👤 Su Cheong Yeom

Keratin 17 (K17) is a stress-responsive intermediate filament protein that is upregulated in chronic skin diseases and in several carcinomas. We previously showed that K17 is induced in epidermal keratinocytes following exposure to DNA-damaging agents, promoting keratinocyte survival and chemically induced papilloma formation in mouse skin. Molecularly, K17 is recruited to the nucleus, where it impacts nuclear architecture, gene expression, and the DNA damage response (DDR). Here, we report on efforts to delineate K17-dependent processes during DDR by focusing on its interacting partners. Using mass spectrometry, we identified a network of K17-interacting Rho GTPase signaling proteins, including Rac1 and its activator Dock7. Biochemically, we confirmed that Rac1 and K17 interact directly in vitro and in A431 tumor keratinocytes, both at baseline and after ionizing radiation. We show that Show less

Pediatric Low-Grade Glioma (pLGG) is the most common pediatric brain tumor, and radiomics-based machine learning (ML) models have shown promise in identifying BRAF fusion and BRAF p.V600E mutation. This bicentric retrospective study included 495 children diagnosed between 1999 and 2023. The local hospital dataset comprised Magnetic Resonance Imaging (MRI) scans of patients with BRAF fusion (n = 190), BRAF p.V600E mutation (n = 95), FGFR1 (n = 25), and other molecular subtypes (n = 144), while an external dataset included BRAF fusion (n = 32) and BRAF p.V600E mutation (n = 9) cases. Radiomics features were extracted from Fluid-Attenuated Inversion Recovery images, and Random Forest classifiers were trained using Monte Carlo data splits and leave-one-out validation. The best-performing model achieved an average one-vs-the-rest area under receiver operating characteristic curve of 0.819 (95% confidence interval [0.791, 0.848]). This study highlights the potential of radiomics-based ML models for molecular subtype differentiation in pLGG, with per-patient predictions enabling outlier identification and subgroup performance evaluation. Show less

Recently, clinical trials of adeno-associated virus-mediated replacement therapy have suggested long-term therapeutic effects for several genetic diseases of the liver, including hemophilia. However, there remain concerns regarding decreased therapeutic effects when the liver is regenerated or when physiological proliferation occurs. Although genome editing using the clustered regularly interspaced short palindromic repeats/Cas9 system provides an opportunity to solve this problem, low knock-in efficiency may limit its application for therapeutically relevant expression. Here, we identified a novel gene, Show less

The plasma proteome of 51 non-metastatic breast cancer patients receiving neoadjuvant chemotherapy (NCT) was prospectively analyzed by high-resolution mass spectrometry coupled with nano-flow liquid chromatography using blood drawn at the time of diagnosis. Plasma proteins were identified as potential biomarkers, and their correlation with clinicopathological variables and survival outcomes was analyzed. Of 51 patients, 20 (39.2%) were HR+/HER2-, five (9.8%) were HR+/HER2+, five (9.8%) were HER2+, and 21 (41.2%) were triple-negative subtype. During a median follow-up of 52.0 months, there were 15 relapses (29.4%) and eight deaths (15.7%). Four potential biomarkers were identified among differentially expressed proteins: APOC3 had higher plasma concentrations in the pathological complete response (pCR) group, whereas MBL2, ENG, and P4HB were higher in the non-pCR group. Proteins statistically significantly associated with survival and capable of differentiating low- and high-risk groups were MBL2 and P4HB for disease-free survival, P4HB for overall survival, and MBL2 for distant metastasis-free survival (DMFS). In the multivariate analysis, only MBL2 was a consistent risk factor for DMFS (HR: 9.65, 95% CI 2.10-44.31). The results demonstrate that the proteomes from non-invasive sampling correlate with pCR and survival in breast cancer patients receiving NCT. Further investigation may clarify the role of these proteins in predicting prognosis and thus their therapeutic potential for the prevention of recurrence. Show less

Renal dysfunction, a major complication of type 2 diabetes, can be predicted from estimated glomerular filtration rate (eGFR) and protein markers such as albumin concentration. Urinary protein biomarkers may be used to monitor or predict patient status. Urine samples were selected from patients enrolled in the retrospective diabetic kidney disease (DKD) study, including 35 with good and 19 with poor prognosis. After removal of albumin and immunoglobulin, the remaining proteins were reduced, alkylated, digested, and analyzed qualitatively and quantitatively with a nano LC-MS platform. Each protein was identified, and its concentration normalized to that of creatinine. A prognostic model of DKD was formulated based on the adjusted quantities of each protein in the two groups. Of 1296 proteins identified in the 54 urine samples, 66 were differentially abundant in the two groups (area under the curve (AUC): Show less