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Background: Mitochondrial outer membrane permeabilisation (MOMP) plays a pivotal role in cellular death and immune activation. A deeper understanding of the impact of tumour MOMP on immunity will aid in guiding more effective immunotherapeutic strategies. Methods: A comprehensive pan‐cancer dataset comprising 30 cancer‐type transcriptomic cohorts, 20 immunotherapy transcriptomic cohorts and three immunotherapy scRNA‐seq datasets was collected and analysed to determine the influence of tumour MOMP activity on clinical prognosis, immune infiltration and immunotherapy effectiveness. Leveraging 65 scRNA‐Seq datasets, the MOMP signature (MOMP.Sig) was developed to accurately reflect tumour MOMP activity. The clinical predictive value of MOMP.Sig was explored through machine learning models. Integration of the MOMP.Sig model and a pan‐cancer immunotherapy CRISPR screen further investigated potential targets to overcome immunotherapy resistance, which subsequently underwent clinical validation. Results: Our research revealed that elevated MOMP activity reduces mortality risk in cancer patients, drives the formation of an anti‐tumour immune environment and enhances the response to immunotherapy. This finding emphasises the potential clinical application value of MOMP activity in immunotherapy. MOMP.Sig, offering a more precise indicator of tumour cell MOMP activity, demonstrated outstanding predictive efficacy in machine‐learning models. Moreover, with the assistance of the MOMP.Sig model, FOXO1 was identified as a core modulator that promotes immune resistance. Finally, these findings were successfully validated in clinical immunotherapy cohorts of skin cutaneous melanoma and triple‐negative breast cancer patients. Conclusions: This study enhances our understanding of MOMP activity in immune modulation, providing valuable insights for more effective immunotherapeutic strategies across diverse tumours. [ABSTRACT FROM AUTHOR]

This article discusses the use of machine learning methods to predict intraventricular hemorrhage (IVH) in preterm infants. IVH is a common complication of preterm birth and is associated with adverse neurodevelopmental outcomes. The study used electronic medical records and antenatal data to develop a predictive model for IVH. The model achieved good accuracy and stability, with an area under the curve (AUC) of 0.88. The study identified several important risk factors for IVH, including gestational age, birth weight, hepatitis B and C infection, and red cell distribution width. However, the study has limitations, including being a single-center study and potential selection bias. Further research is needed to validate and improve the predictive model. [Extracted from the article]