Biomedical subjects

Woong-Yang Park

Publications and source records attributed to Woong-Yang Park.

3 recordsLinked to original sources

PubMed2025

Identification of GREM-1 and GAS6 as Specific Biomarkers for Cancer-Associated Fibroblasts Derived from Patients with Non-Small-Cell Lung Cancer.

Background/Objectives: Cancer-associated fibroblasts (CAFs) play a pivotal role in the tumor microenvironment. We conducted an analysis using RNA sequencing to identify specific markers for CAFs compared to normal fibroblasts (NFs) in non-small-cell carcinoma (NSCLC). Methods: CAFs and NFs were isolated and cultured from tumor tissues (primary tumor or metastatic lymph nodes) and matched non-tumor tissues, respectively. Bulk RNA sequencing was conducted on isolated CAFs and normal fibroblast NFs. Differential expressions, gene set enrichment, and CAF subpopulation prediction analyses were performed. Results: During the study period, 27 CAFs and 12 NFs were isolated and cultured from tumor and non-tumor tissues in patients with treatment-naïve NSCLC. Among them, 22 CAFs and 11 NFs were included in the RNA sequencing analysis. The 22 CAF samples consisted of 12 adenocarcinomas and 10 squamous cell carcinomas (SqCC), with 16 samples from the lungs and 6 samples from the lymph nodes. Notably, COL11A1, GREM1, CD36, and GAS6 showed a higher expression in CAFs than in NFs, whereas TNC and CXCL2 were more abundantly expressed in NFs. CD36 levels were elevated in CAFs from lymph nodes (LN-CAFs) compared with those from lung specimens (Lung-CAFs) and NFs. COL11A1 levels in Lung-CAFs surpassed those in LN-CAFs and NFs. Both GREM1 and GAS6 showed a strong expression in Lung-CAFs and LN-CAFs relative to NFs. CAFs exhibited features of the myofibroblast CAF subpopulation, whereas NFs displayed traits of the antigen-presenting CAF subtype. In the co-culture model of CAFs and THP-1 cells, the knockdown of GREM1 or GAS6 in CAFs significantly decreased the M2 marker expression in macrophages. Conclusions: In NSCLC, GREM1 and GAS6 can be valuable diagnostic targets for CAFs from primary tumors and metastatic sites; they warrant further study.

cancer-associated fibroblast↗

PubMed2025

Paradoxical Effect of Myosteatosis on the Immune Checkpoint Inhibitor Response in Metastatic Renal Cell Carcinoma.

BACKGROUND: Treatment for metastatic renal cell carcinoma (mRCC) has shifted from tyrosine kinase inhibitor (TKI) therapy to immune checkpoint inhibitor (ICI)-based therapy, improving outcomes but with variable individual responses. This study investigated the prognostic implications of pretreatment low skeletal muscle mass (LSMM) and myosteatosis in patients with mRCC undergoing first-line ICI-based therapies, comparing outcomes between PD-1 inhibitor + CTLA-4 inhibitor and PD-1 inhibitor + TKI, incorporating single-cell RNA sequencing. METHODS: A retrospective analysis was performed on 90 patients with mRCC treated with ICI-based therapies between November 2019 and March 2023. Patients were grouped based on whether they received PD-1 inhibitor + CTLA-4 inhibitor or PD-1 inhibitor + TKI combinations. LSMM was defined as skeletal muscle index below 40.8 cm2/m2 for men and 34.9 cm2/m2 for women. Myosteatosis was defined using skeletal muscle density, with cut-off values < 41 HU for BMI < 25 kg/m2 and < 33 HU for BMI ≥ 25 kg/m2. Progression-free survival (PFS) and overall survival (OS) were compared using Kaplan-Meier curves and multivariable models. Single-cell RNA sequencing was performed on pretreatment samples to compare the immune microenvironment between patients with and without myosteatosis. RESULTS: The study cohort (26.7% female; median age: 60.5 years) included 59 patients (65.6%) treated with PD-1 inhibitor + CTLA-4 inhibitor and 31 patients (34.4%) treated with PD-1 inhibitor + TKI. LSMM was present in 18.9% of patients, and myosteatosis in 41.1%, with comparable proportions across groups. During follow-up, 29 patients (32.2%) died: 16 in the PD-1 inhibitor + CTLA-4 inhibitor group and 13 in the PD-1 inhibitor + TKI group. The overall 1-year mortality rate was 22.2%, and PFS rate was 53.3%. Myosteatosis predicted poor OS (HR, 5.389; p = 0.008) and PFS (HR, 2.930; p = 0.022) in the PD-1 inhibitor + TKI group but was protective for PFS (HR, 0.461; p = 0.049) in the PD-1 inhibitor + CTLA-4 inhibitor group. LSMM did not significantly affect outcomes in either group. Single-cell RNA sequencing revealed higher CTLA-4 expression in regulatory T cells and more effector memory CD8+ T cells in patients with myosteatosis, whereas patients without myosteatosis had more anti-tumoural non-classical monocytes. CONCLUSIONS: Myosteatosis negatively impacts OS and PFS in patients with mRCC treated with PD-1 inhibitor + TKI therapy but is protective for PFS in those treated with PD-1 inhibitor + CTLA-4 inhibitor therapy. Altered checkpoint expression and immune cell composition associated with myosteatosis may contribute to these differential responses.

Humans↗

PubMed2024

Quantification of escape from X chromosome inactivation with single-cell omics data reveals heterogeneity across cell types and tissues.

Several X-linked genes escape from X chromosome inactivation (XCI), while differences in escape across cell types and tissues are still poorly characterized. Here, we developed scLinaX for directly quantifying relative gene expression from the inactivated X chromosome with droplet-based single-cell RNA sequencing (scRNA-seq) data. The scLinaX and differentially expressed gene analyses with large-scale blood scRNA-seq datasets consistently identified the stronger escape in lymphocytes than in myeloid cells. An extension of scLinaX to a 10x multiome dataset (scLinaX-multi) suggested a stronger escape in lymphocytes than in myeloid cells at the chromatin-accessibility level. The scLinaX analysis of human multiple-organ scRNA-seq datasets also identified the relatively strong degree of escape from XCI in lymphoid tissues and lymphocytes. Finally, effect size comparisons of genome-wide association studies between sexes suggested the underlying impact of escape on the genotype-phenotype association. Overall, scLinaX and the quantified escape catalog identified the heterogeneity of escape across cell types and tissues.

X Chromosome Inactivation↗