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.

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.

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.

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.

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.

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.

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.