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.

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.

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).

Fluorescent Sensor Based on Magnetic Separation and Strand Displacement Amplification for the Sensitive Detection of Ochratoxin A.

Ochratoxin A (OTA) is a common mycotoxin, and it is a significant threat to human health throughout the food chain. In this study, a sensitive and specific fluorescent sensor based on magnetic separation technology combined with chain displacement amplification was developed for fast and easy detection of OTA in food. The designed strand displacement amplification can improve the sensitivity for the detection, and the magnetic nanomaterials can provide a large surface area, thus enhancing the capture efficiency of the target from the sample. Based on those designs, the experimental results showed that the proposed method displayed excellent performance. The linearity range was 0.5-128.0 ng/mL. The detection limit was 0.125 ng/mL; the relative standard deviations were 3.92-7.71%. Additionally, the developed method was satisfactorily applied to determine OTA in wheat, corn, and red wine samples at three spiked levels (1.0, 8.0, and 64.0 ng/mL). The recoveries ranged from 85.45 to 107.8% for wheat flour, 101.34 to 108.35% for corn flour, and 91.15 to 93.80% for red wine, respectively. Compared with high-performance liquid chromatography, the proposed method showed a lower limit of detection and equal recovery. Hence, the designed method is a potential and good detecting tool for OTA residue analysis in complex matrix samples.

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.

Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas.

OBJECTIVES: To investigate whether radiomic features extracted from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular subtypes of adult diffuse gliomas, and to further develop and validate a multimodal radiomic model by integrating radiomic features from conventional and perfusion MRI. METHODS: We extracted 1197 radiomic features from each sequence of conventional MRI and DSC-PWI, respectively. The Boruta algorithm was used for feature selection and combination, and a three-class random forest method was applied to construct the models. We also constructed a combined model by integrating radiomic features and clinical metrics. The models' diagnostic performance for discriminating the molecular subtypes (IDH wild type [IDHwt], IDH mutant and 1p/19q-noncodeleted [IDHmut-noncodel], and IDH mutant and 1p/19q-codeleted [IDHmut-codel]) was compared using AUCs in the validation set. RESULTS: We included 272 patients (training set, n = 166; validation set, n = 106) with grade II-IV gliomas (mean age, 48.7 years; range, 19-77 years). The proportions of the molecular subtypes were 66.2% IDHwt, 15.1% IDHmut-noncodel, and 18.8% IDHmut-codel. Nineteen radiomic features (13 from conventional MRI and 6 from DSC-PWI) were selected to build the multimodal radiomic model. In the validation set, the multimodal radiomic model showed better performance than the conventional radiomic model did in predicting the IDHwt and IDHmut-codel subtypes, which was comparable to the conventional radiomic model in predicting the IDHmut-noncodel subtype. The multimodal radiomic model yielded similar performance as the combined model in predicting the three molecular subtypes. CONCLUSIONS: Adding DSC-PWI to conventional MRI can improve molecular subtype prediction in patients with diffuse gliomas. KEY POINTS: • The multimodal radiomic model outperformed conventional MRI when predicting both the IDH wild type and IDH mutant and 1p/19q-codeleted subtypes of gliomas. • The multimodal radiomic model showed comparable performance to the combined model in the prediction of the three molecular subtypes. • Radiomic features from T1-weighted gadolinium contrast-enhanced and relative cerebral blood volume images played an important role in the prediction of molecular subtypes.

Correction to "Fluorescent Sensor Based on Magnetic Separation and Strand Displacement Amplification for the Sensitive Detection of Ochratoxin A".

[This corrects the article DOI: 10.1021/acsomega.3c01408.].

Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers.

This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply groups according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomics classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selecting methods and classifiers was calculated separately to compare the performance of the models. Obtain and compare the best combination results with the performance of differentiation through visual observation in clinical diagnosis. 191 patients were included in this study. 3918 stable features were extracted from each patient. Least absolute shrinkage and selection operator (LASSO) and logistic regression model was selected as the optimal combinations after comparing the AUC calculated by models, which predicted the blood supply of vestibular schwannoma by K-Fold cross-validation method with a mean AUC = 0.88 and F1-score = 0.83. Radiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.

Effects of Preoperative Carbamazepine Treatment on Microvascular Decompression for Classical Trigeminal Neuralgia.

BACKGROUND: Carbamazepine (CBZ) is the first-line therapy for trigeminal neuralgia (TN), and microvascular decompression (MVD) is considered to be an effective surgical treatment for TN. However, the effect of preoperative CBZ treatment on MVD outcome is not clear. METHODS: From 2013 to 2019, 63 patients with classical TN underwent MVD at the First Affiliated Hospital of Zhengzhou University, China. Data were collected through telephone follow-up and electronic medical records in April 2020. Short-term surgical outcome and long-term follow-up data were estimated by univariate and multivariate analysis. RESULTS: Multivariate analysis indicated that preoperative CBZ treatment was not a significant predictor for short-term outcomes of MVD (P > 0.05). Multivariate analysis for the long-term outcome of MVD indicated that preoperative CBZ treatment could predict postoperative recurrence of TN (P < 0.05). CONCLUSIONS: For patients with classical TN, a longer preoperative medication history of CBZ treatment had no significant effect on short-term outcome of MVD, but CBZ treatment was associated with a poor long-term outcome following MVD.

Preoperative Predictors of Early Mortality Risk in Isocitrate Dehydrogenase-Wild-Type Glioblastoma Patients Treated with Standard Therapy.

PURPOSE: Early identification of early mortality for glioblastoma (GBM) patients based on laboratory findings at the time of diagnosis could improve the overall survival. The study aimed to explore preoperative factors associated with higher risk of early death (within 1 year after surgery) for isocitrate dehydrogenase (IDH) -wild-type (wt) GBM patients. PATIENTS AND METHODS: We conducted a retrospective analysis of 194 IDH-wt GBM patients who underwent standard treatment. The probability of dying within 1 year after gross total resection (GTR) was defined as the end point "early mortality". Retrospective collection of predictive factors including clinical characteristics and laboratory data at diagnosis. RESULTS: Median follow-up time after GTR was 16 months (3-41 months). Forty-two patients died within 1 year after surgery (1-year mortality rate: 21.6%). All potential predictive factors were assessed on univariate analyses, which revealed the following factors as associated with higher risk of early death: older age (P = 0.013), occurrence of non-seizures symptoms (P = 0.042), special tumor positions (P = 0.046), higher neutrophil-to-lymphocyte ratio (NLR) (P = 0.015), higher red blood cell distribution width (RDW) (P = 0.019), higher lactate dehydrogenase (LDH) (P = 0.005), and higher fibrinogen (FIB) (P = 0.044). In a multivariate analysis, tumor location (P = 0.012), NLR (P = 0.032) and LDH (P = 0.002) were independent predictors of early mortality. The C-index of the nomogram was 0.795. The calibration curve showed good agreement between prediction by nomogram and actual observation. CONCLUSION: Tumor location, preoperative elevated NLR and serum LDH level were independent predictors for 1-year mortality after GTR. We indicate that increased preoperative NLR or LDH may guide patients to review head magnetic resonance imaging (MRI) more frequently and regularly to monitor tumor progression.

A Hematological-Related Prognostic Scoring System for Patients With Newly Diagnosed Glioblastoma.

BACKGROUND: Glioblastoma is the most common primary malignant brain tumor. Recent studies have shown that hematological biomarkers have become a powerful tool for predicting the prognosis of patients with cancer. However, most studies have only investigated the prognostic value of unilateral hematological markers. Therefore, we aimed to establish a comprehensive prognostic scoring system containing hematological markers to improve the prognostic prediction in patients with glioblastoma. PATIENTS AND METHODS: A total of 326 patients with glioblastoma were randomly divided into a training set and external validation set to develop and validate a hematological-related prognostic scoring system (HRPSS). The least absolute shrinkage and selection operator Cox proportional hazards regression analysis was used to determine the optimal covariates that constructed the scoring system. Furthermore, a quantitative survival-predicting nomogram was constructed based on the hematological risk score (HRS) derived from the HRPSS. The results of the nomogram were validated using bootstrap resampling and the external validation set. Finally, we further explored the relationship between the HRS and clinical prognostic factors. RESULTS: The optimal cutoff value for the HRS was 0.839. The patients were successfully classified into different prognostic groups based on their HRSs (P < 0.001). The areas under the curve (AUCs) of the HRS were 0.67, 0.73, and 0.78 at 0.5, 1, and 2 years, respectively. Additionally, the 0.5-, 1-y, and 2-y AUCs of the HRS were 0.51, 0.70, and 0.79, respectively, which validated the robust prognostic performance of the HRS in the external validation set. Based on both univariate and multivariate analyses, the HRS possessed a strong ability to predict overall survival in both the training set and validation set. The nomogram based on the HRS displayed good discrimination with a C-index of 0.81 and good calibration. In the validation cohort, a high C-index value of 0.82 could still be achieved. In all the data, the HRS showed specific correlations with age, first presenting symptoms, isocitrate dehydrogenase mutation status and tumor location, and successfully stratified them into different risk subgroups. CONCLUSIONS: The HRPSS is a powerful tool for accurate prognostic prediction in patients with newly diagnosed glioblastoma.

Dietary natural astaxanthin at an early stage inhibits N-nitrosomethylbenzylamine-induced esophageal cancer oxidative stress and inflammation via downregulation of NFκB and COX2 in F344 rats.

Purpose: Esophageal cancer is a common malignant tumor that develops rapidly and has a poor prognosis clinically. Astaxanthin (AST) is a carotenoid pigment with strong antioxidant, anti-inflammation, and antitumor activities. However, little is known about the effects of astaxanthin in esophageal cancer. The present study aimed to investigate the protective effects and related mechanisms of natural astaxanthin against N-nitrosomethylbenzylamine (NMBA)-induced esophageal cancer in rats. Methods: F344 rats were induced subcutaneously with NMBA dissolved in dimethyl sulfoxide (0.35 mg/kg body weight three times per week for 5 weeks). Rats were fed normal diets with or without 25 mg/kg/day AST at different stages. At different time points, levels of oxidative stress factors in serum and esophagus tissue were analyzed. Western blotting was performed to observe the expression of NFκB and COX2 in esophagus tissue. Results: AST clearly reduced the incidence of visible tumors in esophageal cancer during the early-stage intervention group. Furthermore, when compared with the simple exposed group, AST significantly increased levels of GPx and SOD activity, decreased the activity level of malondialdehyde (all P<0.05). Early-stage and whole-stage intervention groups effectively attenuated expression levels of NFκB and COX2 proteins compared with the simple exposed group (all P<0.05). Conclusion: Natural AST significantly suppressed the occurrence of esophageal cancer by increasing antioxidant capacity and anti-inflammation capacity by inhibiting expression levels of NFκB and COX2 proteins.