screen models. Results: Children with asthmatic bronchitis and asthma exhibit distinct degrees of immune dysregulation characterized by divergent patterns of humoral and cellular immune responses. CD8⁺ T cells and B cells were more dominant in differentiating the two diseases among many immunological biomarkers. Random forest showed a comprehensive high performance compared with other models in learning and training the dataset of immunological biomarkers. Conclusions: This study developed a prediction model for early differential diagnosis of asthmatic bronchitis and asthma using immunological biomarkers. Evaluation of the immune status of patients may provide additional clinical information for those children transforming from asthmatic bronchitis to asthma under recurrent attacks.

[This corrects the article DOI: 10.3389/fgene.2025.1646032.].