Identification of potential targets of the curcumin analog CCA-1.1 for glioblastoma treatment : integrated computational analysis and in vitro study

Abstract

The treatment of glioblastoma multiforme (GBM) is challenging owing to its localization in the brain, the limited capacity of brain cells to repair, resistance to conventional therapy, and its aggressiveness. Curcumin has anticancer activity against aggressive cancers, such as leukemia, and GBM; however, its application is limited by its low solubility and bioavailability. Chemoprevention curcumin analog 1.1 (CCA-1.1), a curcumin analog, has better solubility and stability than those of curcumin. In this study, we explored potential targets of CCA-1.1 in GBM (PTCGs) by an integrated computational analysis and in vitro study. Predicted targets of CCA-1.1 obtained using various databases were subjected to comprehensive downstream analyses, including functional annotation, disease and drug association analyses, protein-protein interaction network analyses, analyses of genetic alterations, expression, and associations with survival and immune cell infiltration. Our integrative bioinformatics analysis revealed four candidate targets of CCA-1.1 in GBM: TP53, EGFR, AKT1, and CASP3. In addition to targeting specific proteins with regulatory effects in GBM, CCA-1.1 has the capacity to modulate the immunological milieu. Cytotoxicity of CCA-1.1 was lower than TMZ with an IC50 value of 9.8 μM compared to TMZ with an IC50 of 40 μM. mRNA sequencing revealed EGFR transcript variant 8 was upregulated, whereas EGFRvIII was downregulated in U87 cells after treatment with CCA-1.1. Furthermore, a molecular docking analysis suggested that CCA-1.1 inhibits EGFR with various mutations in GBM, which was confirmed using molecular dynamics simulation, wherein the binding between CCA-1.1 with the mutant EGFR L861Q was stable. For successful clinical translation, the effects of CCA-1.1 need to be confirmed in laboratory studies and clinical trials.

Figure 1

Chemical structures of curcumin, PGV-1, and CCA-1.1.

Figure 2

Work flow of the study.

Figure 3

(A) Venn diagram showing 268 potential targets of CCA-1.1 against GBM (PTCG). (B) Gene Ontology enrichment analysis of the PTCGs. (C) Disease–gene association analysis of the PTCGs. (D) Drug–gene association analysis of the PTCGs. (E) Protein–protein interaction network of the PTCGs. (F) Top 10 protein in the network, ranked by degree, as analyzed by CytoScape.

Figure 4

(A) Summary of alterations in 10 PTCG reported in GBM studies using cBioportal. (B) OncoPrint analysis of 10 PTCGs in patients with GBM from TCGA PanCancer Atlas study, as analyzed using cBioportal. (C) Pathway enrichment analysis related to genetic alterations in 10 PTCGs in patients with GBM from TCGA PanCancer Atlas, as analyzed using cBioportal. (D) Copy number alterations of 10 PTCGs in patients with GBM from TCGA PanCancer Atlas, as analyzed using cBioportal. Statistical analysis was performed using one-way ANOVA with Tukey’s multiple comparison test. Mutation profiles of (E) EGFR and (F) TP53 in patients with GBM from TCGA PanCancer Atlas, as analyzed using cBioportal.

Figure 5

(A) Gene expression analysis of 10 PTCGs in patients with GBM and adjacent normal tissues from TCGA, as analyzed using GEPIA. (B) Relationships between the overall survival of patients with GBM and the expression of 10 PTCGs, as analyzed using TIMER 2.0.

Figure 6

(A) Cytotoxicity of CCA-1.1 and (B) TMZ in U87 glioblastoma cells. Cytotoxicity was determined using an MTT assay and presented as cell viability as explained in the methods section. Results are shown as the average of the three independent experiments (mean ± SD). (C) Heat map of top 100 DEGs between the U87 cells treated with CCA-1.1 and the DMSO.

Figure 7

(A) 3D visualization of EGFR mutations, E709K (Glu709 Lys709), T263P (Thr263 Pro263), V774M (Val774 Met774), and L861Q (Leu861 Gln861). (B) Visualization of molecular docking results for wild-type EGFR and mutant EGFR (E709K, T263P, V774M, and L861Q) against Curcumin, CCA-1.1, and PGV-1. (C) Visualization of the binding interaction of compounds (Curcumin, CCA-1.1, and PGV-1) against mutant EGFR L861Q at the initial time and after 1 ns MD simulation. (D) Root mean squared deviation (RMSD) of compounds (Curcumin, CCA-1.1, and PGV-1) in complex with mutant EGFR L861Q after 1 ns MD, shown in 100 frames.