Biomedical subjects

Zijun Wang

Publications and source records attributed to Zijun Wang.

2 recordsLinked to original sources

PubMed2026

KEAP1 loss-of-function suppresses immunogenic ferroptosis and limits PD-1 blockade efficacy through an NRF2-FSP1 pathway.

Loss-of-function mutations in Kelch-like ECH-associated protein 1 (KEAP1) frequently occur in lung adenocarcinoma and are associated with poor prognosis and limited benefit from immunotherapy. However, the mechanisms linking KEAP1 deficiency to immune evasion remain elusive. We combined clinical data analysis, in vivo tumor models, and in vitro co-culture systems to investigate how KEAP1 deficiency shapes dendritic cell (DC) biology and response to PD-1 blockade. Ferroptosis induction assays, damage-associated molecular patterns (DAMPs) quantification, cytokine profiling, and mechanistic interrogation of the FSP1-CoQ10 axis were performed to delineate pathways.KEAP1 mutations correlated with poor response to PD-1 blockade and reduced DC infiltration. In mice, KEAP1-deficient tumors exhibited accelerated growth and reduced DC and CD8+ T-cell infiltration, consistent with an immune-cold phenotype. Mechanistically, KEAP1 loss impaired DC function in vitro, as evidenced by reduced maturation, phagocytosis, and naïve CD8+ T-cell priming capacity. This defect was linked to two mechanisms. First, KEAP1-deficient tumor cells resisted ferroptosis and failed to release immunogenic DAMPs, including extracellular ATP, HMGB1, and calreticulin. Second, KEAP1 deficiency reprogrammed the cytokine secretion profile, with downregulation of CCL2, IL-6, CXCL1, and CXCL2, thereby diminishing DC recruitment and inflammatory signaling. Notably, inhibition of the FSP1-CoQ10 antioxidant axis restored ferroptosis-associated immunogenic cell death. Our study identifies KEAP1 deficiency as a driver of immune-cold tumor microenvironments and resistance to PD-1 blockade, acting through impaired ferroptosis-induced immunogenic cell death and disrupted DC function. Genetic FSP1 deletion restored ferroptosis-associated immunogenicity and DC activation in KEAP1-deficient cells, supporting FSP1 as a potential therapeutic target for further in vivo evaluation.

DAMPs↗

PubMed2025

Multimodal deep learning for immunotherapy response prediction and biomarker discovery in non-small cell lung cancer.

OBJECTIVE: Immunotherapy has emerged as a promising treatment for advanced non-small cell lung cancer (NSCLC), but accurately predicting which patients will benefit from it remains a major clinical challenge. To address this, we aim to develop a novel multimodal method, DeepAFM, that integrates histopathology, genomic features, and clinical information to predict patient responses to anti-PD-(L)1 immunotherapy. MATERIALS AND METHODS: A total of 93 patients with advanced NSCLC were included in this study. Histopathological whole-slide images were processed using a self-supervised VQVAE2 for representation learning. PCA and K-means clustering were then applied for dimensionality reduction and feature grouping. Key regions of interest were visualized through permutation importance evaluation and color-coding techniques. The extracted histopathological features, along with genomic alterations and clinical variables, were integrated into the DeepAFM multimodal prediction model. RESULTS: The DeepAFM achieved a high predictive performance with an area under the curve (AUC) of 0.77 (95% confidence interval: 0.69-1.00). Attention-based heatmaps revealed that the model could identify critical pathological patterns, genomic mutations, and clinical indicators associated with patient responses to immunotherapy. DISCUSSION: The integration of multimodal data enabled the model to capture complex interactions among pathology, genomics, and clinical characteristics, enhancing the interpretability and predictive power of immunotherapy response prediction. The visualization techniques facilitated the identification of biologically meaningful features and potential biomarkers. CONCLUSION: This study demonstrates the effectiveness of the DeepAFM in predicting responses to immunotherapy in advanced NSCLC. The approach not only improves prediction accuracy but also provides valuable insights for personalized treatment strategies and biomarker discovery.

Humans↗