Aug 2026· PLoS ONE· Vol 21, pp. e0355984· 0 citations· 59 references
Medicine
TL;DR
Findings indicate that CASP4 and TLR8, together with their associated regulatory miRNAs, may represent promising biomarkers and potential therapeutic targets for future ASD research and contribute to a better understanding of the pathophysiological mechanisms underlying ASD.
Abstract
Background Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders whose underlying molecular mechanisms and biological processes remain incompletely understood. In this study, we used a multi-layered systems biology approach to prioritize candidate genes and regulatory factors associated with ASD. Method Gene expression data from peripheral blood samples were obtained from the Gene Expression Omnibus (GEO) database (GSE18123). Using analyses performed in R software, differentially expressed genes (DEGs) in patients with ASD were identified (p-value < 0.05 and |log2FC| > 0.5). These DEGs were used to perform weighted gene co-expression network analysis (WGCNA) and construct a protein–protein interaction (PPI) network. By integrating the results of these network analyses with feature selection techniques (LASSO and random forest feature importance), candidate genes associated with ASD were prioritized and evaluated using qRT-PCR in the valproic acid (VPA)-induced rat model of autism. Furthermore, a gene regulatory network (GRN) was constructed to identify the regulatory factors associated with DEGs. Result TLR8 and CASP4 were prioritized as candidate genes that may be associated with ASD, because they were located within the co-expression module that showed the strongest correlation with ASD, were identified as key nodes of the PPI network, and were selected by feature selection algorithms. Our experimental validation showed increased expression of TLR8 and CASP4 in the autism model compared with controls; TLR8 was upregulated in both the hippocampus and peripheral blood, whereas CASP4 was upregulated only in the hippocampus. Furthermore, GRN analysis identified miR-891b and miR-627-3p as potential regulators of TLR8, and miR-26b-5p as associated with CASP4. Conclusion These findings indicate that CASP4 and TLR8, together with their associated regulatory miRNAs, may represent promising biomarkers and potential therapeutic targets for future ASD research and contribute to a better understanding of the pathophysiological mechanisms underlying ASD.
This study integrated mRNA expression profiles from five post-mortem brain tissue GEO datasets to identify ASD-associated genes and found that EIF4A1 mRNA expression was significantly elevated in ASD subjects and rescued by treatment with the antipsychotics olanzapine or risperidone.
Alzheimer’s disease (AD) is the most common cause of dementia, and one of the most common health problems all over the world. However, the molecular mechanisms of AD remain incompletely understood. The current investigation aimed to elucidate potential key candidate genes and signaling pathways in AD. Next generation sequencing (NGS) dataset GSE203206 was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 39 AD samples and 8 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R bioconductor package. DEGs were subsequently investigated by Gene ontology (GO) and pathway enrichment analysis, and a protein–protein interaction (PPI) network and modules were constructed and analyzed. The miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network construction analysis were performed to predict key microRNAs (miRNAs), transcription factors (TFs) and small drug molecules. The receiver operating characteristic (ROC) curve analysis was performed to estimate the clinical diagnostic value of the hub genes. Conduct molecular docking with hub genes and corresponding active molecules. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes were screened between AD and normal control samples. GO and pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in response to stimulus, cytoplasm, small molecule binding and signal transduction, whereas down regulated genes were mainly enriched in multicellular organism development, cell junction, ion binding and cardiac conduction. The PPI network contained 4886 nodes and 10,342 edges. HSP90AA1, FN1, KIT, YAP1, LSM2, SKP1, EIF5A2, TAF9, DDX39B and CDK7 were identified as the top hub genes. The regulatory network analysis revealed that miRNAs include hsa-mir-545-3p and hsa-miR-548f-5p, and TFs include PLAG1 and MEF2A might be involved in the development of AD. Drug molecules were predicted including Sulindac, Infliximab, Norfloxacin and Gemcitabine for treatment of AD. Molecular docking analysis revealed that Isocryptomerin and Macrophylloside D were the main active compounds with good binding activities to the HSP90AA1 and FN1. These findings provide new insights into the pathogenesis of AD. The hub genes, miRNAs and TFs have the potential to be used as diagnostic and therapeutic markers.
RNA-sequencing, 3-dimensional protein-centric chromatin conformation, and whole genome DNA methylation sequencing approaches are used to investigate hippocampal tissue from an ASD mouse model to determine if multi-omic data integration improves the resolution of key molecular pathways contributing to the complex ASD phenotype.
Carolina D Alberca, Kwangmoon Park, Ligia A. Papale et al.· Molecular Psychiatry· 0 citations
Parkinson’s disease (PD) pathogenesis involves complex molecular mechanisms, with emerging evidence implicating RNA modifications (RM). This study sought to explore RM-associated key genes and their roles in PD progression.
Transcriptomic datasets GSE6613 (training) and GSE72267 (validation) were analyzed. Differentially expressed genes (DEGs) between PD and controls were recognized. WGCNA was performed to identify RM-associated module genes. Machine learning algorithms, Receiver Operating Characteristic (ROC) analysis, and gene expression validation were applied to screen key genes. Nomogram construction, functional enrichment, immune infiltration analysis were conducted to investigate the biological mechanisms and therapeutic potential of the key genes. The bioinformatics findings were further supported by RT-qPCR experiments in a small clinical cohort (
n
= 5 per group), though these preliminary results require validation in larger independent samples.
Through intersection of 507 DEGs and 2,091 RM-associated module genes, 63 candidate genes related to RM in PD were identified. Machine learning, ROC analysis, gene expression validation, and clinical experiments were further employed to identify two key genes (NOL7 and TXLNA). A nomogram constructed based on key genes demonstrated moderate diagnostic efficacy for PD, with an area under the curve of 0.792. Enrichment analyses revealed associations of the key genes with neuroactive pathways, such as spliceosome and ribosome. Immune infiltration analysis suggested a negative correlation between NOL7 and NKT (cor = -0.30,
P
< 0.05). Furthermore, danazol was predicted to be a compound associated with both NOL7 and TXLNA. Molecular docking analysis revealed that danazol exhibited a relatively favorable binding affinity for NOL7 (-6.3 kcal/mol), whereas its binding affinity for TXLNA was weaker (-4.7 kcal/mol).
NOL7 and TXLNA were validated as blood biomarkers for PD derived from an RNA modification-associated transcriptional module, offering insights into epigenetic dysregulation and immune interactions. The nomogram provided a preliminary framework for PD risk assessment, and the drug prediction offered potential candidates for future therapeutic exploration.
Meiling Chen, Peng Chen, Liya Suo et al.· BMC Neurology· 0 citations
Background The genesis of Parkinson’s disease (PD), a common central neurodegenerative disorder, involves dysregulation of protein posttranslational modifications (PTM). The primary objective of this study was to screen key PTM-associated genes (PTMGs) serving as diagnostic indicators and potential therapeutic targets in PD. Methods Peripheral blood transcriptomic data for PD cohorts and healthy controls were retrieved from publicly accessible repositories. Candidate genes were identified by overlapping differentially expressed genes (DEGs) with established PTMGs via differential expression profiling. Machine learning-based screening approaches were used for the selection of feature genes. Key genes were validated via receiver operating characteristic curve assessment combined with verification of expression levels. Subsequently, enrichment analysis, immune infiltration assessment, chromosome mapping, prediction of ribonucleic acid (RNA) modification sites, and compound screening were further explored. Results A total of 404 DEGs were identified, 19 of which overalpped with PTMGs and were thus selected as candidate genes. ML-based analysis narrowed these to eight feature genes, among which those coding for beta-1,4-galactosyltransferase 3 (B4GALT3), ring finger and FYVE-like domain-containing E3 ubiquitin protein ligase (RFFL), and GABA type A receptor-associated protein (GABARAP) were validated as key genes based on their diagnostic performance and consistent downregulation in the PD group (p < 0.05). Gene set enrichment analysis demonstrated significant enrichment within immune signaling cascades. Analysis of immune cell infiltration revealed diminished populations of activated B lymphocytes, activated CD4-positive T cells, and natural killer T cell subsets in the PD group, which exhibited predominantly positive associations with the identified key genes (p < 0.05). Chromosome mapping localized B4GALT3 to chromosome 1 and RFFL/GABARAP to chromosome 17. High-confidence m6A methylation sites were predicted for B4GALT3 and RFFL. Compound screening identified 34 potential compounds targeting these genes, including valproic acid and phenobarbital. Conclusion This study identified B4GALT3, RFFL, and GABARAP as key PTMGs in PD, highlighting their roles in PD genesis and potential as diagnostic biomarkers.
Bangzhi Wang, Zhuo Huang, Rong Wang et al.· PLoS ONE· 0 citations
This study provides suggestive evidence that elevated adenine and proline may be potential risk factors for ASD and suggests possible involvement of the mitochondrial–Hippo–microtubule pathway, and proposes benzo[a]pyrene as a candidate environmental toxicant that may perturb CSF metabolism.
Dan Zhao, Jun-Zhi Guo, Ying Zhang et al.· Genes· 0 citations
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