Identification of key genes and immune infiltration in peripheral blood biomarker analysis of delayed cerebral ischemia: Valproic acid as a potential therapeutic drug.

BACKGROUND: Delayed cerebral ischemia (DCI) is a common and serious complication of subarachnoid hemorrhage (SAH). Its pathogenesis is not fully understood. Here, we developed a predictive model based on peripheral blood biomarkers and validated the model using several bioinformatic multi-analysis methods. METHODS: Six datasets were obtained from the GEO database. Characteristic genes were screened using weighted correlation network analysis (WGCNA) and differentially expressed genes. Three machine learning algorithms, elastic networks-LASSO, support vector machines (SVM-RFE) and random forests (RF), were also used to construct diagnostic prediction models for key genes. To further evaluate the performance and predictive value of the diagnostic models, nomogram model were constructed, and the clinical value of the models was assessed using Decision Curve Analysis (DCA), Area Under the Check Curve (AUC), Clinical Impact Curve (CIC), and validated in the mouse single-cell RNA-seq dataset. Mendelian randomization(MR) analysis explored the causal relationship between SAH and stroke, and the intermediate influencing factors. We validated this by retrospectively analyzing the qPCR levels of the most relevant genes in SAH and SAH-DCI patients. This experiment demonstrated a statistically significant difference between SAH and SAH-DCI and normal group controls. Finally, potential small molecule compounds interacting with the selected features were screened from the Comparative Toxicogenomics Database (CTD). RESULTS: The fGSEA results showed that activation of Toll-like receptor signaling and leukocyte transendothelial cell migration pathways were positively correlated with the DCI phenotype, whereas cytokine signaling pathways and natural killer cell-mediated cytotoxicity were negatively correlated. Consensus feature selection of DEG genes using WGCNA and three machine learning algorithms resulted in the identification of six genes (SPOCK2, TRRAP, CIB1, BCL11B, PDZD8 and LAT), which were used to predict DCI diagnosis with high accuracy. Three external datasets and the mouse single-cell dataset showed high accuracy of the diagnostic model, in addition to high performance and predictive value of the diagnostic model in DCA and CIC. MR analysis looked at stroke after SAH independent of SAH, but associated with multiple intermediate factors including Hypertensive diseases, Total triglycerides levels in medium HDL and Platelet count. qPCR confirmed that significant differences in DCI signature genes were observed between the SAH and SAH-DCI groups. Finally, valproic acid became a potential therapeutic agent for DCI based on the results of target prediction and molecular docking of the characterized genes. CONCLUSION: This diagnostic model can identify SAH patients at high risk for DCI and may provide potential mechanisms and therapeutic targets for DCI. Valproic acid may be an important future drug for the treatment of DCI.

CRISPR-Cas9 library screening combined with an exosome-targeted delivery system addresses tumorigenesis/TMZ resistance in the mesenchymal subtype of glioblastoma.

Rationale: The large-scale genomic analysis classifies glioblastoma (GBM) into three major subtypes, including classical (CL), proneural (PN), and mesenchymal (MES) subtypes. Each of these subtypes exhibits a varying degree of sensitivity to the temozolomide (TMZ) treatment, while the prognosis corresponds to the molecular and genetic characteristics of the tumor cell type. Tumors with MES features are predominantly characterized by the NF1 deletion/alteration, leading to sustained activation of the RAS and PI3K-AKT signaling pathways in GBM and tend to acquire drug resistance, resulting in the worst prognosis compared to other subtypes (PN and CL). Here, we used the CRISPR/Cas9 library screening technique to detect TMZ-related gene targets that might play roles in acquiring drug resistance, using overexpressed KRAS-G12C mutant GBM cell lines. The study identified a key therapeutic strategy to address the chemoresistance against the MES subtype of GBM. Methods: The CRISPR-Cas9 library screening was used to discover genes associated with TMZ resistance in the U87-KRAS (U87-MG which is overexpressed KRAS-G12C mutant) cells. The patient-derived GBM primary cell line TBD0220 was used for experimental validations in vivo and in vitro. Chromatin isolation by RNA purification (ChIRP) and chromatin immunoprecipitation (ChIP) assays were used to elucidate the silencing mechanism of tumor suppressor genes in the MES-GBM subtype. The small-molecule inhibitor EPIC-0412 was obtained through high-throughput screening. Transmission electron microscopy (TEM) was used to characterize the exosomes (Exos) secreted by GBM cells after TMZ treatment. Blood-derived Exos-based targeted delivery of siRNA, TMZ, and EPIC-0412 was optimized to tailor personalized therapy in vivo. Results: Using the genome-wide CRISPR-Cas9 library screening, we found that the ERBIN gene could be epigenetically regulated in the U87-KRAS cells. ERBIN overexpression inhibited the RAS signaling and downstream proliferation and invasion effects of GBM tumor cells. EPIC-0412 treatment inhibited tumor proliferation and EMT progression by upregulating the ERBIN expression both in vitro and in vivo. Genome-wide CRISPR-Cas9 screening also identified RASGRP1(Ras guanine nucleotide-releasing protein 1) and VPS28(Vacuolar protein sorting-associated protein 28) genes as synthetically lethal in response to TMZ treatment in the U87-KRAS cells. We found that RASGRP1 activated the RAS-mediated DDR pathway by promoting the RAS-GTP transformation. VPS28 promoted the Exos secretion and decreased intracellular TMZ concentration in GBM cells. The targeted Exos delivery system encapsulating drugs and siRNAs together showed a powerful therapeutic effect against GBM in vivo. Conclusions: We demonstrate a new mechanism by which ERBIN is epigenetically silenced by the RAS signaling in the MES subtype of GBM. Restoration of the ERBIN expression with EPIC-0412 significantly inhibits the RAS signaling downstream. RASGRP1 and VPS28 genes are associated with the promotion of TMZ resistance through RAS-GDP to RAS-GTP transformation and TMZ efflux, as well. A quadruple combination therapy based on a targeted Exos delivery system demonstrated significantly reduced tumor burden in vivo. Therefore, our study provides new insights and therapeutic approaches for regulating tumor progression and TMZ resistance in the MES-GBM subtype.

EPIC-1042 as a potent PTRF/Cavin1-caveolin-1 interaction inhibitor to induce PARP1 autophagic degradation and suppress temozolomide efflux for glioblastoma.

BACKGROUND: Temozolomide (TMZ) treatment efficacy in glioblastoma is determined by various mechanisms such as TMZ efflux, autophagy, base excision repair (BER) pathway, and the level of O6-methylguanine-DNA methyltransferase (MGMT). Here, we reported a novel small-molecular inhibitor (SMI) EPIC-1042 (C20H28N6) with the potential to decrease TMZ efflux and promote PARP1 degradation via autolysosomes in the early stage. METHODS: EPIC-1042 was obtained from receptor-based virtual screening. Co-immunoprecipitation and pull-down assays were applied to verify the blocking effect of EPIC-1042. Western blotting, co-immunoprecipitation, and immunofluorescence were used to elucidate the underlying mechanisms of EPIC-1042. In vivo experiments were performed to verify the efficacy of EPIC-1042 in sensitizing glioblastoma cells to TMZ. RESULTS: EPIC-1042 physically interrupted the interaction of PTRF/Cavin1 and caveolin-1, leading to reduced secretion of small extracellular vesicles (sEVs) to decrease TMZ efflux. It also induced PARP1 autophagic degradation via increased p62 expression that more p62 bound to PARP1 and specially promoted PARP1 translocation into autolysosomes for degradation in the early stage. Moreover, EPIC-1042 inhibited autophagy flux at last. The application of EPIC-1042 enhanced TMZ efficacy in glioblastoma in vivo. CONCLUSION: EPIC-1042 reinforced the effect of TMZ by preventing TMZ efflux, inducing PARP1 degradation via autolysosomes to perturb the BER pathway and recruitment of MGMT, and inhibiting autophagy flux in the later stage. Therefore, this study provided a novel therapeutic strategy using the combination of TMZ with EPIC-1042 for glioblastoma treatment.

Mechanisms underlying the therapeutic effects of cinobufagin in treating melanoma based on network pharmacology, single-cell RNA sequencing data, molecular docking, and molecular dynamics simulation.

Malignant melanoma is one of the most aggressive of cancers; if not treated early, it can metastasize rapidly. Therefore, drug therapy plays an important role in the treatment of melanoma. Cinobufagin, an active ingredient derived from Venenum bufonis, can inhibit the growth and development of melanoma. However, the mechanism underlying its therapeutic effects is unclear. The purpose of this study was to predict the potential targets of cinobufagin in melanoma. We gathered known and predicted targets for cinobufagin from four online databases. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were then performed. Gene expression data were downloaded from the GSE46517 dataset, and differential gene expression analysis and weighted gene correlation network analysis were performed to identify melanoma-related genes. Using input melanoma-related genes and drug targets in the STRING online database and applying molecular complex detection (MCODE) analysis, we identified key targets that may be the potential targets of cinobufagin in melanoma. Moreover, we assessed the distribution of the pharmacological targets of cinobufagin in melanoma key clusters using single-cell data from the GSE215120 dataset obtained from the Gene Expression Omnibus database. The crucial targets of cinobufagin in melanoma were identified from the intersection of key clusters with melanoma-related genes and drug targets. Receiver operating characteristic curve (ROC) analysis, survival analysis, molecular docking, and molecular dynamics simulation were performed to gain further insights. Our findings suggest that cinobufagin may affect melanoma by arresting the cell cycle by inhibiting three protein tyrosine/serine kinases (EGFR, ERBB2, and CDK2). However, our conclusions are not supported by relevant experimental data and require further study.