Super-enhancer-driven IRF2BP2 enhances ALK activity and promotes neuroblastoma cell proliferation.

BACKGROUND: Super-enhancers (SEs) typically govern the expression of critical oncogenes and play a fundamental role in the initiation and progression of cancer. Focusing on genes that are abnormally regulated by SE in cancer may be a new strategy for understanding pathogenesis. In the context of this investigation, we have identified a previously unreported SE-driven gene IRF2BP2 in neuroblastoma (NB). METHODS: The expression and prognostic value of IRF2BP2 were detected in public databases and clinical samples. The effect of IRF2BP2 on NB cell growth and apoptosis was evaluated through in vivo and in vitro functional loss experiments. The molecular mechanism of IRF2BP2 was investigated by the study of chromatin regulatory regions and transcriptome sequencing. RESULTS: The sustained high expression of IRF2BP2 results from the activation of a novel SE established by NB master transcription factors MYCN, MEIS2 and HAND2, and they form a new complex that regulates the gene network associated with the proliferation of NB cell populations. We also observed a significant enrichment of the AP-1 family at the binding sites of IRF2BP2. Remarkably, within NB cells, AP-1 plays a pivotal role in shaping the chromatin accessibility landscape, thereby exposing the binding site for IRF2BP2. This orchestrated action enables AP-1 and IRF2BP2 to collaboratively stimulate the expression of the NB susceptibility gene ALK, thereby upholding the highly proliferative phenotype characteristic of NB. CONCLUSION: Our findings indicate that SE-driven IRF2BP2 can bind to AP-1 to maintain the survival of tumor cells via regulating chromatin accessibility of NB susceptibility gene ALK.

Microvolt T-wave alternans complemented with electrophysiologic study for prediction of ventricular tachyarrhythmias in patients with arrhythmogenic right ventricular cardiomyopathy: a long-term follow-up study.

BACKGROUND: The long-term predicted value of microvolt T-wave alternans (MTWA) for ventricular tachyarrhythmia in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) remains unclear. Our study explored the characteristics of MTWA and its prognostic value when combined with an electrophysiologic study (EPS) in patients with ARVC. METHODS: All patients underwent non-invasive MTWA examination with modified moving average (MMA) analysis and an EPS. A positive event was defined as the first occurrence of sudden cardiac death, documented sustained ventricular tachycardia (VT), ventricular fibrillation, or the administration of appropriate implantable cardioverter defibrillator therapy including shock or anti-tachycardia pacing. RESULTS: Thirty-five patients with ARVC (age 38.6 ±â€Š11.0 years; 28 males) with preserved left ventricular (LV) function were recruited. The maximal TWA value (MaxValt) was 17.0 (11.0-27.0) µV. Sustained VT was induced in 22 patients by the EPS. During a median follow-up of 99.9 ±â€Š7.7 months, 15 patients had positive clinical events. When inducible VT was combined with the MaxValt, the area under the curve improved from 0.739 to 0.797. The receiver operating characteristic curve showed that a MaxValt of 23.5 µV was the optimal cutoff value to identify positive events. The multivariate Cox regression model for survival showed that MTWA (MaxValt, hazard ratio [HR], 1.06; 95% confidence interval [CI], 1.01-1.11; P = 0.01) and inducible VT (HR, 5.98; 95% CI, 1.33-26.8; P = 0.01) independently predicted positive events in patients with ARVC. CONCLUSIONS: MTWA assessment with MMA analysis complemented by an EPS might provide improved prognostic ability in patients with ARVC with preserved LV function during long-term follow-up.

A model driven approach to imbalanced data sampling in medical decision making.

Classification is an important medical decision support function that can be seriously affected by disproportionate class distribution in the training data. In medical decision making, the rate of misclassification and the cost of misclassifying a minority (positive) class as a majority (negative) class are especially high. In this paper, we propose a new model-driven sampling approach to balancing data samples. Most existing data sampling methods produce new data points based on local, deterministic information. Our approach extends the idea of generative sampling to produce new data points based on an induced probabilistic graphical model. We present the motivation and the design of the proposed algorithm, and compare it with two representative imbalanced data sampling approaches on four medical data sets varying in size, imbalance ratio, and dimension. The empirical study helped identify the challenges in imbalanced data problems in medicine, and highlighted the strengths and limitations of the relevant sampling approaches. Performance of the model driven approach is shown to be comparable with existing approaches; potential improvements could be achieved by incorporating domain knowledge.