Machine learning prediction models for in-hospital postoperative functional outcome after moderate-to-severe traumatic brain injury.

AIM: This study aims to utilize machine learning (ML) and logistic regression (LR) models to predict surgical outcomes among patients with traumatic brain injury (TBI) based on admission examination, assisting in making optimal surgical treatment decision for these patients. METHOD: We conducted a retrospective review of patients hospitalized in our department for moderate-to-severe TBI. Patients admitted between October 2011 and October 2022 were assigned to the training set, while patients admitted between November 2022 and May 2023 were designated as the external validation set. Five ML algorithms and LR model were employed to predict the postoperative Glasgow Outcome Scale (GOS) status at discharge using clinical and routine blood data collected upon admission. The Shapley (SHAP) plot was utilized for interpreting the models. RESULTS: A total of 416 patients were included in this study, and they were divided into the training set (n = 396) and the external validation set (n = 47). The ML models, using both clinical and routine blood data, were able to predict postoperative GOS outcomes with area under the curve (AUC) values ranging from 0.860 to 0.900 during the internal cross-validation and from 0.801 to 0.890 during the external validation. In contrast, the LR model had the lowest AUC values during the internal and external validation (0.844 and 0.567, respectively). When blood data was not available, the ML models achieved AUCs of 0.849 to 0.870 during the internal cross-validation and 0.714 to 0.861 during the external validation. Similarly, the LR model had the lowest AUC values (0.821 and 0.638, respectively). Through repeated cross-validation analysis, we found that routine blood data had a significant association with higher mean AUC values in all ML and LR models. The SHAP plot was used to visualize the contributions of all predictors and highlighted the significance of blood data in the lightGBM model. CONCLUSION: The study concluded that ML models could provide rapid and accurate predictions for postoperative GOS outcomes at discharge following moderate-to-severe TBI. The study also highlighted the crucial role of routine blood tests in improving such predictions, and may contribute to the optimization of surgical treatment decision-making for patients with TBI.

Machine learning models for predicting in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury.

AIM: This study aims to develop prediction models for in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury (TBI). METHOD: We conducted a retrospective review of patients hospitalized for moderate-to-severe TBI in our department from 2011 to 2020. Five machine learning (ML) algorithms and the conventional logistic regression (LR) model were employed to predict in-hospital mortality and the Glasgow Outcome Scale (GOS) functional outcomes. These models utilized clinical and routine blood data collected upon admission. RESULTS: This study included a total of 196 patients who received only non-surgical treatment after moderate-to-severe TBI. When predicting mortality, ML models achieved area under the curve (AUC) values of 0.921 to 0.994 using clinical and routine blood data, and 0.877 to 0.982 using only clinical data. In comparison, LR models yielded AUCs of 0.762 and 0.730 respectively. When predicting the GOS outcome, ML models achieved AUCs of 0.870 to 0.915 using clinical and routine blood data, and 0.858 to 0.927 using only clinical data. In comparison, the LR model yielded AUCs of 0.798 and 0.787 respectively. Repeated internal validation showed that the contributions of routine blood data for prediction models may depend on different prediction algorithms and different outcome measurements. CONCLUSION: The study reported ML-based prediction models that provided rapid and accurate predictions on short-term outcomes after non-surgical treatment among patients with moderate-to-severe TBI. The study also highlighted the superiority of ML models over conventional LR models and proposed the complex contributions of routine blood data in such predictions.

Lollipop-Like Costal Cartilage Graft with a Peach-Shaped Head for East Asian Rhinoplasty.

BACKGROUND: Nasal tip refinement is a challenging step of East Asian rhinoplasty due to complex anatomical defects. Autologous costal cartilage grafts are commonly used to provide nasal tip support. This study aims to evaluate the efficacy and safety of a modified technique using a lollipop-like cartilage graft with a peach-shaped head. METHODS: A retrospective review was conducted on Chinese patients who underwent primary rhinoplasty with the modified technique between November 2018 and March 2021 at our center. Preoperative and postoperative facial photographs, patient-reported outcome measures including the visual analog scale (VAS) and the rhinoplasty outcome evaluation (ROE), as well as surgery-related complications, were collected for outcome assessment. RESULTS: A total of 31 adult patients were included in this retrospective cohort study, with a mean follow-up period of 17.12 ± 3.89 months. The majority of patients (28/31; 90.3%) expressed satisfaction with the aesthetic outcomes, as evidenced by a significant increase in mean VAS score from 3.97 ± 1.52 preoperatively to 7.39 ± 0.22 postoperatively (P < 0.01), and a significant increase in mean ROE score from 11.77 ± 2.33 to 17.22 ± 2.47 (P < 0.01). Photogrammetric analysis also demonstrated significant improvements in nasal anatomical measurements. Among the three patients with unsatisfactory feedback, two had mild columella deviations and one had a mild dorsum deviation. No other notable complications were reported. CONCLUSION: The current study supports the safety and utility of the modified technique utilizing the lollipop-like costal cartilage graft with a peach-shaped head for major tip refinement in East Asian patients. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Cholesterol homeostasis confers glioma malignancy triggered by hnRNPA2B1-dependent regulation of SREBP2 and LDLR.

BACKGROUND: Dysregulation of cholesterol metabolism is a significant characteristic of glioma, yet the underlying mechanisms are largely unknown. N6-methyladenosine (m6A) modification has been implicated in promoting tumor development and progression. The aim of this study was to determine the key m6A regulatory proteins involved in the progression of glioma, which is potentially associated with the reprogramming of cholesterol homeostasis. METHODS: Bioinformatics analysis was performed to determine the association of m6A modification with glioma malignancy from The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Glioma stem cell (GSC) self-renewal was determined by tumor sphere formation and bioluminescence image assay. RNA sequencing and lipidomic analysis were performed for cholesterol homeostasis analysis. RNA immunoprecipitation and luciferase reporter assay were performed to determine hnRNPA2B1-dependent regulation of sterol regulatory element-binding protein 2 (SREBP2) and low-density lipoprotein receptor (LDLR) mRNA. The methylation status of hnRNPA2B1 promoter was determined by bioinformatic analysis and methylation-specific PCR assay. RESULTS: Among the m6A-regulatory proteins, hnRNPA2B1 was demonstrated the most important independent prognostic risk factor for glioma. hnRNPA2B1 ablation exhibited a significant tumor-suppressive effect on glioma cell proliferation, GSC self-renewal and tumorigenesis. hnRNPA2B1 triggers de novo cholesterol synthesis by inducing HMGCR through the stabilization of SREBP2 mRNA. m6A modification of SREBP2 or LDLR mRNA is required for hnRNPA2B1-mediated mRNA stability. The hypomethylation of cg21815882 site on hnRNPA2B1 promoter confers elevated expression of hnRNPA2B1 in glioma tissues. The combination of targeting hnRNPA2B1 and cholesterol metabolism exhibited remarkable antitumor effects, suggesting valuable clinical implications for glioma treatment. CONCLUSIONS: hnRNPA2B1 facilitates cholesterol uptake and de novo synthesis, thereby contributing to glioma stemness and malignancy.

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study.

OBJECTIVES: This study aims at examining the correlation of intraosseous temperature change with drilling impulse data during osteotomy and establishing real-time temperature prediction models. MATERIALS AND METHODS: A combination of in vitro bovine rib model and Autonomous Dental Implant Robotic System (ADIR) was set up, in which intraosseous temperature and drilling impulse data were measured using an infrared camera and a six-axis force/torque sensor respectively. A total of 800 drills with different parameters (e.g., drill diameter, drill wear, drilling speed, and thickness of cortical bone) were experimented, along with an independent test set of 200 drills. Pearson correlation analysis was done for linear relationship. Four machining learning (ML) algorithms (e.g., support vector regression [SVR], ridge regression [RR], extreme gradient boosting [XGboost], and artificial neural network [ANN]) were run for building prediction models. RESULTS: By incorporating different parameters, it was found that lower drilling speed, smaller drill diameter, more severe wear, and thicker cortical bone were associated with higher intraosseous temperature changes and longer time exposure and were accompanied with alterations in drilling impulse data. Pearson correlation analysis further identified highly linear correlation between drilling impulse data and thermal changes. Finally, four ML prediction models were established, among which XGboost model showed the best performance with the minimum error measurements in test set. CONCLUSION: The proof-of-concept study highlighted close correlation of drilling impulse data with intraosseous temperature change during osteotomy. The ML prediction models may inspire future improvement on prevention of thermal bone injury and intelligent design of robot-assisted implant surgery.

Genome-wide DNA methylation analysis identifies potent CpG signature for temzolomide response in non-G-CIMP glioblastomas with unmethylated MGMT promoter: MGMT-dependent roles of GPR81.

PURPOSES: To identify potent DNA methylation candidates that could predict response to temozolomide (TMZ) in glioblastomas (GBMs) that do not have glioma-CpGs island methylator phenotype (G-CIMP) but have an unmethylated promoter of O-6-methylguanine-DNA methyltransferase (unMGMT). METHODS: The discovery-validation approach was planned incorporating a series of G-CIMP-/unMGMT GBM cohorts with DNA methylation microarray data and clinical information, to construct multi-CpG prediction models. Different bioinformatic and experimental analyses were performed for biological exploration. RESULTS: By analyzing discovery sets with radiotherapy (RT) plus TMZ versus RT alone, we identified a panel of 64 TMZ efficacy-related CpGs, from which a 10-CpG risk signature was further constructed. Both the 64-CpG panel and the 10-CpG risk signature were validated showing significant correlations with overall survival of G-CIMP-/unMGMT GBMs when treated with RT/TMZ, rather than RT alone. The 10-CpG risk signature was further observed for aiding TMZ choice by distinguishing differential outcomes to RT/TMZ versus RT within each risk subgroup. Functional studies on GPR81, the gene harboring one of the 10 CpGs, indicated its distinct impacts on TMZ resistance in GBM cells, which may be dependent on the status of MGMT expression. CONCLUSIONS: The 64 TMZ efficacy-related CpGs and in particular the 10-CpG risk signature may serve as promising predictive biomarker candidates for guiding optimal usage of TMZ in G-CIMP-/unMGMT GBMs.

Non-invasive prenatal testing for fetal Ss, Kidd, and CTL2 blood group prediction by multiplex digital droplet PCR.

Background: Some blood groups, such as S and s blood groups in the MNS blood group system, and Kidd and CTL2 blood group systems, can cause severe fetal and newborn alloimmune disorders. Non-invasive prenatal testing (NIPT) to predict fetal blood groups and knowledge of local blood group gene frequency are both important for pregnancy management decisions. Droplet digital PCR (ddPCR) has high specificity and sensitivity in detecting fetal single nucleotide variation. Objectives: The objective is to predict fetal Ss, Kidd, and CTL2 blood groups using multiplex ddPCR. The gene frequencies of three blood groups were detected by ddPCR in northwest China. Design: This is a prospective study. Methods: Cell-free fetal DNA isolated from 26 healthy single pregnant women at different gestational stages was tested with QX200 Droplet Digital PCR. Results were compared with fetal genotypes. DNA samples purified from 20 blood pools containing a total of 1000 donors in northwest China were subjected to ddPCR to detect the gene frequency of three blood groups. Results: Ss, Kidd, and CTL2 blood groups of 26 pregnant fetuses were accurately detected by multiplex ddPCR. The multiplex ddPCR results were consistent with the Sanger sequencing results of 26 fetal blood samples after birth. The gene frequencies of the three blood groups detected by ddPCR were 9.30% for S, 90.70% for s, 48.43% for Jka, 51.57% for Jkb, 66.57% for HNA-3A, and 33.43% for HNA-3B. Conclusions: It is reliable to predict fetal Ss, Kidd, and CTL2 blood groups by multiplex ddPCR. Meanwhile, we designed a simple and efficient method for inferring the gene frequency of three blood groups based on ddPCR.

Machine learning algorithms for improved prediction of in-hospital outcomes after moderate-to-severe traumatic brain injury: a Chinese retrospective cohort study.

AIM: Controversy remains high over the superiority of advanced machine learning (ML) algorithms to conventional logistic regression (LR) in the prediction of prognosis after traumatic brain injury (TBI). This study aimed to compare the performance of ML and LR models in predicting in-hospital prognosis after TBI. METHOD: In a single-center retrospective cohort of adult patients hospitalized for moderate-to-severe TBI (Glasgow coma score ≤12) in our hospital from 2011 to 2020, LR and three ML algorithms (XGboost, lightGBM, and FT-transformer) were run to build prediction models for in-hospital mortality and the Glasgow Outcome Scale (GOS) functional outcomes using either all 19 clinical and laboratory features or the 10 non-laboratory ones collected at admission to the neurological intensive care unit. The Shapley (SHAP) value was used for model interpretation. RESULT: In total, 482 patients had an in-hospital mortality rate of 11.0%. A total of 23.0% of the patients had good functional scores (GOS ≥ 4) at discharge. All ML models performed better than the LR model in predicting in-hospital prognosis after TBI, among which the lightGBM model showed the best performance: When predicting mortality, the lightGBM model yielded an area under the curve (AUC) of 0.953 using all 19 features (the LR model: 0.813) and an AUC of 0.935 using 10 non-laboratory features (the LR model: 0.803); when predicting GOS functional outcomes, it yielded an AUC of 0.913 using all 19 features (the LR model: 0.832) and an AUC of 0.889 using non-laboratory data (the LR model: 0.818). The SHAP method identified key contributors to explain the lightGBM models. Finally, the integration of the lightGBM models with different prediction purposes was found to provide refined prognostic information, particularly for patients who survived moderate-to-severe TBI. CONCLUSION: The study supported the superiority of ML to LR in predicting prognosis after moderate-to-severe TBI and highlighted its potential use for clinical application.

Potent predictive CpG signature for temozolomide response in non-glioma-CpG island methylator phenotype glioblastomas with methylated MGMT promoter.

Aim: We aimed to identify potent CpG signatures predicting temozolomide (TMZ) response in glioblastomas (GBMs) that do not have the glioma-CpG island methylator phenotype (G-CIMP) but have a methylated promoter of MGMT (meMGMT). Materials & methods: Different datasets of non-G-CIMP meMGMT GBMs with molecular and clinical data were analyzed. Results: A panel of 77 TMZ efficacy-related CpGs and a seven-CpG risk signature were identified and validated for distinguishing differential outcomes to radiotherapy plus TMZ versus radiotherapy alone in non-G-CIMP meMGMT GBMs. An integrated classification scheme was also proposed for refining a MGMT-based TMZ-guiding approach in all G-CIMP-GBMs. Conclusion: The CpG signatures may serve as promising predictive biomarker candidates for guiding optimal TMZ usage in non-G-CIMP meMGMT GBMs.

Glioblastomas that do not have the glioma-CpG island methylator phenotype (G-CIMP) but have a methylated promoter of the MGMT gene (meMGMT) show considerable variability in their response to temozolomide (TMZ). Powerful biomarkers that provide predictive information on optimal TMZ decision-making can be clinically useful. This study has identified and validated a panel of 77 TMZ efficacy-related CpGs and a seven-CpG risk signature for predicting TMZ usage in non-G-CIMP meMGMT glioblastomas. An integrated classification scheme is proposed for refining a MGMT-based TMZ-guiding approach in non-G-CIMP glioblastomas.

TFG mutation induces haploinsufficiency and drives axonal Charcot-Marie-Tooth disease by causing neurite degeneration.

AIMS: TFG-related axonal Charcot-Marie-Tooth (CMT) disease is a late-onset, autosomal dominant, hereditary motor, and sensory neuropathy characterized by slowly progressive weakness and atrophy of the distal muscles. The objective of this study was to determine the common pathogenic mechanism of TFG-related CMT type 2 (CMT2) caused by different mutations and establish a direct association between TFG haploinsufficiency and neurodegeneration. METHODS: Three individuals carrying the TFG p.G269V mutation but with varying disease durations were studied. The effect of the p.G269V mutation was confirmed by analyzing protein samples extracted from the blood of two individuals. The functional consequences of both CMT2 mutant gene products were evaluated in vitro. The effect of TFG deficiency in the nervous system was examined using zebrafish models and cultured mouse neurons. RESULTS: Overexpression of p.G269V TFG failed to enhance soluble TFG levels by generating insoluble TFG aggregates. TFG deficiency disrupted neurite outgrowth and induced neuronal apoptosis both in vivo and in vitro and further impaired locomotor capacity in zebrafish, which was consistent with the phenotype in patients. Wnt signaling was activated as a protective factor in response to TFG deficiency. CONCLUSION: CMT2-related TFG mutation induces TFG haploinsufficiency within cells and drives disease by causing progressive neurite degeneration.

Neuropathy-associated Fars2 deficiency affects neuronal development and potentiates neuronal apoptosis by impairing mitochondrial function.

BACKGROUND: Neurodegenerative diseases encompass an extensive and heterogeneous group of nervous system disorders which are characterized by progressive degeneration and death of neurons. Many lines of evidence suggest the participation of mitochondria dysfunction in these diseases. Mitochondrial phenylalanyl-tRNA synthetase, encoded by FARS2, catalyzes the transfer of phenylalanine to its cognate tRNA for protein synthesis. As a member of mt-aaRSs genes, FARS2 missense homozygous mutation c.424G > T (p.D142Y) found in a Chinese consanguineous family first built the relationship between pure hereditary spastic paraplegia (HSP) and FARS2 gene. More FARS2 variations were subsequently found to cause heterogeneous group of neurologic disorders presenting three main phenotypic manifestations: infantile-onset epileptic mitochondrial encephalopathy, later-onset spastic paraplegia and juvenile onset refractory epilepsy. Studies showed that aminoacylation activity is frequently disrupt in cases with FARS2 mutations, indicating a loss-of-function mechanism. However, the underlying pathogenesis of neuropathy-associated Fars2 deficiency is still largely unknown. RESULTS: Early gestation lethality of global Fars2 knockout mice was observed prior to neurogenesis. The conditional Fars2 knockout-mouse model delayed lethality to late-gestation, resulting in a thinner cortex and an enlarged ventricle which is consist with the MRI results revealing cortical atrophy and reduced cerebral white matter volume in FARS2-deficient patients. Delayed development of neurite outgrowth followed by neuronal apoptosis was confirmed in Fars2-knockdown mouse primary cultured neurons. Zebrafish, in which fars2 was knocked down, exhibited aberrant motor neuron function including reduced locomotor capacity which well restored the spastic paraplegia phenotype of FARS2-deficient patients. Altered mitochondrial protein synthesis and reduced levels of oxidative phosphorylation complexes were detected in Fars2-deficient samples. And thus, reduced ATP, total NAD levels and mitochondrial membrane potential, together with increased ROS production, revealed mitochondrial dysfunction both in vitro and in vivo. Dctn3 is a potential downstream molecule in responds to Fars2 deficient in neurons, which may provide some evidence for the development of pathogenesis study and therapeutic schedule. CONCLUSIONS: The Fars2 deficiency genetic models developed in this study cover the typical clinical manifestations in FARS2 patients, and help clarify how neuropathy-associated Fars2 deficiency, by damaging the mitochondrial respiratory chain and impairing mitochondrial function, affects neuronal development and potentiates neuronal cell apoptosis.

A Novel Pseudogene Methylation Signature to Predict Temozolomide Outcome in Non-G-CIMP Glioblastomas.

Objective: Alterations in the methylation state of pseudogenes may serve as clinically useful biomarkers of glioblastomas (GBMs) that do not have glioma-CpG island methylator phenotype (G-CIMP). Methods: Non-G-CIMP GBM datasets were included for evaluation, and a RISK-score signature was determined from the methylation state of pseudogene loci. Both bioinformatic and experimental analyses were performed for biological validation. Results: By integrating clinical information with DNA methylation microarray data, we screened a panel of eight CpGs from discovery cohorts of non-G-CIMP GBMs. Each CpG could accurately and independently predict the prognosis of patients under a treatment regime that combined radiotherapy (RT) and temozolomide (TMZ). The 8-CpG signature appeared to show opposite prognostic correlations between patients treated with RT/TMZ and those treated with RT monotherapy. The analyses further indicated that this signature had predictive value for TMZ efficacy because different survival benefits between RT/TMZ and RT therapies were observed in each risk subgroup. The incorporation of other risk factors, such as age and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, with our pseudogene methylation signature could provide precise risk classification. In vitro experimental data revealed that two locus-specific pseudogenes (ZNF767P and CLEC4GP1) may modulate TMZ resistance via distinct mechanisms in GBM cells. Conclusion: The biologically and clinically relevant RISK-score signature, based on pseudogene methylation loci, may offer information for predicting TMZ responses of non-G-CIMP GBMs, that is independent from, but complementary to, MGMT-based approaches.

Integrative analysis identifies an immune-relevant epigenetic signature for prognostication of non-G-CIMP glioblastomas.

The clinical and molecular implications of DNA methylation alterations remain unclear among the majority of glioblastomas (GBMs) without glioma-CpGs island methylator phenotype (G-CIMP); integrative multi-level molecular profiling may provide useful information. Independent cohorts of non-G-CIMP GBMs or IDH wild type (wt) lower-grade gliomas (LGGs) from local and public databases with DNA methylation and gene expression microarray data were included for discovery and validation of a multimarker signature, combined using a RISK score model. Bioinformatic and in vitro functional analyses were employed for biological validation. Using a strict multistep selection approach, we identified eight CpGs, each of which was significantly correlated with overall survival (OS) of non-G-CIMP GBMs, independent of age, the O-6-methylguanine-DNA methyltransferase (MGMT) methylation status, treatments and other identified CpGs. An epigenetic RISK signature of the 8 CpGs was developed and validated to robustly and independently prognosticate prognosis in different cohorts of not only non-G-GIMP GBMs, but also IDHwt LGGs. It also showed good discriminating value in stratified cohorts by current clinical and molecular factors. Bioinformatic analysis revealed consistent correlation of the epigenetic signature to distinct immune-relevant transcriptional profiles of GBM bulks. Functional experiments showed that S100A2 appeared to be epigenetically regulated by one identified CpG and was associated with GBM cell proliferation, apoptosis, invasion, migration and immunosuppression. The prognostic 8-CpGs RISK score signature may be of promising value for refining current glioma risk classification, and its potential links to distinct immune phenotypes make it a promising biomarker candidate for predicting response to anti-glioma immunotherapy.

Novel predictive epigenetic signature for temozolomide in non-G-CIMP glioblastomas.

OBJECTIVE: To identify novel epigenetic signatures that could provide predictive information that is complementary to promoter methylation status of the O-6-methylguanine-DNA methyltransferase (MGMT) gene for predicting temozolomide (TMZ) response, among glioblastomas (GBMs) without glioma-CpGs island methylator phenotype (G-CIMP) METHODS: Different cohorts of primary non-G-CIMP GBMs with genome-wide DNA methylation microarray data were included for discovery and validation of a multimarker signature, combined using a RISK score model. Different statistical analyses and functional experiments were performed for clinical and biological validation. RESULTS: By employing discovery cohorts with radiotherapy (RT) and TMZ versus RT alone and a strict multistep selection strategy, we identified seven CpGs, each of which was significantly correlated with overall survival (OS) of non-G-CIMP GBMs with RT/TMZ, independent of age, MGMT promoter methylation status, and other identified CpGs. A RISK score signature of the 7 CpGs was developed and validated to distinguish non-G-CIMP GBMs with differential survival outcomes to RT/TMZ, but not to RT alone. The interaction analyses also showed differential outcomes to RT/TMZ versus RT alone within the RISK score-based subgroups. The signature could also improve the risk classification by age and MGMT promoter methylation status. Functional experiments showed that HSBP2 appeared to be epigenetically regulated by one identified CpG and was associated with TMZ resistance, but it was not associated with cell proliferation or apoptosis in GBM cell lines. The predictive value of the single CpG methylation of HSBP2 by pyrosequencing was observed in a local cohort of isocitrate dehydrogenase 1 (IDH1) R132H wild-type GBMs. CONCLUSIONS: This novel epigenetic signature might be a promising predictive (but not a general prognostic) biomarker and be helpful for refining the MGMT-based guiding approach to TMZ usage in non-G-CIMP GBMs.

A five-CpG signature of microRNA methylation in non-G-CIMP glioblastoma.

AIMS: DNA methylation has been found to regulate microRNAs (miRNAs) expression, but the prognostic value of miRNA-related DNA methylation aberration remained largely elusive in cancers including glioblastomas (GBMs). This study aimed to investigate the clinical and biological feature of miRNA methylation in GBMs of non-glioma-CpG island methylator phenotype (non-G-CIMP). METHODS: Prognostic miRNA methylation loci were analyzed, with TCGA and Rennes cohort as training sets, and independent datasets of GBMs and low-grade gliomas (LGGs) were obtained as validation sets. Different statistical and bioinformatic analysis and experimental validations were performed to clinically and biologically characterize the signature. RESULTS: We identified and validated a risk score based on methylation status of five miRNA-associated CpGs which could predict survival of GBM patients in a series of training and validation sets. This signature was independent of age and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The risk subgroup was associated with angiogenesis and accordingly differential responses to bevacizumab-contained therapy. MiRNA target analysis and in vitro experiments further confirmed the accuracy of this signature. CONCLUSION: The five-CpG signature of miRNA methylation was biologically relevant and was of potential prognostic and predictive value for GBMs. It might be of help for improving individualized treatment.

A novel prognostic six-CpG signature in glioblastomas.

AIMS: We aimed to identify a clinically useful biomarker using DNA methylation-based information to optimize individual treatment of patients with glioblastoma (GBM). METHODS: A six-CpG panel was identified by incorporating genome-wide DNA methylation data and clinical information of three distinct discovery sets and was combined using a risk-score model. Different validation sets of GBMs and lower-grade gliomas and different statistical methods were implemented for prognostic evaluation. An integrative analysis of multidimensional TCGA data was performed to molecularly characterize different risk tumors. RESULTS: The six-CpG risk-score signature robustly predicted overall survival (OS) in all discovery and validation cohorts and in a treatment-independent manner. It also predicted progression-free survival (PFS) in available patients. The multimarker epigenetic signature was demonstrated as an independent prognosticator and had better performance than known molecular indicators such as glioma-CpG island methylator phenotype (G-CIMP) and proneural subtype. The defined risk subgroups were molecularly distinct; high-risk tumors were biologically more aggressive with concordant activation of proangiogenic signaling at multimolecular levels. Accordingly, we observed better OS benefits of bevacizumab-contained therapy to high-risk patients in independent sets, supporting its implication in guiding usage of antiangiogenic therapy. Finally, the six-CpG signature refined the risk classification based on G-CIMP and MGMT methylation status. CONCLUSIONS: The novel six-CpG signature is a robust and independent prognostic indicator for GBMs and is of promising value to improve personalized management.

Integrative analysis of novel hypomethylation and gene expression signatures in glioblastomas.

Molecular and clinical heterogeneity critically hinders better treatment outcome for glioblastomas (GBMs); integrative analysis of genomic and epigenomic data may provide useful information for improving personalized medicine. By applying training-validation approach, we identified a novel hypomethylation signature comprising of three CpGs at non-CpG island (CGI) open sea regions for GBMs. The hypomethylation signature consistently predicted poor prognosis of GBMs in a series of discovery and validation datasets. It was demonstrated as an independent prognostic indicator, and showed interrelationships with known molecular marks such as MGMT promoter methylation status, and glioma CpG island methylator phenotype (G-CIMP) or IDH1 mutations. Bioinformatic analysis found that the hypomethylation signature was closely associated with the transcriptional status of an EGFR/VEGFA/ANXA1-centered gene network. The integrative molecular analysis finally revealed that the gene network defined two distinct clinically relevant molecular subtypes reminiscent of different immature neuroglial lineages in GBMs. The novel hypomethylation signature and relevant gene network may provide new insights into prognostic classification, molecular characterization, and treatment development for GBMs.

Dec1 expression predicts prognosis and the response to temozolomide chemotherapy in patients with glioma.

Differentiated embryo chondrocyte expressed gene 1 (Dec1), a crucial cell differentiation mediator and apoptosis inhibitor, is abundantly expressed in various types of human cancer and is associated with malignant tumor progression. As poor differentiation and low apoptosis are closely associated with poor survival rates and a poor response to radio/chemotherapy in patients with cancer, the prognostic value of Dec1 expression was examined in the present study and its correlation with response to temozolomide (TMZ) chemotherapy was analyzed in patients with glioma. Dec1 expression was analyzed by immunohistochemistry in 157 samples of newly diagnosed glioma and 63 recurrent glioblastoma cases that relapsed during TMZ chemotherapy. Correlations with clinical variables, prognosis and the response to TMZ chemotherapy were analyzed in the newly diagnosed gliomas. Dec1 expression was also compared with the apoptosis index determined by TdT­mediated dUTP nick ending­labeling assay in recurrent glioblastomas. The antiglioma effect of TMZ in nude mice xenografts with Dec1 expression was examined in vivo. High expression of Dec1, which was significantly associated with high pathological tumor grade and poor response to TMZ chemotherapy, was demonstrated to be an unfavorable independent prognostic factor and predicted poor survival in patients with newly diagnosed glioma. In patients with recurrent glioblastoma, there was a negative correlation between Dec1 expression and the apoptotic index. In nude mice treated with TMZ, Dec1 overexpression potentiated proliferation, but attenuated TMZ­induced apoptosis. In conclusion, Dec1 is a prognostic factor for the clinical outcome and a predictive factor for the response to TMZ chemotherapy in patients with glioma.

Resveratrol suppresses human glioblastoma cell migration and invasion via activation of RhoA/ROCK signaling pathway.

Numerous studies have demonstrated that resveratrol has a potential use in cancer prevention and treatment. However, the effects of resveratrol on cancer cell motility and invasiveness remain unclear. The current study aimed to examine the effects of resveratrol on cell migration and invasion in human glioblastoma cells, and to explore the underlying molecular mechanisms. In wound-healing and Matrigel transwell assays, resveratrol was found to significantly inhibit the migration and invasion of U87MG, T98G and U251 glioblastoma cells in vitro. Results from western blot analysis and gelatin zymography revealed that resveratrol also suppressed the expression and activity of matrix metalloproteinase 2 (MMP-2; P<0.05), an important mediator of cell migration and invasion. Furthermore, using a pull-down assay, increased activation of RhoA was observed in glioblastoma cells treated with resveratrol vs. controls (P<0.05). Notably, inhibition of the RhoA/Rho-associated kinase (ROCK) pathway by C3 transferase or Y-27362 was found to attenuate the resveratrol-induced reductions in cell migration and invasion (P<0.05), and also partially rescued the decreased expression and activity of MMP-2 induced by resveratrol (P<0.05). Taken together, the results suggest that resveratrol may inhibit glioblastoma cell motility and invasiveness via activating the RhoA/ROCK signaling pathway.

Risks and benefits of stress ulcer prophylaxis in adult neurocritical care patients: a systematic review and meta-analysis of randomized controlled trials.

INTRODUCTION: Neurocritical care patients are at high risk for stress-related upper gastrointestinal (UGI) bleeding. The aim of this meta-analysis was to evaluate the risks and benefits of stress ulcer prophylaxis (SUP) in this patient group. METHODS: A systematic search of major electronic literature databases was conducted. Eligible studies were randomized controlled trials (RCTs) in which researchers compared the effects of SUP (with proton pump inhibitors or histamine 2 receptor antagonists) with placebo or no prophylaxis in neurocritical care patients. The primary outcome was UGI bleeding, and secondary outcomes were all-cause mortality and nosocomial pneumonia. Study heterogeneity was sought and quantified. The results were reported as risk ratios/relative risks (RRs) with 95 % confidence intervals (CIs). RESULTS: We included 8 RCTs comprising an aggregate of 829 neurocritical care patients. Among these trials, one study conducted in a non-intensive care unit setting that did not meet our inclusion criteria was ultimately included based on further evaluation. All studies were judged as having a high or unclear risk of bias. SUP was more effective than placebo or no prophylaxis at reducing UGI bleeding (random effects: RR 0.31; 95 % CI 0.20-0.47; P < 0.00001; I (2) = 45 %) and all-cause mortality (fixed effects: RR 0.70; 95 % CI 0.50-0.98; P = 0.04; I (2) = 0 %). There was no difference between SUP and placebo or no prophylaxis regarding nosocomial pneumonia (random effects: RR 1.14; 95 % CI 0.67-1.94; P = 0.62; I (2) = 42 %). The slight asymmetry of the funnel plots raised the concern of small trial bias, and apparent heterogeneity existed in participants, interventions, control treatments, and outcome measures. CONCLUSIONS: In neurocritical care patients, SUP seems to be more effective than placebo or no prophylaxis in preventing UGI bleeding and reducing all-cause mortality while not increasing the risk of nosocomial pneumonia. The robustness of this conclusion is limited by a lack of trials with a low risk of bias, sparse data, heterogeneity among trials, and a concern regarding small trial bias. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) identifier: CRD42015015802 . Date of registration: 6 Jan 2015.