Sangsoo Han, Miyoung Choi, Bora Lee, Hye-Won Lee, Seong Hee Kang, Yuri Cho, Sang Bong Ahn, Do Seon Song, Dae Won Jun, Jieun Lee, and Jeong-Ju Yoo. Gut and Liver 2022;16:952-63. https://doi.org/10.5009/gnl210391

Do Seon Song, U Im Chang, Sung-Goo Kang, Sang-Wook Song, and Jin Mo Yang. Gut and Liver 2019;13:658-68. https://doi.org/10.5009/gnl18439

Background/Aims: Sarcopenia is an independent prognostic factor of liver cirrhosis (LC). However, the association between LC-related systemic inflammation and sarcopenia is unclear.Methods: Sprague-Dawley rats were treated with thioacetamide (TAA) or saline as a control. Rifaximin was administered to TAA-induced LC rats. Enzyme-linked immunosorbent assay was performed to measure inflammatory mediators in rat serum. RT-PCR was performed to measure the molecular expression in tissues. Hematoxylin and eosin (H&E) staining and immunohistochemistry were performed to investigate tissue pathology. Serum tumor necrosis factor-α levels, liver stiffness (LS), and the L3 skeletal muscle index (L3SMI) were measured in 60 patients with chronic liver disease.Results: LC and sarcopenia were successfully induced by TAA. Serum TNF-α levels were increased in LC rats and correlated with myostatin expression, muscle weight, and myofiber diameter. The expression of intestinal occludin and zona occludens-1 was reduced in LC rats and associated with serum TNF-α levels and sarcopenia. In patients with LS ≥7 kPa or sarcopenia, serum TNF-α levels were significantly increased, which was also confirmed when we raised the LS cutoff to 10 kPa. The L3SMI was inversely correlated with serum TNF-α levels in patients with LS ≥7 kPa. TNF-α was reduced by rifaximin, which might have resulted in reduced expression of muscular MuRF1 and myostatin and improvements in myofiber diameters within muscle tissues.Conclusions: These results suggest that serum TNF-α is associated with LC-related sarcopenia. Rifaximin might be effective in reducing serum TNF-α levels and improving sarcopenia in LC, but these results need to be validated in future studies.

Hana Park, Seung Kak Shin, Ijin Joo, Do Seon Song, Jeong Won Jang, and Joong-Won Park. Gut and Liver 1999;0:. https://doi.org/10.5009/gnl19163

Purpose: Given its heterogeneity and diverse clinical outcomes, precise subclassification of Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) is required for appropriately determining patient prognosis and selecting treatment. Experimental Design: We recruited 2,626 patients with BCLC-C HCC from multiple centers, comprising training/test (n = 1,693) and validation cohorts (n = 933). The XGBoost model was chosen for maximum performance among the machine learning (ML) models. Patients were categorized into low-, intermediate-, high-, and very high-risk subgroups based on the estimated prognosis, and this subclassification was named the CLAssification via Machine learning of BCLC-C (CLAM-C). Results: The areas under the receiver operating characteristic curve of the CLAM-C for predicting the 6-, 12-, and 24-month survival of patients with BCLC-C were 0.800, 0.831, and 0.715, respectively—significantly higher than those of the conventional models, which were consistent in the validation cohort. The four subgroups had significantly different median overall survivals, and this difference was maintained among various patient subgroups and treatment modalities. Immune-checkpoint inhibitors and transarterial therapies were associated with significantly better survival than tyrosine kinase inhibitors (TKI) in the low- and intermediate-risk subgroups. In cases with first-line systemic therapy, the CLAM-C identified atezolizumab–bevacizumab as the best therapy, particularly in the high-risk group. In cases with later-line systemic therapy, nivolumab had better survival than TKIs in the low-to-intermediate-risk subgroup, whereas TKIs had better survival in the high- to very high-risk subgroup. Conclusions: ML modeling effectively subclassified patients with BCLC-C HCC, potentially aiding treatment allocation. Our study underscores the potential utilization of ML modeling in terms of prognostication and treatment allocation in patients with BCLC-C HCC.