Communications Medicine

OCT 7, 2026 · JOURNAL ARTICLE

Co-targeting CDK2 and CDK9 inhibits MYC oncogenic programs and has anti-tumor effects in CIC-rearranged sarcoma

Communications Medicine

Abstract Background CIC -rearranged sarcoma is a rare and highly aggressive cancer that primarily affects children and young adults and has few effective treatment options. Previou

OCT 5, 2026 · JOURNAL ARTICLE

Glucose and lipid metabolism indicators associate with metabolic syndrome risk prediction in adults with hypertension

Communications Medicine

Hypertensive adults are at high risk of metabolic syndrome (MetS), yet accurate risk prediction tools for this group are scarce. We prospectively followed 130,489 Chinese hypertens

OCT 5, 2026 · JOURNAL ARTICLE

AI expert collaborative generative model for predicting pathologic response to neoadjuvant chemoimmunotherapy in NSCLC

Communications Medicine

Accurate prediction of pathologic response to neoadjuvant chemo-immunotherapy for NSCLC at baseline is important for personalized perioperative treatment selection. However, as a n

SEP 19, 2026 · JOURNAL ARTICLE

Sequencing analysis reveals evidence of immune activation in advanced HER2 negative breast cancer responders treated with the combination of entinostat, nivolumab, and ipilimumab

Communications Medicine

Advanced HER2-negative breast cancer remains difficult to treat, and mechanisms associated with response to immunotherapy-based combinations are not fully understood. This study ai

AUG 14, 2026 · JOURNAL ARTICLE

Data-driven phenotypic clustering of sleep-disordered breathing and long-term cardiovascular risk

Communications Medicine

Rigorous prospective cohort study with adjudicated hard cardiovascular endpoints, clear phenotype-based risk stratification with internal and external validation, showing clinically meaningful hazard ratios that outperform standard AHI scoring.

AUG 4, 2026 · JOURNAL ARTICLE

Polygenic risk-informed white matter integrity improves deep learning-based prediction of youth depression

Communications Medicine

A deep learning model combining polygenic risk scores and neuroimaging achieves moderate discrimination (AUC 0.61–0.67) for predicting youth depression cross-sectionally and at 2-year follow-up, with cross-ethnic validation, but lacks comparison to clinical gold standards and requires external prospective validation.