Inner Mongolia People's Hospital

The prognosis for hepatocellular carcinoma remains grim. Combining radiotherapy with immune checkpoint blockade (ICB) has shown potential to enhance therapeutic outcomes, yet there is a pressing need for further advancements. Our previous research demonstrated that this combined approach suppresses ALKBH5 gene expression and increases m6A modification levels in hepatocellular carcinoma tissues. High-throughput sequencing and detailed molecular analysis revealed that inhibiting ALKBH5 amplifies CIITA m6A modifications post-therapy. This modulation triggers MHC II molecule expression in tumors, facilitating the presentation of tumor-associated antigens to CD4 + T lymphocytes and the recruitment of CD8 + T cells for an anti-tumor immune response. Building on these findings, we engineered a CIITA vector with a specific site mutation to confirm that the regulation of CIITA by the combined radiotherapy and immunotherapy is mediated through m6A methylation. Consequently, we established a comprehensive network involving ALKBH5, CIITA, MHC II, and CD4+ and CD8 + T cells. To elucidate the role and underlying molecular mechanisms of this combined therapy in reshaping the tumor immune microenvironment for hepatocellular carcinoma, we employed multi-omics approaches across in vitro, animal model, and clinical multi-dimensional studies, offering novel insights for enhancing treatment efficacy.

The critical role of metabolic reprogramming as a potential therapeutic target in the management of endometrial cancer (EC) progression requires further investigation. This study investigated the anti-cancer effect of palmitic acid (PA) on EC progression using cellular and xenograft models, combined with integrated transcriptomic and metabolomic analyses to elucidate the molecular pathways through which PA induces ferroptosis and inhibits tumor growth. The anti-cancer effects of PA were assessed through comprehensive in vitro assays, including cell viability, proliferation, migration, invasion, adhesion, clonogenicity, cell-cycle distribution, apoptosis, and epithelial-mesenchymal transition (EMT). In vivo, the therapeutic efficacy of PA was evaluated using a xenograft mouse model. Transcriptomic and metabolomic profiling identified differentially expressed genes (DEGs) and metabolites, with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses further exploring PA's mechanistic impact. PA significantly reduced the viability, migration, invasion, clonogenic potential, and EMT of EC cells, while inducing cell-cycle arrest and apoptosis. In xenograft models, PA effectively suppressed tumor growth. Mechanistically, transcriptomic and metabolomic analyses, together with changes in ferroptosis-related markers, indicated that PA exerts its anti-cancer effects at least in part through ferroptosis activation. This conclusion was further supported by multiple ferroptosis hallmarks and ferrostatin-1 rescue, which substantially attenuated PA-induced phenotypic and biochemical alterations. PA suppressed EC progression by inducing ferroptosis, providing novel mechanistic insights into PA's anti-cancer properties and underscoring its potential as a therapeutic candidate for EC treatment.

To investigate the role of CD44 in papillary thyroid carcinoma (PTC) lymph node metastasis and its connotation with Treg-mediated immunosuppression through TGF-β and IL-10 signaling. Eighty-two benign thyroid nodule patients were compared with 122 PTC patients. Differentially expressed proteins (DEPs) were identified in both tissue and serum. CD44 expression was validated by ELISA, IHC and flow cytometry. The proportion of Treg and cytokines were analyzed in parallel. The tumor microenvironment architecture was examined using 3D tissue clearing and whole-mount imaging. The functional role of CD44 was further explored using TPC-1 human PTC cell line through gain-of-function and loss-of-function assays in vitro. Proteomic analysis identified 34 tissue and 17 serum DEPs involved in metastasis. CD44 levels in serum were significantly higher in PTC patients than in controls (345.45 ± 44.88 pg/mL vs. 73.33 ± 25.64 pg/mL, P < 0.001). Furthermore, CD44 expression in the serum of CLNM patients was further elevated (782.01 ± 168.38 pg/mL vs. 248.77 ± 55.33 pg/mL, P < 0.001). Multivariate analysis identified CD44 as an independent risk factor for PTC (OR = 5.271, 95% CI: 1.741 ~ 15.959) and CLNM (OR = 3.995, 95% CI: 1.298 ~ 12.298). CD44 levels showed positive correlations with increased TGF-β, IL-10 levels, and Treg frequency. CD44 levels were connected with amplified TGF-β, IL-10, and Treg frequency. IHC and 3D imaging also demonstrated colocalization of CD44⁺ tumor cells and Tregs in H&E sections and whole-mount. Importantly, CD44 knockdown suppressed, while CD44 overexpression enhanced, TPC-1 cell proliferation in vitro, suggesting a direct role of CD44 in promoting tumor cell growth. Our data suggest that CD44 may contribute to PTC metastasis by modulating the immunosuppressive microenvironment through TGF-β/IL-10 mediated Treg accumulation and by directly driving tumor cell proliferation. These findings highlight a potential pathogenic role of CD44 in PTC progression.

Personalized repetitive transcranial magnetic stimulation (rTMS) increasingly relies on resting-state functional magnetic resonance imaging (fMRI) to select stimulation sites, yet most pipelines depend on user-defined thresholds and atlas masks, which can shift individualized targets. We propose a watershed-based approach, implemented in a graphical user interface, that performs threshold-independent segmentation of functional images to support rTMS target localization. As a proof-of-concept, we focused on Alzheimer's disease-related circuits within the default mode network, designating the posterior cingulate cortex (PCC) as the deep effective region and the inferior parietal lobule (IPL) as the superficial stimulation target. In a cohort of 21 healthy participants, quantitative comparison with a conventional threshold-based, mask-constrained peak strategy revealed high concordance for PCC peaks but a median spatial displacement of 6.0 mm (95 % CI: 0.0-12.7 mm) for IPL targets. Qualitative examples further illustrate that watershed segmentation reduces bias from neighboring functional clusters, truncation by atlas boundaries, and ambiguity among multiple local peaks. By decoupling target definition from user-chosen thresholds and packaging the method in an accessible toolbox, this framework offers a generalizable tool for individualized fMRI-guided rTMS.

BackgroundUltrasound (US)-guided microwave ablation (MWA) for primary hyperparathyroidism (PHPT) is a relatively novel minimally invasive treatment. However, definitive evidence for the efficacy of thermal ablation in treating PHPT is not well characterized. This work aimed to evaluate the effectiveness and safety of US-guided MWA in patients with PHPT.MethodsThis retrospective study analyzed the data of patients diagnosed with PHPT who underwent US-guided MWA at Peking University First Hospital between October 2020 and October 2024. Serum levels of parathyroid hormone (PTH), calcium, and phosphate were measured preoperatively and at 3, 6, and 12 hours post-MWA to assess immediate biochemical changes. Long-term therapeutic outcomes were evaluated by monitoring the volume of ablation areas and serum PTH, calcium, and phosphate levels at 1, 3, 6, and 12 months post-MWA or at the last follow-up.ResultsA total of 30 clinical records were reviewed, of which 25 (15 female and 10 male; mean age: 60.04±17.38 years) met the inclusion criteria and were included in the final analysis. No clinically significant complications were observed during or following the MWA procedure. Serum PTH levels showed a significant reduction at 3, 6, and 12 hours post-MWA compared to baseline. Serum calcium levels began to decrease significantly at 3 hours post-MWA, whereas serum phosphate levels showed no significant changes within the first 12 hours. Serum PTH levels were significantly lower at the last follow-up (mean duration: 7.48 ± 4.8 months) compared to baseline (P=0.002). Serum calcium levels were significantly lower at 1 month and at the last follow-up (P=0.0021) compared to baseline. At the last follow-up, the volume reduction ratio (VRR) of the ablated masses was 71.5%±29%.ConclusionsOur study provides evidence that US-guided MWA is safe and effective for managing PHPT adenomas.

IntroductionBreast cancer is associated with significant restructuring of the gut ecosystem. Gut microbial composition and function may influence cancer development and progression through immune modulation, metabolic regulation, and inflammation-related pathways.MethodsUsing shotgun metagenomic sequencing of fecal samples from 38 stage I–III breast cancer patients and 36 age- and body mass index-matched healthy controls. Machine learning models were constructed to evaluate the diagnostic potential of integrated microbial and metabolic features.ResultsSignificant alterations were observed in gut microbiota composition, including depletion of beneficial taxa (Limosilactobacillus fermentum, Blautia sp.) and enrichment of Prevotella copri. Pathways involved in short-chain fatty acid and purine metabolism were reduced. The gut phageome exhibited structural changes and altered correlations with bacterial hosts. Predictive analysis revealed depletion of short-chain fatty acids (butyrate, propionate), purine intermediates (hypoxanthine, xanthine), and nicotinate in patients. A machine learning model integrating microbial and predicted metabolic features achieved an area under the curve values of 0.78 in the discovery cohort and 0.73 (recall = 0.74) in an independent validation cohort.DiscussionCoordinated gut microbiome, phageome, and metabolome alterations characterize breast cancer, offering potential non-invasive biomarkers and mechanistic insights for disease detection and intervention.

IntroductionSepsis, a life-threatening and costly condition, necessitates early detection for effective management. However, diagnostic and therapeutic delays are common. Furthermore, sepsis complexity and dynamics challenge existing early warning systems, impeding timely intervention.MethodsIn this study, we introduce the Bayes-Optimized Boosting-Mamba Tab Model (BOBM), a novel deep learning-based algorithm that incorporates a bidirectional optimization learning mechanism within an ensemble framework. We designed dynamic agent models to enhance computational efficiency and adaptability across diverse clinical scenarios. Additionally, the model architecture was optimized for different temporal phases to enable a more precise estimation of the timing and progression of sepsis risk. To improve interpretability, we implemented SHapley Additive exPlanations value analysis, providing clinicians with actionable insights into potential patterns of sepsis progression.ResultsThe experimental evaluation demonstrated that our model attained the highest AUC (AUC: 0.86–0.95) in the MIMIC-IV dataset across different temporal windows prior to sepsis onset, confirming its adaptability and robust generalizability across various prediction timeframes. Furthermore, the model demonstrated superior performance (AUC: 0.978–0.982) when validated on two independent large-scale external datasets.ConclusionIn contrast to traditional prediction approaches, our model extends the potential intervention timeframe significantly, while incorporating dynamic monitoring capabilities spanning the 7- to 28-day window before clinical suspicion, thereby establishing a foundation for regional sepsis surveillance systems.