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Page 8 of 25 Han et al. Cancer Drug Resist 2024;7:16 https://dx.doi.org/10.20517/cdr.2024.01

Tumor formation experiment in nude mice
In our study, 16 six-week-old specific pathogen-free (SPF) BALB/c nude mice were initially transplanted
6
with 5 × 10 U251 cells in a 100 µL PBS volume in the right upper axilla. The mice were then divided into
two groups (IsocuB group at 2 mg/kg and DMSO group) on day 6 after transplantation. During the
experiment, tumor size was measured daily in both groups of nude mice. On day 18, the masses were
removed and weighed. We assessed and compared the supervisors. These studies were conducted in
compliance with the ARRIVE guidelines [22,23] and the National Research Council Guide for the Care and Use
of Laboratory Animals. All animal experiments followed the regulations of ethics committee of the
Experimental Animal Administration of Sichuan University (NO. K2024006).

Statistical analysis
To ensure the accuracy of the experimental results, each set of experiments was repeated at least three times.
The final data were presented as the mean ± standard deviation (SD), and the differences were statistically
analyzed using GraphPad software (P < 0.05 was considered statistically significant).

RESULTS
Network pharmacology and databases analysis
In this study, the targets of isocuB [Figure 1A] were predicted using the CTD, Swiss Target Prediction, and
PharmMapper databases. A total of 301 predicted targets were identified as potential targets of isocuB. In
this study, we identified potential therapeutic target genes for glioma using the GeneCards, OMIM, and
CTD databases. The results were compiled, yielding 22,320 relevant therapeutic targets. We imported the
280 common genes [Figure 1B, Supplementary Table 1] into the STRING database and obtained the PPI
information for 280 nodes and 631 edges [Figure 1C]. The corresponding PPI information was imported
into Cytoscape software. The analysis revealed that the top five genes affected by isocuB in glioma were
RXRα, AKT1, ESR1, MAPK1, and HSP90AA1 [Figure 1D, Supplementary Table 2]. A total of 826 GO
analyses were performed, and the first 10 enrichment results are visualized in Figure 1E based on the P
value. The analyses were mainly divided into three categories: (1) 278 BP, including response to protein
binding, peptidyl-tyrosine phosphorylation, and protein autophosphorylation; (2) 279 CC, including
cytosol, extracellular exosome, and extracellular region; (3) 279 MF, including signaling receptor activator
activity and identical protein binding activity. KEGG analysis revealed 152 major pathways, with the 20
most significantly enriched pathways shown in Figure 1F. These pathways included the PI3K/AKT signaling
pathway, MAPK signaling pathway, and proteoglycans in cancer signaling pathways. The top five targets
were found to be highly connected in the network according to the PPI analysis, located at the core of the
network, and identified as the most important nodes. The binding affinities of isocuB to RXRα (3HOA),
AKT1 (4EJN), ESR1 (6V8T), MAPK1 (6G54), and HSP90AA1 (4BQG) were -7.41, -6.67, -7.65, -7.95, and
-7.68 kcal/mol [Figure 1G]. In addition, a coupling fraction of less than 0 kcal/mol indicates that the
component can spontaneously bind to the target, less than -4.25 kcal/mol indicates a good affinity coupling,
and less than -7 kcal/mol is considered as strong affinity coupling . The results indicated a strong binding
[24]
activity between the main components and the hub gene (affinity < -6.00 kcal/mol).

Based on RNA-Seq data from the UALCAN database, a total of 7932 case samples were included. The
mRNA expression of RXRα, AKT1, ESR1, MAPK1, and HSP90AA1 was analyzed in different tumor tissues.
The mRNA expression levels of AKT1 and MAPK1 were relatively high in glioma, while those of RXRα,
ESR1, and HSP90AA1 were relatively low [Figure 2A]. The mRNA expression of RXRα, AKT1, ESR1,
MAPK1, and HSP90AA1 in tumor and normal tissues was analyzed using the GEPIA database. The mRNA
expression of RXRα, ESR1, and HSP90AA1 did not show significant differences between tumor tissues and
normal tissues [Figure 2B]. AKT1 and MAPK1 mRNA expression levels were significantly different in