Kwon Joong Na

This study uses spatial transcriptomics to compare the tumor-stroma border in HBV-related and NBNC HCC, identifying conserved desmoplastic niches and etiology-specific immune signaling differences.

This study identifies spatial tumor microenvironment features of chemoradiotherapy-resistant residual esophageal squamous cell carcinoma using spatial transcriptomics and single-cell analysis, revealing SPP1-associated macrophage programs and microvascular rarefaction as key factors.

This study uses spatial transcriptomics to compare immune and metabolic differences between treatment-responsive and resistant non-small cell lung cancer tumors after neoadjuvant chemoimmunotherapy.

This study uses spatial transcriptomics to identify SPP1+ macrophages as key contributors to CCRT resistance in hypopharyngeal SCC by modulating the tumor microenvironment.

STopover analyzes spatial transcriptomics data to identify cell-cell interactions and colocalization patterns in the tumor microenvironment.

The paper analyzes unmapped RNA-seq data from cancer spatial transcriptomes to study microbial RNA distribution and function in cancer tissues.

This study uses spatial transcriptomics to analyze cancer-associated fibroblasts in lung squamous cell carcinoma, linking their spatial organization to tumor progression and patient outcomes.

CellDART adapts neural networks to infer cell type spatial distribution from single-cell and spatial transcriptomic data, showing effectiveness in human and mouse tissues.

The paper identifies potential imaging and therapeutic targets for severe COVID-19 inflammation by analyzing single-cell RNA-seq data from patient samples.

This study develops a 3D culture model of human alveolar type 2 cells to study SARS-CoV-2 infection and reveals rapid viral replication and immune response.

The paper develops a deep learning-based risk stratification model for lung adenocarcinoma using gene coexpression networks and validates its prognostic value across multiple datasets.