Background The nuclear factor kappa B (NFκB) regulatory pathways downstream of tumor necrosis factor (TNF) play a critical role in carcinogenesis. However, the widespread influence of NFκB in cells can result in off-target effects, making it a challenging therapeutic target. Ensemble learning is a machine learning technique where multiple models are combined to improve the performance and robustness of the prediction. Accordingly, an ensemble learning model could uncover more precise targets within the NFκB/TNF signaling pathway for cancer therapy. Methods In this study, we trained an ensemble learning model on the transcriptome profiles from 16 cancer types in the TCGA database to identify a robust set of genes that are consistently associated with the NFκB/TNF pathway in cancer. Our model uses cancer patients as features to predict the genes involved in the NFκB/TNF signaling pathway and can be adapted to predict the genes for different cancer types by switching the cancer type of patients. We also performed functional analysis, survival analysis, and a case study of triple-negative breast cancer to demonstrate our model's potential in translational cancer medicine. Results Our model accurately identified genes regulated by NFκB in response to TNF in cancer patients. The downstream analysis showed that the identified genes are typically involved in the canonical NFκB-regulated pathways, particularly in adaptive immunity, anti-apoptosis, and cellular response to cytokine stimuli. These genes were found to have oncogenic properties and detrimental effects on patient survival. Our model also could distinguish patients with a specific cancer subtype, triple-negative breast cancer (TNBC), which is known to be influenced by NFκB-regulated pathways downstream of TNF. Furthermore, a functional module known as mononuclear cell differentiation was identified that accurately predicts TNBC patients and poor short-term survival in non-TNBC patients, providing a potential avenue for developing precision medicine for cancer subtypes. Conclusions In conclusion, our approach enables the discovery of genes in NFκB-regulated pathways in response to TNF and their relevance to carcinogenesis. We successfully categorized these genes into functional groups, providing valuable insights for discovering more precise and targeted cancer therapeutics.

Previous studies suggested that severe epilepsies e.g., developmental and epileptic encephalopathies (DEE) are mainly caused by ultra-rare de novo genetic variants. For milder phenotypes, rare genetic variants could contribute to the phenotype. To determine the importance of rare variants for different epilepsy types, we analyzed a whole-exome sequencing cohort of 9,170 epilepsy-affected individuals and 8,436 controls. Here, we separately analyzed three different groups of epilepsies: severe DEEs, genetic generalized epilepsy (GGE), and non-acquired focal epilepsy (NAFE). We required qualifying rare variants (QRVs) to occur in controls at a minor allele frequency ≤ 1:1,000, to be predicted as deleterious (CADD≥20), and to have an odds ratio in epilepsy cases ≥2. We identified genes enriched with QRVs in DEE (n=21), NAFE (n=72), and GGE (n=32). Enrichment was strongest in NAFE and weakest in DEE. This suggests that rare variants may play a more important role for causality of NAFE than in DEE. Moreover, we found that QRV-carrying genes e.g., HSGP2, FLNA or TNC are involved in structuring the brain extracellular matrix. The present study confirms an involvement of rare variants for NAFE, while in DEE and GGE, the contribution of such variants appears more limited.

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Background Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identification and risk stratification of patients with HF. Methods The first study, with a case-control study design to address data imbalance issue, included 431 subjects with HF exhibiting typical symptoms and a left ventricular ejection fraction (LVEF) less than 45% and 1545 control participants with no history of HF (non-HF). Carotid pressure waveforms were obtained from all the participants using applanation tonometry. The HF score, representing the probability of HF, was derived from a one-dimensional deep neural network (DNN) model trained with characteristics of the normalized carotid pressure waveform. In the second study of HF patients, we constructed a Cox regression model with 83 candidate clinical variables along with the HF score to predict the risk of all-cause mortality with rehospitalization. Results To identify subjects using the HF score, the sensitivity, specificity, accuracy, F1 score, and area under receiver operating characteristic curve were 0.867, 0.851, 0.874, 0.878, and 0.93, respectively, from the 10-fold cross-validation of the DNN, which was better than other machine learning models, including logistic regression, support vector machine, and random forest. With a median follow-up of 5.8 years, the multivariable Cox model using the HF score and other clinical variables outperformed the other HF risk prediction models with concordance index of 0.71, in which only the HF score and five clinical variables were independent significant predictors (p < 0.05), including age, history of percutaneous coronary intervention, concentration of sodium in the emergency room, N-terminal pro-brain natriuretic peptide, and hemoglobin. Conclusions Our study demonstrated the diagnostic and prognostic utility of arterial waveforms in subjects with HF using a DNN model. Pulse wave contains valuable information that can benefit the clinical care of patients with HF.