Glioblastoma (GBM) remains the most lethal primary brain tumor, characterized by dismal survival rates. Novel molecular targets are urgently required to enhance therapeutic outcomes. A combination of bioinformatics analysis and experimental validation was employed to investigate the role of EGFLAM in GBM. The Chinese Glioma Genome Atlas provided a platform for gene expression profiling, while siRNA-mediated knockdown and overexpression assays in GBM cell lines, alongside in vivo tumorigenesis models, facilitated functional validation. EGFLAM was found to be significantly overexpressed in GBM tissues, correlating with adverse prognostic factors and higher tumor grades, particularly in patients over the age of 41. Functional assays indicated that EGFLAM is vital for maintaining GBM cell proliferation, viability, and invasiveness. Knockdown of EGFLAM expression led to a marked decrease in tumorigenic capabilities. Proteomic interactions involving EGFLAM, such as with NUP205, were implicated in cell cycle regulation, providing insight into its oncogenic mechanism. In vivo studies further demonstrated that silencing EGFLAM expression could inhibit tumor growth, underscoring its therapeutic potential. The study identifies EGFLAM as a pivotal oncogenic factor in GBM, serving as both a prognostic biomarker and a viable therapeutic target. These findings lay the groundwork for future research into EGFLAM-targeted therapies, aiming to improve clinical outcomes for GBM patients.

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.

Hematological and neurological expressed 1 (HN1), also known as Jupiter microtubule associated homolog 1 (JPT1), is a highly conserved protein with widespread expression in various tissues. Ectopic elevation of HN1 has been observed in multiple cancers, highlighting its role in tumorigenesis and progression. Both proteomics and transcriptomics reveal that HN1 is closely associated with severe disease progression, poor prognostic and shorter overall survival. HN1\'s involvement in cancer cell proliferation and metastasis has been extensively investigated. Overexpression of HN1 is associated with increased tumor growth and disease progression, while its depletion leads to cell cycle arrest and apoptosis. The pivotal role of HN1 in cancer progression, particularly in proliferation, migration, and invasion, underscores its significance in cancer metastasis. Validation of the efficacy and safety of HN1 inhibition, along with the development of diagnostic methods to determine HN1 expression levels in patients, is essential for the translation of HN1-targeted therapies into clinical practice. Overall, HN1 emerges as a valuable prognostic marker and therapeutic target in cancer, and further investigations hold the potential to improve patient outcomes by impeding metastasis and enhancing treatment strategies.

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.

Mitochondria are dynamic organelles that continuously undergo fusion/fission to maintain normal cell physiological activities and energy metabolism. When mitochondrial dynamics is unbalanced, mitochondrial homeostasis is broken, thus damaging mitochondrial function. Accumulating evidence demonstrates that impairment in mitochondrial dynamics leads to lung tissue injury and pulmonary disease progression in a variety of disease models, including inflammatory responses, apoptosis, and barrier breakdown, and that the role of mitochondrial dynamics varies among pulmonary diseases. These findings suggest that modulation of mitochondrial dynamics may be considered as a valid therapeutic strategy in pulmonary diseases. In this review, we discuss the current evidence on the role of mitochondrial dynamics in pulmonary diseases, with a particular focus on its underlying mechanisms in the development of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD), asthma, pulmonary fibrosis (PF), pulmonary arterial hypertension (PAH), lung cancer and bronchopulmonary dysplasia (BPD), and outline effective drugs targeting mitochondrial dynamics-related proteins, highlighting the great potential of targeting mitochondrial dynamics in the treatment of pulmonary disease.

Alterations in vascular extracellular matrix (ECM) components, interactions, and mechanical properties influence both the formation and stability of atherosclerotic plaques. This review discusses the contribution of the ECM microenvironment in vascular homeostasis and remodeling in atherosclerosis, highlighting Cartilage oligomeric matrix protein (COMP) and its degrading enzyme ADAMTS7 as examples, and proposes potential avenues for future research aimed at identifying novel therapeutic targets for atherosclerosis based on the ECM microenvironment.

Cluster of differentiation 36 (CD36) is a multiligand receptor with important roles in lipid metabolism, angiogenesis and innate immunity, and its diverse effects may depend on the binding of specific ligands in different contexts. CD36 is expressed not only on immune cells in the tumor microenvironment (TME) but also on some hematopoietic cells. CD36 is associated with the growth, metastasis and drug resistance in some hematologic tumors, such as leukemia, lymphoma and myelodysplastic syndrome. Currently, some targeted therapeutic agents against CD36 have been developed, such as anti-CD36 antibodies, CD36 antagonists (small molecules) and CD36 expression inhibitors. This paper not only innovatively addresses the role of CD36 in some hematopoietic cells, such as erythrocytes, hematopoietic stem cells and platelets, but also pays special attention to the role of CD36 in the development of hematologic tumors, and suggests that CD36 may be a potential cancer therapeutic target in hematologic tumors.

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.

OBJECTIVE: Valve interstitial cells (VICs) undergo a transition to intermediate state cells before ultimately transforming into the osteogenic cell population, which is a pivotal cellular process in calcific aortic valve disease (CAVD). Herein, this study successfully delineated the stages of VIC osteogenic transformation and elucidated a novel key regulatory role of lumican (LUM) in this process.
METHODS: Single-cell RNA-sequencing (scRNA-seq) from nine human aortic valves was used to characterize the pathological switch process and identify key regulatory factors. The in vitro, ex vivo, in vivo, and double knockout mice were constructed to further unravel the calcification-promoting effect of LUM. Moreover, the multi-omic approaches were employed to analyse the molecular mechanism of LUM in CAVD.
RESULTS: ScRNA-seq successfully delineated the process of VIC pathological transformation and highlighted the significance of LUM as a novel molecule in this process. The pro-calcification role of LUM is confirmed on the in vitro, ex vivo, in vivo level, and ApoE-/-//LUM-/- double knockout mice. The LUM induces osteogenesis in VICs via activation of inflammatory pathways and augmentation of cellular glycolysis, resulting in the accumulation of lactate. Subsequent investigation has unveiled a novel LUM driving histone modification, lactylation, which plays a role in facilitating valve calcification. More importantly, this study has identified two specific sites of histone lactylation, namely, H3K14la and H3K9la, which have been found to facilitate the process of calcification. The confirmation of these modification sites\' association with the expression of calcific genes Runx2 and BMP2 has been achieved through ChIP-PCR analysis.
CONCLUSIONS: The study presents novel findings, being the first to establish the involvement of lumican in mediating H3 histone lactylation, thus facilitating the development of aortic valve calcification. Consequently, lumican would be a promising therapeutic target for intervention in the treatment of CAVD.