Background/Aims: Hepatocellular carcinoma (HCC) exhibits high de novo recurrence rates post-resection. Current post-surgery recurrence prediction methods are limited, emphasizing the need for reliable biomarkers to assess recurrence risk. We aimed to develop methylation-based markers for classifying HCC patients and predicting their risk of de novo recurrence post-surgery. Methods: In this retrospective cohort study, we analyzed data from HCC patients who underwent surgical resection in Korea, excluding those with recurrence within one year post-surgery. Using the Infinium Methylation EPIC array on 140 samples in the discovery cohort, we classified patients into low- and high-risk groups based on methylation profiles. Distinctive markers were identified through random forest analysis. These markers were validated in the cancer genome atlas (n=217), Validation cohort 1 (n=63) and experimental Validation using a methylation-sensitive high-resolution melting (MS-HRM) assay in Validation cohort 1 and Validation cohort 2 (n=63). Results: The low-risk recurrence group (methylation group 1; MG1) showed a methylation average of 0.73 (95% confidence interval [CI] 0.69–0.77) with a 23.5% recurrence rate, while the high-risk group (MG2) had an average of 0.17 (95% CI 0.14–0.20) with a 44.1% recurrence rate (P<0.03). Validation confirmed the applicability of methylation markers across diverse populations, showing high accuracy in predicting the probability of HCC recurrence risk (area under the curve 96.8%). The MS-HRM assay confirmed its effectiveness in predicting de novo recurrence with 95.5% sensitivity, 89.7% specificity, and 92.2% accuracy. Conclusions Methylation markers effectively classified HCC patients by de novo recurrence risk, enhancing prediction accuracy and potentially offering personalized management strategies.

Background/Aims: Hepatocellular carcinoma (HCC) exhibits high de novo recurrence rates post-resection. Current post-surgery recurrence prediction methods are limited, emphasizing the need for reliable biomarkers to assess recurrence risk. We aimed to develop methylation-based markers for classifying HCC patients and predicting their risk of de novo recurrence post-surgery. Methods: In this retrospective cohort study, we analyzed data from HCC patients who underwent surgical resection in Korea, excluding those with recurrence within one year post-surgery. Using the Infinium Methylation EPIC array on 140 samples in the discovery cohort, we classified patients into low- and high-risk groups based on methylation profiles. Distinctive markers were identified through random forest analysis. These markers were validated in the cancer genome atlas (n=217), Validation cohort 1 (n=63) and experimental Validation using a methylation-sensitive high-resolution melting (MS-HRM) assay in Validation cohort 1 and Validation cohort 2 (n=63). Results: The low-risk recurrence group (methylation group 1; MG1) showed a methylation average of 0.73 (95% confidence interval [CI] 0.69–0.77) with a 23.5% recurrence rate, while the high-risk group (MG2) had an average of 0.17 (95% CI 0.14–0.20) with a 44.1% recurrence rate (P<0.03). Validation confirmed the applicability of methylation markers across diverse populations, showing high accuracy in predicting the probability of HCC recurrence risk (area under the curve 96.8%). The MS-HRM assay confirmed its effectiveness in predicting de novo recurrence with 95.5% sensitivity, 89.7% specificity, and 92.2% accuracy. Conclusions Methylation markers effectively classified HCC patients by de novo recurrence risk, enhancing prediction accuracy and potentially offering personalized management strategies.

Background/Aims: Hepatocellular carcinoma (HCC) exhibits high de novo recurrence rates post-resection. Current post-surgery recurrence prediction methods are limited, emphasizing the need for reliable biomarkers to assess recurrence risk. We aimed to develop methylation-based markers for classifying HCC patients and predicting their risk of de novo recurrence post-surgery. Methods: In this retrospective cohort study, we analyzed data from HCC patients who underwent surgical resection in Korea, excluding those with recurrence within one year post-surgery. Using the Infinium Methylation EPIC array on 140 samples in the discovery cohort, we classified patients into low- and high-risk groups based on methylation profiles. Distinctive markers were identified through random forest analysis. These markers were validated in the cancer genome atlas (n=217), Validation cohort 1 (n=63) and experimental Validation using a methylation-sensitive high-resolution melting (MS-HRM) assay in Validation cohort 1 and Validation cohort 2 (n=63). Results: The low-risk recurrence group (methylation group 1; MG1) showed a methylation average of 0.73 (95% confidence interval [CI] 0.69–0.77) with a 23.5% recurrence rate, while the high-risk group (MG2) had an average of 0.17 (95% CI 0.14–0.20) with a 44.1% recurrence rate (P<0.03). Validation confirmed the applicability of methylation markers across diverse populations, showing high accuracy in predicting the probability of HCC recurrence risk (area under the curve 96.8%). The MS-HRM assay confirmed its effectiveness in predicting de novo recurrence with 95.5% sensitivity, 89.7% specificity, and 92.2% accuracy. Conclusions Methylation markers effectively classified HCC patients by de novo recurrence risk, enhancing prediction accuracy and potentially offering personalized management strategies.

Tuberculous pericarditis is an extrapulmonary manifestation of tuberculosis that is most commonly associated with pericardial thickening, effusion, and calcification. We present a case of tuberculous pericarditis mimicking a malignant pericardial tumor in a 77-year-old male. CT revealed an irregular and nodular pericardial thickening. MRI revealed high signal intensity on T1-weighted fat-suppressed images and peripheral rim enhancement after gadolinium administration. MRI can be helpful in determining the differential diagnoses in cases of tuberculous pericarditis with nonspecific imaging findings.

Tuberculous pericarditis is an extrapulmonary manifestation of tuberculosis that is most commonly associated with pericardial thickening, effusion, and calcification. We present a case of tuberculous pericarditis mimicking a malignant pericardial tumor in a 77-year-old male. CT revealed an irregular and nodular pericardial thickening. MRI revealed high signal intensity on T1-weighted fat-suppressed images and peripheral rim enhancement after gadolinium administration. MRI can be helpful in determining the differential diagnoses in cases of tuberculous pericarditis with nonspecific imaging findings.

Tuberculous pericarditis is an extrapulmonary manifestation of tuberculosis that is most commonly associated with pericardial thickening, effusion, and calcification. We present a case of tuberculous pericarditis mimicking a malignant pericardial tumor in a 77-year-old male. CT revealed an irregular and nodular pericardial thickening. MRI revealed high signal intensity on T1-weighted fat-suppressed images and peripheral rim enhancement after gadolinium administration. MRI can be helpful in determining the differential diagnoses in cases of tuberculous pericarditis with nonspecific imaging findings.

Background/Aims: The major histocompatibility class II (MHC II) transactivator, known as CIITA, is induced by Interferon gamma (IFN-γ) and plays a well-established role in regulating the expression of class II MHC molecules in antigen-presenting cells. Methods: Primary human hepatocytes (PHH) were isolated via therapeutic hepatectomy from two donors. The hepatocellular carcinoma (HCC) cell lines HepG2 and Huh7 were used for the mechanistic study, and HBV infection was performed in HepG2-NTCP cells. HBV DNA replication intermediates and secreted antigen levels were measured using Southern blotting and ELISA, respectively. Results: We identified a non-canonical function of CIITA in the inhibition of hepatitis B virus (HBV) replication in both HCC cells and patient-derived PHH. Notably, in vivo experiments demonstrated that HBV DNA and secreted antigen levels were significantly decreased in mice injected with the CIITA construct. Mechanistically, CIITA inhibited HBV transcription and replication by suppressing the activity of HBV-specific enhancers/promoters. Indeed, CIITA exerts antiviral activity in hepatocytes through ERK1/2-mediated down-regulation of the expression of hepatocyte nuclear factor 1α (HNF1α) and HNF4α, which are essential factors for virus replication. In addition, silencing of CIITA significantly abolished the IFN-γ-mediated anti-HBV activity, suggesting that CIITA mediates the anti-HBV activity of IFN-γ to some extent. HBV X protein (HBx) counteracts the antiviral activity of CIITA via direct binding and impairing its function. Conclusions Our findings reveal a novel antiviral mechanism of CIITA that involves the modulation of the ERK pathway to restrict HBV transcription. Additionally, our results suggest the possibility of a new immune avoidance mechanism involving HBx.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.