Identification of biomarker associated with Trop2 in breast cancer: implication for targeted therapy.

PURPOSE: Trop2, a cell membrane glycoprotein, is overexpressed in almost all epithelial cancers. This study aimed to explore the mutational characteristics and significance of Trop2 in breast cancer (BC). METHODS: Patients diagnosed with BC (n = 77) were enrolled to investigate expression level and clinical characteristics of Trop2. Database of cBioPortal and Kaplan-Meier Plotter were used to evaluate the effects of Trop2 (TACSTD2) genomic ateration and mRNA expression levels on disease-free survival (DFS) and relapse-free survival (RFS), respectively. Based on next generation sequencing analysis, the Trop2 mutation characteristics of BC patients were deeply depicted. In addition, Trop2 expression, mutation and methylation signature associated with Trop2 mutations were analyzed. RESULTS: Trop2 mutation and high expression of Trop2 were predictive biomarker for shorter DFS and RFS in BC. The positive rate of Trop2 expression in these 77 BC patients was 96.1% (74/77). Based on the Trop2 expression level, the patients were classified into Trop2 negative group, medium expression group and high expression group. The mutation frequencies of MAP3K1, NOTCH2, PTEN and MAGI2 were significantly higher in Trop2 medium expression group than high expression group. Moreover, we investigated the effect of the Trop2 mutations on other genes, including co-expressed genes, differentially mutated genes, differentially expressed genes, gene methylation and phosphorylation. We found that MED8, DPH2, KDM4A, EBNA1BP2, USP1, IPO13, CGAS, PRKAA2, NCOA7, ASCC3 and ABRACL were differentially expressed, mutated and methylated between Trop2 mutation group and wild group. CONCLUSION: MAP3K1, NOTCH2, PTEN and MAGI2 mutations were significantly different between Trop2 medium expression and Trop2 high expression BC patients. The effects of Trop2 mutation on the expression, variation, methylation, and phosphorylation of other genes were comprehensively revealed. High expression level of Trop2 and Trop2 mutation were predictive biomarker for poor prognosis and targeted therapy in BC.

Proangiogenic potential of plasma exosomes from prostate cancer patients.

Angiogenesis plays a pivotal role in the progression and metastasis of solid cancers, including prostate cancer (PCa). While small extracellular vesicles derived from PCa cell lines induce a proangiogenic phenotype in vascular endothelial cells, the contribution of plasma exosomes from patients with PCa to this process remains unclear. Here, we successfully extracted and characterized plasma exosomes. Notably, a ring of PKH67-labeled exosomes was observed around the HUVEC nucleus using fluorescence microscopy, indicating the uptake of exosomes by HUVEC. At the cellular level, PCa plasma exosomes enhanced angiogenesis, proliferation, invasion, and migration of HUVEC cells. Moreover, PCa plasma exosomes promoted angiogenesis and aortic sprouting. MicroRNAs are the most common genetic material in exosomes, and to identify miRNAs associated with the angiogenic response, we performed small RNA sequencing followed by RT-qPCR and bioinformatics analysis. These analyses revealed distinct miRNA profiles in plasma exosomes from patients with PCa compared to healthy individuals. Notably, hsa-miR-184 emerged as a potential regulator implicated in the proangiogenic effects of PCa plasma exosomes.

Integrating plasma exosomal miRNAs, ultrasound radiomics and tPSA for the diagnosis and prediction of early prostate cancer: a multi-center study.

INTRODUCTION: This multi-center study aims to explore the roles of plasma exosomal microRNAs (miRNAs), ultrasound (US) radiomics, and total prostate-specific antigen (tPSA) levels in early prostate cancer detection. METHODS: We analyzed the publicly available dataset GSE112264 to identify the differentially expressed miRNAs associated with prostate cancer. Then, PyRadiomics was used to extract image features, and least absolute shrinkage and selection operator (LASSO) was used to screen the data. Subsequently, according to strict inclusion and exclusion criteria, the internal dataset (n = 199) was used to construct a diagnostic model, and the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and DeLong test were used to evaluate its diagnostic performance. Finally, we used an external dataset (n = 158) for further validation. RESULTS: The number of features extracted by PyRadiomics was 851, and the number of features screened by LASSO was 23. We combined the hsa-miR-320c, hsa-miR-944, radiomics, and tPSA features to construct a joint model. The area under the ROC curve of the combined model was 0.935. In the internal validation, the area under the curve (AUC) of the training set was 0.943, and the AUC of the test set was 0.946. The AUC of the external data set was 0.910. The calibration curve and decision curve were consistent with the performance of the combined model. There was a significant difference in the prediction ability between the combined prediction model and the single index prediction model, indicating the high credibility and accuracy of the combined model in predicting PCa. CONCLUSIONS: The combined prediction model, consisting of plasma exosomal miRNAs (hsa-miR-320c and hsa-miR-944), US radiomics, and clinical tPSA, can be utilized for the early diagnosis of prostate cancer.

DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation.

Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs. To address this limitation, we propose a novel model named DockingGA that combines Transformer neural networks and genetic algorithms to generate molecules with better binding affinity for specific targets. In order to generate high quality molecules, we chose the Self-referencing Chemical Structure Strings to represent the molecule and optimize the binding affinity of the molecules to different targets. Compared to other baseline models, DockingGA proves to be the optimal model in all docking results for the top 1, 10 and 100 molecules, while maintaining 100% novelty. Furthermore, the distribution of physicochemical properties demonstrates the ability of DockingGA to generate molecules with favorable and appropriate properties. This innovation creates new opportunities for the application of generative models in practical drug discovery.

MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data.

Advancements in single-cell sequencing research have revolutionized our understanding of cellular heterogeneity and functional diversity through the analysis of single-cell transcriptomes and genomes. A crucial step in single-cell RNA sequencing (scRNA-seq) analysis is identifying cell types. However, scRNA-seq data are often high dimensional and sparse, and manual cell type identification can be time-consuming, subjective, and lack reproducibility. Consequently, analyzing scRNA-seq data remains a computational challenge. With the increasing availability of well-annotated scRNA-seq datasets, advanced methods are emerging to aid in cell type identification by leveraging this information. Deep learning neural networks have great potential for analyzing single-cell data. This paper proposes MulCNN, a multi-level convolutional neural network that uses a unique cell type-specific gene expression feature extraction method. This method extracts critical features through multi-scale convolution while filtering noise. Extensive testing using datasets from various species and comparisons with popular classification methods show that MulCNN has outstanding performance and offers a new and scalable direction for scRNA-seq analysis.

QuantumTox: Utilizing quantum chemistry with ensemble learning for molecular toxicity prediction.

Molecular toxicity prediction plays an important role in drug discovery, which is directly related to human health and drug fate. Accurately determining the toxicity of molecules can help weed out low-quality molecules in the early stage of drug discovery process and avoid depletion later in the drug development process. Nowadays, more and more researchers are starting to use machine learning methods to predict the toxicity of molecules, but these models do not fully exploit the 3D information of molecules. Quantum chemical information, which provides stereo structural information of molecules, can influence their toxicity. To this end, we propose QuantumTox, the first application of quantum chemistry in the field of drug molecule toxicity prediction compared to existing work. We extract the quantum chemical information of molecules as their 3D features. In the downstream prediction phase, we use Gradient Boosting Decision Tree and Bagging ensemble learning methods together to improve the accuracy and generalization of the model. A series of experiments on various tasks show that our model consistently outperforms the baseline model and that the model still performs well on small datasets of less than 300.

DNMG: Deep molecular generative model by fusion of 3D information for de novo drug design.

Deep learning is improving and changing the process of de novo molecular design at a rapid pace. In recent years, great progress has been made in drug discovery and development by using deep generative models for de novo molecular design. However, most of the existing methods are string-based or graph-based and are limited by the lack of some very important properties, such as the three-dimensional information of molecules. We propose DNMG, a deep generative adversarial network (GAN) combined with transfer learning. Specifically, we use a Wasserstein-variant GAN based network architecture that considers the 3D grid spatial information of the ligand with atomic physicochemical properties to generate a representation of the molecule, which is then parsed into SMILES strings using an improved captioning network. Comprehensive in experiments demonstrate the ability of DNMG to generate valid and novel drug-like ligands. The DNMG model is used to design inhibitors for three targets, MK14, FNTA, and CDK2. The computational results show that the molecules generated by DNMG have better binding ability to the target proteins and better physicochemical properties. Overall, our deep generative model has excellent potential to generate molecules with high binding affinity for targets and explore the space of drug-like chemistry.