Purpose: Urothelial carcinoma has various molecular subtypes, each with different tumor characteristics. Although it is known that molecular changes occur during tumor progression, little is known about the specifics of these changes. In this study, we performed transcriptional analysis to understand the molecular changes during tumor progression. Materials and Methods: Formalin-fixed, paraffin-embedded tumor tissues were obtained from 12 patients with muscle-invasive bladder cancer (MIBC). The invasive and non-invasive papillary areas were identified in papillary urothelial carcinoma specimens.Immunohistochemistry (IHC) and mRNA sequencing were performed for each tumor area. Results: Patients with CK5/6-negative and CK20-positive non-invasive papillary areas were selected and classified into the IHC switch subgroup (CK5/6-positive and CK20-negative in the invasive area) and the IHC unchanged subgroup (CK5/6-negative and CK20-positive in the invasive area) according to the IHC results of the invasive area. We identified differences in the mRNA expression between the non-invasive papillary and invasive areas of the papillary MIBC tissue samples. In both the non-invasive papillary and invasive areas, the IHC switch subgroup showed basal subtype gene expression, while the IHC unchanged subgroup demonstrated luminal subtype gene expression. Conclusions The non-invasive papillary area showed a gene expression pattern similar to that of the invasive area. Therefore, even if the non-invasive papillary area exhibits a luminal phenotype on IHC, it can have a basal subtype gene expression depending on the invasive area.

Background: Dipeptidyl peptidase-4 (DPP4) inhibitors are frequently prescribed for patients with type 2 diabetes; however, their cost can pose a significant barrier for those with impaired kidney function. This study aimed to estimate the economic benefits of substituting non-renal dose-adjusted (NRDA) DPP4 inhibitors with renal dose-adjusted (RDA) DPP4 inhibitors in patients with both impaired kidney function and type 2 diabetes. Methods: This retrospective cohort study was conducted from January 1, 2012 to December 31, 2018, using data obtained from common data models of five medical centers in Korea. Model 1 applied the prescription pattern of participants with preserved kidney function to those with impaired kidney function. In contrast, model 2 replaced all NRDA DPP4 inhibitors with RDA DPP4 inhibitors, adjusting the doses of RDA DPP4 inhibitors based on individual kidney function. The primary outcome was the cost difference between the two models. Results: In total, 67,964,996 prescription records were analyzed. NRDA DPP4 inhibitors were more frequently prescribed to patients with impaired kidney function than in those with preserved kidney function (25.7%, 51.3%, 64.3%, and 71.6% in patients with estimated glomerular filtration rates [eGFRs] of ≥60, <60, <45, and <30 mL/min/1.73 m2, respectively). When model 1 was applied, the cost savings per year were 7.6% for eGFR <60 mL/min/1.73 m2 and 30.4% for eGFR <30 mL/min/1.73 m2. According to model 2, 15.4% to 51.2% per year could be saved depending on kidney impairment severity. Conclusion Adjusting the doses of RDA DPP4 inhibitors based on individual kidney function could alleviate the economic burden associated with medical expenses.

This retrospective cohort study aimed to compare coronavirus disease 2019 (COVID-19)-related clinical outcomes between patients with and without gout. Electronic health recordbased data from two centers (Seoul National University Hospital [SNUH] and Boramae Medical Center [BMC]), from January 2021 to April 2022, were mapped to a common data model. Patients with and without gout were matched using a large-scale propensityscore algorithm based on population-level estimation methods. At the SNUH, the risk for COVID-19 diagnosis was not significantly different between patients with and without gout (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.59–1.84). Within 30 days after COVID-19 diagnosis, no significant difference was observed in terms of hospitalization (HR, 0.57; 95% CI, 0.03–3.90), severe outcomes (HR, 2.90; 95% CI, 0.54–13.71), or mortality (HR, 1.35; 95% CI, 0.06–16.24). Similar results were obtained from the BMC database, suggesting that gout does not increase the risk for COVID-19 diagnosis or severe outcomes.

Objectives: The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. Methods: A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model. Results: The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.

Purpose: In the present study, we aimed to establish a liquid biopsy-based monitoring method using peripheral blood cell-free DNA (cfDNA) for patients with cervical cancer who underwent radical radiotherapy (RT). Materials and Methods: Twenty-five patients with cervical cancer were prospectively recruited and treated with external beam RT and brachytherapy. In all patients, except one, chemotherapy was administered concurrently during RT. Whole peripheral blood samples were obtained at least twice from each patient. We performed next-generation sequencing (NGS) for the target-captured libraries (67 oncogenes and human papillomavirus [HPV] type 16/18) using 64 plasma cfDNA samples from the 25 participants. The ratio of HPV cfDNA and the variant allele frequency (VAF) in cfDNA was calculated, and their dynamic changes were monitored. The median follow-up duration was 25.4 months. Results: In total, we identified 21,866 cfDNA variants. ARID1A and frameshift variants occupied the largest portion of altered genes and HIGH-grade variant types, respectively. In most cases, tumor shrinkage was followed by a decrease in the HPV ratio; however, an increase in HPV ratio indicated distant metastasis, despite the reduced tumor size. The initial HPV ratio reflecting the tumor burden was likely associated with treatment outcomes (p = 0.16). We did not determine a role for serial changes in the VAF in cfDNA. Conclusion Our findings suggest that the HPV cfDNA ratio, calculated after targeted NGS, may be valuable for monitoring and predicting treatment responses. Accordingly, further validation of these findings is warranted.

Background: The genetically predicted lipid-lowering effect of HMGCR or PCSK9 variant can be used to assess drug proxy effects on kidney function. Methods: Mendelian randomization (MR) analysis-identified HMGCR and PCSK9 genetic variants were used to predict the low-density lipoprotein (LDL) cholesterol-lowering effects of medications targeting related molecules. Primary summary-level outcome data for log-estimated glomerular filtration rate (eGFR; creatinine) were provided by the CKDGen Consortium (n = 1,004,040 European) from a meta-analysis of CKDGen and UK Biobank data. We also conducted a separate investigation of summary-level data from CKDGen (n = 567,460, log-eGFR [creatinine]) and UK Biobank (n = 436,581, log-eGFR [cystatin C]) samples. Summary-level MRs using an inverse variance weighted method and pleiotropy-robust methods were performed. Results: Summary-level MR analysis indicated that the LDL-lowering effect predicted genetically by HMGCR variants (50-mg/dL decrease) was significantly associated with a decrease in eGFR (–1.67%; 95% confidence interval [CI], –2.20% to –1.13%). Similar significance was found in results from the pleiotropy-robust MR methods when the CKDGen and UK Biobank data were analyzed separately. However, the LDL-lowering effect predicted genetically by PCSK9 variants was significantly associated with an increase in eGFR (+1.17%; 95% CI, 0.10%–2.25%). The results were similarly supported by the weighted median method and in each CKDGen and UK Biobank dataset, but the significance obtained by MR-Egger regression was attenuated. Conclusion Genetically predicted HMG-CoA reductase inhibition was associated with low eGFR, while genetically predicted PCSK9 inhibition was associated with high eGFR. Clinicians should consider that the direct effect of different types of lipid-lowering medication on kidney function can vary.