ACSM2A
- Known as:
- ACSM2A
- Catalog number:
- 001051A
- Product Quantity:
- 250ul
- Category:
- -
- Supplier:
- ABM
- Gene target:
- ACSM2A
Related genes to: ACSM2A
- Gene:
- ACSM2A NIH gene
- Name:
- acyl-CoA synthetase medium chain family member 2A
- Previous symbol:
- ACSM2
- Synonyms:
- A-923A4.1, MGC150530
- Chromosome:
- 16p12.3
- Locus Type:
- gene with protein product
- Date approved:
- 2007-06-20
- Date modifiied:
- 2019-03-22
Related products to: ACSM2A
Related articles to: ACSM2A
- Ischemic stroke (IS) is a leading cause of mortality and long-term disability worldwide, with ultra-early diagnosis critical for effective reperfusion therapy but challenged by nonspecific symptoms and limited sensitivity of imaging in the hyperacute phase. Plasma metabolomic alterations occur rapidly after ischemia onset, offering potential for accessible biomarkers and deeper insights into underlying pathogenic mechanisms. We therefore aimed to explore plasma metabolic alterations and their associated regulatory networks in IS. We conducted a multicenter case–control study involving 493 IS patients and 493 controls individually matched for age, sex, and body mass index (BMI). Untargeted LC–MS-based metabolomic profiling was performed on plasma samples to quantify and identify differentially abundant metabolites. Machine learning models, including LASSO and XGBoost, were used to select diagnostic biomarkers and construct a classification model. Model performance was evaluated using ten-fold cross-validation, an internal test set, and two independent external validation cohorts. Core regulatory genes were identified by integrating metabolite-related genes from HMDB with transcriptomic data from the GEO dataset. Functional enrichment, single-cell RNA sequencing, and drug-gene interaction analyses were further employed. We identified 319 endogenous differential metabolites, among which 18 core metabolites were selected to build an XGBoost diagnostic model. L-Methionine (Log2FC = 4.97), Purine (Log2FC = − 3.12), and Threonic acid (Log2FC = − 1.74) were the most influential contributors. The metabolite-based model showed strong internal discrimination under ten-fold cross-validation and internal testing; however, external validation was heterogeneous, with substantially reduced discrimination in the Ningbo cohort and preserved performance in the Suzhou cohort. This pattern suggests center-dependent variability and potential optimism in internally estimated performance, warranting cautious interpretation. Furthermore, ten core regulatory genes (e.g., MAOA, MSRB2, ACSM2A) were identified and implicated in neurotransmitter metabolism, oxidative stress, and fatty acid activation. Single-cell analysis revealed cell-type-specific expression patterns of the detectable core genes in endothelial cells and microglia. Drug prediction highlighted several repurposable compounds, including levodopa and tryptamine, with predicted binding affinity to target proteins. This multi-omics study characterizes a plasma metabolic signature associated with IS while highlighting cohort-dependent variability in external validation. Although internally derived diagnostic performance was high, heterogeneous external validation underscores the importance of cautious interpretation and prospective real-world validation. Overall, these findings should be regarded as hypothesis-generating and require confirmation in larger, consecutively recruited real-world populations. - Source: PubMed
Publication date: 2026/03/24
Guo ZhiyuanZhang HaijunLv LiweiDing PingQi LingyanWang XiaokunChen YanYao YingshuiHan Liyuan - To investigate the causal relationship between mitochondrial genes and the pathogenesis of carotid plaque (CP), a multiomics-integrated Mendelian randomization (MR) analysis was performed in this study. - Source: PubMed
Publication date: 2025/11/01
Yu ZhuyuanMeng XiangyuanZong ZiyuSong QiHuo YingchaoChen Hao - Given the crucial role of mitochondria in the prognosis and treatment of hepatocellular carcinoma (HCC), we aim to develop two independent mitochondrial scoring systems to separately predict patient prognosis and the likelihood of transarterial chemoembolization non-response (TACE NR). Mitochondria-related candidate genes were selected and analyzed using univariate Cox and LASSO Cox regression analyses to create a risk prognosis score (RPS). Univariate and LASSO logistic regression analyses were used to establish the risk diagnosis score (RDS). Alternative therapies for patients with TACE NR were explored using TIDE and oncoPredict algorithms. The Seurat package was used to study the involvement of the RDS genes in HCC differentiation. The RPS accurately predicts the 1-5 year survival rates of patients with HCC, where higher RPS values were associated with poorer survival outcomes. The RDS model demonstrated a commendable performance in diagnosing TACE NR, as patients with a higher RDS exhibited a greater likelihood of TACE NR. RDS was associated with the infiltration of various immune cells, and patients with lower RDS tended to have higher response rates to immunotherapy and increased sensitivity to JAK1, rapamycin, and AZD2014. By contrast, patients with higher RDS values and a higher probability of TACE NR had more responsive to paclitaxel, dasatinib, and vincristine, suggesting that these drugs are potential alternative therapies. Single-cell sequencing studies have identified ACSM2A as a key player in HCC differentiation and a potential target for therapeutic intervention. The RPS and RDS are important reference points for predicting outcomes and guiding treatment decisions in patients with HCC. Additionally, ACSM2A shows promise as a potential therapeutic target for HCC. - Source: PubMed
Publication date: 2025/01/01
Ma Jian-YingWei WeiWang Yi-XianZhao Zhen-YuXiong Zhen-YuMei JieWu Wen-ZeGuo Jia-Wei - Histone deacetylase (HDAC) family can remove acetyl groups from histone lysine residues, and their high expression is closely related to the poor prognosis of hepatocellular carcinoma (HCC) patients. Recently, it has been reported to play an immunosuppressive role in the microenvironment, but little is known about the mechanism. - Source: PubMed
Publication date: 2022/09/30
Teng LinxinLi ZhengjunShi YipengGao ZihanYang YangWang YunshanBi Lei - Goat milk is rich in fat and protein, thus, has high nutritional values and benefits human health. However, goaty flavour is a major concern that interferes with consumer acceptability of goat milk and the 4-alkyl-branched-chain fatty acids (vBCFAs) are the major substances relevant to the goaty flavour in goat milk. Previous research reported that the acyl-coenzyme A synthetases (ACSs) play a key role in the activation of fatty acids, which is a prerequisite for fatty acids entering anabolic and catabolic processes and highly involved in the regulation of vBCFAs metabolism. Although ACS genes have been identified in humans and mice, they have not been systematically characterized in goats. In this research, we performed genome-wide characterization of the ACS genes in goats, identifying that a total of 25 ACS genes (without ) were obtained in the and each ACS protein contained the conserved AMP-binding domain. Phylogenetic analysis showed that out of the 25 genes, 21 belonged to the ACSS, ACSM, ACSL, ACSVL, and ACSBG subfamilies. However, , , , and genes were not classified in the common evolutionary branch and belonged to the ACS superfamily. The genes in the same clade had similar conserved structures, motifs and protein domains. The expression analysis showed that the majority of ACS genes were expressed in multi tissues. The comparative analysis of expression patterns in non-lactation and lactation mammary glands of goat, sheep and cow indicated that and genes may participate in the formation mechanisms of goaty flavour in goat milk. In conclusion, current research provides important genomic resources and expression information for ACSs in goats, which will support further research on investigating the formation mechanisms of the goaty flavour in goat milk. - Source: PubMed
Publication date: 2022/09/07
Zhang FuhongLuo JunShi ChenboZhu LuHe QiuyaTian HuibinWu JiaoZhao JianqingLi Cong