BackgroundGlioblastoma (GBM) is one of the most malignant types of tumors in the central nervous system, and the 5-year survival remains low. Several studies have shown that preoperative peripheral blood tests and preoperative conventional Magnetic Resonance Imaging (MRI) examinations affect the prognosis of GBM patients. Therefore, it is necessary to construct a risk score based on a preoperative peripheral blood test and conventional MRI and develop a multielement prognostic nomogram for GBM.MethodsThis study retrospectively analyzed 131 GBM patients. Determination of the association between peripheral blood test variables and conventional MRI variables and prognosis was performed by univariate Cox regression. The nomogram model, which was internally validated using a cohort of 56 GBM patients, was constructed by multivariate Cox regression. RNA sequencing data from Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA datasets were used to determine peripheral blood test-related genes based on GBM prognosis.ResultsThe constructed risk score included the neutrophil/lymphocyte ratio (NLR), lymphocyte/monocyte ratio (LMR), albumin/fibrinogen (AFR), platelet/lymphocyte ratio (PLR), and center point–to-ventricle distance (CPVD). A final nomogram was developed using factors associated with prognosis, including age, sex, the extent of tumor resection, IDH mutation status, radiotherapy status, chemotherapy status, and risk. The Area Under Curve (AUC) values of the receiver operating characteristic curve (ROC) curve were 0.876 (12-month ROC), 0.834 (24-month ROC) and 0.803 (36-month ROC) in the training set and 0.906 (12-month ROC), 0.800 (18-month ROC) and 0.776 (24-month ROC) in the validation set. In addition, vascular endothelial growth factor A (VEGFA) was closely associated with NLR and LMR and identified as the most central negative gene related to the immune microenvironment and influencing immune activities.ConclusionThe risk score was established as an independent predictor of GBM prognosis, and the nomogram model exhibit appropriate predictive power. In addition, VEGFA is the key peripheral blood test-related gene that is significantly associated with poor prognosis.

Paclitaxel is an herbal active ingredient used in clinical practice that shows anti-tumor effects. However, its biological activity, mechanism, and cancer cell-killing effects remain unknown. Information on the chemical gene interactions of paclitaxel was obtained from the Comparative Toxicogenomics Database, SwishTargetPrediction, Binding DB, and TargetNet databases. Gene expression data were obtained from the GSE4290 dataset. Differential gene analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology analyses were performed. Gene set enrichment analysis was performed to evaluate disease pathway activation; weighted gene co-expression network analysis with diff analysis was used to identify disease-associated genes, analyze differential genes, and identify drug targets via protein-protein interactions. The Molecular Complex Detection (MCODE) analysis of critical subgroup networks was conducted to identify essential genes affected by paclitaxel, assess crucial cluster gene expression differences in glioma versus standard samples, and perform receiver operator characteristic mapping. To evaluate the pharmacological targets and signaling pathways of paclitaxel in glioblastoma, the single-cell GSE148196 dataset was acquired from the Gene Expression Omnibus database and preprocessed using Seurat software. Based on the single-cell RNA-sequencing dataset, 24 cell clusters were identified, along with marker genes for the two different cell types in each cluster. Correlation analysis revealed that the mechanism of paclitaxel treatment involves effects on neurons. Paclitaxel may affect glioblastoma by improving glucose metabolism and processes involved in modulating immune function in the body.