DKPE-GraphSYN: a drug synergy prediction model based on joint dual kernel density estimation and positional encoding for graph representation.

Introduction: Synergistic medication, a crucial therapeutic strategy in cancer treatment, involves combining multiple drugs to enhance therapeutic effectiveness and mitigate side effects. Current research predominantly employs deep learning models for extracting features from cell line and cancer drug structure data. However, these methods often overlook the intricate nonlinear relationships within the data, neglecting the distribution characteristics and weighted probability densities of gene expression data in multi-dimensional space. It also fails to fully exploit the structural information of cancer drugs and the potential interactions between drug molecules. Methods: To overcome these challenges, we introduce an innovative end-to-end learning model specifically tailored for cancer drugs, named Dual Kernel Density and Positional Encoding (DKPE) for Graph Synergy Representation Network (DKPEGraphSYN). This model is engineered to refine the prediction of drug combination synergy effects in cancer. DKPE-GraphSYN utilizes Dual Kernel Density Estimation and Positional Encoding techniques to effectively capture the weighted probability density and spatial distribution information of gene expression, while exploring the interactions and potential relationships between cancer drug molecules via a graph neural network. Results: Experimental results show that our prediction model achieves significant performance enhancements in forecasting drug synergy effects on a comprehensive cancer drug and cell line synergy dataset, achieving an AUPR of 0.969 and an AUC of 0.976. Discussion: These results confirm our model's superior accuracy in predicting cancer drug combinations, providing a supportive method for clinical medication strategy in cancer.

MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction.

Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is particularly important given the increase in the number of available drug classes and potential drug-drug interactions. Existing methods for predicting the synergistic effects of drug combinations primarily focus on extracting structural features of drug molecules and cell lines, but neglect the interaction mechanisms between cell lines and drug combinations. Consequently, there is a deficiency in comprehensive understanding of the synergistic effects of drug combinations. To address this issue, we propose a drug combination synergy prediction model based on multi-source feature interaction learning, named MFSynDCP, aiming to predict the synergistic effects of anti-tumor drug combinations. This model includes a graph aggregation module with an adaptive attention mechanism for learning drug interactions and a multi-source feature interaction learning controller for managing information transfer between different data sources, accommodating both drug and cell line features. Comparative studies with benchmark datasets demonstrate MFSynDCP's superiority over existing methods. Additionally, its adaptive attention mechanism graph aggregation module identifies drug chemical substructures crucial to the synergy mechanism. Overall, MFSynDCP is a robust tool for predicting synergistic drug combinations. The source code is available from GitHub at https://github.com/kkioplkg/MFSynDCP .

miR-4461 Inhibits Liver Cancer Stem Cells Expansion and Chemoresistance via Regulating SIRT1.

MicroRNAs (miRNAs) were involved in tumorigenesis, progression, recurrence and drug resistance of hepatocellular carcinoma (HCC). However, few miRNAs have been identified and entered clinical practice. We show here that miR-4461 expression is reduced in liver cancer stem cells (CSCs) and predicts the poor prognosis of HCC patients. Knockdown of miR-4461 enhances the self-renewal and tumorigenicity of liver CSCs. Conversely, forced miR-4461 expression inhibits liver CSCs self-renewal and tumorigenesis. Mechanically, miR-4461 directly targets sirtuin 1 (SIRT1) via binding to its 3'-UTR in liver CSCs. The correlation of miR-4461 and SIRT1 was confirmed in human HCC patients' tissues. Additionally, we found that miR-4461 overexpression hepatoma cells are more sensitive to cisplatin treatment. PDXs also showed that miR-4461 high HCC xenografts are sensitive to cisplatin treatment. Clinical cohort analysis further confirmed that HCC patients with high miR-4461 are benefited more from transcatheter arterial chemoembolization (TACE) treatment. In conclusion, our findings revealed the crucial role of the miR-4461 in liver CSCs expansion and cisplatin response, rendering miR-4461 as an optimal target for the prevention and intervention of HCC.

Exosome loaded genipin crosslinked hydrogel facilitates full thickness cutaneous wound healing in rat animal model.

Full thickness cutaneous wound therapy and regeneration remains a critical challenge in clinical therapeutics. Recent reports have suggested that mesenchymal stem cells exosomes therapy is a promising technology with great potential to efficiently promote tissue regeneration. Multifunctional hydrogel composed of both synthetic materials and natural materials is an effective carrier for exosomes loading. Herein, we constructed a biodegradable, dual-sensitive hydrogel encapsulated human umbilical cord-mesenchymal stem cells (hUCMSCs) derived exosomes to facilitate wound healing and skin regeneration process. The materials characterization, exosomes identification, and in vivo full-thickness cutaneous wound healing effect of the hydrogels were performed and evaluated. The in vivo results demonstrated the exosomes loaded hydrogel had significantly improved wound closure, re-epithelialization rates, collagen deposition in the wound sites. More skin appendages were observed in exosomes loaded hydrogel treated wound, indicating the potential to achieve complete skin regeneration. This study provides a new access for complete cutaneous wound regeneration via a genipin crosslinked dual-sensitive hydrogel loading hUCMSCs derived exosomes.

miR-186 Inhibits Liver Cancer Stem Cells Expansion via Targeting PTPN11.

MicroRNAs (miRNAs) participated in the regulation of tumorigenesis, progression, metastasis, recurrence and chemo-resistance of cancers. However, the potential function of miRNAs in cancer stem cells (CSCs) or tumor-initiating cells (T-ICs) was not clearly elucidated. In the present study, we found that miR-186 expression was reduced in liver CSCs. Functional studies showed that miR-186 knockdown facilitated liver CSCs self-renewal and tumorigenesis. Conversely, forced miR-186 expression suppressed liver CSCs self-renewal and tumorigenesis. Mechanically, miR-186 downregulated PTPN11 via binding to its 3'-UTR in liver CSCs. The correlation of miR-186 and PTPN11 was confirmed in Hepatocellular carcinoma (HCC) patients' tissues. Further study showed that interference of PTPN11 can abolished the discrepancy between miR-186 mimic and control HCC cells in self-renewal and the proportion of CSCs. Additionally, we found that miR-186 overexpression HCC cells were more sensitive to cisplatin treatment. Clinical cohort analysis showed that HCC patients with high miR-186 were benefited more from transcatheter arterial chemoembolization (TACE) treatment. In conclusion, our study demonstrates a new regulation mechanism of liver CSCs, a new target for HCC, and a biomarker for postoperative TACE.

The interaction between gut microbiome and anti-tumor drug therapy.

A large number of symbiotic gut microbiome exists in the human gastrointestinal micro-ecosystem. The daily diet, lifestyle, and body constitution influence the type and quantity of gut microbiome in the body. Increasing evidence demonstrates that the gut microbiome can affect tumor development and progress. We discuss in this paper how the gut microbiome impacts tumor pathology through DNA damage, production of dietary and microbial metabolites, altered cellular signaling pathways, immune system suppression, and involvement in pro-inflammatory pathways changing gut microbiome composition. The gut microbiome acts on different types of the anti-tumor drug through bacterial translocation, immuno-modulation, metabolic modulation, enzymatic degradation, and reduction of microbial diversity. This article summarized the aforementioned by reviewing recent studies on the interaction among the gut microbiome, tumor development, and antitumor drugs.