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Osteoarthritis (OA) is a common degenerative joint disease characterized by progressive cartilage degradation and inflammation. In recent years, mesenchymal stem cells (MSCs) derived exosomes (MSCs-Exo) have attracted widespread attention for their potential role in modulating OA pathology. However, the unpredictable therapeutic effects of exosomes have been a significant barrier to their extensive clinical application. In this study, we investigated whether fucoidan-pretreated MSC-derived exosomes (F-MSCs-Exo) could better protect chondrocytes in osteoarthritic joints and elucidate its underlying mechanisms. In order to evaluate the role of F-MSCs-Exo in osteoarthritis, both in vitro and in vivo studies were conducted. MiRNA sequencing was employed to analyze MSCs-Exo and F-MSCs-Exo, enabling the identification of differentially expressed genes and the exploration of the underlying mechanisms behind the protective effects of F-MSCs-Exo in osteoarthritis. Compared to MSCs-Exo, F-MSCs-Exo demonstrated superior effectiveness in inhibiting inflammatory responses and extracellular matrix degradation in rat chondrocytes. Moreover, F-MSCs-Exo exhibited enhanced activation of autophagy in chondrocytes. MiRNA sequencing of both MSCs-Exo and F-MSCs-Exo revealed that miR-146b-5p emerged as a promising candidate mediator for the chondroprotective function of F-MSCs-Exo, with TRAF6 identified as its downstream target. In conclusion, our research results demonstrate that miR-146b-5p encapsulated in F-MSCs-Exo effectively inhibits TRAF6 activation, thereby suppressing inflammatory responses and extracellular matrix degradation, while promoting chondrocyte autophagy for the protection of osteoarthritic cartilage cells. Consequently, the development of a therapeutic approach combining fucoidan with MSC-derived exosomes provides a promising strategy for the clinical treatment of osteoarthritis. [ABSTRACT FROM AUTHOR]

The present study was aimed at identifying the potential prognostic biomarkers of the immune-related long noncoding RNA (IRL) signature for patients with hepatocellular carcinoma (HCC). RNA-sequencing data and clinical information about HCC were obtained from The Cancer Genome Atlas. The IRLs were determined with regard to the coexpression of immune-related genes and differentially expressed lncRNAs. The survival IRLs were obtained using the univariate Cox analysis. Subsequently, the prognosis model was constructed via the multivariate Cox analysis. Subsequently, functional annotation was conducted using Gene Set Enrichment Analysis (GSEA) and principal component analysis (PCA). In total, 341 IRLs were identified, and 6 IRLs were found to have a highly significant association with the prognosis of patients with HCC. The immune prognosis model was constructed with these 6 IRLs (AC099850.4, negative regulator of antiviral response, AL031985.3, PRRT3-antisense RNA1, AL365203.2, and long intergenic nonprotein coding RNA 1224) using the multivariate Cox regression analysis. In addition, immune-related prognosis signatures were confirmed as an independent prognostic factor. The association between prognostic signatures and immune infiltration indicated that the 6 lncRNAs could reflect the immune status of the tumor. Collectively, the present study demonstrates that six-lncRNA signatures may be potential biomarkers to predict the prognosis of patients with HCC. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies. Recent studies reveal that tumor microenvironment (TME) components significantly affect HCC growth and progression, particularly the infiltrating stromal and immune cells. Thus, mining of TME-related biomarkers is crucial to improve the survival of patients with HCC. Public access of The Cancer Genome Atlas (TCGA) database allows convenient performance of gene expression-based analysis of big data, which contributes to the exploration of potential association between genes and prognosis of a variety of malignancies, including HCC. The "Estimation of STromal and Immune cells in MAlignant Tumors using Expression data" algorithm renders the quantification of the stromal and immune components in TME possible by calculating the stromal and immune scores. Differentially expressed genes (DEGs) were screened by dividing the HCC cohort of TCGA database into high- and low-score groups according to stromal and immune scores. Further analyses of functional enrichment and protein-protein interaction networks show that the DEGs are mainly involved in immune response, cell adhesion, and extracellular matrix. Finally, seven DEGs have significant association with HCC poor outcomes. These genes contain FABP3, GALNT5, GPR84, ITGB6, MYEOV, PLEKHS1, and STRA6 and may be candidate biomarkers for HCC prognosis. [ABSTRACT FROM AUTHOR]

A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies. [ABSTRACT FROM AUTHOR]