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Seon-Kyu Kim

Publications and source records attributed to Seon-Kyu Kim.

3 recordsLinked to original sources
PubMed2026

Quantum computing-assisted validation of a conserved macrophage suppression module shared by ASFV and PEDV.

BACKGROUND: African swine fever virus (ASFV) and porcine epidemic diarrhea virus (PEDV) differ in viral biology and cellular tropism, yet both pathogens suppress macrophage-mediated immune responses in pigs. OBJECTIVE: To identify a conserved macrophage suppression module shared by ASFV and PEDV and evaluate quantum computing as an independent framework for biological network validation. METHODS: Integrated analysis of publicly available GEO datasets (GSE231435 for ASFV and GSE306895) identified 471 shared downregulated genes. A network- and multi-omics-informed 20-gene core was selected and encoded as a 20-qubit modularity-based Quadratic Unconstrained Binary Optimization (QUBO) problem. Community detection was benchmarked using the Quantum Approximate Optimization Algorithm (QAOA) on both the IBM Quantum Aer simulator and the 156-qubit IBM Fez (Heron r2) quantum processor and compared with brute-force enumeration and simulated annealing. RESULTS: A conserved macrophage suppression module shared by ASFV and PEDV was identified. For the STRING protein-protein interaction network, QAOA at circuit depth p = 3 reproduced the brute-force optimum with an approximation ratio of 1.000. In contrast, performance progressively declined in the denser co-expression network with increasing circuit depth, consistent with noise accumulation under current Noisy Intermediate-Scale Quantum (NISQ) conditions. Multi-run consensus analysis identified stable hub genes, including MMP9 and SLA-DOA, as well as genes exhibiting variable community assignments. CONCLUSION: These findings reveal a conserved macrophage suppression module shared between ASFV and PEDV and demonstrate that quantum computing can serve as an independent validation framework for biologically meaningful host-response networks. Network topology emerged as a key determinant of QAOA performance on real NISQ hardware.

Animals↗
PubMed2026

Transcriptome-based high-frequency recurrence index predicts frequent recurrence in non-muscle-invasive bladder cancer after Bacillus Calmette-Guérin therapy.

BACKGROUND: High-frequency recurrence (HfR,&#x2009;&#x2265;&#x2009;2 recurrences) in non-muscle-invasive bladder cancer (NMIBC) poses a significant clinical burden. Current risk models, such as the European Organization for Research and Treatment of Cancer (EORTC), the European Association of Urology (EAU), and the UROMOL classification, offer limited predictive accuracy for identifying patients at risk for frequent recurrence despite appropriate treatment. METHODS: A 75-gene high-frequency recurrence index (HfRI) was constructed by selecting recurrence-associated genes using differential expression and Cox regression analyses. The HfRI was computed as a weighted sum of normalized gene expression values. The model was trained on a discovery cohort and validated in multiple cohorts (n&#x2009;=&#x2009;1379) using machine-learning approaches. Clinical relevance was assessed using recurrence-free survival (RFS) and Cox models, and predictive performance was compared with that of the EORTC, EAU, and UROMOL classifications using the area under the curve (AUC) and the concordance index (c-index). RESULTS: The HfRI robustly stratified patients into high-risk and low-risk groups across six independent NMIBC cohorts. Patients classified as HfRI-high had a significantly greater likelihood of experiencing&#x2009;&#x2265;&#x2009;2 recurrences (&#x3c7;2, p&#x2009;=&#x2009;0.001) and showed markedly reduced RFS (log-rank test, p&#x2009;<&#x2009;0.001). The adverse prognostic effect of the HfRI persisted even among patients treated with BCG therapy (log-rank test, p&#x2009;=&#x2009;0.02). Multivariate analysis revealed that the HfRI was an independent predictor of HfR (HR&#x2009;=&#x2009;2.82, 95% CI&#x2009;=&#x2009;1.89-4.20, p&#x2009;<&#x2009;0.001). Compared with established clinical risk classifiers, the HfRI demonstrated superior predictive performance (AUC&#x2009;=&#x2009;0.736, c-index&#x2009;=&#x2009;0.673) in terms of the EORTC (AUC&#x2009;=&#x2009;0.594), EAU (AUC&#x2009;=&#x2009;0.557) risk groups, and UROMOL2021 (AUC&#x2009;=&#x2009;0.596) classification. Pathway analysis revealed that HfRI-high tumors were characterized by upregulation of cell cycle progression and DNA replication pathways, accompanied by suppression of immune signaling pathways. These biological features provide a mechanistic explanation for the reduced responsiveness to intravesical BCG therapy, underscoring the role of HfRI not only as a predictor of recurrence risk but also as a biomarker capable of identifying patients unlikely to benefit from standard BCG treatment. CONCLUSIONS: HfRI represents a robust, transcriptome-based tool for predicting frequent recurrence in NMIBC patients. The HfRI supports earlier identification of patients at risk of high-frequency recurrence, thereby supporting personalized treatment strategies.

Humans↗
PubMed2025

Precise progerin targeting using RfxCas13d: A therapeutic avenue for Hutchinson-Gilford progeria syndrome.

Hutchinson-Gilford progeria syndrome (HGPS), an extremely rare progressive genetic disorder, is caused by a point mutation in LMNA that induces progerin production, which disrupts cellular function and triggers premature aging and mortality. Despite extensive efforts, HPGS remains incurable. We successfully implemented a strategy using RfxCas13d to selectively target progerin mRNA at specific junction regions, without unintended cleavage and reduce its expression. This technique discriminated between normal lamin A and progerin, thus providing a safe and targeted therapeutic avenue to treat HGPS. Our approach effectively restored aberrant gene expression and progerin-induced cellular phenotypes, including senescence, mitochondrial dysfunction, and DNA damage in cells with HGPS and LMNAG608G/G608G mice. Notably, LMNAG608G/G608G mice exhibited improved progeroid phenotypes, suggesting a potential therapeutic application of this approach for other diseases resulting from abnormal RNA splicing.

Progeria↗