The molecular structure of Panaxadiol was downloaded from the PubChem database, with a Compound CID of 73,498 (Wang et al., 2012).

To obtain target information related to Panaxadiol, we utilized multiple databases for prediction and collection. These databases included the Comparative Toxicogenomics Database (Davis et al., 2023) (CTD, https://ctdbase.org/), SEA Search Server (https://sea.bkslab.org/) (Jin et al., 2022), SwissTargetPrediction (Daina et al., 2019) (http://www.swisstargetprediction.ch/) and TargetNet (Yao et al., 2016) (http://targetnet.scbdd.com/home/index/). During this process, the 2D structure and Isomeric SMILES information of Panaxadiol were obtained from the PubChem database.

We downloaded GBM transcriptomic data in FPKM format from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) (Zhao et al., 2023). The dataset comprised a total of 171 samples derived from tumor tissues, alongside five samples from normal tissues Subsequently, we processed these data using the “limma” R package to prepare for subsequent analyses.

To obtain differentially expressed genes (DEGs) in the TCGA-GBM cohort, We conducted an analysis of differential gene expression utilizing the “limma” package in R. The screening thresholds were set as |logFC| > 2 and P < 0.05.

We utilized the “clusterProfiler” package in R to explore the potential functions of Panaxadiol, focusing on Disease Ontology, Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes (Kanehisa and Goto, 2000; Schriml et al., 2022).

To investigate the potential target network of Panaxadiol in treating GBM, we first used the “VennSchemram” R package to intersect Panaxadiol-related targets with GBM DEGs and visualized the results. Subsequently, We submitted the identified intersecting targets to the STRING database (https://cn.string-db.org/) in order to develop a protein-protein interaction network (Szklarczyk et al., 2025). We chose Homo sapiens as the species of interest and established a medium confidence threshold of 0.4 to endure the reliability of the interaction network (Szklarczyk et al., 2021).

We imported the protein-protein interaction network into Cytoscape (v3.10.3) for further analysis and visualization. Using the CytoHubba (Chin et al., 2014) plugin in Cytoscape (Kohl et al., 2011), we performed topological analysis of the network nodes based on algorithms such as MNC, MCC, and Degree. We pinpointed the ten targets that exhibited the highest scores, which were regarded as potential key genes. Degree(Degree Centrality) is an algorithm that assesses the importance of a node by calculating the number of edges directly connected to it. The higher the degree of a node, the more important it is considered to be within the network. MNC (Maximum Neighborhood Component) is an algorithm that evaluates the importance of nodes by identifying key nodes with a larger neighborhood range within the network. MCC (Maximum Clique Centrality) refers to the algorithm that assesses the centrality of nodes by calculating the number of maximum cliques to which a node belongs, thereby identifying nodes that are in key positions within the network.

Then used “VennSchemram” in R to intersect these targets to determine the final hub genes. Additionally, to parse the modular structure the network of interactions between proteins, we employed the MCODE plugin (Bader and Hogue, 2003) with parameters set as degree cutoff = 2, Node Score cutoff = 0.2, K-Core = 2, and Max. Depth = 100 to identify highly interacting subnetwork modules.

The protein configurations of the hub genes are available from the PDB database, including GRIA2 (PDB-ID: 2wjx), GRIN1 (PDB-ID: 8vuv), GRIN2B (PDB-ID: 5ewj), GRM1 (PDB-ID: 3 ks9), GRM5 (PDB-ID: 7p2l), HTR1A (PDB-ID: 8pjk), and HTR2A (PDB-ID: 7wc4) (Noguchi and Akiyama, 2003). Subsequently, we performed molecular docking simulations using the 2D structure of Panaxadiol on the CB-Dock2 platform (cadd.labshare.cn) to investigate the binding capabilities of Panaxadiol with these hub genes (Liu et al., 2022).

U251 cells were purchased from Procell Corp, and U87 cells were sourced from the National Collection of Authenticated Cell Cultures. The cells were cultured in DMEM medium supplemented with 10% fetal bovine serum (FBS). The cells were cultured in an incubator set at 37°C with 5% CO₂.

The objective of this study is to evaluate the effects of Panaxadiol on the survival rates of the U251 and U87 cell lines. Cells were seeded at a density of approximately 3,000 cells per well in 96-well plates, and were subsequently exposed to various concentrations of Panaxadiol. After a continuous incubation for 48 h, aspirate the culture medium from the wells,and then add the CCK-8 reagent, followed by incubation in the cell culture incubator for approximately 1 h.

The objective of this research is to investigate the effects of extended exposure to Panaxadiol on the growth of GBM cells, U251 and U87 cell lines were maintained in 6-well plates, with a seeding density of 800 cells per well. After 24 h of culture to allow cell attachment, cells were subjected to varying concentrations of Panaxadiol for treatment. Following a culture period of 2 weeks, the cells were subjected to fixation using 4% paraformaldehyde. Subsequently, they were stained with crystal violet to facilitate further observation and quantification.

We employed the EdU Cell Proliferation Kit labeled to assess the impact of Panaxadiol on the proliferation capabilities of GBM cells. First, Approximately 3 × 10⁵ cells were plated in confocal dishes and seeded under standard conditions for a duration of 24 h. To label proliferating cells, EdU was added to the culture medium for a period of 2 h. Subsequent to the incubation phase, the cells were subject to fixation using a 4% paraformaldehyde solution for a period of 15 min, followed by permeabilization utilizing a 0.3% Triton X-100 solution for a duration of 10 min. After permeabilization, the cells were placed in the Click Reaction Mixture for incubation, which was prepared following the manufacturer’s guidelines, for a duration of 30 min in a dark environment to facilitate the fluorescent labeling of proliferating cells. Following this, the cells were incubated with Hoechst 33,342 dye for a period of 10 min.

In the apoptosis experiment, we used the Annexin V-FITC/PI Apoptosis Detection Kit (MedChemExpress). Following treatment with Panaxadiol, the cells underwent a washing and digestion process. The resulting cell suspension was then prepared in 195 μL of binding buffer, followed by the addition of 10 μL of Annexin V-FITC and 5 μL of propidium iodide (PI) for staining. The cells were incubated in a dark setting at room temperature for a duration of 15–20 min before analysis was performed using flow cytometry.

We used the Fluo-4 Calcium Assay (Beyotime Biotech) to detect the calcium levels in cells. Following the instructions outlined by the manufacturer, the Fluo-4 Staining Solution was formulated. GBM cells treated with Panaxadiol and seeded in 6-well plates or confocal dishes were washed with PBS. Subsequently, an appropriate volume of Fluo-4 Staining Solution (1 mL for 6-well plates or confocal dishes) was added and incubated at a temperature of 37°C in a dark environment for 30 min. Detection was performed using confocal microscopy and flow cytometry.

To evaluate the effects of Panaxadiol in vivo, we performed tumor xenograft experiments utilizing nude mice that were aged between 4 and 5 weeks. A total of 4 × 10⁶ U87 cells were subcutaneously injected into the mice (n = 5). Mice assigned to the experimental group were administered intraperitoneal injections of Panaxadiol at a dosage of 10 mg/kg every 48 h. Upon completion of the experiment, the mice were humanely euthanized, and the tumors were surgically removed. Tumor volume was calculated using the formula: volume (mm³) = width² × length/2. All experimental procedures received approval from the Institutional Animal Care and Use Committee (IACUC) with approval number SUMC 2023-021 and followed the ARRIVE guidelines established by the National Centre for the Replacement, Refinement, and Reduction of Animals in Research (NC3Rs).

In this research, statistical analysis were conducted utilizing GraphPad Prism (v10.1.2) and R software (v4.3.2). For comparisons between two groups, Student’s t-test was used to assess differences; for comparisons involving more than two groups, one-way analysis of variance (ANOVA) was employed to determine differences. The data are expressed as the mean value along with the standard error of the mean (SEM). Every experiment was conducted independently on three separate occasions. A p-value of less than 0.05 was deemed to indicate statistical significance.

The two-dimensional (2D) and three-dimensional (3D) chemical representations of Panaxadiol are illustrated in (Figures 1A, B), respectively. To obtain targets related to Panaxadiol, we used the CTD, SEA, SwissTargetPrediction, and TargetNet databases to predict and collect its targets, ultimately obtaining a total of 520 related targets (Figures 2A, B; Supplementary Table S1). Subsequently, we downloaded the transcriptomic data of the TCGA-GBM cohort from the TCGA database and conducted differential expression analysis with the “limma” package in R. By setting the threshold to |logFC| > 2 and P < 0.05, we identified 2,129 differentially expressed genes (DEGs) (Figure 2C; Supplementary Table S2). To further explore which diseases the targets of Panaxadiol might be involved in, we performed disease ontology enrichment analysis using these targets (Figure 2D). The results suggest that the targets associated with Panaxadiol are significantly linked to various diseases such as arteriosclerotic cardiovascular disease, musculoskeletal system cancer, and high-grade glioma. Finally, using the “VennSchemram” R package, we intersected the targets of Panaxadiol with the DEGs of GBM, obtaining 66 intersection targets related to both Panaxadiol and GBM (Figure 2E).

The 66 intersection targets related to Panaxadiol and GBM are shown in (Figure 3A). To investigate the potential functional pathways through which Panaxadiol acts on GBM, We conducted enrichment analyses utilizing GO and KEGG frameworks centered on these identified targets. The GO enrichment is primarily focused on physiological processes, including the signaling pathway of G protein-coupled serotonin receptors, blood vessel diameter maintenance, and phosphatidylinositol phospholipase C activity (Figures 3B, C). Significantly, various pathways associated with calcium were notably highlighted, including the voltage-gated calcium channel complex, calcium channel activity, and the overall calcium channel complex (Supplementary Table S3). This suggests that the therapeutic effect of Panaxadiol on GBM may be mediated through calcium. The findings from the KEGG enrichment analysis lent additional credence to this hypothesis, revealing significant enrichment in the Calcium signaling pathway, the cAMP signaling pathway, and the cGMP-PKG signaling pathway (Figures 3D, E). The Calcium signaling pathway exhibited significant enrichment, aligning with the findings from GO enrichment analysis. Consequently, we hypothesize that Panaxadiol predominantly exerts its effects via the calcium signaling pathway, along with other associated pathways, to modulate the biological behavior of GBM cells.

To further explore the association network among the intersection targets, we successfully constructed and visualized a PPI network of the intersection targets by uploading the 66 targets to the STRING database and using Cytoscape software (Figure 4A). Based on this network, the MCODE plugin was employed to pinpoint the most favorable subnetwork, comprising 21 nodes and 84 edges, which achieved a score of 8.4 (Figure 4B). Subsequently, using the MNC, MCC, and Degree algorithms of the CytoHubba plugin, we screened out the top 10 genes from each algorithm. Through the intersection of the outcomes generated by the three algorithms, we were able to pinpoint the most pivotal hub genes, which were GRIA2, GRIN1, GRIN2B, GRM1, GRM5, HTR1A, and HTR2A (Figure 4C).

After identifying the hub genes through which Panaxadiol acts on GBM, we used the CB-Dock database to perform molecular docking simulations to assess the binding capabilities of Panaxadiol with these hub genes. The Vina score results were as follows: GRIA2-2wjx: −10.7, GRIN1-8vuv: −9.6, GRIN2B-5ewj: −8.9, GRM1-3 ks9: −9.2, GRM5-7p2l: −8.7, HTR1A-8pjk: −11, and HTR2A-7wc4: −10.1 (Figures 5A–G; Table 1).

To evaluate the therapeutic effects of Panaxadiol on GBM, we investigated its impact on cell proliferation through in vitro experiments. The findings indicated that Panaxadiol markedly reduced the viability of GBM cells in a concentration-dependent manner, as demonstrated by the CCK-8 assay (Figures 6A–C). Similarly, in the colony formation assay, the quantity of colonies observed in the Panaxadiol-treated cohort was markedly diminished in comparison to the untreated cohort (Figures 6D, E), indicating that Panaxadiol inhibits the long-term proliferation and clonogenic ability of GBM. Furthermore, the EdU assay demonstrated a notable reduction in the number of EdU-positive cells, suggesting that Panaxadiol significantly inhibits DNA synthesis in GBM cells, thereby impeding their proliferative capacity (Figures 6F, G).

Flow cytometry apoptosis assays showed that Panaxadiol significantly induces apoptosis in glioma cells. Further experiments indicated that Panaxadiol inhibits GBM cell proliferation and induces apoptosis by increasing intracellular calcium ion influx (Figure 7A). Intracellular calcium ion levels were detected using flow cytometry and confocal microscopy with Fluo-4 staining. The findings indicated that the intensity of green fluorescence was markedly increased in the group treated with Panaxadiol when compared to the control group, indicating a significant increase in intracellular calcium ion levels (Figures 7B, C). Additionally, we validated the results of network pharmacology and in vitro cell experiments through a U87 cell xenograft tumor experiment in nude mice. In vivo experiments, Panaxadiol was administered via intraperitoneal injection in mice to evaluate its effects on tumor progression. The findings indicated that Panaxadiol exhibited a substantial inhibitory effect on tumor proliferation within the U87 xenograft model. The tumor volume and weight of the excised tumors are shown in (Figure 7D), further confirming the antitumor effects of Panaxadiol.