JIHYUN KIM

Dr. JIHYUN KIM


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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11 July 2023 in Cancers
Verified
Journal Article
Open access
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JKJIHYUN KIM
JIHYUN KIM
SKSunshin Kim
Sunshin Kim
SPSeog-Yun Park
Seog-Yun Park
GLGeon Kook Lee
Geon Kook Lee
KLKun Young Lim
Kun Young Lim
JHJi-Youn Han
Ji-Youn Han
Although molecular subtypes of small-cell lung cancer (SCLC) have been proposed, their clinical relevance and therapeutic implications are not fully understood. Thus, we aimed to refine molecular subtypes and to uncover therapeutic targets. We classified the subtypes based on gene expression (n = 81) and validated them in our samples (n = 87). Non-SCLC samples were compared with SCLC subtypes to identify the early development stage of SCLC. Single-cell transcriptome analysis was applied to dissect the TME of bulk samples. Finally, to overcome platinum resistance, we performed drug screening of patient-derived cells and cell lines. Four subtypes were identified: the ASCL1+ (SCLC-A) subtype identified as TP53/RB-mutated non-SCLC representing the early development stage of SCLC; the immune activation (SCLC-I) subtype, showing high CD8+/PD-L1+ T-cell infiltration and endothelial-to-mesenchymal transition (EndMT); the NEUROD1 (SCLC-N) subtype, which showed neurotransmission process; and the POU2F3+ (SCLC-P) subtype with epithelial-to-mesenchymal transition (EMT). EndMT was associated with the worst prognosis. While SCLC-A/N exhibited platinum sensitivity, the EndMT signal of SCLC-I conferred platinum resistance. A BET inhibitor suppressed the aggressive angiogenesis phenotype of SCLC-I. We revealed that EndMT development contributed to a poor outcome in SCLC-I. Moreover, heterogenous TME development facilitated platinum resistance. BET inhibitors are novel candidates for overcoming platinum resistance.

30 December 2025 in Scientific Reports
Verified
Journal Article
Open access
PDF file
HJHa-Eun Jeong
Ha-Eun Jeong
KKKyung-Hyun Kim
Kyung-Hyun Kim
YKYoungjun Kim
Youngjun Kim
HYHo-Yeong Yu
Ho-Yeong Yu
DSDong Mun Shin
Dong Mun Shin
JKJIHYUN KIM
JIHYUN KIM
Whole-genome sequencing technologies have advanced, leading to an increase in uncharacterized variants with unknown functions. This study focuses on characterizing cardiomyopathy-associated genes harboring theses rare variants and uncovering their cellular contexts using single-cell transcriptomics. We investigated whole genome sequencing on 245 unrelated Korean patients with either dilated (48.2%) and hypertrophic (47.8%) cardiomyopathy. Rare variants were identified and subjected to burden analysis to detect potentially causative candidates. To understand their biological impact, we profiled single-cell transcriptomes from an independent dataset, highlighting cell populations and interactions relevant to disease pathogenesis. A total of 3584 rare variants were discovered, including 50 pathogenic or likely pathogenic variants in 41 patients. Among the remaining 3534 variants of uncertain significance (VUS), burden analysis revealed 144 gene signatures significantly enriched in pathways related to cardiac muscle tissue development, heart morphogenesis, and endocrine system development (FDR < 0.003). These gene signatures were strongly correlated with cardiomyopathy − associated phenotypes, including HCM (HP:0001639) and DCM (HP:0001644) from the Human Phenotype Ontology (HPO) database. Single-cell transcriptomic analysis of 11,664 heart tissue cells demonstrated that the expression of these gene is influenced by cellular heterogeneity, which may contribute to disease manifestation. Further analysis identified five major cell types in heart tissue, revealing dynamic interactions between cardiomyocytes and endothelial cells. Rare genetic variants, including VUS, are closely linked to cardiac developmental processes and the sarcolemma integrity in cardiomyopathy. Our findings underscore the critical involvement of both cardiomyocytes and non-cardiomyocytes in disease etiology, highlighting the utility of integrating genomic and single-cell data for mechanistic insights.