L-Dopa-Decarboxylase (DDC) Is a Positive Prognosticator for Breast Cancer Patients and Epinephrine Regulates Breast Cancer Cell (MCF7 and T47D) Growth In Vitro According to Their Different Expression of Gi- Protein- Coupled Receptors

Constructing a Novel Amino Acid Metabolism Signature: A New Perspective on Pheochromocytoma Diagnosis, Immune Landscape, and Immunotherapy

Pheochromocytoma/paraganglioma (PGPG) is a rare neuroendocrine tumor. Amino acid metabolism is crucial for energy production, redox balance, and metabolic pathways in tumor cell proliferation. This study aimed to build a risk model using amino acid metabolism-related genes, enhancing PGPG diagnosis and treatment decisions. We analyzed RNA-sequencing data from the PCPG cohort in the GEO dataset as our training set and validated our findings using the TCGA dataset and an additional clinical cohort. WGCNA and LASSO were utilized to identify hub genes and develop risk prediction models. The single-sample gene set enrichment analysis, MCPCOUNTER, and ESTIMATE algorithm calculated the relationship between amino acid metabolism and immune cell infiltration in PCPG. The TIDE algorithm predicted the immunotherapy efficacy for PCPG patients. The analysis identified 292 genes with differential expression, which are involved in amino acid metabolism and immune pathways. Six genes (DDC, SYT11, GCLM, PSMB7, TYRO3, AGMAT) were identified as crucial for the risk prediction model. Patients with a high-risk profile demonstrated reduced immune infiltration but potentially higher benefits from immunotherapy. Notably, DDC and SYT11 showed strong diagnostic and prognostic potential. Validation through quantitative Real-Time Polymerase Chain Reaction and immunohistochemistry confirmed their differential expression, underscoring their significance in PCPG diagnosis and in predicting immunotherapy response. This study's integration of amino acid metabolism-related genes into a risk prediction model offers critical clinical insights for PCPG risk stratification, potential immunotherapy responses, drug development, and treatment planning, marking a significant step forward in the management of this complex condition.

Molecular signature of stem-like glioma cells (SLGCs) from human glioblastoma and gliosarcoma

Glioblastoma multiforme (GBM) and the GBM variant gliosarcoma (GS) are among the tumors with the highest morbidity and mortality, providing only palliation. Stem-like glioma cells (SLGCs) are involved in tumor initiation, progression, therapy resistance, and relapse. The identification of general features of SLGCs could contribute to the development of more efficient therapies. Commercially available protein arrays were used to determine the cell surface signature of eight SLGC lines from GBMs, one SLGC line obtained from a xenotransplanted GBM-derived SLGC line, and three SLGC lines from GSs. By means of non-negative matrix factorization expression metaprofiles were calculated. Using the cophenetic correlation coefficient (CCC) five metaprofiles (MPs) were identified, which are characterized by specific combinations of 7-12 factors. Furthermore, the expression of several factors, that are associated with GBM prognosis, GBM subtypes, SLGC differentiation stages, or neural identity was evaluated. The investigation encompassed 24 distinct SLGC lines, four of which were derived from xenotransplanted SLGCs, and included the SLGC lines characterized by the metaprofiles. It turned out that all SLGC lines expressed the epidermal growth factor EGFR and EGFR ligands, often in the presence of additional receptor tyrosine kinases. Moreover, all SLGC lines displayed a neural signature and the IDH1 wildtype, but differed in their p53 and PTEN status. Pearson Correlation analysis identified a positive association between the pluripotency factor Sox2 and the expression of FABP7, Musashi, CD133, GFAP, but not with MGMT or Hif1α. Spherical growth, however, was positively correlated with high levels of Hif1α, CDK4, PTEN, and PDGFRβ, whereas correlations with stemness factors or MGMT (MGMT expression and promoter methylation) were low or missing. Factors highly expressed by all SLGC lines, irrespective of their degree of stemness and growth behavior, are Cathepsin-D, CD99, EMMPRIN/CD147, Intβ1, the Galectins 3 and 3b, and N-Cadherin.

A computational study on structural and functional consequences of nsSNPs in human dopa decarboxylase

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson's disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches.Communicated by Ramaswamy H. Sarma.

Active site serine-193 modulates activity of human aromatic amino acid decarboxylase

Aromatic amino acid decarboxylase is a pyridoxal 5'-phosphate-dependent enzyme responsible for the synthesis of the neurotransmitters, dopamine and serotonin. Here, by a combination of bioinformatic predictions and analyses, phosphorylation assays, spectroscopic investigations and activity measurements, we determined that Ser-193, a conserved residue located at the active site, can be phosphorylated, increasing catalytic efficiency. In order to determine the molecular basis for this functional improvement, we determined the structural and kinetic properties of the site-directed variants S193A, S193D and S193E. While S193A retains 27% of the catalytic efficiency of wild-type, the two acidic side chain variants are impaired in catalysis with efficiencies of about 0.15% with respect to the wild-type. Thus, even if located at the active site, Ser-193 is not essential for enzyme activity. We advance the idea that this residue is fundamental for the correct architecture of the active site in terms of network of interactions triggering catalysis. This role has been compared with the properties of the Ser-194 of the highly homologous enzyme histidine decarboxylase whose catalytic loop is visible in the spatial structure, allowing us to propose the validation for the effect of the phosphorylation. The effect could be interesting for AADC deficiency, a rare monogenic disease, whose broad clinical phenotype could be also related to post translational AADC modifications.

RNA modification regulator DDC in endometrial cancer affects the tumor microenvironment and patient prognosis

Uterine corpus endometrial carcinoma (UCEC) is infiltrated by immune cells, which are involved in the growth and proliferation of malignant tumors and resistance to immunotherapy. This study suggested that RNA modification regulators played an important role in the development and prognosis of UCEC. Many studies confirmed that RNA modification played an essential role in tumor immune regulation, and abnormal RNA modification contributed to tumorigenesis and cancer progression. Based on the RNA modification regulatory factors, the UCEC samples from TCGA (The Cancer Genome Atlas) were classified into two clusters, namely Cluster A and Cluster B, using unsupervised consensus clustering. We obtained DEG (differentially expressed genes) between the two clusters, and constructed a risk model of RNA modification-related genes using DEGs. Cluster A had lower RNA modification regulatory factors, richer immune cell infiltration, and better prognosis. The differentially expressed genes between the two clusters were obtained, and these genes were used for modeling. This model divided patients with UCEC into two groups. The low-risk group had better immune infiltration, and the ROC (receiver operating characteristic) curve showed that this model had good predictive efficacy. The low-risk group had a better response to immunotherapy by immune checkpoint prediction. We obtained the key gene L-dopa decarboxylase (DDC) through the intersection of LASSO model genes and GEO dataset GSE17025. We evaluated the potential biological functions of DDC. The differences in the expression of DDC were verified by immunohistochemistry. We evaluated the relationship between DDC and immune cell infiltration and verified this difference using immunofluorescence. Cluster A with low expression of RNA modification regulators has better prognosis and richer immune cell infiltration, therefore, we believed that RNA modification regulators in UCEC were closely related to the tumor microenvironment. Also, the risk score could well predict the prognosis of patients and guide immunotherapy, which might benefit patients with UCEC.

Multi-omics profiles refine L-dopa decarboxylase (DDC) as a reliable biomarker for prognosis and immune microenvironment of clear cell renal cell carcinoma

Background

Increasing evidence indicates that L-dopa decarboxylase (DDC), which mediates aberrant amino acid metabolism, is significantly associated with tumor progression. However, the impacts of DDC are not elucidated clearly in clear cell renal cell carcinoma (ccRCC). This study aimed to evaluate DDC prognostic value and potential mechanisms for ccRCC patients.

Methods

Transcriptomic and proteomic expressions of and clinical data including 532 patients with ccRCC (The Cancer Genome Atlas RNA-seq data), 226 ccRCC samples (Gene Expression Omnibus), 101 ccRCC patients from the E-MTAB-1980 cohort, and 232 patients with ccRCC with proteogenomic data (Fudan University Shanghai Cancer Center) were downloaded and analyzed to investigate the prognostic implications of DDC expression. Cox regression analyses were implemented to explore the effect of DDC expression on the prognosis of pan-cancer. The "limma" package identified the differentially expressed genes (DEGs) between high DDC subgroups and low DDC groups. Functional enrichments were performed based DEGs between DDC subgroups. The differences of immune cell infiltrations and immune checkpoint genes between DDC subgroups were analyzed to identify potential influence on immune microenvironment.

Results

We found significantly decreased DDC expression in ccRCC tissues compared with normal tissues from multiple independent cohorts based on multi-omics data. We also found that DDC expression was correlated with tumor grades and stages.The following findings revealed that lower DDC expression levels significantly correlated with shorter overall survival (P <0.001) of patients with ccRCC. Moreover, we found that DDC expression significantly correlated with an immunosuppressive tumor microenvironment, higher intra-tumoral heterogeneity, elevated expression of immune checkpoint CD274, and possibly mediated malignant behaviors of ccRCC cells via the PI3k/Akt signaling pathway.

Conclusion

The present study is the first to our knowledge to indicate that decreased DDC expression is significantly associated with poor survival and an immune-suppressive tumor microenvironment in ccRCC. These findings suggest that DDC could serve as a biomarker for guiding molecular diagnosis and facilitating the development of novel individual therapeutic strategies for patients with advanced ccRCC.

Neural regulations of the tumor microenvironment

The identification of nerves in the tumor microenvironment has ushered in a new area of research in cancer biology. Numerous studies demonstrate the presence of various types of peripheral nerves (sympathetic, parasympathetic, sensory) within the tumor microenvironment; moreover, an increased density of nerves in the tumor microenvironment correlates with worse prognosis. In this review, we address the current understanding of nerve-mediated alterations of the tumor microenvironment and how they impact disease through a variety of processes, including direct nerve-cancer cell communication, alteration of the infiltrative immune population, and alteration of stromal components.