Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.02.23.580874

Emodin modulates PI3K-AKT pathway to inhibit proliferation, invasion and induce apoptosis in glioma cells.

Abstract

Glioma is a type of tumor that begins in glial cells and occurs in the brain and spinal cord. Glioma forms a major health challenge worldwide. They are hard to treat, not only because of the deregulation in multiple signaling transduction pathways affecting various cellular processes but also because they are not contained in a well-defined mass with clear borders. One of the main pathways deregulated in glioma is PI3K-AKT and its associated downstream targets like NF-B which affects different proteins/transcription factors influencing many aspects of gliomagenesis like epithelial to mesenchymal transition (EMT). A combination of in-silico and in-vitro approaches targeted against specific catalytic isoform (p110{delta}) of Class IA PI3K with potent and selective inhibitors would maximize the chances of tumor regression. We adopted an in-silico approach to screen a range of natural molecules for a potent p110{delta} inhibitor and among them, "emodin" was found to be a potential candidate. In vitro, emodin treatment inhibits proliferation, induces apoptosis, modulates astrocytic phenotype, and decreases cell density of glioma cells. Emodin induces changes in the astrocytic phenotype of glioma cells to elongated form with rounded-off, shrunken-down morphology. Emodin was found to contribute to ROS production which leads to apoptosis of glioma cells. The apoptosis induced by emodin was confirmed by propidium iodide staining and ascertained by FACS analysis. We evaluated the effect of emodin on various proteins of PI3K-AKT and downstream targets. We found that emodin treatment decreases the expression of p-AKT, increases expression levels of I-B, inhibits nuclear translocation of NF-B, and upregulates the phosphorylated form of GSK-3{beta}. Changes at the molecular level of these proteins result in the inhibition/degradation of downstream proteins and transcription factors associated with the growth and proliferation of glioma cells. Inhibition of nuclear translocation of NF-B also inhibits nuclear activation of various protumorigenic signaling pathway mediators involved in EMT such as N-cadherin, {beta}-catenin, Claudin-1. These EMT markers promote invasion, proliferation, migration, and growth in glioma cells. Emodin treatment resulted in changed expression profiles of these EMT markers involved in promoting gliomagenesis. In essence these results suggest that in-vitro emodin treatment remarkably reduces the proliferation of glioma cells possibly targeting multiple pathways involved in tumor growth, proliferation, and development, supporting the rationale and relevance of using multipronged strategies for effective treatment of glioma.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mir, A. H., Banday, M. Z., Aadil, F., Ganie, S. A., Shah, E. H.. 2024-02-27. Emodin modulates PI3K-AKT pathway to inhibit proliferation, invasion and induce apoptosis in glioma cells.. https://doi.org/10.1101/2024.02.23.580874

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

BAP1 loss and PRAME expression converge to remodel the tumor-immune ecosystem during uveal melanoma progression

Uveal melanoma (UM) is characterized by a small number of recurrent genetic alterations that determine metastatic propensity. BAP1 loss and PRAME expression define the dominant prognostic axes in UM, yet how they promote malignant progression remains unclear. We profiled 190,535 cells from normal uvea, uveal nevus, primary and metastatic UM using single-cell transcriptomics, T cell receptor sequencing, spatial transcriptomics and isogenic perturbation models. Normal melanocytes, nevus cells and UM cells formed a transcriptional continuum marked by loss of differentiation and emergence of neural crest-like, stress-responsive, hypoxic-glycolytic and immune-interacting states. BAP1 loss and PRAME expression imposed distinct but convergent immunoregulatory programs, inducing interferon and TNF-NFkB signaling and MHC-I expression, with HLA-E showing the strongest response. These alterations were accompanied by macrophage and CD8+ T cell remodeling. PRAME-enriched tumor regions formed spatially organized niches enriched for macrophages and plasma cells. These findings define BAP1 loss and PRAME expression as distinct but convergent axes of tumor-immune coevolution and nominate HLA-E as a candidate mediator of immune resistance.

cancer biology↗

A plasma metabolomics workflow for breast cancer detection using quantitative GC/MS and machine learning

Blood-based metabolomic profiling has been widely investigated for breast cancer (BC) detection; however, clinical implementation remains limited due to variability in sample handling, analytical reproducibility, and overfitting during statistical analysis. We established a plasma GC/MS metabolomics workflow for discriminating BC from healthy controls (HC) using conventional machine-learning algorithms. Plasma samples (n = 360; BC = 180, HC = 180) were collected prospectively under standardized preanalytical conditions before surgery and the initiation of systematic anticancer therapy and analyzed using a quantitative GC/MS platform with automated derivatization. Feature selection and model development were conducted using three machine-learning (ML) algorithms (Lasso logistic regression (LR), random forest classifier (RFC), and support vector machine (SVM)). A total of 45 metabolite candidate biomarkers were identified, and the optimal number of metabolite features for each algorithm was estimated by a recursive feature elimination (RFE)-based strategy. The best-performing models achieved area under the ROC curve values (AUC) of 0.910 (LR), 0.893 (RFC), and 0.843 (SVM). We selected prioritizing candidate biomarkers consistently expressed across the multi-algorithm pipeline. A bagging ensemble model improved stability (AUC = 0.911) and reduced false-positive predictions in the independent HC dataset. In addition, model stability with respect to false-positive predictions was assessed using an independent HC cohort (n = 15) that was collected at a separate institution. These results indicate that a plasma metabolomics workflow combined with conventional multi-algorithm ML, algorithm-specific feature selection, and independent assessment provides stable discrimination between BC and HC in a moderately sized cohort.

cancer biology↗

Prognostic value, signal interaction network, and immune infiltration characteristics of MET gene expression in gastric cancer analyzed by multi-database bioinformatics

Objective Based on the bioinformatics method of multi-database integration, this study systematically analyzes the expression characteristics, clinical pathological correlation, prognostic value, potential molecular mechanisms, and immune infiltration patterns of hepatocyte growth factor receptor (MET) in gastric cancer. Methods The UALCAN and GEPIA databases were employed to examine the differential expression of MET between gastric cancer and normal gastric mucosal tissues, as well as its associations with clinicopathological features. Kaplan-Meier Plotter was utilized to evaluate the impact of MET expression on overall survival (OS) and progression-free survival (PFS). Protein-protein interaction (PPI) network was constructed via LinkedOmics, followed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of co-expressed genes. Four algorithms, TIMER, CIBERSORT, EPIC, and MCPcounter, were used to cross evaluate the correlation between MET expression and immune cell infiltration; Further validate the cell type specific expression of MET using the gastric cancer single-cell sequencing queues (GSE134520, GSE167297) built into the TISCH database. Results MET expression was significantly elevated in gastric cancer tissues compared with normal gastric mucosa (P < .05), and its expression level was significantly correlated with tumor grade and TNM stage. Patients with high MET expression exhibited significantly poorer OS and PFS than those with low MET expression (P < .05). The PPI network revealed that MET could interact with 20 key proteins, including EGFR, ERBB2, HGF, STAT3, and GRB2 etc. GO enrichment analysis suggests that differentially expressed genes are significantly enriched in functions such as the ERBB signaling pathway, cadherin binding, and DNA repair complexes; KEGG enrichment analysis showed that MET related genes were significantly enriched in pathways such as homologous recombination, nuclear cytoplasmic transport, mismatch repair, and oxidative phosphorylation. Immune infiltration analysis showed that the negative association between MET and B cells infiltration has cross algorithm robustness, while the association with neutrophils, CD8+ T cells, CD4+ T cells, and macrophages exhibits algorithmic heterogeneity or insignificance; There is no significant correlation between MET and common immune checkpoint molecules such as PD-1, PD-L1, CTLA4, etc. Single cell validation further confirmed that MET is mainly enriched in malignant epithelial cells and endothelial cells, and is almost not expressed in immune cells. Conclusions Multidimensional bioinformatic analyses demonstrate that elevated MET expression serves as an independent risk factor for unfavorable prognosis in gastric cancer. MET may mediate dual drug resistance in gastric cancer via crosstalk with multiple signaling molecules (including EGFR, ERBB2, HGF, STAT3 and GRB2) and dysregulation of the homologous recombination repair pathway. Results from multiple-algorithm immune infiltration analysis, single-cell dataset analysis and immune checkpoint correlation analysis indicate that MET exerts only modest direct regulatory effects on the gastric cancer immune microenvironment. This exploratory study offers systematic bioinformatic evidence supporting MET as a candidate prognostic biomarker and potential therapeutic target for gastric cancer. Further functional experiments and prospective cohort studies are required to validate its molecular mechanisms and clinical utility.

cancer biology↗