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Ji-Yong Sung

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Open access Jul 2026

Cancer-associated fibroblast subtype signature gene predicts survival and immunotherapy response in sarcoma

Background Sarcomas show heterogeneous responses to immune-checkpoint blockade (ICB), and cancer-associated fibroblasts (CAFs) are considered to shape the tumor immune microenvironment, yet CAF programs that predict ICB outcomes in sarcoma remain unclear. Methods We investigated the interaction between different cell types in the sarcoma tumor microenvironment and their effects on immune-checkpoint blockade response, with a focus on identifying signature genes and molecular mechanisms that distinguish tumor-promoting from tumor-suppressive CAFs at the single-cell level. We analyzed single-cell data from two different sarcoma cohorts, transcriptome profiles of 206 sarcoma patients recruited from The Cancer Genome Atlas, and predicted immune-checkpoint blockade (ICB) response data inferred using the TIDE algorithm from 64 TCGA sarcoma patients. Results We found 134 stem-like CAF-related signature genes in the recurrent group and eight signature genes in the metastasis group, defining three CAF subtypes (myofibroblastic CAF, antigen-presenting CAF, and inflammatory CAF). SIG134 and SIG8 were associated with TIDE-inferred ICB response in subtype-specific analyses: SIG134 in STLMS and ULMS, and SIG8 in MFS, STLMS, and ULMS. In addition, the MDK-NCL ligand-receptor signal transduction pathway was linked to the infCAF subtype and myoCAFs in metastatic sarcoma. Furthermore, SIG4 (MDK, SDC2, LRP1, and NCL) was highly expressed in inflammatory CAFs. Conclusions Single-cell-derived CAF signatures may reflect sarcoma subtype-specific stromal programs associated with predicted ICB response and clinical outcome. SIG4 is proposed as a candidate prognostic signature that warrants further validation.

Ji-Yong Sung, Jin-Hong Kim, Yi-Jun Kim · 0 citations
Review Open access Jul 2026

Advancing genomics and integration of multi-omics for precision oncology using quantum machine learning.

This Perspective highlights how quantum algorithms-such as Quantum Support Vector Machines, Quantum Principal Component Analysis, and quantum generative models-could enhance key tasks in precision oncology, including multi-omics integration, spatial transcriptomics, and neoantigen prediction.

Ji-Yong Sung, Jae-Ho Cheong · 0 citations