TiRank prioritizes phenotypic niches in tumor microenvironment for clinical biomarker discovery

作者：Yuxiang Lin, Zening Huang, Ziyan Lin, Yating Lin, Jinsheng Song, L. Luo, Jiayao Chi, Yeyang Zheng, Youxin Gao, Y. S. Lin, Xinyu Li, Chenyu Liang, Lei Zhang, Xinkang Wang, Yuqin Sun, Rongshan Yu, Qiyue Chen, Mengsha Tong · 发表于：Genome Medicine · 年份：2026 · DOI：10.1186/s13073-026-01604-2 · 被引用次数：2 · 研究领域：Single-cell and spatial transcriptomics、Cancer Immunotherapy and Biomarkers、Cell Image Analysis Techniques

BACKGROUND: Tumor microenvironment (TME) plays a crucial role in cancer progression, metastasis, and treatment response. Recent advances in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have provided valuable insights into the cellular diversity and spatial organization of the TME. However, prioritizing clinically relevant cellular subpopulations in spatial contexts using high-dimensional and sparse data remains a challenge. METHODS: We introduce TiRank, a novel framework designed to prioritize clinically relevant spatial niches. TiRank incorporates a relative expression ordering (REO)-transformation module to mitigate systematic biases across modalities and utilizes a multitask transfer learning framework to align scRNA-seq, ST, and bulk transcriptomes into a unified embedding space. We benchmarked TiRank using multiple public datasets and pan-cancer clinical cohorts, demonstrating its capability to identify phenotypic cell subpopulations and spatial niches. RESULTS: By integrating scRNA-seq, ST, and bulk transcriptomics with clinical phenotypes, TiRank demonstrates high accuracy in identifying drug-sensitive cells and clinically relevant spatial niches across various cancer types. As a case study, we applied TiRank to gastric cancer (GC) to prioritize spatial niches associated with patient outcomes. In our clinical cohort, TiRank successfully revealed a distinct spatial niche at the tumor boundary, characterized by an enrichment of cancer-associate...