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Page 4 of 25 Han et al. Cancer Drug Resist 2024;7:16 https://dx.doi.org/10.20517/cdr.2024.01

Obtaining intersection target genes of isocuB and glioma
We utilized the Bioinformatics & Evolutionary Genomics website (http://bioinformatics.psb.ugent.be/
webtools/Venn/) to identify common targets at the intersection of the aforementioned glioma therapeutic
targets and isocuB target genes. Subsequently, we generated a Venn diagram.

Protein-protein interaction network construction
We imported the previously obtained intersection target genes of isocuB and glioma into the Search Tool
for the Retrieval of Interacting Genes (STRING) database (https://string-db.org/). We specified the species
as “Homo sapiens” and selected the parameter with the highest confidence level of 0.900 to retrieve a related
file in the “.tsv” format. We then conducted a topological analysis using the Cytoscape software (https://
cytoscape.org/) to identify the core genes with the strongest correlation.

Analysis of biological functions and pathways
To predict the correlation pathway and function of isocuB in glioma, we used the Database for Annotation,
Visualization, and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) to conduct Gene Ontology
(GO) process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses (P <
0.05). Based on the above results, we utilized the R programming language to visualize the GO and KEGG
pathway analyses. GO enrichment involves analyses of molecular function (MF), cell component (CC), and
biological process (BP). KEGG is a bioinformatics resource used to identify a comprehensive list of genes
that significantly impact metabolic pathways.

Molecular docking analysis
To improve the prediction of the relationship between isocuB and the top five related genes in the protein-
protein interaction (PPI) analysis, we performed molecular association prediction. First, we obtained the
structure of the gene from the Protein Data Bank (PDB) (http://www.rcsb.org) and acquired the 3D
structure of isocuB from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Next, we removed
the hydrogen and ligands and dehydrated the hub gene proteins using the PyMOL 1.7.x software (http://
www.pymol.org/). The AutoDock Tools 1.5.6 software (https://autodock.scripps.edu/) was used to convert
the file to the PDBQT format. The PyMOL software was used to visualize the docking results.

Research based on database data
The hub genes (RXRα, AKT1, ESR1, MAPK1, and HSP90AA1) were identified, and their mRNA expression
levels in various tumor tissues were analyzed using the University of Alabama at Birmingham (UALCAN)
platform (https://ualcan.path.uab.edu/analysis.html). We analyzed the mRNA expression levels in glioma
and related factors using the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/).
The top five gene mutations and copy number variants were analyzed using the cBioPortal for Cancer
Genomics database (https://www.cbioportal.org/). The CGGA database was utilized to analyze the
correlation between the mRNA expression levels of each gene and the clinical parameters and prognosis
associated with glioma. The GEPIA database, which includes all glioma data and group cutoffs from The
Cancer Genome Atlas (TCGA), was selected as the reference. The mRNA expression levels of each gene in
the gliomas were confirmed using GEPIA. The mRNA expression levels of each gene in gliomas were
verified, and their relationship with prognosis was analyzed.

Experimental verification
Cell culture and drug
Human glioma cells (U251 and U87, obtained from Shanghai Zhong Qiao Xin Zhou Biotechnology Co.,
Ltd., China) were cultured in complete DMEM (Shanghai Zhong Qiao Xin Zhou Biotechnology Co., Ltd.,