Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, characterized by high incidence and poor survival rates. Glycosylation, a fundamental post-translational modification, influences protein stability, signaling, and tumor progression, with aberrations implicated in immune evasion and metastasis. This study investigates the role of glycosylation-related genes (Glycosylation-RGs) in CRC using machine learning and bioinformatics. Data from The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB) were analyzed to identify 67 differentially expressed Glycosylation-RGs. These genes were used to classify CRC patients into two subgroups with distinct survival outcomes, highlighting their prognostic value. Weighted gene coexpression network analysis (WGCNA) revealed key modules associated with CRC traits, including pathways like glycan biosynthesis and PI3K–Akt signaling. A machine-learning-based prognostic model demonstrated strong predictive performance, stratifying patients into high- and low-risk groups with significant survival differences. Additionally, the model revealed correlations between risk scores and immune cell infiltration, providing insights into the tumor immune microenvironment. Drug sensitivity analysis identified potential therapeutic agents, including Trametinib, SCH772984, and Oxaliplatin, showing differential efficacy between risk groups. These findings enhance our understanding of glycosylation in CRC, identifying it as a critical factor in disease progression and a promising target for future therapeutic strategies.

Three new 11,20-epoxybriaranes—fragilides U–W (1–3), as well as two known metabolites, junceellonoid D (4) and junceellin (5), were obtained from the octocoral Junceella fragilis. The structures of briaranes 1–3 were elucidated by spectroscopic methods and briaranes 3 and 5 displayed inhibition effects on inducible nitric oxide synthase (iNOS) release from RAW264.7.

Background: In several human cancers, Krüppel-like factor 5 (KLF5), a zinc finger transcription factor, can contribute to both tumor progression or suppression; however, the precise role of KLF5 in nasopharyngeal carcinoma (NPC) remains poorly understood. In this study, the association between KLF5 and microRNA-145-5p (miR-145-5p) in NPC cells was elucidated. Results: Our results showed that KLF5 expression was up-regulated in NPC group compared to normal group. We found that KLF5 exhibited an oncogenic role in NPC cells. The upregulation of miR-145-5p inhibited the proliferation, migration, and invasion of NPC cells. It was observed that miR-145-5p could down-regulate the mRNA and protein expression of KLF5 in NPC cell lines. Additionally, the activity of focal adhesion kinase (FAK), a migration marker, was regulated by miR-145-5p and KLF5 in NPC cells. Conclusions: The results of this study indicated that miR-145-5p could repress the proliferation, migration, and invasion of NPC cells via KLF5/FAK regulation, and could be a potential therapeutic target for patients with NPC.

Globally, healthcare is concerned about the rising prevalence of type 2 diabetes. Phytochemicals from medicinal plants have shown great promise in improving human health. The present study aimed to determine the secondary metabolites of Ziziphus oenopolia (L.) fruit extract that contribute to its anti-diabetic activity. The anti-diabetic properties were assessed by in vitro and in silico approaches using α-amylase and α-glucosidase inhibitory assays. Gas chromatography and mass spectroscopy analyses were used to profile Z. oenopolia fruit contents, and a total of four bioactive chemicals and eight phytocompounds were tentatively identified, including flavonoids, terpenoids, phenols, steroids, tannins, and saponins. The Z. oenopolia fruit hydroalcoholic extract inhibits α-amylase and α-glucosidase enzymes in a dose-dependent manner (IC₅₀ = 328.76 and 337.28 µg/mL, R ² = 0.979 and 0.981). Additionally, phytochemicals found in Z. oenopolia fruit exhibit the ability to inhibit anti-diabetic targets, specifically α-amylase and α-glucosidase (2QV4 vs 3A4A; correlation coefficient, r = 0.955), as demonstrated by computational analysis. This establishes the fruit as a promising and environmentally friendly option for treating hyperglycemia, highlighting the positive correlation between anti-diabetic objectives.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, charac-terized by high incidence and poor survival rates. Glycosylation, a fundamental post-translational modification, influences protein stability, signaling, and tumor pro-gression, with aberrations implicated in immune evasion and metastasis. This study investigates the role of glycosylation-related genes (Glycosylation-RGs) in CRC using machine learning and bioinformatics. Data from The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB) were analyzed to identify 67 differentially expressed Glycosylation-RGs. These genes were used to classify CRC patients into two subgroups with distinct survival outcomes, highlighting their prognostic value. Weighted Gene Coexpression Network Analysis (WGCNA) revealed key modules as-sociated with CRC traits, including pathways like glycan biosynthesis and PI3K-Akt signaling. A machine learning-based prognostic model demonstrated strong predictive performance, stratifying patients into high- and low-risk groups with significant sur-vival differences. Additionally, the model revealed correlations between risk scores and immune cell infiltration, providing insights into the tumor immune microenvironment. Drug sensitivity analysis identified potential therapeutic agents, including Trametinib, SCH772984, and Oxaliplatin, showing differential efficacy between risk groups. These findings enhance our understanding of glycosylation in CRC, identifying it as a critical factor in disease progression and a promising target for future therapeutic strategies.