Mitochondrial dysfunction induced by HIF-1α under hypoxia contributes to the development of gastric mucosal lesions.

INTRODUCTION: Hypoxia is an important characteristic of gastric mucosal diseases, and hypoxia-inducible factor-1α (HIF-1α) contributes to microenvironment disturbance and metabolic spectrum abnormalities. However, the underlying mechanism of HIF-1α and its association with mitochondrial dysfunction in gastric mucosal lesions under hypoxia have not been fully clarified. OBJECTIVES: To evaluate the effects of hypoxia-induced HIF-1α on the development of gastric mucosal lesions. METHODS: Portal hypertensive gastropathy (PHG) and gastric cancer (GC) were selected as representative diseases of benign and malignant gastric lesions, respectively. Gastric tissues from patients diagnosed with the above diseases were collected. Portal hypertension (PHT)-induced mouse models in METTL3 mutant or NLRP3-deficient littermates were established, and nude mouse gastric graft tumour models with relevant inhibitors were generated. The mechanisms underlying hypoxic condition, mitochondrial dysfunction and metabolic alterations in gastric mucosal lesions were further analysed. RESULTS: HIF-1α, which can mediate mitochondrial dysfunction via upregulation of METTL3/IGF2BP3-dependent dynamin-related protein 1 (Drp1) N6-methyladenosine modification to increase mitochondrial reactive oxygen species (mtROS) production, was elevated under hypoxic conditions in human and mouse portal hypertensive gastric mucosa and GC tissues. While blocking HIF-1α with PX-478, inhibiting Drp1-dependent mitochondrial fission via mitochondrial division inhibitor 1 (Mdivi-1) treatment or METTL3 mutation alleviated this process. Furthermore, HIF-1α influenced energy metabolism by enhancing glycolysis via lactate dehydrogenase A. In addition, HIF-1α-induced Drp1-dependent mitochondrial fission also enhanced glycolysis. Drp1-dependent mitochondrial fission and enhanced glycolysis were associated with alterations in antioxidant enzyme activity and dysfunction of the mitochondrial electron transport chain, resulting in massive mtROS production, which was needed for activation of NLRP3 inflammasome to aggravate the development of the PHG and GC. CONCLUSIONS: Under hypoxic conditions, HIF-1α enhances mitochondrial dysfunction via Drp1-dependent mitochondrial fission and influences the metabolic profile by altering glycolysis to increase mtROS production, which can trigger NLRP3 inflammasome activation and mucosal microenvironment alterations to contribute to the development of benign and malignant gastric mucosal lesions.

Nuclear autoantigenic sperm protein facilitates glioblastoma progression and radioresistance by regulating the ANXA2/STAT3 axis.

AIMS: Although radiotherapy is a core treatment modality for various human cancers, including glioblastoma multiforme (GBM), its clinical effects are often limited by radioresistance. The specific molecular mechanisms underlying radioresistance are largely unknown, and the reduction of radioresistance is an unresolved challenge in GBM research. METHODS: We analyzed and verified the expression of nuclear autoantigenic sperm protein (NASP) in gliomas and its relationship with patient prognosis. We also explored the function of NASP in GBM cell lines. We performed further mechanistic experiments to investigate the mechanisms by which NASP facilitates GBM progression and radioresistance. An intracranial mouse model was used to verify the effectiveness of combination therapy. RESULTS: NASP was highly expressed in gliomas, and its expression was negatively correlated with the prognosis of glioma. Functionally, NASP facilitated GBM cell proliferation, migration, invasion, and radioresistance. Mechanistically, NASP interacted directly with annexin A2 (ANXA2) and promoted its nuclear localization, which may have been mediated by phospho-annexin A2 (Tyr23). The NASP/ANXA2 axis was involved in DNA damage repair after radiotherapy, which explains the radioresistance of GBM cells that highly express NASP. NASP overexpression significantly activated the signal transducer and activator of transcription 3 (STAT3) signaling pathway. The combination of WP1066 (a STAT3 pathway inhibitor) and radiotherapy significantly inhibited GBM growth in vitro and in vivo. CONCLUSION: Our findings indicate that NASP may serve as a potential biomarker of GBM radioresistance and has important implications for improving clinical radiotherapy.

ELOVL6 promotes the progression of head and neck squamous cell carcinoma via activating WNT/ß-catenin pathway.

This study was to explore the role of ELOVL6 in the development of head and neck squamous cell carcinoma (HNSCC). Considering its previously identified oncogenic role in hepatocellular carcinoma. ELOVL6 gene expression, clinicopathological analysis, enrichment analysis, and immune infiltration analysis were based on the data from Gene Expression Omnibus and The Cancer Genome Atlas, with additional bioinformatics analyses performed. Human HNSCC tissue microarray and cell lines were used. The expression of ELOVL6 in HNSCC was detected by quantitative polymerase chain reaction, immunohistochemistry assay, and western blot analysis. The proliferation ability of HNSCC cells, invasion, and apoptosis were evaluated using cell counting kit-8 method, Transwell assay, and flow cytometry, respectively. Based on the data derived from the cancer databases and our HNSCC cell and tissue studies, we found that ELOVL6 was overexpressed in HNSCC. Moreover, ELOVL6 expression level had a positive correlation with clinicopathology of HNSCC. Gene set enrichment analysis showed that ELOVL6 affected the occurrence of HNSCC through WNT signaling pathway. Functional experiments demonstrated that ELOVL6 knockdown inhibited the proliferation and invasion of HNSCC cells while promoting apoptosis. Additionally, compound 3f, an agonist of WNT/ß-catenin signaling pathway, enhances the effect of ELOVL6 on the progression of HNSCC cells. ELOVL6 is upregulated in HNSCC and promotes the development of HNSCC cells by inducing WNT/ß-catenin signaling pathway. ELOVL6 stands a potential target for the treatment of HNSCC and a prognosis indicator of human HNSCC.

Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

Activated hepatic stellate cell-derived Bmp-1 induces liver fibrosis via mediating hepatocyte epithelial-mesenchymal transition.

Liver fibrosis is a reparative response to injury that arises from various etiologies, characterized by activation of hepatic stellate cells (HSCs). Periostin, a secreted matricellular protein, has been reported to participate in tissue development and regeneration. However, its involvement in liver fibrosis remains unknown. This study investigated the roles and mechanisms of Periostin in phenotypic transition of HSCs and relevant abnormal cellular crosstalk during liver fibrosis. The fate of hepatic stellate cells (HSCs) during liver fibrogenesis was investigated using single-cell and bulk RNA sequencing profiles, which revealed a significant proliferation of activated HSCs (aHSCs) in fibrotic livers of both humans and mice. αSMA-TK mice were used to demonstrate that depletion of proliferative aHSCs attenuates liver fibrosis induced by carbon tetrachloride and 3,5-diethoxycarbonyl-1,4-dihydrocollidine. Through integrating data from single-cell and bulk sequencing, Periostin was identified as a distinctive hallmark of proliferative aHSC subpopulation. Elevated levels of Periostin were detected in fibrotic livers of both humans and mice, primarily within aHSCs. However, hepatic Periostin levels were decreased along with depletion of proliferative aHSCs. Deficiency of Periostin led to reduced liver fibrosis and suppressed hepatocyte epithelial-mesenchymal transition (EMT). Periostin-overexpressing HSCs, exhibiting a proliferative aHSC phenotype, release bone morphogenetic protein-1 (Bmp-1), which activates EGFR signaling, inducing hepatocyte EMT and contributing to liver fibrosis. In conclusion, Periostin in aHSCs drives their acquisition of a proliferative phenotype and the release of Bmp-1. Proliferative aHSC subpopulation-derived Bmp-1 induces hepatocyte EMT via EGFR signaling, promoting liver fibrogenesis. Bmp-1 and Periostin should be potential therapeutic targets for liver fibrosis.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities.

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

NMGMDA: a computational model for predicting potential microbe-drug associations based on minimize matrix nuclear norm and graph attention network.

The prediction of potential microbe-drug associations is of great value for drug research and development, especially, methods, based on deep learning, have been achieved significant improvement in bio-medicine. In this manuscript, we proposed a novel computational model named NMGMDA based on the nuclear norm minimization and graph attention network to infer latent microbe-drug associations. Firstly, we created a heterogeneous microbe-drug network in NMGMDA by fusing the drug and microbe similarities with the established drug-microbe associations. After this, by using GAT and NNM to calculate the predict scores. Lastly, we created a fivefold cross validation framework to assess the new model NMGMDA's progressiveness. According to the simulation results, NMGMDA outperforms some of the most advanced methods, with a reliable AUC of 0.9946 on both MDAD and aBioflm databases. Furthermore, case studies on Ciprofloxacin, Moxifoxacin, HIV-1 and Mycobacterium tuberculosis were carried out in order to assess the effectiveness of NMGMDA even more. The experimental results demonstrated that, following the removal of known correlations from the database, 16 and 14 medications as well as 19 and 17 microbes in the top 20 predictions were validated by pertinent literature. This demonstrates the potential of our new model, NMGMDA, to reach acceptable prediction performance.

Leveraging a disulfidptosis­related lncRNAs signature for predicting the prognosis and immunotherapy of glioma.

BACKGROUND: Gliomas, a prevalent form of primary brain tumors, are linked with a high mortality rate and unfavorable prognoses. Disulfidptosis, an innovative form of programmed cell death, has received scant attention concerning disulfidptosis-related lncRNAs (DRLs). The objective of this investigation was to ascertain a prognostic signature utilizing DRLs to forecast the prognosis and treatment targets of glioma patients. METHODS: RNA-seq data were procured from The Cancer Genome Atlas database. Disulfidptosis-related genes were compiled from prior research. An analysis of multivariate Cox regression and the least absolute selection operator was used to construct a risk model using six DRLs. The risk signature's performance was evaluated via Kaplan-Meier survival curves and receiver operating characteristic curves. Additionally, functional analysis was carried out using GO, KEGG, and single-sample GSEA to investigate the biological functions and immune infiltration. The research also evaluated tumor mutational burden, therapeutic drug sensitivity, and consensus cluster analysis. Reverse transcription quantitative PCR was conducted to validate the expression level of DRLs. RESULTS: A prognostic signature comprising six DRLs was developed to predict the prognosis of glioma patients. High-risk patients had significantly shorter overall survival than low-risk patients. The robustness of the risk model was validated by receiver operating characteristic curves and subgroup survival analysis. Risk model was used independently as a prognostic indicator for the glioma patients. Notably, the low-risk patients displayed a substantial decrease in the immune checkpoints, the proportion of immune cells, ESTIMATE and immune score. IC50 values from the different risk groups allowed us to discern three drugs for the treatment of glioma patients. Lastly, the potential clinical significance of six DRLs was determined. CONCLUSIONS: A novel six DRLs signature was developed to predict prognosis and may provide valuable insights for patients with glioma seeking novel immunotherapy and targeted therapy.

Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma.

BACKGROUND: To develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes. METHODS: A total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test. RESULTS: Radscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes. CONCLUSION: Our findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

Ferritin light chain promotes the reprogramming of glioma immune microenvironment and facilitates glioma progression.

Background: Tumor-associated macrophages (TAMs), the most abundant non-tumor cell population in the glioma microenvironment, play a crucial role in immune evasion and immunotherapy resistance of glioblastoma (GBM). However, the regulatory mechanism of the immunosuppressive TME of GBM remains unclear. Methods: Bioinformatics were used to analyse the potential role of ferritin light chain (FTL) in GBM immunology and explore the effects of FTL on the reprogramming of the GBM immune microenvironment and GBM progression. Results: The FTL gene was found to be upregulated in TAMs of GBM at both the bulk and single-cell RNA-seq levels. FTL contributed to the protumor microenvironment by promoting M2 polarization in TAMs via inhibiting the expression of iPLA2ß to facilitate the ferroptosis pathway. Inhibition of FTL in TAMs attenuated glioma angiogenesis, promoted the recruitment of T cells and sensitized glioma to anti-PD1 therapy. Conclusion: Our study suggested that FTL promoted the development of an immunosuppressive TME by inducing M2 polarization in TAMs, and inhibition of FTL in TAMs reprogrammed the TME and sensitized glioma to anti-PD1 therapy, providing a new strategy for improving the therapeutic effect of anti-PD1.

Dissolution changes in drug-amino acid/biotin co-amorphous systems: Decreased/increased dissolution during storage without recrystallization.

Co-amorphous systems have been proven to be a promising strategy to address the poor water solubility of poorly water-soluble drugs. Generally, the initial dissolution behaviors after co-amorphous system preparation and the potential recrystallization during storage are used to evaluate the performance of co-amorphous systems. However, this study reveals that decreased dissolution and unexpected increased dissolution were observed during storage though the co-amorphous systems maintained amorphous form. Three drugs (valsartan, tadalafil, mebendazole) and three co-formers (arginine, tryptophan, biotin) were used to prepare co-amorphous systems and the samples were stored for different times. After stored for 80 d, most of the co-amorphous systems maintained amorphous form, however, decreased and increased intrinsic dissolution rates (IDRs) were both observed in these non-recrystallized co-amorphous systems. The moisture changes of the systems during storage and the possible drug-co-former molecular interactions showed no effect on the dissolution changes, while phase separation might play a role in it. In conclusion, more attention should be paid to the dissolution changes of co-amorphous systems during storage. Focusing on the initial dissolution behaviors after sample preparation and the physical recrystallization during storage is not enough for the development of co-amorphous systems in future.

Hyaluronic acid modified carbon nanotubes using for photothermal therapy by promoting apoptosis of nasopharyngeal carcinoma cells.

Background: The present work illustrates the role of multi-walled carbon nanotubes in photothermal therapy. Nasopharyngeal carcinoma (NPC) is a malignant tumor of the head and neck with significant ethnic and geographic differences, and conventional treatment options are no longer suitable to improve the prognosis and survival of patients. Photothermal therapy (PTT) has emerged as a new strategy for oncology treatment in recent years and is now used in the treatment of many common cancers. Multi-walled carbon nanotubes (MWCNT) have been used to advantage in several fields due to their excellent thermal conductivity. The aim of this paper is to investigate the promotion of apoptosis of nasopharyngeal cancer cells by multi-walled carbon nanotubes as an adjuvant nanomaterial for nasopharyngeal cancer photothermal therapy. Methods: Carboxylated multi-walled carbon nanotubes and prepared multi-walled carbon nanotube-hyaluronic acid (MWCNT-HA) composites were used for cell proliferation-related experiments such as CCK-8 assay, live-dead staining and flow cytometric analysis and inverted fluorescence microscopy to determine the expression level of apoptotic factors and confocal microscopy cell morphology analysis on nasopharyngeal carcinoma CNE-1 cells under near-infrared laser irradiation. The effects of multi-walled carbon nanotubes on the proliferation and apoptosis of tumor cells under NIR response were elucidated, and the mechanism of apoptosis was explored. Results: TEM and SEM demonstrated that MWCNT had good appearance morphology and the temperature rise curve indicated excellent photothermal stability. And MWCNT and MWCNT-HA could significantly inhibit the proliferation of tumor cells and change the normal morphology of cells under NIR laser irradiation. Cellular immunofluorescence analysis confirmed that MWCNT-HA significantly upregulated the expression level of apoptosis factor Caspase-3 and significantly downregulated the expression level of anti-apoptosis factor Bcl-2. Conclusion: In this study, MWCNT inhibited the proliferation of tumor cells and promoted apoptosis through the use of multi-walled carbon nanotubes as an adjuvant nanomaterial for photothermal therapy. In addition, multi-walled carbon nanotubes could inhibit the mitochondrial pathway of CNE-1 cells to cause cell death. These studies suggest that multi-walled carbon nanotubes can function as efficient photothermal conversion materials for tumor photothermal therapy.

National Brain Tumour Registry of China (NBTRC) statistical report of primary brain tumours diagnosed in China in years 2019-2020.

Background: The lack of a well-designed brain tumour registry with standardized pathological diagnoses in underdeveloped countries hinders the ability to compare epidemiologic data across the globe. The National Brain Tumour Registry of China (NBTRC), created in January 2018, is the first multi-hospital-based brain tumour registry in China. Patient data reported to the NBTRC in years 2019-2020 were assessed. Methods: Tumour pathology was based on the 2016 World Health Organization (WHO) classification of tumours of the central nervous system and ICD-O-3. The anatomical site was coded per the Surveillance, Epidemiology, and End Results (SEER) solid tumour module (version of July 2019). The cases were tabulated by histology and anatomical site. Categorical variables were reported as numbers (percentages). The distribution of tumours by age (0-14, 15-19, 20-39, 40-64, and 65+ years) was analysed. Findings: There were a total of 25,537 brain tumours, foremost among them meningioma (23.63%), followed by tumours of the pituitary (23.42%), and nerve sheath tumours (9.09%). Glioblastoma, the most common and lethal form of primary brain cancer in adults, represented 8.56% of all cases. Of note, 6.48% of the malignant tumours were located in the brain stem. The percentage of malignant brain tumours decreased with increasing age, 24.08% in adults (40+ years), 30.25% in young adults (20-39 years), 35.27% in adolescents (15-19 years), and 49.83% in children (0-14 years). Among the 2107 paediatric patients, the most common sites were ventricle (17.19%), brainstem (14.03%), pituitary and craniopharyngeal duct (13.4%), and cerebellum (12.3%), a distribution that differed from that of the entire cohort. The histology distribution was also unique in children, with glioblastoma much less incident compared to the whole cohort (3% vs. 8.47%, p < 0.01). 58.80% of all patients chose higher-level neurosurgical hospitals outside of their province of residence. The median in-hospital length of stay (LOS) for the various pathologies ranged from 11 to 19 days. Interpretation: The histological and anatomical site distribution of brain tumours in the NBTRC was statistically different in the subgroup of children (0-14 years). Patient choice of pursuing trans-provincial treatment was common and the in-hospital LOS was longer compared to that reported in similar European and American patient populations, which merits further attention. Funding: The National Key Research and Development Program of China (2015BAI12B04, 2013BAI09B03, 2014BAI04B01, and 2021YFF1201104) and Chinese National Natural Science Foundation of China (81971668).

TRIM25 promotes temozolomide resistance in glioma by regulating oxidative stress and ferroptotic cell death via the ubiquitination of keap1.

Resistance to temozolomide (TMZ) remains an important cause of treatment failure in patients with glioblastoma multiforme (GBM). TRIM25, as a tripartite motif-containing (TRIM) family member, plays a significant role in cancer progression and chemoresistance. However, the function of TRIM25 and its precise mechanism in regulating GBM progression and TMZ resistance remain poorly understood. We found that the expression of TRIM25 was upregulated in GBM, and it was associated with tumor grade and TMZ resistance. Elevated TRIM25 expression predicted a poor prognosis in GBM patients and enhanced tumor growth in vitro and in vivo. Further analysis revealed that elevated TRIM25 expression inhibited oxidative stress and ferroptotic cell death in glioma cells under TMZ treatment. Mechanistically, TRIM25 regulates TMZ resistance by promoting the nuclear import of nuclear factor erythroid 2-related factor 2(Nrf2) via keap1 ubiquitination. Knockdown of Nrf2 abolished the ability of TRIM25 to promote glioma cell survival and TMZ resistance. Our results support the targeting of TRIM25 as a new therapeutic strategy for glioma.

Diffusion tensor imaging-based machine learning for IDH wild-type glioblastoma stratification to reveal the biological underpinning of radiomic features.

INTRODUCTION: This study addresses the lack of systematic investigation into the prognostic value of hand-crafted radiomic features derived from diffusion tensor imaging (DTI) in isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM), as well as the limited understanding of the biological interpretation of individual DTI radiomic features and metrics. AIMS: To develop and validate a DTI-based radiomic model for predicting prognosis in patients with IDH wild-type GBM and reveal the biological underpinning of individual DTI radiomic features and metrics. RESULTS: The DTI-based radiomic signature was an independent prognostic factor (p < 0.001). Incorporating the radiomic signature into a clinical model resulted in a radiomic-clinical nomogram that predicted survival better than either the radiomic model or clinical model alone, with a better calibration and classification accuracy. Four categories of pathways (synapse, proliferation, DNA damage response, and complex cellular functions) were significantly correlated with the DTI-based radiomic features and DTI metrics. CONCLUSION: The prognostic radiomic features derived from DTI are driven by distinct pathways involved in synapse, proliferation, DNA damage response, and complex cellular functions of GBM.

A novel signature of cuproptosis-related lncRNAs predicts prognosis in glioma: Evidence from bioinformatic analysis and experiments.

Background: Glioma patients often experience unfavorable outcomes and elevated mortality rates. Our study established a prognostic signature utilizing cuproptosis-associated long non-coding RNAs (CRLs) and identified novel prognostic biomarkers and therapeutic targets for glioma. Methods: The expression profiles and related data of glioma patients were obtained from The Cancer Genome Atlas, an accessible online database. We then constructed a prognostic signature using CRLs and evaluated the prognosis of glioma patients by means of Kaplan-Meier survival curves and receiver operating characteristic curves. A nomogram based on clinical features was employed to predict the individual survival probability of glioma patients. Functional enrichment analysis was conducted to identify crucial CRL-related enriched biological pathways. The role of LEF1-AS1 in glioma was validated in two glioma cell lines (T98 and U251). Results: We developed and validated a prognostic model for glioma with 9 CRLs. Patients with low-risk had a considerably longer overall survival (OS). The prognostic CRL signature may serve independently as an indicator of prognosis for glioma patients. In addition, functional enrichment analysis revealed significant enrichment of multiple immunological pathways. Notable differences were observed between the two risk groups in terms of immune cell infiltration, function, and immune checkpoints. We further identified four drugs based on their different IC50 values from the two risk groups. Subsequently, we discovered two molecular subtypes of glioma (cluster one and cluster two), with the cluster one subtype exhibiting a remarkably longer OS compared to the cluster two subtype. Finally, we observed that inhibition of LEF1-AS1 curbed the proliferation, migration, and invasion of glioma cells. Conclusion: The CRL signatures were confirmed as a reliable prognostic and therapy response indicator for glioma patients. Inhibition of LEF1-AS1 effectively suppressed the growth, migration, and invasion of gliomas; therefore, LEF1-AS1 presents itself as a promising prognostic biomarker and potential therapeutic target for glioma.

Nifuroxazide inhibits the growth of glioblastoma and promotes the infiltration of CD8 T cells to enhance antitumour immunity.

INTRODUCTION: Glioblastoma is a primary intracranial tumour with extremely high disability and fatality rates among adults. Existing diagnosis and treatment methods have not significantly improved the overall poor prognosis of patients. Nifuroxazide, an oral antibiotic, has been reported to act as a tumour suppressor in a variety of tumours and to participate in the process of antitumour immunity. However, whether it can inhibit the growth of glioma is still unclear. METHODS: We explored the potential mechanism of nifuroxazide inhibiting the growth of glioblastoma cells through in vitro and in vivo experiments. RESULTS: nifuroxazide can inhibit the proliferation of glioblastoma cells, promote G2 phase arrest, induce apoptosis, and inhibit epithelial-mesenchymal transition through the MAP3K1/JAK2/STAT3 pathway. Similarly, clinical sample analysis confirmed that MAP3K1 combined with STAT3 can affect the prognostic characteristics of patients with glioma. In addition, nifuroxazide can drive the M1 polarization of microglioma cells, inhibit the expression of CTLA4 and PD-L1 in tumour cells, and promote the infiltration of CD8 T cells to exert antitumour effects. Combination treatment with PD-L1 inhibitors can significantly prolong the survival time of mice. CONCLUSION: we found that nifuroxazide can inhibit the growth of glioblastoma and enhance antitumour immunity. Thus, nifuroxazide is an effective drug for the treatment of glioblastoma and has great potential for clinical application.