Molecular subtypes based on PANoptosis-related genes and tumor microenvironment infiltration characteristics in lower-grade glioma.

The growth of cancer, the effectiveness of treatment, and prognosis are all closely related to PANoptosis (include pyroptosis, apoptosis, and necroptosis). It remains unclear whether PANoptosis genes (PANGs) may contribute to lower-grade glioma (LGG) tumor microenvironment (TME). In this study, we collected 1203 LGG samples from three public databases and reported that PANoptosis involves TME interaction and prognosis. Firstly, we provided a comprehensive review of the pan-cancer landscape of PANGs in terms of expression characteristics, prognostic value, mutational profile, and pathway regulation. Then, we identified two distinct PANclusters, each with its own molecular, clinical, and immunological profile. We then developed a scoring system for LGG patients called PANscore. As well as investigating immune characteristics, tumor mutational characteristics, and drug sensitivity, we examined the differences between groups with high PANscores and those with low PANscores. Based on this PANscore and clinicopathological variables, an instant nomogram for predicting clinical survival in LGG patients was developed. Our thorough examination of PANGs in LGG revealed their probable function in TME, as well as their clinicopathological characteristics and prognosis. These discoveries could deepen our comprehension of PANGs in LGG and provide doctors fresh perspectives on how to forecast prognosis and create more efficient, individualized treatment plans.

CRISPR-cas9 screening identified lethal genes enriched in Hippo kinase pathway and of predictive significance in primary low-grade glioma.

BACKGROUND: Low-grade gliomas (LGG) are a type of brain tumor that can be lethal, and it is essential to identify genes that are correlated with patient prognosis. In this study, we aimed to use CRISPR-cas9 screening data to identify key signaling pathways and develop a genetic signature associated with high-risk, low-grade glioma patients. METHODS: The study used CRISPR-cas9 screening data to identify essential genes correlated with cell survival in LGG. We used RNA-seq data to identify differentially expressed genes (DEGs) related to cell viability. Moreover, we used the least absolute shrinkage and selection operator (LASSO) method to construct a genetic signature for predicting overall survival in patients. We performed enrichment analysis to identify pathways mediated by DEGs, overlapping genes, and genes shared in the Weighted correlation network analysis (WGCNA). Finally, the study used western blot, qRT-PCR, and IHC to detect the expression of hub genes from signature in clinical samples. RESULTS: The study identified 145 overexpressed oncogenes in low-grade gliomas using the TCGA database. These genes were intersected with lethal genes identified in the CRISPR-cas9 screening data from Depmap database, which are enriched in Hippo pathways. A total of 19 genes were used to construct a genetic signature, and the Hippo signaling pathway was found to be the predominantly enriched pathway. The signature effectively distinguished between low- and high-risk patients, with high-risk patients showing a shorter overall survival duration. Differences in hub gene expression were found in different clinical samples, with the protein and mRNA expression of REP65 being significantly up-regulated in tumor cells. The study suggests that the Hippo signaling pathway may be a critical regulator of viability and tumor proliferation and therefore is an innovative new target for treating cancerous brain tumors, including low-grade gliomas. CONCLUSION: Our study identified a novel genetic signature associated with high-risk, LGG patients. We found that the Hippo signaling pathway was significantly enriched in this signature, indicating that it may be a critical regulator of tumor viability and proliferation in LGG. Targeting the Hippo pathway could be an innovative new strategy for treating LGG.

Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma.

BACKGROUND: Lower-grade glioma (LGG) is a highly heterogeneous disease that presents challenges in accurately predicting patient prognosis. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence cell death mechanisms, which are critical in tumorigenesis and progression. However, the prognostic significance of the interplay between mitochondrial function and cell death in LGG requires further investigation. METHODS: We employed a robust computational framework to investigate the relationship between mitochondrial function and 18 cell death patterns in a cohort of 1467 LGG patients from six multicenter cohorts worldwide. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations. Ultimately, we devised the mitochondria-associated programmed cell death index (mtPCDI) using machine learning models that exhibited optimal performance. RESULTS: The mtPCDI, generated by combining 18 highly influential genes, demonstrated strong predictive performance for prognosis in LGG patients. Biologically, mtPCDI exhibited a significant correlation with immune and metabolic signatures. The high mtPCDI group exhibited enriched metabolic pathways and a heightened immune activity profile. Of particular importance, our mtPCDI maintains its status as the most potent prognostic indicator even following adjustment for potential confounding factors, surpassing established clinical models in predictive strength. CONCLUSION: Our utilization of a robust machine learning framework highlights the significant potential of mtPCDI in providing personalized risk assessment and tailored recommendations for metabolic and immunotherapy interventions for individuals diagnosed with LGG. Of particular significance, the signature features highly influential genes that present further prospects for future investigations into the role of PCD within mitochondrial function.

DNA methylation regulator-mediated modification patterns and risk of intracranial aneurysm: a multi-omics and epigenome-wide association study integrating machine learning, Mendelian randomization, eQTL and mQTL data.

BACKGROUND: Intracranial aneurysms (IAs) pose a significant and intricate challenge. Elucidating the interplay between DNA methylation and IA pathogenesis is paramount to identify potential biomarkers and therapeutic interventions. METHODS: We employed a comprehensive bioinformatics investigation of DNA methylation in IA, utilizing a transcriptomics-based methodology that encompassed 100 machine learning algorithms, genome-wide association studies (GWAS), Mendelian randomization (MR), and summary-data-based Mendelian randomization (SMR). Our sophisticated analytical strategy allowed for a systematic assessment of differentially methylated genes and their implications on the onset, progression, and rupture of IA. RESULTS: We identified DNA methylation-related genes (MRGs) and associated molecular pathways, and the MR and SMR analyses provided evidence for potential causal links between the observed DNA methylation events and IA predisposition. CONCLUSION: These insights not only augment our understanding of the molecular underpinnings of IA but also underscore potential novel biomarkers and therapeutic avenues. Although our study faces inherent limitations and hurdles, it represents a groundbreaking initiative in deciphering the intricate relationship between genetic, epigenetic, and environmental factors implicated in IA pathogenesis.

Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor.

OBJECTIVE: The clinical ability of radiomics to predict intracranial aneurysm rupture risk remains unexplored. This study aims to investigate the potential uses of radiomics and explore whether deep learning (DL) algorithms outperform traditional statistical methods in predicting aneurysm rupture risk. METHODS: This retrospective study included 1740 patients with 1809 intracranial aneurysms confirmed by digital subtraction angiography at two hospitals in China from January 2014 to December 2018. We randomly divided the dataset (hospital 1) into training (80%) and internal validation (20%). External validation was performed using independent data collected from hospital 2. The prediction models were developed based on clinical, aneurysm morphological, and radiomics parameters by logistic regression (LR). Additionally, the DL model for predicting aneurysm rupture risk using integration parameters was developed and compared with other models. RESULTS: The AUCs of LR models A (clinical), B (morphological), and C (radiomics) were 0.678, 0.708, and 0.738, respectively (all p < 0.05). The AUCs of the combined feature models D (clinical and morphological), E (clinical and radiomics), and F (clinical, morphological, and radiomics) were 0.771, 0.839, and 0.849, respectively. The DL model (AUC = 0.929) outperformed the machine learning (ML) (AUC = 0.878) and the LR models (AUC = 0.849). Also, the DL model has shown good performance in the external validation datasets (AUC: 0.876 vs 0.842 vs 0.823, respectively). CONCLUSION: Radiomics signatures play an important role in predicting aneurysm rupture risk. DL methods outperformed conventional statistical methods in prediction models for the rupture risk of unruptured intracranial aneurysms, integrating clinical, aneurysm morphological, and radiomics parameters. KEY POINTS: • Radiomics parameters are associated with the rupture risk of intracranial aneurysms. • The prediction model based on integrating parameters in the deep learning model was significantly better than a conventional model. • The radiomics signature proposed in this study could guide clinicians in selecting appropriate patients for preventive treatment.

An innovative prognostic model based on autophagy-related long noncoding RNA signature for low-grade glioma.

Autophagy is a highly conserved lysosomal degradation process essential in tumorigenesis. However, the involvement of autophagy-related long noncoding RNAs (lncRNAs) in low-grade glioma (LGG) remains unclear. In this study, we established an autophagy-related lncRNA prognostic signature for patients with LGG and assess its underlying functions. We used univariate Cox, least absolute shrinkage and selection operator and multivariate Cox regression models to establish an autophagy-related lncRNA prognostic signature. Kaplan-Meier survival analysis, receiver operating characteristic curve, nomogram, C-index, calibration curve and clinical decision-making curve were used to assess the predictive capability of the identified signature. A signature comprising nine autophagy-related lncRNAs (AL136964.1, ARHGEF26-AS1, PCED1B-AS1, AS104072.1, PRKCQ-AS1, LINC00957, AS125616.1, PSMB8-AS1 and AC087741.1) was identified as a prognostic model. Patients with LGG were divided into the high- and low-risk cohorts based on the median model-based risk score. The survival analysis revealed a 10-year survival rate of 9.3% (95% CI 1.91-45.3%) and 13.48% (95% CI 4.52-40.2%) in high-risk patients in the training and validation sets, respectively, and 48.4% (95% CI 24.7-95.0%) and 48.4% (95% CI 28.04-83.4%) in low-risk patients in the training and validation sets, respectively. This finding suggested a relatively low survival in high-risk patients. In addition, the lncRNA signature was independently prognostic and potentially associated with the progression of LGG. Therefore, the 9-autophagy-related-lncRNA signature may play a crucial role in the diagnosis and treatment of LGG, which may offer new avenues for tumour-targeted therapy.

Early sexual activity lowers the incidence of intracranial aneurysm: a Mendelian randomization investigation.

Objective: Investigate the potential correlation between the age of initial sexual contact, the lifetime accumulation of sexual partners, and the occurrence of intracranial aneurysm (IA) employing a two-sample Mendelian randomization approach. Methods: This research aims to elucidate the causal relationship between intracranial aneurysm (IA) and sexual variables. Two distinct sexual variables, specifically the age had first sexual intercourse (n = 406,457) and the lifetime number of sexual partners (n = 378,882), were employed as representative parameters in a two-sample Mendelian randomization (MR) study. Outcome data from 23 cohorts, comprising 5,140 cases and 71,934 controls, were gathered through genome-wide association studies (GWAS). To bolster analytical rigor, five distinct methodologies were applied, encompassing MR-Egger technique, weighted median, inverse variance weighted, simple modeling, and weighted modeling. Results: Our investigation unveiled a causal relationship between the age first had sexual intercourse and the occurrence of intracranial aneurysm (IA), employing the Inverse Variance Weighted (IVW) approach [Odds Ratio (OR): 0.609, p-value: 5.684E-04, 95% Confidence Interval (CI): 0.459-0.807]. This association was notably significant in the context of unruptured intracranial aneurysms (uIA) using the IVW approach (OR: 0.392, p-value: 6.414E-05, 95% CI: 0.248-0.621). Conversely, our findings did not reveal any discernible link between the lifetime number of sexual partners and the occurrence of IA (IA group: OR: 1.346, p-value: 0.415, 95% CI: 0.659-2.749; SAH group: OR: 1.042, p-value: 0.943, 95% CI: 0.338-3.209; uIA group: OR: 1.990, p-value: 0.273, 95% CI: 0.581-6.814). Conclusion: The two-sample Mendelian Randomization (MR) study presented herein provides evidence supporting a correlation between the age of initial engagement in sexual activity and the occurrence of intracranial aneurysm (IA), with a noteworthy emphasis on unruptured intracranial aneurysms (uIA). Nevertheless, our investigation failed to establish a definitive association between IA and the cumulative lifetime number of sexual partners.

Appraising the causal association among depression, anxiety and intracranial aneurysms: Evidence from genetic studies.

BACKGROUND: The risk of intracranial aneurysms (IAs) is increased in individuals with depression and anxiety. This indicates that depression and anxiety may contribute to the development of physical disorders. Herein, to investigate the association between genetic variants related to depression and anxiety and the risk of IA, two-sample Mendelian randomization was performed. METHODS: The genome-wide association study (GWAS) comprised genome-wide genotype data of 2248 clinically well-characterized patients with anxiety and 7992 ethnically matched controls from four European countries. Sex-specific summary-level outcome data were obtained from the GWAS of IA, including 23 cohorts with a total of 10,754 cases and 306,882 controls of European and East Asian ancestry. To improve validity, five varying Mendelian randomization techniques were used in the analysis, namely Mendelian randomization-Egger, weighted median, inverse variance weighted, simple mode, and weighted mode. RESULTS: The inverse variance weighted results indicated the causal effect of depression on IA (P = 0.03, OR = 1.32 [95 % CI, 1.03-1.70]) and unruptured IA (UIA) (P = 0.02, OR = 1.68 [95 % CI, 1.08-2.61]). However, the causal relationship between depression and subarachnoid hemorrhage (SAH) was not found (P = 0.16). We identified 43 anxiety-associated single-nucleotide polymorphisms as genetic instruments and found no causal relationship between anxiety and IA, UIA, and SAH. LIMITATIONS: Potential pleiotropy, possible weak instruments, and low statistical power limited our findings. CONCLUSION: Our MR study suggested a possible causal effect of depression on the increased risk of UIAs. Future research is required to investigate whether rational intervention in depression treatment can help to decrease the societal burden of IAs.

Nomogram for predicting survival in T1-T2 stage patients with supraglottic squamous cell carcinoma.

BACKGROUND: Supraglottic squamous cell carcinoma (SGSCC) is characterized by low differentiation, rapid growth, and inconspicuous initial manifestations. Early detection and prompt treatment can significantly improve survival rates. The main focus of treatment is to maintain optimal laryngeal function. METHODS: Using the Surveillance, Epidemiology, and End Results (SEER) database, we conducted univariate and multivariate Cox regression analyses to identify independent prognostic factors for T1-T2 SGSCC. We also enrolled 109 patients with T1-T2 SGSCC from the First Affiliated Hospital of Xinjiang Medical University as an external validation set. In addition, we developed a nomogram to predict the prognosis of T1-T2 SGSCC, assessed the predictive accuracy and discriminatory ability of the nomogram using the area under the curve (AUC), C-index, receiver operating characteristic (ROC) curve and calibration curve, and confirmed the clinical validity of the nomogram using decision curve analysis (DCA). RESULTS: Our investigation identified nine prognostic indicators for T1-T2 SGSCC: age (≥ 65 years), marital status, American Joint Committee on Cancer (AJCC) stage (II-IV), grade (III-IV), M stage (M1), radiotherapy, chemotherapy, sex (female), and surgery. These variables were used to create accurate nomograms that predict overall and specific survival rates at 1, 3, and 5 years. The nomograms demonstrated superior prognostic value and accuracy compared to AJCC staging. Laryngectomy with partial laryngectomy is the preferred treatment option for T1-T2 SGSCC cases, providing superior overall survival (OS) and cancer-specific survival (CSS). Radiotherapy also improves OS and CSS. Our results were based on a comprehensive analysis of various indicators, including the C-index, ROC curve, calibration curve, and DCA curve. CONCLUSION: Nomograms provide significant advantages in treatment decision making and diagnosis. Laryngectomy with partial laryngectomy is the most appropriate method for T1-T2 SGSCC cases. However, radiotherapy can also be used. Thus, patients with T1-T2 SGSCC should be evaluated to determine if combination therapy is the optimal treatment approach. Nevertheless, further research is needed to understand the role of chemotherapy. Overall, this study identified nine key predictors of future outcomes, aiding healthcare professionals in assessing risks and making treatment decisions for T1-T2 SGSCC patients.

Radiogenomic landscape: Assessment of specific phagocytosis regulators in lower-grade gliomas.

Genome-wide CRISPR-Cas9 knockout screens have emerged as a powerful method for identifying key genes driving tumor growth. The aim of this study was to explore the phagocytosis regulators (PRs) specifically associated with lower-grade glioma (LGG) using the CRISPR-Cas9 screening database. Identifying these core PRs could lead to novel therapeutic targets and pave the way for a non-invasive radiogenomics approach to assess LGG patients' prognosis and treatment response. We selected 24 PRs that were overexpressed and lethal in LGG for analysis. The identified PR subtypes (PRsClusters, geneClusters, and PRs-score models) effectively predicted clinical outcomes in LGG patients. Immune response markers, such as CTLA4, were found to be significantly associated with PR-score. Nine radiogenomics models using various machine learning classifiers were constructed to uncover survival risk. The area under the curve (AUC) values for these models in the test and training datasets were 0.686 and 0.868, respectively. The CRISPR-Cas9 screen identified novel prognostic radiogenomics biomarkers that correlated well with the expression status of specific PR-related genes in LGG patients. These biomarkers successfully stratified patient survival outcomes and treatment response using The Cancer Genome Atlas (TCGA) database. This study has important implications for the development of precise clinical treatment strategies and holds promise for more accurate therapeutic approaches for LGG patients in the future.

N7-methylguanosin regulators-mediated methylation modification patterns and characterization of the immune microenvironment in lower-grade glioma.

N7-methylguanosine (m7G) modification signature has recently emerged as a crucial regulator of tumor progression and treatment in cancer. However, there is limited information available on the genomic profile of lower-grade gliomas (LGGs) related to m7G methylation modification genes' function in tumorigenesis and progression. In this study, we employed bioinformatics methods to characterize m7G modifications in individuals with LGG from The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). We used gene set enrichment analysis (GSEA), single sample GSEA (ssGSEA), CIBERSORT algorithm, ESTIMATE algorithm, and TIDE to evaluate the association between m7G modification patterns, tumor microenvironment (TME) cell infiltration properties, and immune infiltration markers. The m7G scoring scheme using principal component analysis (PCA) was employed to investigate the m7G modification patterns quantitatively. We examined the m7G modification hub genes' expression levels in normal samples, refractory epilepsy samples, and LGG samples using immunohistochemistry, western-blotting, and qRT-PCR. Our findings revealed that individuals with LGG could be categorized into two groups based on m7G scores (high and low) according to the properties of m7G. Moreover, we observed that high m7G score was associated with significant clinical benefit and prolonged survival duration in the anti-PD-1 cohort, while low m7G score was associated with improved prognostic outcomes and increased likelihood of complete or partial response in the anti-PD-L1 cohort. Different m7G subtypes also showed varying Tumor Mutational Burden (TMB) and immune profiles and might have distinct responses to immunotherapy. Furthermore, we identified five potential genetic markers that were highly correlated with the m7G score signature index. These findings provide insight into the features and classification associated with m7G methylation modifications and may aid in improving the clinical outcome of LGG.

TSPO exacerbates acute cerebral ischemia/reperfusion injury by inducing autophagy dysfunction.

Autophagy is considered a double-edged sword, with a role in the regulation of the pathophysiological processes of the central nervous system (CNS) after cerebral ischemia-reperfusion injury (CIRI). The 18-kDa translocator protein (TSPO) is a highly conserved protein, with its expression level in the nervous system closely associated with the regulation of pathophysiological processes. In addition, the ligand of TSPO reduces neuroinflammation in brain diseases, but the potential role of TSPO in CIRI is largely undiscovered. On this basis, we investigated whether TSPO regulates neuroinflammatory response by affecting autophagy in microglia. In our study, increased expression of TSPO was detected in rat brain tissues with transient middle cerebral artery occlusion (tMCAO) and in BV2 microglial cells exposed to oxygen-glucose deprivation or reoxygenation (OGD/R) treatment, respectively. In addition, we confirmed that autophagy was over-activated during CIRI by increased expression of autophagy activation related proteins with Beclin-1 and LC3B, while the expression of p62 was decreased. The degradation process of autophagy was inhibited, while the expression levels of LAMP-1 and Cathepsin-D were significantly reduced. Results of confocal laser microscopy and transmission electron microscopy (TEM) indicated that autophagy flux was disordered. In contrast, inhibition of TSPO prevented autophagy over-activation both in vivo and in vitro. Interestingly, suppression of TSPO alleviated nerve cell damage by reducing reactive oxygen species (ROS) and pro-inflammatory factors, including TNF-α and IL-6 in microglia cells. In summary, these results indicated that TSPO might affect CIRI by mediating autophagy dysfunction and thus might serve as a potential target for ischemic stroke treatment.

Genomic Profiling of Lower-Grade Gliomas Subtype with Distinct Molecular and Clinicopathologic Characteristics via Altered DNA-Damage Repair Features.

Lower WHO grade II and III gliomas (LGGs) exhibit significant genetic and transcriptional heterogeneity, and the heterogeneity of DNA damage repair (DDR) and its relationship to tumor biology, transcriptome, and tumor microenvironment (TME) remains poorly understood. In this study, we conducted multi-omics data integration to investigate DDR alterations in LGG. Based on clinical parameters and molecular characteristics, LGG patients were categorized into distinct DDR subtypes, namely, DDR-activated and DDR-suppressed subtypes. We compared gene mutation, immune spectrum, and immune cell infiltration between the two subtypes. DDR scores were generated to classify LGG patients based on DDR subtype features, and the results were validated using a multi-layer data cohort. We found that DDR activation was associated with poorer overall survival and that clinicopathological features of advanced age and higher grade were more common in the DDR-activated subtype. DDR-suppressed subtypes exhibited more frequent mutations in IDH1. In addition, we observed significant upregulation of activated immune cells in the DDR-activated subgroup, which suggests that immune cell infiltration significantly influences tumor progression and immunotherapeutic responses. Furthermore, we constructed a DDR signature for LGG using six DDR genes, which allowed for the division of patients into low- and high-risk groups. Quantitative real-time PCR results showed that CDK1, CDK2, TYMS, SMC4, and WEE1 were significantly upregulated in LGG samples compared to normal brain tissue samples. Overall, our study sheds light on DDR heterogeneity in LGG and provides insight into the molecular pathways of DDR involved in LGG development.

Modification Patterns of DNA Methylation-Related lncRNAs Regulating Genomic Instability for Improving the Clinical Outcomes and Tumour Microenvironment Characterisation of Lower-Grade Gliomas.

Background: DNA methylation is an important epigenetic modification that affects genomic instability and regulates gene expression. Long non-coding RNAs (lncRNAs) modulate gene expression by interacting with chromosomal modifications or remodelling factors. It is urgently needed to evaluate the effects of DNA methylation-related lncRNAs (DMlncRNAs) on genome instability and further investigate the mechanism of action of DMlncRNAs in mediating the progression of lower-grade gliomas (LGGs) and their impact on the immune microenvironment. Methods: LGG transcriptome data, somatic mutation profiles and clinical features analysed in the present study were obtained from the CGGA, GEO and TCGA databases. Univariate, multivariate Cox and Lasso regression analyses were performed to establish a DMlncRNA signature. The KEGG and GO analyses were performed to screen for pathways and biological functions associated with key genes. The ESTIMATE and CIBERSORT algorithms were used to determine the level of immune cells in LGGs and the immune microenvironment fraction. In addition, DMlncRNAs were assessed using survival analysis, ROC curves, correlation analysis, external validation, independent prognostic analysis, clinical stratification analysis and qRT-PCR. Results: We identified five DMlncRNAs with prognostic value for LGGs and established a prognostic signature using them. The Kaplan-Meier analysis revealed 10-years survival rate of 10.10% [95% confidence interval (CI): 3.27-31.40%] in high-risk patients and 57.28% (95% CI: 43.17-76.00%) in low-risk patients. The hazard ratio (HR) and 95% CI of risk scores were 1.013 and 1.009-1.017 (p < 0.001), respectively, based on the univariate Cox regression analysis and 1.009 and 1.004-1.013 (p < 0.001), respectively, based on the multivariate Cox regression analysis. Therefore, the five-lncRNAs were identified as independent prognostic markers for patients with LGGs. Furthermore, GO and KEGG analyses revealed that these lncRNAs are involved in the prognosis and tumorigenesis of LGGs by regulating cancer pathways and DNA methylation. Conclusion: The findings of the study provide key information regarding the functions of lncRNAs in DNA methylation and reveal that DNA methylation can regulate tumour progression through modulation of the immune microenvironment and genomic instability. The identified prognostic lncRNAs have high potential for clinical grouping of patients with LGGs to ensure effective treatment and management.

N6-methyladenosine RNA methylation regulator-related alternative splicing gene signature as prognostic predictor and in immune microenvironment characterization of patients with low-grade glioma.

Background: N6-methyladenosine (m6A) RNA methylation is an important epigenetic modification affecting alternative splicing (AS) patterns of genes to regulate gene expression. AS drives protein diversity and its imbalance may be an important factor in tumorigenesis. However, the clinical significance of m6A RNA methylation regulator-related AS in the tumor microenvironment has not been investigated in low-grade glioma (LGG). Methods: We used 12 m6A methylation modulatory genes (WTAP, FTO, HNRNPC, YTHDF2, YTHDF1, YTHDC2, ALKBH5, YTHDC1, ZC3H13, RBM15, METTL14, and METTL3) from The Cancer Genome Atlas (TCGA) database as well as the TCGA-LGG (n = 502) dataset of AS events and transcriptome data. These data were downloaded and subjected to machine learning, bioinformatics, and statistical analyses, including gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Univariate Cox, the Least Absolute Shrinkage and Selection Operator (LASSO), and multivariable Cox regression were used to develop prognostic characteristics. Prognostic values were validated using Kaplan-Maier survival analysis, proportional risk models, ROC curves, and nomograms. The ESTIMATE package, TIMER database, CIBERSORT method, and ssGSEA algorithm in the R package were utilized to explore the role of the immune microenvironment in LGG. Lastly, an AS-splicing factor (SF) regulatory network was examined in the case of considering the role of SFs in regulating AS events. Results: An aggregate of 3,272 m6A regulator-related AS events in patients with LGG were screened using six machine learning algorithms. We developed eight AS prognostic characteristics based on splice subtypes, which showed an excellent prognostic prediction performance. Furthermore, quantitative prognostic nomograms were developed and showed strong validity in prognostic prediction. In addition, prognostic signatures were substantially associated with tumor immune microenvironment diversity, ICB-related genes, and infiltration status of immune cell subtypes. Specifically, UGP2 has better promise as a prognostic factor for LGG. Finally, splicing regulatory networks revealed the potential functions of SFs. Conclusion: The present research offers a novel perspective on the role of AS in m6A methylation. We reveal that m6A methylation regulator-related AS events can mediate tumor progression through the immune-microenvironment, which could serve as a viable biological marker for clinical stratification of patients with LGG so as to optimize treatment regimens.

Identification of VASH1 as a Potential Prognostic Biomarker of Lower-Grade Glioma by Quantitative Proteomics and Experimental Verification.

Background: VASH1 is a novel angiogenic regulatory factor, that participates in the process of carcinogenesis and the development of diverse tumors. Our study aimed to investigate the expression and prognostic value of the VASH1 in Lower-Grade Glioma (LGG), to explore its functional network in LGG and its effects on biological behaviors. Methods: LGG transcriptome data, somatic mutation profiles and clinical features analyzed in the present study were obtained from the TCGA, GTEx, CCLE, CGGA, UALCAN, and GEPIA2 databases, as well as clinical data and tissue sections of 83 LGG patients in our hospital. The expression characteristics of VASH1 in LGG were investigated by univariate, multivariate, immunohistochemistry, qRT-PCR, and western-blot. Subsequently, we analyzed the prognostic significance of VASH1 in LGG patients by survival analysis, subject operation characteristic curve, correlation analysis, external validation, independent prognostic significance analysis, and clinical stratification, and confirmed its biological effect on glioma cell lines in vitro. Finally, we performed GO, KEGG, and GSEA to clarify biological functions and related pathways. CIBERSORT and ESTIMATE algorithms were used to calculate the proportion of immune cells and immune microenvironment fraction in LGG. Result: We found that VASH1 is highly expressed in LGG tissues and is associated with poor prognosis, WHO grade, IDH1 wild-type, and progressive disease (P < 0.05). Multivariate and the Nomogram model showed that high VASH1 expression was an independent risk factor for glioma prognosis and had better prognostic prediction efficacy in different LGG Patient cohorts (HR = 4.753 and P=0.002). In vitro experiments showed that knockdown of VASH1 expression in glioma cell lines caused increased glioma cell proliferation, invasion, and migration capacity. The mechanism may be related to VASH1 promoting microtubule formation and remodeling of immune microenvironment. Conclusion: Our study firstly found that high VASH1 expression was associated with poor prognosis. In addition, We identified the possible mechanism by which VASH1 functioned in LGG. VASH1 inhibits the invasion and migration of tumor cells by affecting microtubule formation and immune infiltration in the tumor microenvironment. May be an important endogenous anti-tumor factor for LGG and provide a potential biomarker for individualized treatment of LGG.

An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning.

Background: Immunogenic Cell Death (ICD) is a novel way to regulate cell death and can sufficiently activate adaptive immune responses. Its role in immunity is still emerging. However, the involvement of ICD in Intracranial Aneurysms (IA) remains unclear. This study aimed to identify biomarkers associated with ICDs and determine the relationship between them and the immune microenvironment during the onset and progression of IA. Methods: The IA gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in IA were identified and the effects of the ICD on immune microenvironment signatures were studied. Techniques like Lasso, Bayes, DT, FDA, GBM, NNET, RG, SVM, LR, and multivariate analysis were used to identify the ICD gene signatures in IA. A consensus clustering algorithm was used for conducting the unsupervised cluster analysis of the ICD patterns in IA. Furthermore, enrichment analysis was carried out for investigating the various immune responses and other functional pathways. Along with functional annotation, the weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network and module construction, identification of the hub gene, and co-expression analysis were also carried out. Results: The above techniques were used for establishing the ICD gene signatures of HMGB1, HMGN1, IL33, BCL2, HSPA4, PANX1, TLR9, CLEC7A, and NLRP3 that could easily distinguish IA from normal samples. The unsupervised cluster analysis helped in identifying three ICD gene patterns in different datasets. Gene enrichment analysis revealed that the IA samples showed many differences in pathways such as the cytokine-cytokine receptor interaction, regulation of actin cytoskeleton, chemokine signaling pathway, NOD-like receptor signaling pathway, viral protein interaction with the cytokines and cytokine receptors, and a few other signaling pathways compared to normal samples. In addition, the three ICD modification modes showed obvious differences in their immune microenvironment and the biological function pathways. Eight ICD-regulators were identified and showed meaningful associations with IA, suggesting they could severe as potential prognostic biomarkers. Conclusions: A new gene signature for IA based on ICD features was created. This signature shows that the ICD pattern and the immune microenvironment are closely related to IA and provide a basis for optimizing risk monitoring, clinical decision-making, and developing novel treatment strategies for patients with IA.

m6A regulator-mediated RNA methylation modification patterns and immune microenvironment infiltration characterization in patients with intracranial aneurysms.

Background: The role of epigenetic modulation in immunity is receiving increased recognition-particularly in the context of RNA N6-methyladenosine (m6A) modifications. Nevertheless, it is still uncertain whether m6A methylation plays a role in the onset and progression of intracranial aneurysms (IAs). This study aimed to establish the function of m6A RNA methylation in IA, as well as its correlation with the immunological microenvironment. Methods: Our study included a total of 97 samples (64 IA, 33 normal) in the training set and 60 samples (44 IA, 16 normal) in the validation set to systematically assess the pattern of RNA modifications mediated by 22 m6A regulators. The effects of m6A modifications on immune microenvironment features, i.e., immune response gene sets, human leukocyte antigen (HLA) genes, and infiltrating immune cells were explored. We employed Lasso, machine learning, and logistic regression for the purpose of identifying an m6A regulator gene signature of IA with external data validation. For the unsupervised clustering analysis of m6A modification patterns in IA, consensus clustering methods were employed. Enrichment analysis was used to assess immune response activity along with other functional pathways. The identification of m6A methylation markers was identified based on a protein-protein interaction network and weighted gene co-expression network analysis. Results: We identified an m6A regulator signature of IGFBP2, IGFBP1, IGF2BP2, YTHDF3, ALKBH5, RBM15B, LRPPRC, and ELAVL1, which could easily distinguish individuals with IA from healthy individuals. Unsupervised clustering revealed three m6A modification patterns. Gene enrichment analysis illustrated that the tight junction, p53 pathway, and NOTCH signaling pathway varied significantly in m6A modifier patterns. In addition, the three m6A modification patterns showed significant differences in m6A regulator expression, immune microenvironment, and bio-functional pathways. Furthermore, macrophages, activated T cells, and other immune cells were strongly correlated with m6A regulators. Eight m6A indicators were discovered-each with a statistically significant correlation with IA-suggesting their potential as prognostic biological markers. Conclusion: Our study demonstrates that m6A RNA methylation and the immunological microenvironment are both intricately correlated with the onset and progression of IA. The novel insight into patterns of m6A modification offers a foundation for the development of innovative treatment approaches for IA.

In-Stent Stenosis After Pipeline Embolization Device in Intracranial Aneurysms: Incidence, Predictors, and Clinical Outcomes.

BACKGROUND: In-stent stenosis (ISS) is a delayed complication that can occur after pipeline embolization device use when treating intracranial aneurysms (IAs). OBJECTIVE: To assess the incidence, predictors, and outcomes of ISS. METHODS: This was a retrospective, multicenter, observational study. All patient data were collected from a PLUS registry study. We collected data from patients with IA who completed digital subtraction angiography at follow-up and divided patients into "non-ISS," "mild ISS," or "severe ISS" groups. Multivariate logistic regression analysis was conducted to determine predictors of ISS. RESULTS: A total of 1171 consecutive patients with 1322 IAs participated in this study. Angiographic follow-up was available for 662 patients with 728 IAs, and the mean follow-up time was 9 months. ISS was detected in 73 cases (10.03%), including 61 mild ISS cases and 12 severe ISS cases. Univariate and multivariable analysis demonstrated that current smoking history (mild ISS: OR 2.15, 95% CI 1.122-4.118, P = .021; severe ISS: OR 5.858, 95% CI 1.186-28.93, P = .030) and cerebral atherosclerosis (mild ISS: OR 5.694, 95% CI 3.193-10.15, P = .001; severe ISS: OR 6.103, 95% CI 1.384-26.91, P = .017) were independent predictors of ISS. Compared with the other groups, the severe ISS group had higher rate of ischemic stroke (33.3%). CONCLUSION: ISS occurs in approximately 10.03% of cases at a mean follow-up of 9 months. Statistically, current smoking history and cerebral atherosclerosis are the main predictors of ISS. Severe ISS may be associated with higher risk of neurological ischemic events in patients with IA after pipeline embolization device implantation.