ABSTRACT
Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.
ABSTRACT
Alzheimer's disease (AD) accounts for approximately 60% of dementia cases worldwide. Advanced age is the most significant risk factor for AD and approximately two-thirds of cases relate to women. While the previous meta-analysis suggests that estrogen receptor (ESR) genetic polymorphisms are closely associated with dementia, the implications of this observation on a molecular level are not entirely understood. Our study explores this intricate molecular puzzle through the use of a variety of bioinformatics tools. Initially, we attempted to elucidate mechanisms underlying breast cancer development by identifying the high-throughput dataset of ESR1-knockdown breast cancer tissue samples. Surprisingly, KEGG pathway enrichment showed that the most frequently occurring proteins were related to axonal guidance and inflammation-related gene markers. These observations were supported by an external high throughput dataset of AD inflammatory samples in vivo. Our results suggest that ESR1 is modulated by apolipoprotein E (APOE) through CEBPB/ATF4, mir-155-5p, or mir-1-3p. Moreover, sea hare-hydrolysates (SHH), as one of the axonal guidance molecules, could regulate the STAT3/PRDM1/CEBPB pathway and consequently induce cell death through pyroptosis signaling pathways, trigger the secretion of IL1ß, leading to neuroinflammation and worsening AD pathogenesis. Molecular docking verification demonstrated that the predicted natural products scoulerine and genistein displayed strong binding affinities for BACE1 and ESR1, respectively. This strategy can be used to design novel, personalized therapeutic approaches to treatment and a first-in-class clinical lead for the personalised treatment of AD.
