Dietary factors and the incidence of intracranial aneurysms: a Mendelian randomization research.

BACKGROUND: Intracranial aneurysms (IAs) pose a significant threat to morbidity and mortality, yet their etiology remains inadequately comprehended. The present study employs Mendelian randomization (MR) to investigate the relationship among dietary elements with IAs, encompassing unruptured intracranial aneurysms (uIA) as well as aneurysmal subarachnoid hemorrhage (aSAH). METHODS: The current study employed a double-sample MR test utilizing genome-wide association study (GWAS) summary data from the IEU and IAs' meta-analysis to investigate the genetically predicted consumption levels of various dietary factors using GWAS data. Causation was assessed by techniques of MR-Egger, weighted mode, and median, as well as IVW. To guarantee the accuracy of the results, pleiotropy and heterogeneity evaluations were also carried out. RESULTS: The findings of the study indicate a positive correlation between the intake of alcohol, lamb/mutton, and pork with the risk of IAs (IVW all p < 0.05). Conversely, a negative correlation was observed regarding dried fruit consumption and the risk of aSAH (IVW p < 0.05). There was only scant evidence supporting the association between alcohol intake frequency and an elevated risk of uIA (IVW method p < 0.05). The MR analysis outcomes were authenticated by the MR-PRESSO method and were deemed reliable. Furthermore, sensitivity calculations, such as pleiotropy and homogeneity test, leave-one-out evaluation, and funnel charts, validated the robustness of the results. CONCLUSIONS: The findings suggest that reducing alcohol, lamb/mutton, and pork intake, and increasing dried fruit intake may be potential strategies for the prevention of IAs and aSAH. Additional research is necessary to validate these outcomes and elucidate the underlying mechanisms.

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.

Prognostic value of anoikis-related genes revealed using multi-omics analysis and machine learning based on lower-grade glioma features and tumor immune microenvironment.

Background: The investigation explores the involvement of anoikis-related genes (ARGs) in lower-grade glioma (LGG), seeking to provide fresh insights into the disease's underlying mechanisms and to identify potential targets for therapy. Methods: We applied unsupervised clustering techniques to categorize LGG patients into distinct molecular subtypes based on ARGs with prognostic significance. Additionally, various machine learning algorithms were employed to pinpoint genes most strongly correlated with patient outcomes, which were then used to develop and assess risk profiles. Results: Our analysis identified two distinct molecular subtypes of LGG, each with significantly different prognoses. Patients in Cluster 2 had a median survival of 2.036 years, markedly shorter than the 7.994 years observed in Cluster 1 (P < 0.001). We also constructed a six-gene ARG signature that efficiently classified patients into two risk categories, showing median survival durations of 4.084 years for the high-risk group and 10.304 years for the low-risk group (P < 0.001). Significantly, the immune profiles, tumor mutation characteristics, and drug sensitivity varied greatly among these risk groups. The high-risk group was characterized by a "cold" tumor microenvironment (TME), a lower IDH1 mutation rate (61.7 % vs. 91.4 %), a higher TP53 mutation rate (53.7 % vs. 38.9 %), and greater sensitivity to targeted therapies such as QS11 and PF-562271. Furthermore, our nomogram, integrating risk scores with clinicopathological features, demonstrated strong predictive accuracy for clinical outcomes in LGG patients, with an AUC of 0.903 for the first year. The robustness of this prognostic model was further validated through internal cross-validation and across three external cohorts. Conclusions: The evidence from our research suggests that ARGs could potentially serve as reliable indicators for evaluating immunotherapy effectiveness and forecasting clinical results in patients with LGG.