Construction of an ER stress-related prognostic signature for predicting prognosis and screening the effective anti-tumor drug in osteosarcoma.

BACKGROUND: Osteosarcoma is the most common malignant primary bone tumor in infants and adolescents. The lack of understanding of the molecular mechanisms underlying osteosarcoma progression and metastasis has contributed to a plateau in the development of current therapies. Endoplasmic reticulum (ER) stress has emerged as a significant contributor to the malignant progression of tumors, but its potential regulatory mechanisms in osteosarcoma progression remain unknown. METHODS: In this study, we collected RNA sequencing and clinical data of osteosarcoma from The TCGA, GSE21257, and GSE33382 cohorts. Differentially expressed analysis and the least absolute shrinkage and selection operator regression analysis were conducted to identify prognostic genes and construct an ER stress-related prognostic signature (ERSRPS). Survival analysis and time dependent ROC analysis were performed to evaluate the predictive performance of the constructed prognostic signature. The "ESTIMATE" package and ssGSEA algorithm were utilized to evaluate the differences in immune cells infiltration between the groups. Cell-based assays, including CCK-8, colony formation, and transwell assays and co-culture system were performed to assess the effects of the target gene and small molecular drug in osteosarcoma. Animal models were employed to assess the anti-osteosarcoma effects of small molecular drug. RESULTS: Five genes (BLC2, MAGEA3, MAP3K5, STC2, TXNDC12) were identified to construct an ERSRPS. The ER stress-related gene Stanniocalcin 2 (STC2) was identified as a risk gene in this signature. Additionally, STC2 knockdown significantly inhibited osteosarcoma cell proliferation, migration, and invasion. Furthermore, the ER stress-related gene STC2 was found to downregulate the expression of MHC-I molecules in osteosarcoma cells, and mediate immune responses through influencing the infiltration and modulating the function of CD8+ T cells. Patients categorized by risk scores showed distinct immune status, and immunotherapy response. ISOX was subsequently identified and validated as an effective anti-osteosarcoma drug through a combination of CMap database screening and in vitro and in vivo experiments. CONCLUSION: The ERSRPS may guide personalized treatment decisions for osteosarcoma, and ISOX holds promise for repurposing in osteosarcoma treatment.

Unveiling the multi-target compounds of Rhazya stricta: Discovery and inhibition of novel target genes for the treatment of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a prevalent kidney malignancy with a pressing need for innovative therapeutic strategies. In this context, emerging research has focused on exploring the medicinal potential of plants such as Rhazya stricta. Nevertheless, the complex molecular mechanisms underlying its potential therapeutic efficacy remain largely elusive. Our study employed an integrative approach comprising data mining,network pharmacology,tissue cell type analysis, and molecular modelling approaches to identify potent phytochemicals from R. stricta, with potential relevance for ccRCC treatments. Initially, we collected data on R. stricta's phytochemical from public databases. Subsequently, we integrated this information with differentially expressed genes (DEGs) in ccRCC, which were derived from microarray datasets(GSE16441,GSE66270, and GSE76351). We identified potential intersections between R. stricta and ccRCC targets, which enabled us to construct a compound-genes-pathway network using Cytoscape software. This helped illuminate R. stricta's multi-target pharmacological effects on ccRCC. Moreover, tissue cell type analysis added another layer of insight into the cellular specificity of potential therapeutic targets in the kidney. Through further Kaplan-Meier survival analysis, we pinpointed MMP9,ACE,ERBB2, and HSP90AA1 as prospective diagnostic and prognostic biomarkers for ccRCC. Notably, our study underscores the potential of R. stricta derived compounds-namely quebrachamine,corynan-17-ol, stemmadenine,strictanol,rhazinilam, and rhazimolare-to impede ccRCC progression by modulating the activity of MMP9,ACE,ERBB2, and HSP90AA1 genes. Further, molecular docking and dynamic simulations confirmed the plausible binding affinities of these compounds. Despite these promising findings, we recognize the need for comprehensive in vivo and in vitro studies to further investigate the pharmacokinetics and biosafety profiles of these compounds.

MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model.

BACKGROUND: Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex diseases. This can be attributed to the highly correlated nature of the data with various diseases, as well as the comprehensive and complementary information it provides. However, integrating multi-omics data for complex diseases is challenged by data characteristics such as high imbalance, scale variation, heterogeneity, and noise interference. These challenges further emphasize the importance of developing effective methods for multi-omics data integration. RESULTS: We proposed a novel multi-omics data learning model called MODILM, which integrates multiple omics data to improve the classification accuracy of complex diseases by obtaining more significant and complementary information from different single-omics data. Our approach includes four key steps: 1) constructing a similarity network for each omics data using the cosine similarity measure, 2) leveraging Graph Attention Networks to learn sample-specific and intra-association features from similarity networks for single-omics data, 3) using Multilayer Perceptron networks to map learned features to a new feature space, thereby strengthening and extracting high-level omics-specific features, and 4) fusing these high-level features using a View Correlation Discovery Network to learn cross-omics features in the label space, which results in unique class-level distinctiveness for complex diseases. To demonstrate the effectiveness of MODILM, we conducted experiments on six benchmark datasets consisting of miRNA expression, mRNA, and DNA methylation data. Our results show that MODILM outperforms state-of-the-art methods, effectively improving the accuracy of complex disease classification. CONCLUSIONS: Our MODILM provides a more competitive way to extract and integrate important and complementary information from multiple omics data, providing a very promising tool for supporting decision-making for clinical diagnosis.

TOP: A deep mixture representation learning method for boosting molecular toxicity prediction.

At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learning-based framework called TOP (the abbreviation of TOxicity Prediction). TOP integrates specifically designed data preprocessing methods, an RNN based on bidirectional gated recurrent unit (BiGRU), and fully connected neural networks for end-to-end molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction accuracy. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five state-of-the-art methods.

Design, Synthesis, Structure-Activity Relationship and Docking Studies of Novel Functionalized Arylvinyl-1,2,4-Trioxanes as Potent Antiplasmodial as well as Anticancer Agents.

A novel series of synthetic functionalized arylvinyl-1,2,4-trioxanes (8 a-p) has been prepared and assessed for their in vitro antiplasmodial activity against the chloroquine-resistant Pf INDO strain of Plasmodium falciparum by using a SYBR green-I fluorescence assay. Compounds 8 g (IC50 =0.051 µM; SI=589.41) and 8 m (IC50 =0.059 µM; SI=55.93) showed 11-fold and >9-fold more potent antiplasmodial activity, respectively, as compared to chloroquine (IC50 =0.546 µM; SI=36.63). Different in silico docking studies performed on many target proteins revealed that the most active arylvinyl-1,2,4-trioxanes (8 g and 8 m) showed dihydrofolate reductase (DHFR) binding affinities on a par with those of chloroquine and artesunate. The in vitro cytotoxic potentials of 8 a-p were also evaluated against human lung (A549) and liver (HepG2) cancer cell lines along with immortalized normal lung (BEAS-2B) and liver (LO2) cell lines. Following screening, five derivatives viz. 8 a, 8 h, 8 l, 8 m and 8 o (IC50 =1.65-31.7 µM; SI=1.08-10.96) were found to show potent cytotoxic activity against (A549) lung cancer cell lines, with selectivity superior to that of the reference compounds artemisinin (IC50 =100 µM), chloroquine (IC50 =100 µM) and artesunic acid (IC50 =9.85 µM; SI=0.76). In fact, the most active 4-naphthyl-substituted analogue 8 l (IC50 =1.65 µM; SI >10) exhibited >60 times more cytotoxicity than the standard reference, artemisinin, against A549 lung cancer cell lines. In silico docking studies of the most active anticancer compounds, 8 l and 8 m, against EGFR were found to validate the wet lab results. In summary, a new series of functionalized aryl-vinyl-1,2,4-trioxanes (8 a-p) has been shown to display dual potency as promising antiplasmodial and anticancer agents.

The effects of action observation training on improving upper limb motor functions in people with stroke: A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: Action observation training (AOT) has been used as a new intervention for improving upper limb motor functions in people with stroke. This systematic review and meta-analysis aims to investigate the effects of AOT on improving upper limb motor functions in people with stroke. METHODS: We searched ten electronic databases to identify randomized controlled trials (RCTs) about the effects of AOT on upper limb motor functions in stroke survivors. Methodological quality of included studies was assessed by the Risk of Bias Tool in the Cochrane Handbook for Systematic Reviews of Interventions. A random-effects meta-analysis was performed by pooling the standardized mean difference (SMD) of upper limb motor outcomes. RESULTS: Seven studies of 276 participants with stroke were included. Meta-analysis showed a significant effect favoring AOT on improving upper limb motor functions in patients with stroke [SMD = 0.35, 95% confidence interval [CI], 0.10 to 0.61, I2 = 10.14%, p = 0.007]. CONCLUSIONS: AOT appears to be an effective intervention for improving the upper limb motor functions in people after stroke. Further studies need to investigate the neural mechanism underlying the effects of AOT.

Reconstructing Phylogeny by Aligning Multiple Metabolic Pathways Using Functional Module Mapping.

Comparison of metabolic pathways provides a systematic way for understanding the evolutionary and phylogenetic relationships in systems biology. Although a number of phylogenetic methods have been developed, few efforts have been made to provide a unified phylogenetic framework that sufficiently reflects the metabolic features of organisms. In this paper, we propose a phylogenetic framework that characterizes the metabolic features of organisms by aligning multiple metabolic pathways using functional module mapping. Our method transforms the alignment of multiple metabolic pathways into constructing the union graph of pathways, builds mappings between functional modules of pathways in the union graph, and infers phylogenetic relationships among organisms based on module mappings. Experimental results show that the use of functional module mapping enables us to correctly categorize organisms into main categories with specific metabolic characteristics. Traditional genome-based phylogenetic methods can reconstruct phylogenetic relationships, whereas our method can offer in-depth metabolic analysis for phylogenetic reconstruction, which can add insights into traditional phyletic reconstruction. The results also demonstrate that our phylogenetic trees are closer to the classic classifications in comparison to existing classification methods using metabolic pathway data.