Acetyl-CoA synthetase (ACSS2) does not generate butyryl- and crotonyl-CoA

ACSS2 and metabolic diseases: from lipid metabolism to therapeutic target

Elevated incidence of metabolic disorders has been reported worldwide in the recent decade, highlighting the need for developing efficient therapies. These diseases result from a complex interplay of various factors that contribute to disease progression, complications, and resistance to current treatment options. Acetyl-CoA Synthetase Short Chain Family Member 2 (ACSS2) is a nucleo-cytosolic enzyme with both lipogenic and metabolic regulatory roles. Studies on ACSS2 have shown that it is involved in pathways commonly dysregulated in metabolic disorders, leading to fat deposition and disrupted cellular signaling. Although multiple studies have suggested a role of ACSS2 in the metabolic rewiring during tumorigenesis, few studies have examined its involvement in the pathophysiology of metabolic diseases. Recent evidence indicates that ACSS2 may contribute to the pathogenesis of various metabolic disorders making its examination of great interest and potentially aiding in the development of new therapeutic strategies. The objective of this review is to summarize the current understanding of ACSS2's role in metabolic disorders and its potential as a therapeutic target.

Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking

BACKGROUND/OBJECTIVES

Breast cancer is the second most common malignancy worldwide and poses a significant threat to women's health. However, the prognostic biomarkers and therapeutic targets of breast cancer are unclear. A prognostic model can help in identifying biomarkers and targets for breast cancer. In this study, a novel prognostic model was developed to optimize treatment, improve clinical prognosis, and screen potential phosphoglycerate kinase 1 (PGK1) inhibitors for breast cancer treatment.

METHODS

Using data from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified in normal individuals and breast cancer patients. The biological functions of the DEGs were examined using bioinformatics analysis. A novel prognostic model was then constructed using the DEGs through LASSO and multivariate Cox regression analyses. The relationship between the prognostic model, survival, and immunity was also evaluated. In addition, virtual screening was conducted based on the risk genes to identify novel small molecule inhibitors of PGK1 from Chemdiv and Targetmol libraries. The effects of the potential inhibitors were confirmed through cell experiments.

RESULTS

A total of 230 up- and 325 down-regulated DEGs were identified in HER2, LumA, LumB, and TN breast cancer subtypes. A new prognostic model was constructed using ten risk genes. The analysis from The Cancer Genome Atlas (TCGA) indicated that the prognosis was poorer in the high-risk group compared to the low-risk group. The accuracy of the model was confirmed using the ROC curve. Furthermore, functional enrichment analyses indicated that the DEGs between low- and high-risk groups were linked to the immune response. The risk score was also correlated with tumor immune infiltrates. Moreover, four compounds with the highest score and the lowest affinity energy were identified. Notably, D231-0058 showed better inhibitory activity against breast cancer cells.

CONCLUSIONS

Ten genes (ACSS2, C2CD2, CXCL9, KRT15, MRPL13, NR3C2, PGK1, PIGR, RBP4, and SORBS1) were identified as prognostic signatures for breast cancer. Additionally, results showed that D231-0058 (2-((((4-(2-methyl-1H-indol-3-yl)-1,3-thiazol-2-yl)carbamoyl)methyl)sulfanyl)acetic acid) may be a novel candidate for treating breast cancer.