Jiangxi Provincial Cancer Hospital

Diffuse large B-cell lymphoma (DLBCL) is an aggressive subtype of non-Hodgkin lymphoma. Fatty acid synthase (FASN) is a key lipogenic enzyme implicated in tumor progression, but its regulation in DLBCL is poorly understood. The function of FASN in DLBCL was explored using database analysis, clinical sample analysis, and cellular phenotyping experiments. Candidate m6A sites on FASN mRNA were predicted using the SRAMP database. Bioinformatics analyses and experimental techniques were conducted to elucidate the role of methyltransferase-like 3 (METTL3) on cell phenotypes and its regulation of FASN. The findings were further confirmed in mice models and clinical sample analyses. FASN was highly expressed in DLBCL and positively correlated with poor prognosis. FASN knockdown inhibited the malignant phenotypes in DLBCL cells by suppressing the PI3K/AKT and MAPK/ERK signaling pathways, as well as promoting endoplasmic reticulum (ER) stress. The analysis of FASN mRNA revealed the presence of m6A modification sites, and a positive correlation was identified between METTL3 and FASN. The impact of METTL3 knockdown on the malignant phenotypes of DLBCL cells was consistent with the effects induced by FASN knockdown, whereas METTL3 overexpression reversed the effects of FASN knockdown. Mechanistically, METTL3 stabilized FASN expression by mediating m6A modification of FASN mRNA, thereby facilitating DLBCL progression. METTL3 stabilizes FASN expression through m6A modification, thereby facilitating the DLBCL progression by activating the PI3K/AKT and MAPK/ERK signaling pathways and inhibiting the ER stress response pathway. The METTL3/FASN axis represents a potential therapeutic target, especially in METTL3/FASN-high DLBCL subgroups.

This article synthesizes key themes emerging from the CTAD 2025 meeting, highlighting significant advances in Alzheimer's disease (AD) research and clinical practice. New disease-modifying approaches-ranging from next-generation anti-amyloid-β and anti-tau antibodies to small-molecule aggregation inhibitors and gene-based strategies-underscore a growing shift toward multi-target therapeutic frameworks. Blood-based biomarkers, such as p-tau217, p-tau181, glial fibrillary acidic protein, and neurofilament light, are nearing clinical readiness, while digital biomarkers and wearable technologies are enabling remote, continuous assessment of cognitive and physiological functions. Clinical trial design is increasingly oriented toward earlier disease stages and genetically or biomarker-defined high-risk groups, incorporating adaptive methodologies and real-world data to enhance efficiency and generalizability. Collectively, these developments signal an impending transition over the next two to three years from a centralized, cognitive scale-driven model of AD care to a more decentralized, biomarker-guided precision paradigm. CTAD 2025 thus marks a pivotal inflection point in the evolving structure of AD diagnosis and treatment.

This study aims to explore a new application paradigm of single nucleotide polymorphisms (SNP) in precision treatment of lung adenocarcinoma by integrating genomics and machine learning techniques. This study is based on a cohort of 83 lung adenocarcinoma patients diagnosed by pathology. Clinical features and SNP genotype data are integrated, and a gradient boosting decision tree (GBDT) algorithm is used to establish an SNPdriver prediction framework. By adaptively learning the nonlinear interaction effects between SNP features, binary classification prediction of driving factors is achieved. This study randomly divided 83 patients with lung adenocarcinoma into 7:3 groups, and there was no significant difference in baseline characteristics (p > 0.05). The SNPdriver model based on GBDT adopts a 6-decision tree ensemble architecture and achieves mutation state weighted prediction through feature path splitting. The validation showed that the predicted Area Under the Curve (AUC) for EGFR and KRAS mutations were 0.90 and 0.85, respectively, and the calibration curve confirmed that the predicted probability was highly consistent with the actual incidence rate. This study successfully constructed the SNPdriver model for predicting driver gene mutations in lung adenocarcinoma based on SNP feature networks. Its high discriminatory power and clinical consistency validated the potential of SNPs as multi-gene coregulatory biomarkers.

Glioblastoma (GBM) is a highly aggressive and lethal brain tumor, and despite conventional treatments, patient prognosis remains poor. Understanding the molecular mechanisms driving GBM and identifying potential therapeutic targets is critical. MOV10, an RNA helicase, is overexpressed in multiple cancers and is considered an oncogene. Our analysis of datasets from TCGA, GEO, and CGGA showed that MOV10 expression is elevated in GBM and strongly negatively correlated with overall survival (OS). Cox regression confirmed MOV10 as an independent prognostic risk factor for GBM.Functional enrichment analysis revealed that MOV10 is involved in immune regulation and tumor progression pathways. We found that MOV10 expression is closely linked to immune infiltration, immune checkpoint expression, and responses to immunotherapy. Immunofluorescence and Transwell assays confirmed that MOV10 knockdown reduced M2 macrophage migration and invasion in GBM cells. Clinical analysis further validated MOV10 overexpression in GBM tissues.In vitro, MOV10 silencing suppressed GBM cell proliferation, inhibited EMT-like processes, and promoted apoptosis through autophagy modulation. Our findings suggest that MOV10 plays a crucial role in GBM progression and could be a promising molecular target for therapy.