Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes.

BACKGROUND: Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction. METHODS: The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes. RESULTS: Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer. CONCLUSION: The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

Recent advances in the development of DprE1 inhibitors using AI/CADD approaches.

Tuberculosis (TB) is a global lethal disease caused by Mycobacterium tuberculosis (Mtb). The flavoenzyme decaprenylphosphoryl-ß-d-ribose 2'-oxidase (DprE1) plays a crucial part in the biosynthesis of lipoarabinomannan and arabinogalactan for the cell wall of Mtb and represents a promising target for anti-TB drug development. Therefore, there is an urgent need to discover DprE1 inhibitors with novel scaffolds, improved bioactivity and high drug-likeness. Recent studies have shown that artificial intelligence/computer-aided drug design (AI/CADD) techniques are powerful tools in the discovery of novel DprE1 inhibitors. This review provides an overview of the discovery of DprE1 inhibitors and their underlying mechanism of action and highlights recent advances in the discovery and optimization of DprE1 inhibitors using AI/CADD approaches.

Discovery of N-(1-(6-Oxo-1,6-dihydropyrimidine)-pyrazole) Acetamide Derivatives as Novel Noncovalent DprE1 Inhibitors against Mycobacterium tuberculosis.

Decaprenylphosphoryl-ß-d-ribose oxidase (DprE1) is a promising target for treating tuberculosis (TB). Currently, most novel DprE1 inhibitors are discovered through high-throughput screening, while computer-aided drug design (CADD) strategies are expected to promote the discovery process. In this study, with the aid of structure-based virtual screening and computationally guided design, a series of novel scaffold N-(1-(6-oxo-1,6-dihydropyrimidine)-pyrazole) acetamide derivatives with significant antimycobacterial activities were identified. Among them, compounds LK-60 and LK-75 are capable of effectively suppressing the proliferation of Mtb with MICMtb values of 0.78-1.56 µM, comparable with isoniazid and much superior to the phase II candidate TBA-7371 (MICMtb = 12.5 µM). LK-60 is also the most active DprE1 inhibitor derived from CADD so far. Further studies confirmed their high affinity to DprE1, good safety profiles to gut microbiota and human cells, and synergy effects with either rifampicin or ethambutol, indicating their broad potential for clinical applications.