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2025A single-institution demographic study of pathologically proven kidney disease in South Korea over the last 33 years
Hyejin Noh, Ji Yeon Kim, Yeong Jin Choi
IF 3 (2025)
Journal of Pathology and Translational Medicine
BACKGROUND: To date, epidemiological studies on the entire spectrum of kidney disease based on pathology have been rarely reported. METHODS: A retrospective study was conducted on patients diagnosed with kidney disease at Seoul St. Mary's Hospital between 1991 and 2023. RESULTS: Among 7,803 patients with native kidney disease, glomerular disease (70.3%) was the most common, followed by tubulointerstitial (15.1%) and vascular disease (8.8%). In kidney biopsy, glomerular disease (77.8%) showed the highest frequency, particularly in those under 20s (95.6%) (p = .013). Primary glomerulonephritis (GN) (72.8%) was the predominant glomerular disease, with IgA nephropathy (IgAN) (47.3%) being the most common one. Tubulointerstitial and vascular diseases increased with age, showing the highest prevalence in those over 60 years (p = .008 and p = .032, respectively). Glomerular disease was diagnosed at a younger age (39.7 ± 16.7 years) than tubulointerstitial (49.1 ± 16.2) and vascular (48.1 ± 15.3) diseases (p < .001). When glomerular diseases were classified morphologically, proliferative GN (57.9%) was the most common, followed by non-proliferative (39.6%) and sclerosing (1.6%). When classified by etiology, primary GN accounted for the most (72.8%), followed by secondary (19.3%) and hereditary GN (5.7%). In nephrectomy, tubulointerstitial disease (64.6%) was the most common. Those with a tubulointerstitial disease had a higher mean age than those with a glomerular disease (p < .001). In cases where nephrectomy was performed for glomerular diseases, IgAN (34.1%) was the most common diagnosis. CONCLUSIONS: Kidney disease has been increasing in South Korea for 33 years. Glomerular disease was the most common across all age groups, tubulointerstitial and vascular diseases increased over 60 years.
https://doi.org/10.4132/jptm.2025.06.18
Kidney disease
Disease
Kidney
Epidemiology
Vascular disease
2
review
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인용수 4
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2025Professional biobanking education in Korea based on ISO 20387
Jong Ok Kim, Chungyeul Kim, Sang‐Yong Song, Eunah Shin, Ji-Sun Song, Mee Sook Roh, Dong-Chul Kim, Han Kyeom Kim, Joon Mee Kim, Yeong Jin Choi
IF 3 (2025)
Journal of Pathology and Translational Medicine
To ensure high-quality bioresources and standardize biobanks, there is an urgent need to develop and disseminate educational training programs in accordance with ISO 20387, which was developed in 2018. The standardization of biobank education programs is also required to train biobank experts. The subdivision of categories and levels of education is necessary for jobs such as operations manager (bank president), quality manager, practitioner, and administrator. Essential training includes programs tailored for beginner, intermediate, and advanced practitioners, along with customized training for operations managers. We reviewed and studied ways to develop an appropriate range of education and training opportunities for standard biobanking education and the training of experts based on KS J ISO 20387. We propose more systematic and professional biobanking training programs in accordance with ISO 20387, in addition to the certification programs of the National Biobank and the Korean Laboratory Accreditation System. We suggest various training programs appropriate to a student's affiliation or work, such as university biobanking specialized education, short-term job training at unit biobanks, biobank research institute symposiums by the Korean Society of Pathologists, and education programs for biobankers and researchers. Through these various education programs, we expect that Korean biobanks will satisfy global standards, meet the needs of users and researchers, and contribute to the advancement of science.
https://doi.org/10.4132/jptm.2024.11.04
Biobank
Medicine
Medical education
Professional development
Professional association
Bioinformatics
Public relations
Political science
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2024Sex-specific survival gene mutations are discovered as clinical predictors of clear cell renal cell carcinoma
Jia Hwang, Hye‐Eun Lee, Jin Han, Moon Hyung Choi, Sung‐Hoo Hong, Sae Woong Kim, Jihoon Yang, Unsang Park, Eun Sun Jung, Yeong Jin Choi
IF 3.9 (2024)
Scientific Reports
Although sex differences have been reported in patients with clear cell renal cell carcinoma (ccRCC), biological sex has not received clinical attention and genetic differences between sexes are poorly understood. This study aims to identify sex-specific gene mutations and explore their clinical significance in ccRCC. We used data from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), The Renal Cell Cancer-European Union (RECA-EU) and Korean-KIRC. A total of 68 sex-related genes were selected from TCGA-KIRC through machine learning, and 23 sex-specific genes were identified through verification using the three databases. Survival differences according to sex were identified in nine genes (ACSS3, ALG13, ASXL3, BAP1, JADE3, KDM5C, KDM6A, NCOR1P1, and ZNF449). Female-specific survival differences were found in BAP1 in overall survival (OS) (TCGA-KIRC, p = 0.004; RECA-EU, p = 0.002; and Korean-KIRC, p = 0.003) and disease-free survival (DFS) (TCGA-KIRC, p = 0.001 and Korean-KIRC, p = 0.000004), and NCOR1P1 in DFS (TCGA-KIRC, p = 0.046 and RECA-EU, p = 0.00003). Male-specific survival differences were found in ASXL3 (OS, p = 0.017 in TCGA-KIRC; and OS, p = 0.005 in RECA-EU) and KDM5C (OS, p = 0.009 in RECA-EU; and DFS, p = 0.016 in Korean-KIRC). These results suggest that biological sex may be an important predictor and sex-specific tailored treatment may improve patient care in ccRCC.
https://doi.org/10.1038/s41598-024-66525-9
Renal cell carcinoma
Gene
Clear cell renal cell carcinoma
Biology
Cell
Bioinformatics
Oncology
Internal medicine
Medicine
Cancer research
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2024Discovery and Validation of Survival-Specific Genes in Papillary Renal Cell Carcinoma Using a Customized Next-Generation Sequencing Gene Panel
Jia Hwang, Seokhwan Bang, Moon Hyung Choi, Sung‐Hoo Hong, Sae Woong Kim, Hye‐Eun Lee, Jihoon Yang, Un Sang Park, Yeong Jin Choi
IF 4.4 (2024)
Cancers
PURPOSE: Papillary renal cell carcinoma (PRCC), the second most common kidney cancer, is morphologically, genetically, and molecularly heterogeneous with diverse clinical manifestations. Genetic variations of PRCC and their association with survival are not yet well-understood. This study aimed to identify and validate survival-specific genes in PRCC and explore their clinical utility. MATERIALS AND METHODS: Using machine learning, 293 patients from the Cancer Genome Atlas-Kidney Renal Papillary Cell Carcinoma (TCGA-KIRP) database were analyzed to derive genes associated with survival. To validate these genes, DNAs were extracted from the tissues of 60 Korean PRCC patients. Next generation sequencing was conducted using a customized PRCC gene panel of 202 genes, including 171 survival-specific genes. Kaplan-Meier and Log-rank tests were used for survival analysis. Fisher's exact test was performed to assess the clinical utility of variant genes. RESULTS: ) databases. CONCLUSIONS: We discovered and verified genes specific for the survival of PRCC patients in the TCGA-KIRP and Korean-KIRP databases. The survival gene signature, including PCSK2 commonly obtained from the 40 gene signature of TCGA and the 10 gene signature of the Korean database, is expected to provide insight into predicting the survival of PRCC patients and developing new treatment.
https://doi.org/10.3390/cancers16112006
Gene
Papillary renal cell carcinomas
DNA sequencing
Renal cell carcinoma
Biology
Computational biology
Carcinoma
Cancer research
Medicine
Oncology
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2023Development and Validation of a Prediction Model for Differentiation of Benign and Malignant Fat-Poor Renal Tumors Using CT Radiomics
Seokhwan Bang, Hee‐Hwan Wang, Hokun Kim, Moon Hyung Choi, Jiook Cha, Yeong Jin Choi, Sung‐Hoo Hong
IF 2.5 (2023)
Applied Sciences
Objectives: To develop and validate a machine learning-based CT radiomics classification model for distinguishing benign renal tumors from malignant renal tumors. Methods: We reviewed 499 patients who underwent nephrectomy for solid renal tumors at our institution between 2003 and 2021. In this retrospective study, patients who had undergone a computed tomography (CT) scan within 3 months before surgery were included. We randomly divided the dataset in a stratified manner as follows: 75% as the training set and 25% as the test set. By using various feature selection methods and a dimensionality reduction method exclusively for the training set, we selected 160 radiomic features out of 1,288 radiomic features to classify malignant renal tumors. Results: The training set included 396 patients, and the test set included 103 patients. The percentage of extracted radiomic features from patients was 32% (385/1218) after the reproducibility test. In terms of the average Area Under the Receiver Operating Characteristic Curve (AU-ROC) and the average Area Under the Precision-Recall Curve (AU-PRC), the Random Forest model achieved better performance (AU-ROC = 0.725; AU-PRC = 0.899). An average accuracy of 0.778 was obtained on evaluation with the hold-out test set. At the optimal threshold, the Random Forest model showed an F1 score of 0.746, precision of 0.862, sensitivity of 0.657, specificity of 0.651, and Negative Predictive Value (NPV) of 0.364. Conclusions: Our machine learning-based CT radiomics classification model performed well for the independent test set, indicating that it could be a useful tool for discriminating between malignant and benign solid renal tumors.
https://doi.org/10.3390/app132011345
Receiver operating characteristic
Radiomics
Medicine
Random forest
Test set
Artificial intelligence
Radiology
Feature selection
Machine learning
Computer science