The Role of Emodin in the Treatment of Bladder Cancer Based on Network Pharmacology and Experimental Verification.

BACKGROUND AND PURPOSE: Emodin, a compound derived from rhubarb and various traditional Chinese medicines, exhibits a range of pharmacological actions, including antiinflammatory, antiviral, and anticancer properties. Nevertheless, its pharmacological impact on bladder cancer (BLCA) and the underlying mechanism are still unclear. This research aimed to analyze the pharmacological mechanisms of Emodin against BLCA using network pharmacology analysis and experimental verification. METHODS: Initially, network pharmacology was employed to identify core targets and associated pathways affected by Emodin in bladder cancer. Subsequently, the expression of key targets in normal bladder tissues and BLCA tissues was assessed by searching the GEPIA and HPA databases. The binding energy between Emodin and key targets was predicted using molecular docking. Furthermore, in vitro experiments were carried out to confirm the predictions made with network pharmacology. RESULTS: Our analysis identified 148 common genes targeted by Emodin and BLCA, with the top ten target genes including TP53, HSP90AA1, EGFR, MYC, CASP3, CDK1, PTPN11, EGF, ESR1, and TNF. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated a significant correlation between Emodin and the PI3KAKT pathway in the context of BLCA. Molecular docking investigations revealed a strong affinity between Emodin and critical target proteins. In vitro experiments demonstrated that Emodin inhibits T24 proliferation, migration, and invasion while inducing cell apoptosis. The findings also indicated that Emodin reduces both PI3K and AKT protein and mRNA expression, suggesting that Emodin may mitigate BLCA by modulating the PI3K-AKT signaling pathway. CONCLUSION: This study integrates network pharmacology with in vitro experimentation to elucidate the potential mechanisms underlying the action of Emodin against BLCA. The results of this research enhance our understanding of the pharmacological mechanisms by which Emodin may be employed in treating BLCA.

DF-1 cells prevent MG-HS infection through gga-miR-24-3p/RAP1B mediated decreased proliferation and increased apoptosis.

Mycoplasma gallisepticum (MG) is a major poultry pathogen that can induce Chronic Respiratory Disease (CRD) in chickens, causing serious economic losses in the poultry industry worldwide. Increasing evidence suggests that microRNAs (miRNAs) act as a vital role in resisting microbial pathogenesis and maintaining cellular mechanism. Our previous miRNAs sequencing data showed gga-miR-24-3p expression level was significantly increased in MG-infected chicken lungs. The aim of this study is to reveal the cellular mechanism behind the MG-HS infection. We found that gga-miR-24-3p was significantly upregulated and Ras-related protein-B (RAP1B) was downregulated in chicken fibroblast cells (DF-1) with MG infection. Dual luciferase reporting assay and rescue assay confirmed that RAP1B was the target gene of gga-miR-24-3p. Meanwhile, overexpressed gga-miR-24-3p increased the levels of tumor necrosis factor alpha (TNF-α) and interleukin-1ß (IL-1ß), and significantly inhibited cell proliferation as well as promoted MG-infected DF-1 cell apoptosis, whereas inhibition of gga-miR-24-3p had the opposite effect. More importantly, the results of overexpression and knockdown of target gene RAP1B demonstrated that the presence of RAP1B promoted cell proliferation and it saved the reduced or increased cell proliferation caused by overexpression or inhibition of gga-miR-24-3p. Furthermore, the overexpression of gga-miR-24-3p could significantly inhibit the expression of MG-HS adhesion protein. Taken together, these findings demonstrate that DF-1 cells can resist MG-HS infection through gga-miR-24-3p/RAP1B mediated decreased proliferation and increased apoptosis, which provides a new mechanism of resistance to MG infection in vitro.

iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach.

A microRNA (miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating that they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the post-genomic age, it is highly desired to develop computational sequence-based methods for effectively identifying the human pre-miRNAs. Here, we propose a predictor called "iMiRNA-PseDPC", in which the RNA sequences are formulated by a novel feature vector called "pseudo distance-pair composition" (PseDPC) with 10 types of structure statuses. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark data-set showed that our approach has remarkably outperformed the existing ones in either prediction accuracy or efficiency, indicating the new predictor is quite promising or at least may become a complementary tool to the existing predictors in this area. For the convenience of most experimental scientists, a user-friendly web server for the new predictor has been established at http://bioinformatics.hitsz.edu.cn/iMiRNA-PseDPC/, by which users can easily get their desired results without the need to go through the mathematical details. It is anticipated that the new predictor may become a useful high throughput tool for genome analysis particularly in dealing with large-scale data.

Identification of real microRNA precursors with a pseudo structure status composition approach.

Containing about 22 nucleotides, a micro RNA (abbreviated miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the postgenomic age, it is highly desired to develop computational sequence-based methods in this regard. Here two new predictors, called "iMcRNA-PseSSC" and "iMcRNA-ExPseSSC", were proposed for identifying the human pre-microRNAs by incorporating the global or long-range structure-order information using a way quite similar to the pseudo amino acid composition approach. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the two new predictors (accessible at http://bioinformatics.hitsz.edu.cn/iMcRNA/) outperformed or were highly comparable with the best existing predictors in this area.