m6A methylation-mediated regulation of LncRNA MEG3 suppresses ovarian cancer progression through miR-885-5p and the VASH1 pathway.

BACKGROUND: Ovarian cancer poses a serious threat to women's health. Due to the difficulty of early detection, most patients are diagnosed with advanced-stage disease or peritoneal metastasis. We found that LncRNA MEG3 is a novel tumor suppressor, but its role in tumor occurrence and development is still unclear. METHODS: We investigated the expression level of MEG3 in pan-cancer through bioinformatics analysis, especially in gynecological tumors. Function assays were used to detect the effect of MEG3 on the malignant phenotype of ovarian cancer. RIP, RNA pull-down, MeRIP-qPCR, actinomycin D test were carried out to explore the m6A methylation-mediated regulation on MEG3. Luciferase reporter gene assay, PCR and Western blot were implemented to reveal the potential mechanism of MEG3. We further confirmed the influence of MEG3 on tumor growth in vivo by orthotopic xenograft models and IHC assay. RESULTS: In this study, we discovered that MEG3 was downregulated in various cancers, with the most apparent downregulation in ovarian cancer. MEG3 inhibited the proliferation, migration, and invasion of ovarian cancer cells. Overexpression of MEG3 suppressed the degradation of VASH1 by negatively regulating miR-885-5p, inhibiting the ovarian cancer malignant phenotype. Furthermore, we demonstrated that MEG3 was regulated at the posttranscriptional level. YTHDF2 facilitated MEG3 decay by recognizing METTL3­mediated m6A modification. Compared with those injected with vector control cells, mice injected with MEG3 knockdown cells showed larger tumor volumes and faster growth rates. CONCLUSION: We demonstrated that MEG3 is influenced by METTL3/YTHDF2 methylation and restrains ovarian cancer proliferation and metastasis by binding miR-885-5p to increase VASH1 expression. MEG3 is expected to become a therapeutic target for ovarian cancer.

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.

Nanoparticles in tumor microenvironment remodeling and cancer immunotherapy.

Cancer immunotherapy and vaccine development have significantly improved the fight against cancers. Despite these advancements, challenges remain, particularly in the clinical delivery of immunomodulatory compounds. The tumor microenvironment (TME), comprising macrophages, fibroblasts, and immune cells, plays a crucial role in immune response modulation. Nanoparticles, engineered to reshape the TME, have shown promising results in enhancing immunotherapy by facilitating targeted delivery and immune modulation. These nanoparticles can suppress fibroblast activation, promote M1 macrophage polarization, aid dendritic cell maturation, and encourage T cell infiltration. Biomimetic nanoparticles further enhance immunotherapy by increasing the internalization of immunomodulatory agents in immune cells such as dendritic cells. Moreover, exosomes, whether naturally secreted by cells in the body or bioengineered, have been explored to regulate the TME and immune-related cells to affect cancer immunotherapy. Stimuli-responsive nanocarriers, activated by pH, redox, and light conditions, exhibit the potential to accelerate immunotherapy. The co-application of nanoparticles with immune checkpoint inhibitors is an emerging strategy to boost anti-tumor immunity. With their ability to induce long-term immunity, nanoarchitectures are promising structures in vaccine development. This review underscores the critical role of nanoparticles in overcoming current challenges and driving the advancement of cancer immunotherapy and TME modification.