Anti-tumor activity of phenoxybenzamine and its inhibition of histone deacetylases

Identifying new molecular signatures and potential therapeutics for idiopathic pulmonary fibrosis: a network medicine approach

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease characterized by excessive collagen deposition and fibrosis of the lung parenchyma, leading to respiratory failure. The molecular mechanisms underlying IPF pathogenesis remain incompletely understood, hindering the development of effective therapeutic strategies. We have used a network medicine approach to comprehensively analyze molecular interactions and identify novel molecular signatures and potential therapeutics associated with IPF progression. Our integrative analysis revealed dysregulated molecular networks that are central to IPF pathophysiology. We have highlighted key molecular players and signaling pathways that are implicated in aberrant fibrotic processes. This systems-level understanding enables the identification of new biomarkers and therapeutic targets for IPF, providing potential avenues for precision medicine. Drug repurposing analysis revealed several drug candidates with anti-fibrotic, anti-inflammatory, and anti-cancer activities that could potentially slow fibrotic progression and improve patient outcomes. This study offers new insights into the molecular underpinnings of IPF and highlights network medicine approaches in uncovering complex disease mechanisms. The molecular signatures and therapeutic targets identified hold promise for developing precision therapies tailored to individual patients, ultimately advancing the management of this debilitating lung disease.

Beta2-Adrenergic Suppression of Neuroinflammation in Treatment of Parkinsonism, with Relevance for Neurodegenerative and Neoplastic Disorders

There is a preliminary record suggesting that β2-adrenergic agonists may have therapeutic value in Parkinson's disease; recent studies have proposed a possible role of these agents in suppressing the formation of α-synuclein protein, a component of Lewy bodies. The present study focuses on the importance of the prototypical β2-adrenergic agonist epinephrine in relation to the incidence of Parkinson's disease in humans, and its further investigation via synthetic selective β2-receptor agonists, such as levalbuterol. Levalbuterol exerts significant anti-inflammatory activity, a property that may suppress cytokine-mediated degeneration of dopaminergic neurons and progression of Parkinsonism. In a completely novel finding, epinephrine and certain other adrenergic agents modeled in the Harvard/MIT Broad Institute genomic database, CLUE, demonstrated strong associations with the gene-expression signatures of anti-inflammatory glucocorticoids. This prompted in vivo confirmation in mice engrafted with human peripheral blood mononuclear cells (PBMCs). Upon toxic activation with mononuclear antibodies, levalbuterol inhibited (1) the release of the eosinophil attractant chemokine eotaxin-1, which is implicated in CNS and peripheral inflammatory disorders, (2) elaboration of the tumor-promoting angiogenic factor VEGFa, and (3) release of the pro-inflammatory cytokine IL-13 from activated PBMCs. These observations suggest possible translation to Parkinson's disease, other neurodegenerative syndromes, and malignancies, via several mechanisms.

AS-IV enhances the antitumor effects of propofol in NSCLC cells by inhibiting autophagy

Non-small cell lung cancer (NSCLC) is one of the most lethal malignant tumors. It has been shown that the general anesthetic agents, propofol and astragaloside IV (AS-IV) both exert antitumor effects in NSCLC. However, the effects of the combination of propofol with AS-IV in NSCLC remain unclear. Cell counting kit-8, and EdU and Transwell assays were performed to evaluate NSCLC cell viability, proliferation, and migration. Cell apoptosis and autophagy were observed by flow cytometric analysis and TUNEL and LC3 staining, respectively. AS-IV notably enhanced the anti-proliferative, pro-apoptotic, and anti-migratory properties of propofol in NSCLC cells. Moreover, AS-IV remarkably facilitated the anti-autophagy effect of propofol in NSCLC cells by downregulating LC3, Beclin 1, and ATG5. Significantly, the pro-apoptotic ability of the AS-IV/propofol combination in NSCLC cells was further enhanced by the autophagy inhibitor 3-MA, suggesting that autophagy plays a tumor-promoting role in NSCLC cells. Collectively, AS-IV could facilitate the antitumor abilities of propofol in NSCLC cells by inhibiting autophagy. These findings may be beneficial for future studies on the use of AS-IV and propofol for the treatment of NSCLC.

Increased ATP2A1 Predicts Poor Prognosis in Patients With Colorectal Carcinoma

Colorectal cancer is one of the most common malignant tumors in the digestive system. Traditional diagnosis and treatment methods have not significantly improved the overall survival of patients. In this study, we explored the value of ATP2A1 as a biomarker in predicting the prognosis of colorectal cancer patients. We used the TCGA database to reveal the relationship between ATP2A1 mRNA level and prognosis, methylation, and immune invasion in colorectal cancer. The results showed that the expression of ATP2A1 was increased in colorectal cancer. The overall survival of patients with high expression of ATP2A1 was significantly lower than patients with low expression of ATP2A1. Cox regression analysis showed that high expression of ATP2A1 was an independent risk factor for poor prognosis in colorectal cancer patients. In addition, we used three datasets to perform a meta-analysis, which further confirmed the reliability of the results. Furthermore, we revealed that ATP2A1 could significantly inhibit the proliferation of colorectal cancer cells by inhibiting the autophagy process and was associated with several immune cells, especially CD8 + T cells. Finally, four small molecule drugs with potential inhibition of ATP2A1 expression were found by CMap analysis. This study demonstrates for the first time that ATP2A1 is a potential pathogenic factor, which may play a significant role in colorectal cancer.

Immunoprognostic model of lung adenocarcinoma and screening of sensitive drugs

Screening of mRNAs and lncRNAs associated with prognosis and immunity of lung adenocarcinoma (LUAD) and used to construct a prognostic risk scoring model (PRS-model) for LUAD. To analyze the differences in tumor immune microenvironment between distinct risk groups of LUAD based on the model classification. The CMap database was also used to screen potential therapeutic compounds for LUAD based on the differential genes between distinct risk groups. he data from the Cancer Genome Atlas (TCGA) database. We divided the transcriptome data into a mRNA subset and a lncRNA subset, and use multiple methods to extract mRNAs and lncRNAs associated with immunity and prognosis. We further integrated the mRNA and lncRNA subsets and the corresponding clinical information, randomly divided them into training and test set according to the ratio of 5:5. Then, we performed the Cox risk proportional analysis and cross-validation on the training set to construct a LUAD risk scoring model. Based on the risk scoring model, patients were divided into distinct risk group. Moreover, we evaluate the prognostic performance of the model from the aspects of Area Under Curve (AUC) analysis, survival difference analysis, and independent prognostic analysis. We analyzed the differences in the expression of immune cells between the distinct risk groups, and also discuss the connection between immune cells and patient survival. Finally, we screened the potential therapeutic compounds of LUAD in the Connectivity Map (CMap) database based on differential gene expression profiles, and verified the compound activity by cytostatic assays. We extracted 26 mRNAs and 74 lncRNAs related to prognosis and immunity by using different screening methods. Two mRNAs (i.e., KLRC3 and RAET1E) and two lncRNAs (i.e., AL590226.1 and LINC00941) and their risk coefficients were finally used to construct the PRS-model. The risk score positions of the training and test set were 1.01056590 and 1.00925190, respectively. The expression of mRNAs involved in model construction differed significantly between the distinct risk population. The one-year ROC areas on the training and test sets were 0.735 and 0.681. There was a significant difference in the survival rate of the two groups of patients. The PRS-model had independent predictive capabilities in both training and test sets. Among them, in the group with low expression of M1 macrophages and resting NK cells, LUAD patients survived longer. In contrast, the monocyte expression up-regulated group survived longer. In the CMap drug screening, three LUAD therapeutic compounds, such as resveratrol, methotrexate, and phenoxybenzamine, scored the highest. In addition, these compounds had significant inhibitory effects on the LUAD A549 cell lines. The LUAD risk score model constructed using the expression of KLRC3, RAET1E, AL590226.1, LINC00941 and their risk coefficients had a good independent prognostic power. The optimal LUAD therapeutic compounds screened in the CMap database: resveratrol, methotrexate and phenoxybenzamine, all showed significant inhibitory effects on LUAD A549 cell lines.

Comprehensive Analysis of Cell Cycle-Related Genes in Patients With Prostate Cancer

This study aimed to identify critical cell cycle-related genes (CCRGs) in prostate cancer (PRAD) and to evaluate the clinical prognostic value of the gene panel selected. Gene set variation analysis (GSVA) of dysregulated genes between PRAD and normal tissues demonstrated that the cell cycle-related pathways played vital roles in PRAD. Patients were classified into four clusters, which were associated with recurrence-free survival (RFS). Moreover, 200 prognostic-related genes were selected using the Kaplan-Meier (KM) survival analysis and univariable Cox regression. The prognostic CCRG risk score was constructed using random forest survival and multivariate regression Cox methods, and their efficiency was validated in Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70770. We identified nine survival-related genes: CCNL2, CDCA5, KAT2A, CHTF18, SPC24, EME2, CDK5RAP3, CDC20, and PTTG1. Based on the median risk score, the patients were divided into two groups. Then the functional enrichment analyses, mutational profiles, immune components, estimated half-maximal inhibitory concentration (IC50), and candidate drugs were screened of these two groups. In addition, the characteristics of nine hub CCRGs were explored in Oncomine, cBioPortal, and the Human Protein Atlas (HPA) datasets. Finally, the expression profiles of these hub CCRGs were validated in RWPE-1 and three PRAD cell lines (PC-3, C4-2, and DU-145). In conclusion, our study systematically explored the role of CCRGs in PRAD and constructed a risk model that can predict the clinical prognosis and immunotherapeutic benefits.

Further investigation of the potential anti-neoplastic, anti-inflammatory and immunomodulatory actions of phenoxybenzamine using the Broad Institute CLUE platform.. [DOI: 10.1101/767392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Previous clinical studies with the FDA-approved alpha-adrenergic antagonist, phenoxybenzamine, showed apparent efficacy to reverse the symptoms and disabilities of the neuropathic condition, Complex Regional Pain Syndrome; also, the anatomic spread and intensity of this syndrome has a proliferative character and it was proposed that phenoxybenzamine may have an anti-inflammatory, immunomodulatory mode of action. A previous study gave evidence that phenoxybenzamine had anti-proliferative activity in suppression of growth in several human tumor cell cultures. The same report demonstrated that the drug possessed significant histone deacetylase inhibitory activity. Utilizing the Harvard/Massachusetts Institute of Technology Broad Institute genomic database, CLUE, the present study suggests that the gene expression signature of phenoxybenzamine in malignant cell lines is consistent with anti-inflammatory/immunomodulatory activity and suppression of tumor expansion by several possible mechanisms of action. Of particular note, phenoxybenzamine demonstrated signatures that were highly similar to those with glucocorticoid agonist activity. Also, gene expression signatures of phenoxbenzamine were consistent with several agents in each case that were known to suppress tumor proliferation, notably, protein kinase C inhibitors, Heat Shock Protein inhibitors, epidermal growth factor receptor inhibitors, and glycogen synthase kinase inhibitors. Searches in CLUE also confirmed the earlier observations of strong similarities between gene expression signatures of phenoxybenzamine and several histone deacetylase inhibitors.

A Comparative Study of Cluster Detection Algorithms in Protein-Protein Interaction for Drug Target Discovery and Drug Repurposing

The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein–protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.