ACYP1 Human

What is ACYP1 and what are its fundamental characteristics?

ACYP1, also known as acylphosphatase erythrocyte isozyme (ACYPE), is a small cytosolic enzyme that catalyzes the hydrolysis of the carboxyl-phosphate bond of acylphosphates. It belongs to the acylphosphatase family and contains one fibrinogen C-terminal domain. Two acylphosphatase isoenzymes have been isolated: muscle acylphosphatase and erythrocyte acylphosphatase (ACYP1), distinguished by their tissue localization. ACYP1 is the erythrocyte isoenzyme encoded by the ACYP1 gene and functions as a metabolism-related gene associated with tumor initiation and progression .

What experimental approaches are recommended for studying ACYP1 expression?

For studying ACYP1 expression, researchers should consider multiple complementary techniques. Immunohistochemistry using specific anti-ACYP1 antibodies (such as ab231323 from Abcam at 1:100 dilution) is effective for protein detection in tissue samples, with H-scores recommended for quantifying staining intensity . For transcriptional analysis, RNA sequencing and database mining using resources like Oncomine, TIMER, GEPIA, and UALCAN provide valuable expression data across cancer types. For functional studies, recombinant ACYP1 protein expressed in E. coli with His-tags can be utilized for in vitro assays , while gene manipulation via siRNA or overexpression vectors enables investigation of ACYP1's biological roles in cellular and animal models .

How does ACYP1 expression vary across different cancer types?

ACYP1 expression shows significant variation across cancer types, with predominantly elevated expression in most cancers compared to corresponding normal tissues. Analysis of the Oncomine and TIMER databases reveals that ACYP1 is significantly overexpressed in numerous cancer types . Specifically, ACYP1 is upregulated in lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), pancreatic adenocarcinoma (PAAD), skin cutaneous melanoma (SKCM), and thymoma (THYM). Conversely, decreased ACYP1 expression has been observed in uterine carcinosarcoma (UCS), brain lower grade glioma, head and neck cancer, and breast cancer . This differential expression pattern suggests context-dependent roles for ACYP1 in different tumor microenvironments.

What is the prognostic significance of ACYP1 expression in cancer patients?

Interestingly, ACYP1 demonstrates variable prognostic value in other cancers. It acts as an adverse prognostic factor in breast cancer (OS: HR=6.97, p=0.003) and soft tissue cancer (OS: HR=1.58, p=0.025; RFS: HR=1.68, p=0.034), but serves as a protective factor in head and neck squamous cell carcinoma (HNSC) (OS: HR=0.4, p=0.018; RFS: HR=0.33, p=0.047) and lung squamous cell carcinoma (LUSC) (OS: HR=0.73, p=0.028) . This variability highlights the context-dependent functions of ACYP1 in different tumor types.

How does ACYP1 regulate glycolysis in cancer cells?

ACYP1 serves as a key regulator of aerobic glycolysis (the Warburg effect) in cancer cells through its involvement in the ACYP1/HSP90/MYC/LDHA axis. RNA sequencing has revealed that ACYP1 markedly enhances the expression of genes related to aerobic glycolysis, with LDHA (lactate dehydrogenase A) identified as a critical downstream target of ACYP1 . Overexpression of ACYP1 upregulates LDHA levels, which subsequently increases the malignancy potential of cancer cells. Gene Set Enrichment Analysis (GSEA) has further shown enrichment of differentially expressed genes in the MYC pathway, indicating a positive correlation between MYC and ACYP1 levels . The mechanistic pathway involves ACYP1 binding to HSP90, which regulates c-Myc protein expression and stability. This HSP90-dependent interaction activates the MYC/LDHA axis, promoting glycolysis and contributing to tumor progression .

What pathways are enriched in ACYP1-associated gene signatures?

ACYP1 is implicated in multiple signaling pathways, particularly those related to metabolism and immune function. GSEA and GSVA analyses have identified significant enrichment of metabolism-related pathways, including glycolysis gluconeogenesis, sphingolipid metabolism, pyruvate metabolism, mTOR signaling, inositol phosphate metabolism, aminoacyl transfer biosynthesis, pyrimidine metabolism, and purine metabolism . Beyond metabolic pathways, Hallmark GSEA and GSVA analyses have revealed enrichment of immune-related pathways, including TNFα signaling, allograft rejection, inflammatory response, and interferon-gamma response in ACYP1-high expressing cancers . This dual involvement in both metabolic and immune pathways underscores ACYP1's multifaceted role in cancer biology, affecting both energy metabolism and the tumor immune microenvironment.

How does ACYP1 expression correlate with immune cell infiltration in tumors?

ACYP1 expression significantly correlates with the infiltration of various immune cell populations in the tumor microenvironment, suggesting an immunomodulatory role. In LIHC, ACYP1 expression shows positive correlations with multiple immune cell types, including B cells (R=0.264, p=6.66e-07), CD8+ T cells (R=0.125, p=2.05e-02), CD4+ T cells (R=0.201, p=1.75e-04), Tregs (R=0.361, p=4.42e-12), NK cells (R=0.201, p=1.70e-04), dendritic cells (DCs) (R=0.419, p=4.19e-16), and myeloid-derived suppressor cells (MDSCs) (R=0.565, p=1.84e-30) . Notably, ACYP1 shows negative correlation with macrophages (R=-0.414, p=9.43e-16) and monocytes (R=-0.253, p=1.87e-06) . These correlations suggest that ACYP1 may differentially modulate the recruitment and function of specific immune cell populations, potentially contributing to the immunosuppressive tumor microenvironment in LIHC.

What is the relationship between ACYP1 and specific immune cell markers?

ACYP1 expression demonstrates significant correlations with markers of various immune cell subsets in LIHC. Analysis of 49 immune cell markers revealed that ACYP1 expression significantly correlates with 45 of these markers . Particularly strong correlations were observed with markers of MDSCs, Th2 cells, dendritic cells, and macrophages . These correlations extend to markers of T-cell subsets, including CD8+ T cells, Th1 cells, Th17 cells, Tregs, and exhausted T cells, as well as different types of macrophages, including M1, M2, and tumor-associated macrophages. This extensive correlation profile suggests that ACYP1 may influence the differentiation, recruitment, or function of specific immune cell subsets, thereby modulating the tumor immune microenvironment and potentially affecting patient outcomes.

How does ACYP1 contribute to HCC progression and clinical features?

ACYP1 promotes HCC progression through multiple mechanisms and correlates with several clinical features. Functionally, ACYP1 enhances the proliferation, invasion, and migration capacities of HCC cells both in vitro and in vivo . At the molecular level, ACYP1 activates the MYC/LDHA axis to promote glycolysis and drive tumor progression . Clinically, high ACYP1 expression in HCC is associated with specific patient characteristics, including male sex, Asian race, early tumor stage, tumor grade, AJCC T stage, microvascular invasion, history of alcohol consumption, and hepatitis virus infection . Furthermore, ACYP1 is significantly upregulated in patients with early recurrence of HCC, indicating its potential role in disease relapse . These associations make ACYP1 a valuable prognostic indicator for HCC patients.

What is the role of ACYP1 in lenvatinib resistance in HCC?

ACYP1 plays a critical role in driving lenvatinib resistance in HCC through the ACYP1/HSP90/MYC/LDHA axis. Lenvatinib resistance is positively associated with ACYP1 expression levels in HCC . Mechanistically, ACYP1 binds to HSP90, which regulates c-Myc protein expression and stability in an HSP90-dependent manner. This interaction activates the MYC/LDHA axis, promoting glycolysis and contributing to drug resistance . Importantly, targeting ACYP1 remarkably decreases lenvatinib resistance and inhibits progression of HCC tumors with high ACYP1 expression when combined with lenvatinib both in vitro and in vivo . This synergistic effect suggests that ACYP1 inhibition could be a promising strategy to overcome lenvatinib resistance and improve treatment outcomes in HCC patients.

What methodological approaches should be used to investigate ACYP1's protein interactions?

To rigorously investigate ACYP1's protein interactions, researchers should employ a multi-modal approach combining biochemical, proteomic, and structural techniques. Co-immunoprecipitation (Co-IP) assays have successfully identified ACYP1-HSP90 interactions and should be paired with mass spectrometry analysis for unbiased identification of binding partners . Proximity ligation assays and fluorescence resonance energy transfer (FRET) can provide spatial information about these interactions within intact cells. For detailed structural characterization, X-ray crystallography or cryo-electron microscopy of purified recombinant ACYP1 (available as His-tagged protein expressed in E. coli ) complexed with its binding partners can elucidate interaction interfaces. Mutational analysis targeting specific residues can further validate these interaction sites and determine their functional significance. For in vivo validation, transgenic animal models with ACYP1 manipulation should be used to confirm the physiological relevance of identified interactions.

How can the contradictory prognostic roles of ACYP1 across different cancer types be explained?

The contradictory prognostic roles of ACYP1 across cancer types likely reflect context-dependent functions influenced by tissue-specific factors. To elucidate these contradictions, researchers should conduct comprehensive multi-omics analyses comparing ACYP1-high and ACYP1-low tumors across different cancer types, with particular focus on comparing cancers where ACYP1 has opposing prognostic impacts (e.g., HCC vs. HNSC). Single-cell RNA sequencing would reveal cell type-specific ACYP1 expression patterns and associated pathway activations. Comparative pathway analysis between cancer types might identify divergent downstream effectors or compensatory mechanisms. Tumor microenvironment characterization through spatial transcriptomics and multiplexed immunohistochemistry could reveal how ACYP1 differentially modulates immune infiltration across cancer types. Finally, developing conditional knockout models in multiple tissue types would allow direct comparison of ACYP1's functional impact in different cellular contexts, potentially explaining its variable prognostic significance.

What are the most promising approaches for developing ACYP1-targeted therapies?

Developing ACYP1-targeted therapies represents a promising direction, particularly for HCC treatment. Based on current knowledge of the ACYP1/HSP90/MYC/LDHA axis, several approaches should be pursued. Structure-based drug design targeting the ACYP1-HSP90 interaction interface could yield specific inhibitors that disrupt this critical complex. Small molecule screening against ACYP1's enzymatic activity might identify direct inhibitors that block its catalytic function. RNA interference or antisense oligonucleotides could be developed for targeted ACYP1 downregulation. Since ACYP1 modulates glycolysis, combination therapies targeting both ACYP1 and glycolytic enzymes might produce synergistic effects. For HCC specifically, combining ACYP1 inhibition with lenvatinib has already shown promise in preclinical models . Any therapeutic approach would require careful patient stratification based on ACYP1 expression levels and cancer type, given its variable prognostic significance across different malignancies.

How might ACYP1's role in metabolism extend beyond glycolysis to other metabolic pathways?

ACYP1's metabolic influence likely extends beyond glycolysis to multiple metabolic pathways that warrant further investigation. Pathway analyses have implicated ACYP1 in sphingolipid metabolism, pyruvate metabolism, mTOR signaling, inositol phosphate metabolism, aminoacyl transfer biosynthesis, pyrimidine metabolism, and purine metabolism . Metabolomic profiling of cells with ACYP1 manipulation would provide comprehensive insights into its metabolic impact. Stable isotope tracing experiments using 13C-labeled glucose, glutamine, or fatty acids could reveal how ACYP1 affects carbon flux through different metabolic pathways. Investigation of ACYP1's interaction with key metabolic enzymes beyond LDHA might uncover novel regulatory mechanisms. Additionally, examining how ACYP1-mediated metabolic reprogramming influences the tumor microenvironment and immune cell function could explain its correlations with immune cell infiltration. This expanded understanding of ACYP1's metabolic functions could reveal new therapeutic vulnerabilities in ACYP1-overexpressing cancers.

What factors should be considered when designing experiments to study ACYP1 in cancer models?

When designing experiments to study ACYP1 in cancer models, researchers should consider several critical factors to ensure robust and clinically relevant results. Cell line selection should include multiple lines with varying baseline ACYP1 expression levels to capture heterogeneity. Patient-derived xenografts or organoids would provide more physiologically relevant models than traditional cell lines. Gene manipulation should employ both knockdown and overexpression approaches to establish causality. For in vivo studies, both subcutaneous and orthotopic tumor models should be considered, as the latter better recapitulate the native tumor microenvironment. Immune-competent models are essential when studying ACYP1's immunomodulatory effects. Given ACYP1's variable prognostic significance across cancer types, comparative studies in multiple cancer models would provide important insights. Finally, when investigating ACYP1's role in drug resistance, researchers should monitor dynamic changes in ACYP1 expression during treatment rather than relying solely on baseline expression levels.