ArticleHeliyon2024
Screening and identification of potential target of 1'-acetoxychavicol acetate (ACA) in acquired lapatinib-resistant breast cancer.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Recent advances in the investigation of the quinazoline nucleus and derivatives with potential anticancer activities.Future medicinal chemistry · 2025Review
- Lapatinib-Resistant HER2+ Breast Cancer Cells Are Associated with Dysregulation of MAPK and p70S6K/PDCD4 Pathways and Calcium Management, Influence of Cryptotanshinone.International journal of molecular sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.