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Journal of Thoracic Disease Feb 2024var. (RW) is one of the traditional Chinese medicinal materials, which is used to treat angina pectoris (AP). However, the possible underlying mechanisms remains...
BACKGROUND
var. (RW) is one of the traditional Chinese medicinal materials, which is used to treat angina pectoris (AP). However, the possible underlying mechanisms remains unclear. The aim of this study was to explore RW in the treatment of AP and to identify the potential mechanism of the core compounds.
METHODS
In this study, systematic and comprehensive network pharmacology and molecular docking were used for the first time to explore the potential pharmacological mechanisms of RW on AP. First, the relative compounds were obtained by mining the literature, and potential targets of these compounds using target prediction were collected. We then built the AP target database using the DigSee and GeneCards databases. Based on the data, overlapping targets and hub genes were identified with Maximal Clique Centrality (MCC) algorithm in Cytoscape, cytoHubba. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and protein-protein interaction (PPI) analysis were performed to screen the hub targets by topology. Molecular docking was utilized to investigate the receptor-ligand interactions on Autodock Vina and visualized in PyMOL.
RESULTS
A total of 218 known RW therapeutic targets were selected. Systematic analysis identified nine hub targets (, , , , , , , and ) mainly involved in the complex treatment effects associated with the protection of the vascular endothelium, as well as the regulation of glucose metabolism, cellular processes, inflammatory responses, and cellular signal transduction. Molecular docking indicated that the core compounds had good affinity with the core targets.
CONCLUSIONS
The results of this study preliminarily identify the potential targets and signaling pathways of RW in AP therapy and lay a promising foundation for further experimental studies and clinical trials.
PubMed: 38505080
DOI: 10.21037/jtd-23-1891 -
Human Cell May 2024Merkel cell carcinoma (MCC) is an aggressive skin cancer, with a propensity for early metastasis. Therefore, early diagnosis and the identification of novel targets...
Merkel cell carcinoma (MCC) is an aggressive skin cancer, with a propensity for early metastasis. Therefore, early diagnosis and the identification of novel targets become fundamental. The enzyme nicotinamide N-methyltransferase (NNMT) catalyzes the reaction of N-methylation of nicotinamide and other analogous compounds. Although NNMT overexpression was reported in many malignancies, the significance of its dysregulation in cancer cell phenotype was partly clarified. Several works demonstrated that NNMT promotes cancer cell proliferation, migration, and chemoresistance. In this study, we investigated the possible involvement of this enzyme in MCC. Preliminary immunohistochemical analyses were performed to evaluate NNMT expression in MCC tissue specimens. To explore the enzyme function in tumor cell metabolism, MCC cell lines have been transfected with plasmids encoding for short hairpin RNAs (shRNAs) targeting NNMT mRNA. Preliminary immunohistochemical analyses showed elevated NNMT expression in MCC tissue specimens. The effect of enzyme downregulation on cell proliferation, migration, and chemosensitivity was then evaluated through MTT, trypan blue, and wound healing assays. Data obtained clearly demonstrated that NNMT knockdown is associated with a decrease of cell proliferation, viability, and migration, as well as with enhanced sensitivity to treatment with chemotherapeutic drugs. Taken together, these results suggest that NNMT could represent an interesting MCC biomarker and a promising target for targeted anti-cancer therapy.
Topics: Humans; Nicotinamide N-Methyltransferase; Carcinoma, Merkel Cell; Drug Resistance, Neoplasm; Cell Proliferation; Skin Neoplasms; RNA, Small Interfering
PubMed: 38504052
DOI: 10.1007/s13577-024-01047-0 -
Health Data Science 2024Gemcitabine is a first-line chemotherapy for pancreatic adenocarcinoma (PAAD), but many PAAD patients do not respond to gemcitabine-containing treatments. Being able to...
Gemcitabine is a first-line chemotherapy for pancreatic adenocarcinoma (PAAD), but many PAAD patients do not respond to gemcitabine-containing treatments. Being able to predict such nonresponders would hence permit the undelayed administration of more promising treatments while sparing gemcitabine life-threatening side effects for those patients. Unfortunately, the few predictors of PAAD patient response to this drug are weak, none of them exploiting yet the power of machine learning (ML). Here, we applied ML to predict the response of PAAD patients to gemcitabine from the molecular profiles of their tumors. More concretely, we collected diverse molecular profiles of PAAD patient tumors along with the corresponding clinical data (gemcitabine responses and clinical features) from the Genomic Data Commons resource. From systematically combining 8 tumor profiles with 16 classification algorithms, each of the resulting 128 ML models was evaluated by multiple 10-fold cross-validations. Only 7 of these 128 models were predictive, which underlines the importance of carrying out such a large-scale analysis to avoid missing the most predictive models. These were here random forest using 4 selected mRNAs [0.44 Matthews correlation coefficient (MCC), 0.785 receiver operating characteristic-area under the curve (ROC-AUC)] and XGBoost combining 12 DNA methylation probes (0.32 MCC, 0.697 ROC-AUC). By contrast, the hENT1 marker obtained much worse random-level performance (practically 0 MCC, 0.5 ROC-AUC). Despite not being trained to predict prognosis (overall and progression-free survival), these ML models were also able to anticipate this patient outcome. We release these promising ML models so that they can be evaluated prospectively on other gemcitabine-treated PAAD patients.
PubMed: 38486621
DOI: 10.34133/hds.0108 -
Nature Communications Mar 2024Developing clinically predictive model systems for evaluating gene transfer and gene editing technologies has become increasingly important in the era of personalized...
Developing clinically predictive model systems for evaluating gene transfer and gene editing technologies has become increasingly important in the era of personalized medicine. Liver-directed gene therapies present a unique challenge due to the complexity of the human liver. In this work, we describe the application of whole human liver explants in an ex situ normothermic perfusion system to evaluate a set of fourteen natural and bioengineered adeno-associated viral (AAV) vectors directly in human liver, in the presence and absence of neutralizing human sera. Under non-neutralizing conditions, the recently developed AAV variants, AAV-SYD12 and AAV-LK03, emerged as the most functional variants in terms of cellular uptake and transgene expression. However, when assessed in the presence of human plasma containing anti-AAV neutralizing antibodies (NAbs), vectors of human origin, specifically those derived from AAV2/AAV3b, were extensively neutralized, whereas AAV8- derived variants performed efficiently. This study demonstrates the potential of using normothermic liver perfusion as a model for early-stage testing of liver-focused gene therapies. The results offer preliminary insights that could help inform the development of more effective translational strategies.
Topics: Humans; Genetic Vectors; Dependovirus; Antibodies, Neutralizing; Liver; Perfusion
PubMed: 38485924
DOI: 10.1038/s41467-024-46194-y -
Archives of Microbiology Mar 2024Chryseobacterium demonstrates a diverse environmental presence and a significant pathogenic potential across various ecosystems. This clinical case showcases a rare...
Chryseobacterium demonstrates a diverse environmental presence and a significant pathogenic potential across various ecosystems. This clinical case showcases a rare instance of bacterial infection in a 75-year-old male with untreated diabetes and recurrent urinary tract infections (UTIs). The patient presented symptoms of abdominal pain, burning urination, fever, and an elevated eosinophil count. A subsequent urine culture identified a Chryseobacterium-related bacterium as the causative agent, exhibiting sensitivity to piperacillin/tazobactam, trimethoprim/sulfamethoxazole, and nitrofurantoin, which led to successful treatment using oral nitrofurantoin. Analysis of the 16S rRNA gene sequence of APV-1 revealed a close relationship of 98.2% similarity to Chryseobacterium gambrini strain 5-1St1a (AM232810). Furthermore, comparative genome analysis, incorporating Average Nucleotide Identity (ANI), Digital DNA-DNA Hybridization (dDDH) values, and comprehensive phylogenetic assessments utilizing 16S rRNA gene sequences, core genes, and amino acid sequences of core proteins, highlighted the unique phylogenetic positioning of APV-1 within the Chryseobacterium genus. Distinct carbon utilization and assimilation patterns, along with major fatty acid content, set APV-1 apart from C. gambrini strain 5-1St1a. These findings, encompassing phenotypic, genotypic, and chemotaxonomic characteristics, strongly support the proposal of a novel species named Chryseobacterium urinae sp. nov., with APV-1 designated as the type strain (= MCC 50690 = JCM 36476). Despite its successful treatment, the strain displayed resistance to multiple antibiotics. Genomic analysis further unveiled core-conserved genes, strain-specific clusters, and genes associated with antibiotic resistance and virulence. This report underscores the vital importance of elucidating susceptibility patterns of rare pathogens like Chryseobacterium, particularly in immunocompromised individuals. It advocates for further analyses to understand the functional significance of identified genes and their implications in treatment and pathogenesis.
Topics: Aged; Humans; Bacterial Typing Techniques; Base Composition; Chryseobacterium; Diabetes Mellitus; DNA; DNA, Bacterial; Ecosystem; Fatty Acids; Nitrofurantoin; Nucleic Acid Hybridization; Phylogeny; RNA, Ribosomal, 16S; Sequence Analysis, DNA; Urinary Tract Infections; Male
PubMed: 38466448
DOI: 10.1007/s00203-024-03881-0 -
Mathematical Biosciences and... Jan 2024Lung adenocarcinoma, a chronic non-small cell lung cancer, needs to be detected early. Tumor gene expression data analysis is effective for early detection, yet its...
Lung adenocarcinoma, a chronic non-small cell lung cancer, needs to be detected early. Tumor gene expression data analysis is effective for early detection, yet its challenges lie in a small sample size, high dimensionality, and multi-noise characteristics. In this study, we propose a lung adenocarcinoma convolutional neural network (LATCNN), a deep learning model tailored for accurate lung adenocarcinoma prediction and identification of key genes. During the feature selection stage, we introduce a hybrid algorithm. Initially, the fast correlation-based filter (FCBF) algorithm swiftly filters out irrelevant features, followed by applying the k-means-synthetic minority over-sampling technique (k-means-SMOTE) method to address category imbalance. Subsequently, we enhance the particle swarm optimization (PSO) algorithm by incorporating fast-decay dynamic inertia weights and utilizing the classification and regression tree (CART) as the fitness function for the second stage of feature selection, aiming to further eliminate redundant features. In the classifier construction stage, we present an attention convolutional neural network (atCNN) that incorporates an attention mechanism. This improved model conducts feature selection post lung adenocarcinoma gene expression data analysis for classification and prediction. The results show that LATCNN effectively reduces the feature dimensions and accurately identifies 12 key genes with accuracy, recall, F1 score, and MCC of 99.70%, 99.33%, 99.98%, and 98.67%, respectively. These performance metrics surpass those of other comparative models, highlighting the significance of this research for advancing lung adenocarcinoma treatment.
Topics: Humans; Carcinoma, Non-Small-Cell Lung; Lung Neoplasms; Neural Networks, Computer; Adenocarcinoma of Lung; Algorithms
PubMed: 38454716
DOI: 10.3934/mbe.2024133 -
Journal of the European Academy of... Jul 2024The limited therapies available for treating Merkel cell carcinoma (MCC), a highly aggressive skin neoplasm, still pose clinical challenges, and novel treatments are...
BACKGROUND
The limited therapies available for treating Merkel cell carcinoma (MCC), a highly aggressive skin neoplasm, still pose clinical challenges, and novel treatments are required. Targeting retinoid signalling with retinoids, such as all-trans retinoic acid (ATRA), is a promising and clinically useful antitumor approach. ATRA drives tumour cell differentiation by modulating retinoid signalling, leading to anti-proliferative and pro-apoptotic effects. Although retinoid signalling is dysregulated in MCC, ATRA activity in this tumour is unknown. This study aimed to evaluate the impact of ATRA on the pathological phenotype of MCC cells.
METHODS
The effect of ATRA was tested in various Merkel cell polyomavirus-positive and polyomavirus-negative MCC cell lines in terms of cell proliferation, viability, migration and clonogenic abilities. In addition, cell cycle, apoptosis/cell death and the retinoid gene signature were evaluated upon ATRA treatments.
RESULTS
ATRA efficiently impaired MCC cell proliferation and viability in MCC cells. A strong effect in reducing cell migration and clonogenicity was determined in ATRA-treated cells. Moreover, ATRA resulted as strongly effective in arresting cell cycle and inducing apoptosis/cell death in all tested MCC cells. Enrichment analyses indicated that ATRA was effective in modulating the retinoid gene signature in MCC cells to promote cell differentiation pathways, which led to anti-proliferative and pro-apoptotic/cell death effects.
CONCLUSIONS
These results underline the potential of retinoid-based therapy for MCC management and might open the way to novel experimental approaches with other retinoids and/or combinatorial treatments.
Topics: Tretinoin; Carcinoma, Merkel Cell; Humans; Cell Proliferation; Skin Neoplasms; Cell Differentiation; Cell Line, Tumor; Apoptosis; Signal Transduction; Retinoids; Cell Movement; Antineoplastic Agents; Cell Survival
PubMed: 38450801
DOI: 10.1111/jdv.19933 -
BMC Genomics Mar 20245-Methylcytosine (5mC) plays a very important role in gene stability, transcription, and development. Therefore, accurate identification of the 5mC site is of key...
BACKGROUND
5-Methylcytosine (5mC) plays a very important role in gene stability, transcription, and development. Therefore, accurate identification of the 5mC site is of key importance in genetic and pathological studies. However, traditional experimental methods for identifying 5mC sites are time-consuming and costly, so there is an urgent need to develop computational methods to automatically detect and identify these 5mC sites.
RESULTS
Deep learning methods have shown great potential in the field of 5mC sites, so we developed a deep learning combinatorial model called i5mC-DCGA. The model innovatively uses the Convolutional Block Attention Module (CBAM) to improve the Dense Convolutional Network (DenseNet), which is improved to extract advanced local feature information. Subsequently, we combined a Bidirectional Gated Recurrent Unit (BiGRU) and a Self-Attention mechanism to extract global feature information. Our model can learn feature representations of abstract and complex from simple sequence coding, while having the ability to solve the sample imbalance problem in benchmark datasets. The experimental results show that the i5mC-DCGA model achieves 97.02%, 96.52%, 96.58% and 85.58% in sensitivity (Sn), specificity (Sp), accuracy (Acc) and matthews correlation coefficient (MCC), respectively.
CONCLUSIONS
The i5mC-DCGA model outperforms other existing prediction tools in predicting 5mC sites, and it is currently the most representative promoter 5mC site prediction tool. The benchmark dataset and source code for the i5mC-DCGA model can be found in https://github.com/leirufeng/i5mC-DCGA .
Topics: 5-Methylcytosine; Benchmarking; Promoter Regions, Genetic; Research Design; Software
PubMed: 38443802
DOI: 10.1186/s12864-024-10154-z -
American Journal of Human Genetics Mar 2024Variants of uncertain significance (VUSs) in BRCA2 are a common result of hereditary cancer genetic testing. While more than 4,000 unique VUSs, comprised of missense or...
Variants of uncertain significance (VUSs) in BRCA2 are a common result of hereditary cancer genetic testing. While more than 4,000 unique VUSs, comprised of missense or intronic variants, have been identified in BRCA2, the few missense variants now classified clinically as pathogenic or likely pathogenic are predominantly located in the region encoding the C-terminal DNA binding domain (DBD). We report on functional evaluation of the influence of 462 BRCA2 missense variants affecting the DBD on DNA repair activity of BRCA2 using a homology-directed DNA double-strand break repair assay. Of these, 137 were functionally abnormal, 313 were functionally normal, and 12 demonstrated intermediate function. Comparisons with other functional studies of BRCA2 missense variants yielded strong correlations. Sequence-based in silico prediction models had high sensitivity, but limited specificity, relative to the homology-directed repair assay. Combining the functional results with clinical and genetic data in an American College of Medical Genetics (ACMG)/Association for Molecular Pathology (AMP)-like variant classification framework from a clinical testing laboratory, after excluding known splicing variants and functionally intermediate variants, classified 431 of 442 (97.5%) missense variants (129 as pathogenic/likely pathogenic and 302 as benign/likely benign). Functionally abnormal variants classified as pathogenic by ACMG/AMP rules were associated with a slightly lower risk of breast cancer (odds ratio [OR] 5.15, 95% confidence interval [CI] 3.43-7.83) than BRCA2 DBD protein truncating variants (OR 8.56, 95% CI 6.03-12.36). Overall, functional studies of BRCA2 variants using validated assays substantially improved the variant classification yield from ACMG/AMP models and are expected to improve clinical management of many individuals found to harbor germline BRCA2 missense VUS.
Topics: Humans; Female; Genetic Predisposition to Disease; BRCA2 Protein; Genetic Testing; Mutation, Missense; Breast Neoplasms; Germ Cells; DNA
PubMed: 38417439
DOI: 10.1016/j.ajhg.2024.02.002 -
Cancers Feb 2024Merkel cell carcinoma (MCC) and small cell lung cancer (SCLC) can be histologically similar. Immunohistochemistry (IHC) for cytokeratin 20 (CK20) and thyroid...
Merkel cell carcinoma (MCC) and small cell lung cancer (SCLC) can be histologically similar. Immunohistochemistry (IHC) for cytokeratin 20 (CK20) and thyroid transcription factor 1 (TTF-1) are commonly used to differentiate MCC from SCLC; however, these markers have limited sensitivity and specificity. To identify new diagnostic markers, we performed differential gene expression analysis on transcriptome data from MCC and SCLC tumors. Candidate markers included atonal BHLH transcription factor 1 (ATOH1) and transcription factor AP-2β (TFAP2B) for MCC, as well as carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) for SCLC. Immunostaining for CK20, TTF-1, and new candidate markers was performed on 43 MCC and 59 SCLC samples. All three MCC markers were sensitive and specific, with CK20 and ATOH1 staining 43/43 (100%) MCC and 0/59 (0%) SCLC cases and TFAP2B staining 40/43 (93%) MCC and 0/59 (0%) SCLC cases. TTF-1 stained 47/59 (80%) SCLC and 1/43 (2%) MCC cases. CEACAM6 stained 49/59 (83%) SCLC and 0/43 (0%) MCC cases. Combining CEACAM6 and TTF-1 increased SCLC detection sensitivity to 93% and specificity to 98%. These data suggest that ATOH1, TFAP2B, and CEACAM6 should be explored as markers to differentiate MCC and SCLC.
PubMed: 38398178
DOI: 10.3390/cancers16040788