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Biology subjects

Hong, Y.-C.

Publications and source records attributed to Hong, Y.-C..

2 recordsLinked to original sources

Distinct neuroendocrine subtypes predict treatment-induced neuroendocrine prostate cancer prognosis and provide clues for personalized treatment

Background and ObjectiveSecond-generation hormonal therapy inhibits castration-resistant prostate cancer (CRPC), but the tumor eventually recurs as neuroendocrine prostate cancer (NEPC) and turns lethal. Differentiating lineage plasticity that contributed to distinct NEPC subtypes aids in advancing treatments, particularly the recent FDA-approved 177Lu-PSMA-617 radiopharmaceutical therapy. MethodsWe integrated single-cell RNA sequencing data from fresh human CRPC cases. This comprehensive approach allowed us to identify distinct NEPC subpopulations and their respective lineage with high confidence. Key Findings and LimitationsWe uncovered N-Myc and REST as key transcription factors driving distinct neuroendocrine subtypes among 5,797 neuroendocrine-like epithelial cells in CRPC: a REST-dependent subtype (NE I), an N-Myc-dependent subtype (NE II), and a combined N-Myc/REST subtype (NE I+II). These subtypes were validated using multiplex immunofluorescence staining. Trajectory analysis of single-cell RNA sequencing data, along with multi-omics time course analysis of publicly available transcriptomic data recapitulated N-Myc and REST lineages. Additionally, we observed PSMA loss in N-Myc lineage NEPC and identified STMN1 as a biomarker for PSMA-negative subtype. We validated the prognostic value of STMN1 using the TCGA dataset and 60 in-house CRPC tissues. Given that surgery is rarely performed in advanced CRPC, leading to limited sample availability, further validation in larger cohorts is needed. Conclusions and Clinical ImplicationsAdeno-to-neuroendocrine lineage transition in prostate cancer leads to resistance to new therapies. The lethal NEPC phenotype should be revealed earlier in the disease course of patients with CRPC, providing crucial clues for personalized precision medicine.

cancer biology↗

Predicting Lung Cancer in Korean Never-Smokers with Polygenic Risk Scores

In the last few decades, genome-wide association studies (GWAS) with more than 10,000 subjects have identified several loci associated with lung cancer. Hence, recently, genetic data have been used to develop novel risk prediction tools for cancer. The present study aimed to establish a lung cancer prediction model for Korean never-smokers using polygenic risk scores (PRSs). PRSs were calculated using a thresholding-pruning-based approach based on 11 genome-wide significant single nucleotide polymorphisms (SNPs). Overall, the odds ratios tended to increase as PRSs were larger, with the odds ratio of the top 5% PRSs being 1.71 (95% confidence interval: 1.31-2.23), and the area under the curve (AUC) of the prediction model being of 0.76 (95% confidence interval: 0.747-0.774). The receiver operating characteristic (ROC) curves of the prediction model with and without PRSs as covariates were compared using DeLongs test, and a significant difference was observed. Our results suggest that PRSs can be valuable tools for predicting the risk of lung cancer.

bioinformatics↗