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Abstract Background Growth hormone-secreting pituitary neuroendocrine tumors can be pathologically classified into densely granulated (DGGH) and sparsely granulated types (SGGH). SGGH is more aggressive and associated with a poorer prognosis. While epigenetic regulation is vital in tumorigenesis and progression, the role of N6-methyladenosine (m6A) in aggressive behavior has yet to be elucidated. Methods We performed m6A-sequencing on tumor samples from 8 DGGH and 8 SGGH patients, complemented by a suite of assays including ELISA, immuno-histochemistry, -blotting and -fluorescence, qPCR, MeRIP, RIP, and RNA stability experiments, aiming to delineate the influence of m6A on tumor behavior. We further assessed the therapeutic potential of targeted drugs using cell cultures, organoid models, and animal studies. Results We discovered a significant reduction of m6A levels in SGGH compared to DGGH, with an elevated expression of fat mass and obesity-associated protein (FTO), an m6A demethylase, in SGGH subtype. Series of in vivo and in vitro experiments demonstrated that FTO inhibition in tumor cells robustly diminishes hypoxia resistance, attenuates growth hormone secretion, and augments responsiveness to octreotide. Mechanically, FTO-mediated m6A demethylation destabilizes desmoplakin (DSP) mRNA, mediated by the m6A reader FMR1, leading to prohibited desmosome integrity and enhanced tumor hypoxia tolerance. Targeting the FTO-DSP-SSTR2 axis curtailed growth hormone secretion, therefor sensitizing tumors to octreotide therapy. Conclusion Our study reveals the critical role of FTO in the aggressive growth hormone-secreting pituitary neuroendocrine tumors subtype and suggests FTO may represent a new therapeutic target for refractory/persistent SGGH.

Abstract Background Patients with glioblastoma (GBM) have a poor prognosis and limited treatment options. The mRNA decapping enzyme scavenger (DCPS) is a cap-hydrolyzing enzyme. The DCPS inhibitor RG3039 exhibited excellent central nervous system bioavailability in vivo and was safe and well tolerated in healthy volunteers in a phase 1 clinical trial. In this study, we investigated the expression of DCPS in GBM and the anti-tumor activity of RG3039 in various preclinical models of GBM. Methods DCPS expression was examined in human GBM and paired peritumoral tissues. Its prognostic role was evaluated together with clinicopathological characteristics of patients. The anti-GBM effect of RG3039 was determined using GBM cell lines, patient-derived organoids, and orthotopic mouse models. The therapeutic mechanisms of DCPS inhibition were explored. Results DCPS is overexpressed in GBM and is associated with poor survival of patients with GBM. The DCPS inhibitor RG3039 exhibited robust anti-GBM activities in GBM cell lines, patient-derived organoids and orthotopic mouse models, with drug exposure achievable in humans. Mechanistically, RG3039 downregulated STAT5B expression, thereby suppressing proliferation, survival and colony formation of GBM cells. Conclusions DCPS is a promising target for GBM. Inhibition of DCPS with RG3039 at doses achievable in humans downregulates STAT5B expression and reduces proliferation, survival and colony formation of GBM cells. Given the excellent anti-cancer activity and central nervous system bioavailability in vivo and good tolerance in humans, RG3039 warrants further study as a potential GBM therapy.

Abstract Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet’s proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions.

Multisource domain adaptation (MDA) is committed to mining and extracting data concerning target tasks from several source domains. Many recent studies have focused on extracting domain-invariant features to eliminate domain distribution differences. However, there are three aspects that require further consideration. (1) Efforts should be made to ensure the maximum correlation in the potential subspace between the source and target domains. (2) While aligning the marginal distribution, the conditional distribution must also be considered. (3) Merely aligning the source distribution and target distribution cannot guarantee sufficient differentiation for classification tasks. To address these problems, we propose a novel approach named towards discriminability with distribution discrepancy constrains for multisource domain adaptation (TD-DDC). Specifically, TD-DDC first mines features of maximal relations learned from all domains while constructing domain data distribution mean distance metrics for interdomain distribution adaptation. Simultaneously, we integrate discriminability into domain alignment, which means increasing the distance among labels that are distinct from one another while reducing the distance among labels that are the same. Our proposed method not only reduces the interdomain distributional differences but also takes into account the preservation of interdomain correlation and inter-category discrimination. Numerous experiments have shown that TD-DDC performs much better than its competitors on three visual benchmark test databases.