Back

Genomic Discovery of EF-24 Targets Unveils Antitumorigenic Mechanisms in Leukemia Cells

Singh, A. P.; Wax, N.; Duncan, J.; Fernandes, A.; Jacobs, J.

2024-10-16 cancer biology
10.1101/2024.10.14.618197 bioRxiv
Show abstract

Curcumin, a polyphenolic compound derived from the plant Curcuma longa L., has demonstrated a wide range of therapeutic properties, including potential anticancer effects. However, its clinical efficacy is limited due to poor bioavailability and stability. To overcome these challenges, curcumin analogs like EF-24 have been developed with improved pharmacological properties. In this study, we used whole-transcriptome profiling to identify the genome-wide functional impacts of EF-24 treatment in leukemia cells to improve our understanding of its potential mechanisms of action. This approach allowed us to establish a model system for associating druggable genes with clinical disease targets. We used the chronic myeloid leukemia (CML) cell line K-562 and acute myeloid leukemia (AML) cell lines HL-60, Kasumi-1, and THP-1 to conduct EF-24 treatment studies. Cell viability was significantly decreased in the EF-24-treated cells as compared to the untreated controls. We discovered that the genes ATF3, CLU, HSPA6, OSGIN1, ZFAND2A, and CXCL8, which are associated with reduced cell viability and proliferation, were consistently upregulated in all EF-24-treated cell lines. Further analysis of the tested cell lines revealed the activation of various signaling pathways, including the STAT1 regulated S100 family signaling pathway, that controlled downstream gene expression in response to EF-24 treatment. Our results elucidate the molecular mechanisms underlying EF-24s antitumor efficacy against leukemia, highlighting its multifaceted impact on signaling pathways and gene networks that regulate cell survival, proliferation, and immune responses in myeloid leukemia cells. SignificanceThis study reveals how EF-24, a curcumin analog, disrupts key signaling pathways in chronic and acute myeloid leukemia cell lines, offering insights to enhance targeted therapies and provide new targets for investigation. Conflict of Interest StatementThe authors declare no potential conflicts of interest.

Matching journals

The top 16 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.