Background: Parkinson\'s disease (PD) is marked by the loss of dopaminergic neurons in the substantia nigra pars compacta, leading to motor and cognitive dysfunctions. The molecular mechanisms underlying synaptic alterations in PD remain elusive, with a focus on the role of Itga5 in synaptic integrity and motor coordination and TAT-Itga5 was designed to suppress PTEN activity in this investigation. Methods: This study utilized MPTP-induced PD animal models to investigate the expression and role of Itga5 in the striatum. Techniques included quantitative PCR, Western blotting, immunostaining, CRISPR-CasRx-mediated knockdown, electrophysiological assays, behavioral tests, and mass spectrometry. Results: Itga5 expression was significantly reduced in MPTP-induced PD models. In these models, a marked decrease in dendritic spine density and a shift towards thinner spines in striatal GABA neurons were observed, suggesting impaired synaptic integration. Knockdown of Itga5 resulted in reduced dendritic branching, decreased mushroom spines, and increased thin spines, altering synaptic architecture. Electrophysiological analyses revealed changes in action potential and spontaneous excitatory postsynaptic currents, indicating altered synaptic transmission. Motor behavior assessments showed that Itga5 deficiency led to impairments in fine motor control and coordination. Furthermore, Itga5 was found to interact with PTEN, affecting AKT signaling crucial for synaptic development and motor coordination. Conclusion: The study demonstrates that Itga5 plays a critical role in maintaining synaptic integrity and motor coordination in PD. The Itga5-PTEN-AKT pathway represents a potential therapeutic target for addressing synaptic and motor dysfunctions in PD.

UNASSIGNED: Alzheimer\'s disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers.
UNASSIGNED: Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers.
UNASSIGNED: On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors.
UNASSIGNED: Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.

BACKGROUND: Osteosarcoma (OS), the most common primary malignant bone tumor, predominantly affects children and young adults and is characterized by high invasiveness and poor prognosis. Despite therapeutic advancements, the survival rate remains suboptimal, indicating an urgent need for novel biomarkers and therapeutic targets. This study aimed to investigate the prognostic significance of LGMN expression and immune cell infiltration in the tumor microenvironment of OS.
METHODS: We performed an integrative bioinformatics analysis utilizing the GEO and TARGET-OS databases to identify differentially expressed genes (DEGs) associated with LGMN in OS. We conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to explore the biological pathways and functions. Additionally, we constructed protein-protein interaction (PPI) networks, a competing endogenous RNA (ceRNA) network, and applied the CIBERSORT algorithm to quantify immune cell infiltration. The diagnostic and prognostic values of LGMN were evaluated using the area under the receiver operating characteristic (ROC) curve and Cox regression analysis. Furthermore, we employed Consensus Clustering Analysis to explore the heterogeneity within OS samples based on LGMN expression.
RESULTS: The analysis revealed significant upregulation of LGMN in OS tissues. DEGs were enriched in immune response and antigen processing pathways, suggesting LGMN\'s role in immune modulation within the TME. The PPI and ceRNA network analyses provided insights into the regulatory mechanisms involving LGMN. Immune cell infiltration analysis indicated a correlation between high LGMN expression and increased abundance of M2 macrophages, implicating an immunosuppressive role. The diagnostic AUC for LGMN was 0.799, demonstrating its potential as a diagnostic biomarker. High LGMN expression correlated with reduced overall survival (OS) and progression-free survival (PFS). Importantly, Consensus Clustering Analysis identified two distinct subtypes of OS, highlighting the heterogeneity and potential for personalized medicine approaches.
CONCLUSIONS: Our study underscores the prognostic value of LGMN in osteosarcoma and its potential as a therapeutic target. The identification of LGMN-associated immune cell subsets and the discovery of distinct OS subtypes through Consensus Clustering Analysis provide new avenues for understanding the immunosuppressive TME of OS and may aid in the development of personalized treatment strategies. Further validation in larger cohorts is warranted to confirm these findings.

Osteosarcoma is a highly aggressive bone tumor primarily affecting children and adolescents. Despite advancements in treatment modalities, the prognosis for osteosarcoma patients remains poor, emphasizing the need for a deeper understanding of its underlying mechanisms. In recent years, the concept of cancer stem cells (CSCs) has emerged as a crucial factor in tumor initiation, progression, and therapy resistance. These specialized subpopulations of cells possess self-renewal capacity, tumorigenic potential, and contribute to tumor heterogeneity. Sox9, a transcription factor known for its critical role in embryonic development and tissue homeostasis, has been implicated in various malignancies, including osteosarcoma. This review aims to summarize the current knowledge regarding the role of Sox9 in CSCs in osteosarcoma and its potential implications as a prognosis and therapeutic target.

DNA methylation is one of the earliest and most significant epigenetic mechanisms discovered. DNA methylation refers, in general, to the addition of a methyl group to a specific base in the DNA sequence under the catalysis of DNA methyltransferase, with S‑adenosine methionine as the methyl donor, via covalent bonding and chemical modifications. DNA methylation is an important factor in inducing cancer. There are different types of DNA methylation, and methylation at different sites plays different roles. It is well known that the progression of colorectal cancer (CRC) is affected by the methylation of key genes. The present review did not only discuss the potential relationship between DNA methylation and CRC but also discussed how DNA methylation affects the development of CRC by affecting key genes. Furthermore, the clinical significance of DNA methylation in CRC was highlighted, including that of the therapeutic targets and biomarkers of methylation; and the importance of DNA methylation inhibitors was discussed as a novel strategy for treatment of CRC. The present review did not only focus upon the latest research findings, but earlier reviews were also cited as references to older literature.

Protein lysine crotonylation (Kcr) is one conserved form of posttranslational modifications of proteins, which plays an important role in a series of cellular physiological and pathological processes. Lysine ε-amino groups are the primary sites of such modification, resulting in four-carbon planar lysine crotonylation that is structurally and functionally distinct from the acetylation of these residues. High levels of Kcr modifications have been identified on both histone and non-histone proteins. The present review offers an update on the research progression regarding protein Kcr modifications in biomedical contexts and provides a discussion of the mechanisms whereby Kcr modification governs a range of biological processes. In addition, given the importance of protein Kcr modification in disease onset and progression, the potential viability of Kcr regulators as therapeutic targets is elucidated.

UNASSIGNED: Non-small cell lung cancer (NSCLC), including the lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes, is a malignant tumor type with a poor 5-year survival rate. The identification of new powerful diagnostic biomarkers, prognostic biomarkers, and potential therapeutic targets in NSCLC is urgently required.
UNASSIGNED: The UCSC Xena, UALCAN, and GEO databases were used to screen and analyze differentially expressed genes, regulatory modes, and genetic/epigenetic alterations in NSCLC. The UCSC Xena database, GEO database, tissue microarray, and immunohistochemistry staining analyses were used to evaluate the diagnostic and prognostic values. Gain-of-function assays were performed to examine the roles. The ESTIMATE, TIMER, Linked Omics, STRING, and DAVID algorithms were used to analyze potential molecular mechanisms.
UNASSIGNED: NR3C2 was identified as a potentially important molecule in NSCLC. NR3C2 is expressed at low levels in NSCLC, LUAD, and LUSC tissues, which is significantly related to the clinical indexes of these patients. Receiver operating characteristic curve analysis suggests that the altered NR3C2 expression patterns have diagnostic value in NSCLC, LUAD, and especially LUSC patients. Decreased NR3C2 expression levels can help predict poor prognosis in NSCLC and LUAD patients but not in LUSC patients. These results have been confirmed both with database analysis and real-world clinical samples on a tissue microarray. Copy number variation contributes to low NR3C2 expression levels in NSCLC and LUAD, while promoter DNA methylation is involved in its downregulation in LUSC. Two NR3C2 promoter methylation sites have high sensitivity and specificity for LUSC diagnosis with clinical application potential. NR3C2 may be a key participant in NSCLC development and progression and is closely associated with the tumor microenvironment and immune cell infiltration. NR3C2 co-expressed genes are involved in many cancer-related signaling pathways, further supporting a potentially significant role of NR3C2 in NSCLC.
UNASSIGNED: NR3C2 is a novel potential diagnostic and prognostic biomarker and therapeutic target in NSCLC.

Glycogen synthase kinase-3 (GSK3), consisting of GSK3α and GSK3β subtypes, is a complex protein kinase that regulates numerous substrates. Research has observed increased GSK3 expression in the brains of Alzheimer\'s disease (AD) patients and models. AD is a neurodegenerative disorder with diverse pathogenesis and notable cognitive impairments, characterized by Aβ aggregation and excessive tau phosphorylation. This article provides an overview of GSK3\'s structure and regulation, extensively analyzing its relationship with AD factors. GSK3 overactivation disrupts neural growth, development, and function. It directly promotes tau phosphorylation, regulates amyloid precursor protein (APP) cleavage, leading to Aβ formation, and directly or indirectly triggers neuroinflammation and oxidative damage. We also summarize preclinical research highlighting the inhibition of GSK3 activity as a primary therapeutic approach for AD. Finally, pending issues like the lack of highly specific and affinity-driven GSK3 inhibitors, are raised and expected to be addressed in future research. In conclusion, GSK3 represents a target in AD treatment, filled with hope, challenges, opportunities, and obstacles.

OBJECTIVE: The RNA binding protein quaking (QKI) is associated with the development and progression of tumor suppressors in various cancers. However, the clinical implications of QKI expression have not yet been fully elucidated. In this study, we aimed to investigate the clinicopathological and prognostic significance of QKI expression in hepatocellular carcinoma (HCC).
METHODS: We performed QKI, Zinc finger E-box-binding homeobox 1 (ZEB1), E-cadherin, and glutathione peroxidase 4 (GPX4) immunohistochemical staining on 166 HCC patient tissue samples. X-tile bioinformatics software was used to set the cut-off value for high QKI expression. Correlations between QKI expression and various clinicopathological parameters were assessed.
RESULTS: The best cut-off value for high QKI expression was 12.5. High QKI expression was observed in 28 of 166 patients (16.9%) and was an independent prognostic factor for inferior recurrence-free survival (RFS). In addition, high ZEB1 and GPX4 expression correlated with high QKI expression, but not with the loss of E-cadherin expression.
CONCLUSIONS: High QKI expression was identified in HCCs and associated with poor RFS. QKI might be a prognostic biomarker of HCCs associated with epithelial-to-mesenchymal transition and a potential candidate therapeutic target.

Identification of novel biomarkers for prediction of disease course and prognosis is needed to reduce morbidity of liver hepatocellular carcinoma (LIHC/HCC) patients. Although dysregulated Periodic tryptophan protein 1 homolog (PWP1/endonuclein) expression has been detected in several tumors, the potential regulatory effect of PWP1 on LIHC remains uncertain. Here we evaluated the expression of PWP1 using multiple online platforms, and demonstrated that PWP1 upregulation was consistently observed in LIHC relative to non-tumor liver tissues and correlated with unfavorable prognosis. Moreover, HCC prognosis was significantly influenced by the methylation status of various CpG sites in the PWP1 gene. Lastly, we provide direct evidence that PWP1 acts as a driver of HCC progression by showing that siRNA-mediated PWP1 silencing significantly suppressed HCC cell proliferation in vitro. These data strongly suggest that PWP1 silencing may be an effective therapeutic strategy to treat LIHC.