GILT in Thymic Epithelial Cells Facilitates Central CD4 T Cell Tolerance to a Tissue-Restricted, Melanoma-Associated Self-Antigen

Interferon Gamma Inducible Protein 30: from biological functions to potential therapeutic target in cancers

Interferon Gamma Inducible Protein 30 (IFI30), also known as Gamma-Interferon-Inducible Lysosomal Thiol Reductase (GILT), is predominantly found in lysosomes and the cytoplasm. As the sole enzyme identified to catalyze disulfide bond reduction in the endocytic pathway, IFI30 contributes to both major histocompatibility complex (MHC) class I-restricted antigen cross-presentation and MHC class II-restricted antigen processing by decreasing the disulfide bonds of endocytosed proteins. Remarkably, emerging research has revealed that IFI30 is involved in tumorigenesis, tumor development, and the tumor immune response. Targeting IFI30 may provide new strategies for cancer therapy and improve the prognosis of patients. This review provided a comprehensive overview of the research progress on IFI30 in tumor progression, cellular redox status, autophagy, tumor immune response, and drug sensitivity, with a view to providing the theoretical basis for pharmacological intervention of IFI30 in tumor therapy, particularly in immunotherapy.

Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation

Background

Gastric cancer (GC) represents a malignancy with a multi-factorial combination of genetic, environmental, and microbial factors. Targeting lysosomes presents significant potential in the treatment of numerous diseases, while lysosome-related genetic markers for early GC detection have not yet been established, despite implementing this process by assembling artificial intelligence algorithms would greatly break through its value in translational medicine, particularly for immunotherapy.

Methods

To this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. We optimized an AI-based predictor for GC diagnosis. Then, the reliability of the model was initially confirmed by the results of enrichment analyses currently in use. And the immunological implications of the genes comprising the predictor was explored and response of GC patients were evaluated to immunotherapy and chemotherapy. Further, we performed systematic laboratory work to evaluate the build-up of the central genes, both at the expression stage and at the functional aspect, by which we could also demonstrate the reliability of the model to guide cancer immunotherapy.

Results

Eight lysosomal-related genes were selected for predictive model construction based on the inclusion of RMSE as a reference standard and RF algorithm for ranking, namely ADRB2, KCNE2, MYO7A, IFI30, LAMP3, TPP1, HPS4, and NEU4. Taking into account accuracy, precision, recall, and F1 measurements, a preliminary determination of our study was carried out by means of applying the extra tree and random forest algorithms, incorporating the ROC-AUC value as a consideration, the Extra Tree model seems to be the optimal option with the AUC value of 0.92. The superiority of diagnostic signature is also reflected in the analysis of immune features.

Conclusion

In summary, this study is the first to integrate around 20 mainstream ML algorithms to construct an AI-based diagnostic predictor for gastric cancer based on lysosomal-related genes. This model will facilitate the accurate prediction of early gastric cancer incidence and the subsequent risk assessment or precise individualized immunotherapy, thus improving the survival prognosis of GC patients.

Identification of an immune subtype-related prognostic signature of clear cell renal cell carcinoma based on single-cell sequencing analysis

Background

There is growing evidence that immune cells are strongly associated with the prognosis and treatment of clear cell renal cell carcinoma (ccRCC). Our aim is to construct an immune subtype-related model to predict the prognosis of ccRCC patients and to provide guidance for finding appropriate treatment strategies.

Methods

Based on single-cell analysis of the GSE152938 dataset from the GEO database, we defined the immune subtype-related genes in ccRCC. Immediately afterwards, we used Cox regression and Lasso regression to build a prognostic model based on TCGA database. Then, we carried out a series of evaluation analyses around the model. Finally, we proved the role of VMP1 in ccRCC by cellular assays.

Result

Initially, based on TCGA ccRCC patient data and GEO ccRCC single-cell data, we successfully constructed a prognostic model consisting of five genes. Survival analysis showed that the higher the risk score, the worse the prognosis. We also found that the model had high predictive accuracy for patient prognosis through ROC analysis. In addition, we found that patients in the high-risk group had stronger immune cell infiltration and higher levels of immune checkpoint gene expression. Finally, cellular experiments demonstrated that when the VMP1 gene was knocked down, 786-O cells showed reduced proliferation, migration, and invasion ability and increased levels of apoptosis.

Conclusion

Our study can provide a reference for the diagnosis and treatment of patients with ccRCC.

Thymosin Beta 15 Alters the Spatial Development of Thymic Epithelial Cells

The thymus is the most sensitive organ under various pathophysiological conditions, such as aging, starvation, and infection. As a key stromal cell for T cell development, it is well-known that thymic epithelial cells (TECs) play an important role in the thymus response to the external environment. Thymosin beta 15 (Tβ15) is a G-actin binding protein secreted by TECs, it plays an important role in maintaining the dynamic balance of actin, angiogenesis, axonal formation, and wound healing, but the relationship between Tβ15 and TECs is not clear yet. Here, we show the impact of Tβ15 on the TEC's spatial development, as well as the T-cell differentiation and thymic output. As a result, TEC is the main effector cell of Tβ15 in the thymus. Tβ15 OX inhibits the chemotaxis of TECs to the medulla and subsequently blocks the positive selection of thymocytes from CD3⁺TCRβ⁺CD4⁺CD8⁺ double positive cells to CD3⁺TCRβ⁺CD4⁺CD8^- single-positive (CD4SP) cells. Tβ15-knockdown accelerates the reticular differentiation of astral TECs and medullary TECs. Importantly, mice implanted with Tβ15-knockdown iTECs show high thymic output but low peripheral T cell maturity and activity. In a word, our results explain the role of Tβ15 on the differentiation and function of TECs and provide a new perspective for understanding the process of thymus development and degeneration.

GILT Expression in Human Melanoma Cells Enhances Generation of Antigenic Peptides for HLA Class II-Mediated Immune Recognition

Melanoma is an aggressive skin cancer that has become increasingly prevalent in western populations. Current treatments such as surgery, chemotherapy, and high-dose radiation have had limited success, often failing to treat late stage, metastatic melanoma. Alternative strategies such as immunotherapies have been successful in treating a small percentage of patients with metastatic disease, although these treatments to date have not been proven to enhance overall survival. Several melanoma antigens (Ags) proposed as targets for immunotherapeutics include tyrosinase, NY-ESO-1, gp-100, and Mart-1, all of which contain both human leukocyte antigen (HLA) class I and class II-restricted epitopes necessary for immune recognition. We have previously shown that an enzyme, gamma-IFN-inducible lysosomal thiol-reductase (GILT), is abundantly expressed in professional Ag presenting cells (APCs), but absent or expressed at greatly reduced levels in many human melanomas. In the current study, we report that increased GILT expression generates a greater pool of antigenic peptides in melanoma cells for enhanced CD4+ T cell recognition. Our results suggest that the induction of GILT in human melanoma cells could aid in the development of a novel whole-cell vaccine for the enhancement of immune recognition of metastatic melanoma.

Prognostic value of gamma-interferon-inducible lysosomal thiol reductase expression in female patients diagnosed with breast cancer

Because of the high heterogeneity of breast cancer outcome, identification of novel prognostic biomarkers is critical to improve patient stratification and guide precise treatment. We examined the prognostic value of gamma-interferon-inducible lysosomal thiol reductase (GILT) expression in a training set of 416 breast cancer patients and a validation set of 210 patients, and performed functional studies to investigate the functions and underlying mechanisms of GILT on breast cancer prognosis. Our results indicated that high GILT expression in breast cancer cells was associated with improved disease-free survival (DFS; hazard ratio [HR] = 0.189, 95% confidence interval [CI]: 0.099-0.361) and breast cancer-specific survival (BCSS; HR = 0.187, 95% CI: 0.080-0.437) of breast cancer patients both in the training set and the external validation set (HR = 0.453, 95% CI: 0.235-0.873 for DFS, HR = 0.488, 95% CI: 0.245-0.970 for BCSS). In vitro and in vivo studies showed that GILT overexpression inhibited breast cancer cells proliferation, invasion, migration and tumor formation in nude mice and increased sensitivity of breast cancer cells to standard treatment. Proteomics analysis indicated that GILT inhibited reactive oxygen species (ROS) and autophagy activation in breast cancer cells, and GILT overexpression-mediated tumor growth was further enhanced in the presence of autophagy or ROS inhibitors. Our results demonstrate that GILT expression can be effectively used to predict the prognosis and guide treatment strategies of breast cancer patients.