Screening and identification of potential target of 1'-acetoxychavicol acetate (ACA) in acquired lapatinib-resistant breast cancer

Abstract

1'-Acetoxychavicol acetate (ACA) eliminates breast cancer cells via the HER2/MAPK/ERK1/2 and PI3K/AKT pathways, and it also directly influences endocrine resistance by both enhancing pro-apoptotic signals and suppressing pro-survival molecules. This study utilized bioinformatics to assess ACA target genes for lapatinib-resistant breast cancer. We identified differentially expressed genes (DEGs) using GSE16179 microarray data. DEGs from ACA-treated and lapatinib-resistant cells were analyses using Panther DB, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and protein-protein interaction (PPI) network analysis. Genomic mutations, expression levels, prognostic significance, and ROC analysis were examined in selected genes. We used AutoDock Vina to conduct ACA molecular docking with potential target genes. In the PPI network analysis, BCL2, CXCR2, and CDC42 were the three highest-scoring genes. Genetic modification analysis identified PLAU and SSTR3 as the genes most frequently altered in breast cancer samples. The RTK-Ras pathway is likely to be affected by changes in BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET genes. Patients with breast cancer who had lower levels of BCL2, SSTR3, PLAU, ICAM1, IGF1R, and MET had worse overall survival compared to other groups. ACA exhibited moderate binding affinity to BCL2, SSTR3, PLAU, ICAM1, IGF1R, and MET. Overall, ACA might counteract breast cancer resistance to lapatinib by targeting BCL2, SSTR3, PLAU, ICAM1, IGF1R, and MET. Further in vitro studies involving gene silencing could provide more detailed insights into the mechanism by which ACA combats lapatinib resistance.

Keywords: 1′-acetoxychavicol acetate; Bioinformatics; Breast cancer; Lapatinib-resistant; Targeted therapy.

Fig. 1

(a) Structure of 1′-acetoxychavicol acetate (ACA). (b) Venn diagram of protein target prediction of ACA. (c) Protein class of overlapped genes. (d) Gene ontology (GO) analysis of overlapped genes using WebGestalt. (e) Network interaction using STRING. (f) Top-20 genes as potential therapeutic targets of ACA (PTCA).

Fig. 2

cBioPortal examination for genetic alterations and pathway of PTCA. (a) Genetic changes in 26 studies on breast cancer. (b) RTK-RAS signaling pathway of PTCA. (c) Summary of genetic alterations in BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET in The Metastatic Breast Cancer Project (Provisional, December 2021) samples. (d) Mutations of genes BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET in breast cancer samples. Green dots represent missense mutations, yellow dots represent splice mutations, and gray dots represent truncating mutations. (e) Copy number of alterations of BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET across breast cancer samples. 1: deep deletion, 2: shallow deletion, 3: diploid, 4: gain, and 5: amplification.

Fig. 2

cBioPortal examination for genetic alterations and pathway of PTCA. (a) Genetic changes in 26 studies on breast cancer. (b) RTK-RAS signaling pathway of PTCA. (c) Summary of genetic alterations in BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET in The Metastatic Breast Cancer Project (Provisional, December 2021) samples. (d) Mutations of genes BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET in breast cancer samples. Green dots represent missense mutations, yellow dots represent splice mutations, and gray dots represent truncating mutations. (e) Copy number of alterations of BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET across breast cancer samples. 1: deep deletion, 2: shallow deletion, 3: diploid, 4: gain, and 5: amplification.

Fig. 3

Analysis of BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET expression in breast cancer. (a) mRNA expression BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET in normal and breast cancer tissues. (Normal = 291, Tumor = 1085, and p < 0.01). (b) The mRNA levels and tumor stages in breast cancer patients (p < 0.01). (c) Survival prediction of BCL2, CXCR2, CDC42, SSTR3, PLAU, ICAM1, IGF1R, and MET, as analyzed by KMPlotter.

Fig. 4

Molecular docking interactions between the BCL2, PLAU, ICAM1, IGF1R, and MET, their native ligands, and ACA.