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European Neuropsychopharmacology : the... Jun 2024Many individuals with autism spectrum disorder (ASD) experience various degrees of impairment in social interaction and communication, restricted, repetitive behaviours,... (Review)
Review
Many individuals with autism spectrum disorder (ASD) experience various degrees of impairment in social interaction and communication, restricted, repetitive behaviours, interests/activities. These impairments make a significant contribution to poorer everyday adaptive functioning. Yet, there are no pharmacological therapies to effectively treat the core symptoms of ASD. Since symptoms of ASD likely emerge from a complex interplay of vulnerabilities, environmental factors and compensatory mechanisms during the early developmental period, pharmacological interventions arguably would have the greatest impact to improve long-term outcomes when implemented at a young age. It is essential therefore, that clinical development programmes of investigational drugs in ASD include the paediatric population early on in clinical trials. Such trials need to offer the prospect of direct benefit (PDB) for participants. In most cases in drug development this prospect is supported by evidence of efficacy in adults. However, the effectiveness of treatment approaches may be age-dependent, so that clinical trials in adults may not provide sufficient evidence for a PDB in children. In this white paper, we consolidate recommendations from regulatory guidelines, as well as advice from the Food and Drug Administration, USA (FDA) and the Committee for Human Medicinal Products (CHMP) consultations on various development programmes on: 1) elements to support a PDB to participants in early paediatric clinical trials in ASD, including single-gene neurodevelopment disorders, 2) aspects of study design to allow for a PDB. This white paper is intended to be complementary to existing regulatory guidelines in guiding industry and academic sponsors in their conduct of early paediatric clinical trials in ASD.
PubMed: 38917772
DOI: 10.1016/j.euroneuro.2024.05.011 -
Virology Jun 2024Merkel Cell Carcinoma (MCC) is a rare neuroendocrine skin cancer. In our previous work, we decoded genes specifically deregulated by MCPyV early genes as opposed to...
Merkel Cell Carcinoma (MCC) is a rare neuroendocrine skin cancer. In our previous work, we decoded genes specifically deregulated by MCPyV early genes as opposed to other polyomaviruses and established functional importance of NDRG1 in inhibiting cellular proliferation and migration in MCC. In the present work, we found the SET protein, (I2PP2A, intrinsic inhibitor of PP2A) upstream of NDRG1 which was modulated by MCPyV early genes, both in hTERT-HK-MCPyV and MCPyV-positive (+) MCC cell lines. Additionally, MCC dermal tumour nodule tissues showed strong SET expression. Inhibition of the SET-PP2A interaction in hTERT-HK-MCPyV using the small molecule inhibitor, FTY720, increased NDRG1 expression and inhibited cell cycle regulators, cyclinD1 and CDK2. SET inhibition by shRNA and FTY720 also decreased cell proliferation and colony formation in MCPyV(+) MCC cells. Overall, these results pave a path for use of drugs targeting SET protein for the treatment of MCC.
PubMed: 38917692
DOI: 10.1016/j.virol.2024.110143 -
Aging Jun 2024The underlying mechanisms of gastric cancer (GC) remain unknown. Therefore, in this study, we employed a comprehensive approach, combining computational and experimental...
INTRODUCTION
The underlying mechanisms of gastric cancer (GC) remain unknown. Therefore, in this study, we employed a comprehensive approach, combining computational and experimental methods, to identify potential key genes and unveil the underlying pathogenesis and prognosis of GC.
METHODS
Gene expression profiles from GEO databases (GSE118916, GSE79973, and GSE29272) were analyzed to identify DEGs between GC and normal tissues. A PPI network was constructed using STRING and Cytoscape, followed by hub gene identification with CytoHubba. Investigations included expression and promoter methylation analysis, survival modeling, mutational and miRNA analysis, gene enrichment, drug prediction, and assays for cellular behaviors.
RESULTS
A total of 83 DEGs were identified in the three datasets, comprising 41 up-regulated genes and 42 down-regulated genes. Utilizing the degree and MCC methods, we identified four hub genes that were hypomethylated and up-regulated: COL1A1, COL1A2, COL3A1, and FN1. Subsequent validation of their expression and promoter methylation on clinical GC samples through targeted bisulfite sequencing and RT-qPCR analysis further confirmed the hypomethylation and overexpression of these genes in local GC patients. Furthermore, it was observed that these hub genes regulate tumor proliferation and metastasis in and exhibited mutations in GC patients.
CONCLUSION
We found four potential diagnostic and prognostic biomarkers, including COL1A1, COL1A2, COL3A1, and FN1 that may be involved in the occurrence and progression of GC.
PubMed: 38913913
DOI: 10.18632/aging.205965 -
Heliyon Jun 2024Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually....
Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually. However, this process relies on pathologists and can be a time-consuming task. Therefore, to improve the precision of clinical interpretation, researchers have developed computational algorithms that leverage next-generation sequencing technologies for automated mutation analysis. This paper utilized four deep learning classification models with training collections of biomedical texts. These models comprise bidirectional encoder representations from transformers for Biomedical text mining (BioBERT), a specialized language model implemented for biological contexts. Impressive results in multiple tasks, including text classification, language inference, and question answering, can be obtained by simply adding an extra layer to the BioBERT model. Moreover, bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) have been leveraged to produce very good results in categorizing genetic mutations based on textual evidence. The dataset used in the work was created by Memorial Sloan Kettering Cancer Center (MSKCC), which contains several mutations. Furthermore, this dataset poses a major classification challenge in the Kaggle research prediction competitions. In carrying out the work, three challenges were identified: enormous text length, biased representation of the data, and repeated data instances. Based on the commonly used evaluation metrics, the experimental results show that the BioBERT model outperforms other models with an F1 score of 0.87 and 0.850 MCC, which can be considered as improved performance compared to similar results in the literature that have an F1 score of 0.70 achieved with the BERT model.
PubMed: 38912449
DOI: 10.1016/j.heliyon.2024.e32279 -
Identification of key genes as potential diagnostic biomarkers in sepsis by bioinformatics analysis.PeerJ 2024Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential...
BACKGROUND
Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis.
METHODS
Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the "limma" R package. Protein-protein interaction (PPI) network was created using STRING and Cytoscape, and PPI hub genes were identified with the cytoHubba plugin. The PPI hub genes overlapping with the genes in key modules of WGCNA were determined to be the sepsis-related key genes. Subsequently, the key overlapping genes were validated in an external independent dataset and sepsis patients recruited in our hospital. In addition, CIBERSORT analysis evaluated immune cell infiltration and its correlation with key genes.
RESULTS
By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, = 2 - 19). 293 DEGs were identified in the merged datasets. The PPI network was created, and the CytoHubba was used to calculate the top 20 genes based on four algorithms (Degree, EPC, MCC, and MNC). Ultimately, LTF, LCN2, ELANE, MPO and CEACAM8 were identified as key overlapping genes as they appeared in the PPI hub genes and the key module genes of WGCNA. These sepsis-related key genes were validated in an independent external dataset (GSE131761) and sepsis patients recruited in our hospital. Additionally, the immune infiltration profiles differed significantly between sepsis and non-sepsis critical illness groups. Correlations between immune cells and these five key genes were assessed, revealing that plasma cells, macrophages M0, monocytes, T cells regulatory, eosinophils and NK cells resting were simultaneously and significantly associated with more than two key genes.
CONCLUSION
This study suggests a critical role of LTF, LCN2, ELANE, MPO and CEACAM8 in sepsis and may provide potential diagnostic biomarkers and therapeutic targets for the treatment of sepsis.
Topics: Humans; Sepsis; Computational Biology; Biomarkers; Protein Interaction Maps; Gene Expression Profiling; Gene Regulatory Networks; Databases, Genetic
PubMed: 38912048
DOI: 10.7717/peerj.17542 -
Combinatorial Chemistry & High... Jun 2024Variants in the PRRT2 gene are associated with paroxysmal kinesigenic dyskinesia and other episodic disorders. With the employment of variant screening in patients with...
BACKGROUND
Variants in the PRRT2 gene are associated with paroxysmal kinesigenic dyskinesia and other episodic disorders. With the employment of variant screening in patients with episodic dyskinesia, many PRRT2 variants have been discovered. Bioinformatics tools are becoming increasingly important for predicting the functional significance of variants. This study aimed to evaluate the performance of six in silico tools for PRRT2 missense variants.
METHODS
Pathogenic PRRT2 variants were retrieved from the Human Gene Mutation Database (HGMD) and literature from the PubMed database. The benign set of non-deleterious variants was retrieved from the Genome Aggregation Database (gnomAD). The overall accuracy, sensitivity, specificity, positive predictive values, and negative predictive values of SIFT, PolyPhen2, MutationTaster, CADD, Fathmm, and Provean were analyzed. The MCC score and ROC curve were calculated. The GraphPad Prism 8.0 software was used to plot ROC curves for the six bioinformatics software.
RESULTS
A total of 45 missense variants with confirmed pathogenicity were used as a positive set, and 222 missense variants were used as a negative set. The top three tools in accuracy are Fathmm, Provean, and MutationTaster. The top three predictors in sensitivity are SIFT, PolyPhen2, and CADD. Regarding specificity, the top three tools were Provean, Fathmm, and MutationTaster. In terms of the MCC and F-score, the highest degree was observed in Fathmm. Fathmm also had the highest AUC score. The cutoff values of Fathmm, CADD, PolyPhen2, and Provean were between the median prediction scores of the positive and negative sets. In contrast, the cutoff value of SIFT was below the median prediction score of the positive and negative sets. Fathmm had the highest accuracy.
CONCLUSION
The prediction performance of six in silico tools differed among the parameters. Fathmm had the best prediction performance, with the highest accuracy and MCC/F-score for PRRT2 missense variants.
PubMed: 38910474
DOI: 10.2174/0113862073308898240607090256 -
BMC Gastroenterology Jun 2024Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Hepatitis B virus (HBV) is one of the major causes of liver cirrhosis (LC) and HCC....
BACKGROUND
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Hepatitis B virus (HBV) is one of the major causes of liver cirrhosis (LC) and HCC. Therefore, the discovery of common markers for hepatitis B or LC and HCC is crucial for the prevention of HCC.
METHODS
Expressed genes for to chronic active hepaititis B (CAH-B), LC and HCC were obtained from the GEO and TCGA databases, and co-expressed genes were screened using Protein-protein interaction (PPI) networks, least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine - recursive feature elimination (SVM-RFE). The prognostic value of genes was assessed using Kaplan-Meier (KM) survival curves. Columnar line plots, calibration curves and receiver operating characteristic (ROC) curves of individual genes were used for evaluation. Validation was performed using GEO datasets. The association of these key genes with HCC clinical features was explored using the UALCAN database ( https://ualcan.path.uab.edu/index.html ).
RESULTS
Based on WGCNA analysis and TCGA database, the co-expressed genes (565) were screened. Moreover, the five algorithms of MCODE (ClusteringCoefficient, MCC, Degree, MNC, and DMNC) was used to select one of the most important and most closely linked clusters (the top 50 genes ranked). Using, LASSO regression model, RF model and SVM-RFE model, four key genes (UBE2T, KIF4A, CDCA3, and CDCA5) were identified for subsequent research analysis. These 4 genes were highly expressed and associated with poor prognosis and clinical features in HCC patients.
CONCLUSION
These four key genes (UBE2T, KIF4A, CDCA3, and CDCA5) may be common biomarkers for CAH-B and HCC or LC and HCC, promising to advance our understanding of the molecular basis of CAH-B/LC/HCC progression.
Topics: Carcinoma, Hepatocellular; Liver Neoplasms; Humans; Kinesins; Liver Cirrhosis; Computational Biology; Cell Cycle Proteins; Prognosis; Hepatitis B, Chronic; Biomarkers, Tumor; Protein Interaction Maps; Male; Kaplan-Meier Estimate; Support Vector Machine
PubMed: 38890649
DOI: 10.1186/s12876-024-03288-7 -
Cytokine Jun 2024Alveolar echinococcosis (AE) represents one of the deadliest helminthic infections, characterized by an insidious onset and high lethality.
OBJECTIVES
Alveolar echinococcosis (AE) represents one of the deadliest helminthic infections, characterized by an insidious onset and high lethality.
METHODS
This study utilized the Gene Expression Omnibus (GEO) database, applied Weighted Correlation Network Analysis (WGCNA) and Differential Expression Analysis (DEA), and employed the Matthews Correlation Coefficient (MCC) to identify CCL17 and CCL19 as key genes in AE. Immunohistochemistry and immunofluorescence co-localization techniques were used to examine the expression of CCL17 and CCL19 in liver tissue lesions of AE patients. Additionally, a mouse model of multilocular echinococcus larvae infection was developed to study the temporal expression patterns of these genes, along with liver fibrosis and inflammatory responses.
RESULTS
The in vitro model simulating echinococcal larva infection mirrored the hepatic microenvironment post-infection with multilocular echinococcal tapeworms. Quantitative RT-PCR analysis showed that liver fibrosis occurred in AE patients, with proximal activation and increased expression of CCL17 and CCL19 over time post-infection. Notably, expression peaked during the late stages of infection. Similarly, F4/80, a macrophage marker, exhibited corresponding trends in expression. Upon stimulation of normal hepatocytes by vesicular larvae in cellular experiments, there was a significant increase in CCL17 and CCL19 expression at 12 h post-infection, mirroring the upregulation observed with F4/80.
CONCLUSION
CCL17 and CCL19 facilitate macrophage aggregation via the chemokine pathway and their increased expression correlates with the progression of infection, suggesting their potential as biomarkers for AE progression.
PubMed: 38875750
DOI: 10.1016/j.cyto.2024.156669 -
Annals of Human Biology Feb 2024Mitophagy and ferroptosis occur in intracerebral haemorrhage (ICH) but our understanding of mitophagy and ferroptosis-related genes remains incomplete.
BACKGROUND
Mitophagy and ferroptosis occur in intracerebral haemorrhage (ICH) but our understanding of mitophagy and ferroptosis-related genes remains incomplete.
AIM
This study aims to identify shared ICH genes for both processes.
METHODS
ICH differentially expressed mitophagy and ferroptosis-related genes (DEMFRGs) were sourced from the GEO database and literature. Enrichment analysis elucidated functions. Hub genes were selected via STRING, MCODE, and MCC algorithms in Cytoscape. miRNAs targeting hubs were predicted using miRWalk 3.0, forming a miRNA-hub gene network. Immune microenvironment variances were assessed with MCP and TIMER. Potential small molecules for ICH were forecasted CMap database.
RESULTS
64 DEMFRGs and ten hub genes potentially involved in various processes like ferroptosis, TNF signalling pathway, MAPK signalling pathway, and NF-kappa B signalling pathway were discovered. Several miRNAs were identified as shared targets of hub genes. The ICH group showed increased infiltration of monocytic lineage and myeloid dendritic cells compared to the Healthy group. Ten potential small molecule drugs (e.g. Zebularine, TWS-119, CG-930) were predicted CMap.
CONCLUSION
Several shared genes between mitophagy and ferroptosis potentially drive ICH progression TNF, MAPK, and NF-kappa B pathways. These results offer valuable insights for further exploring the connection between mitophagy, ferroptosis, and ICH.
Topics: Mitophagy; Ferroptosis; Cerebral Hemorrhage; Humans; Computational Biology; MicroRNAs; Gene Regulatory Networks
PubMed: 38863372
DOI: 10.1080/03014460.2024.2334719 -
Diagnostic Microbiology and Infectious... Jun 2024Sepsis is a highly lethal disease that poses a serious threat to human health. Increasing evidence indicates that neutrophil extracellular traps (NETs) are key factors...
Sepsis is a highly lethal disease that poses a serious threat to human health. Increasing evidence indicates that neutrophil extracellular traps (NETs) are key factors in the pathological progression of sepsis. This study aims to screen potential biomarkers for sepsis and delve into their regulatory function in the pathogenesis. We downloaded 6 microarray datasets from the Gene Expression Omnibus (GEO) database, with 4 as the training sets and 2 as the validation sets. NETs-related genes (NRGs) were obtained from relevant literature. Differential expression analysis was performed on four training sets separately. We intersected differentially expressed genes (DEGs) from the four training sets and NRGs, finally resulting in 19 NETs-related sepsis genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) unearthed that NETs-related sepsis genes were majorly abundant in functions and pathways such as defense response to bacterium and Neutrophil extracellular trap formation. Using the PPI network, the MCC algorithm, and the MCODE algorithm in the CytoHubba plugin, 7 sepsis hub genes (ELANE, TLR4, MPO, PADI4, CTSG, MMP9, S100A12) were identified. ROC curve for each Hub gene in the training and validation sets were plotted, which revealed that the Area Under Curve (AUC) values are all greater than 0.6, indicating good classification ability. A total of 349 miRNAs targeting Hub genes were predicted in the mirDIP database, and 620 lncRNAs targeting miRNAs were predicted in the ENCORI database. The ceRNA regulatory network was constructed using Cytoscape software. Finally, we employed the cMAP database to predict small molecular complexes as potentially effective drugs for the treatment of sepsis, such as chloroquine, harpagoside, and PD-123319. In conclusion, this project successfully identified 7 core genes, which may serve as promising candidates for novel sepsis biomarkers. Meanwhile, we constructed a related ceRNA network and predicted potential targeted drugs, providing potential therapeutic targets and treatment strategies for sepsis patients.
PubMed: 38852219
DOI: 10.1016/j.diagmicrobio.2024.116380