Machine Learning-Based Models for Advanced Fibrosis and Cirrhosis Diagnosis in Chronic Hepatitis B Patients With Hepatic Steatosis.

BACKGROUND AND AIMS: The global rise of chronic hepatitis B (CHB) superimposed on hepatic steatosis (HS) warrants noninvasive, precise tools for assessing fibrosis progression. This study leveraged machine learning (ML) to develop diagnostic models for advanced fibrosis and cirrhosis in this patient population. METHODS: Treatment-naive CHB patients with concurrent HS who underwent liver biopsy in 10 medical centers were enrolled as a training cohort and an independent external validation cohort (NCT05766449). Six ML models were implemented to predict advanced fibrosis and cirrhosis. The final models, derived from SHAP (Shapley Additive exPlanations), were compared with Fibrosis-4 Index, nonalcoholic fatty liver disease Fibrosis Score, and aspartate aminotransferase-to-platelet ratio index using the area under receiver-operating characteristic curve (AUROC) and decision curve analysis (DCA). RESULTS: Of 1,198 eligible patients, the random forest model achieved AUROCs of 0.778 (95% confidence interval [CI], 0.749-0.807) for diagnosing advanced fibrosis (random forest advanced fibrosis model) and 0.777 (95% CI, 0.748-0.806) for diagnosing cirrhosis (random forest cirrhosis model) in the training cohort, and maintained high AUROCs in the validation cohort. In the training cohort, the random forest advanced fibrosis model obtained an AUROC of 0.825 (95% CI, 0.787-0.862) in patients with hepatitis B virus DNA ≥105 IU/mL, and the random forest cirrhosis model had an AUROC of 0.828 (95% CI, 0.774-0.883) in female patients. The 2 models outperformed Fibrosis-4 Index, nonalcoholic fatty liver disease Fibrosis Score, and aspartate aminotransferase-to-platelet ratio index in the training cohort, and also performed well in the validation cohort. CONCLUSIONS: The random forest models provide reliable, noninvasive tools for identifying advanced fibrosis and cirrhosis in CHB patients with concurrent HS, offering a significant advancement in the comanagement of the 2 diseases. CLINICALTRIALS: gov, Number: NCT05766449.

Ferrostatin-1 improves prognosis and regulates gut microbiota of steatotic liver transplantation recipients in rats.

Aims: To investigate the effects of Ferrostatin-1 (Fer-1) on improving the prognosis of liver transplant recipients with steatotic liver grafts and regulating gut microbiota in rats. Methods: We obtained steatotic liver grafts and established a liver transplantation model. Recipients were divided into sham, liver transplantation and Fer-1 treatment groups, which were assessed 1 and 7 days after surgery (n = 6). Results & conclusion: Fer-1 promotes recovery of the histological structure and function of steatotic liver grafts and the intestinal tract, and improves inflammatory responses of recipients following liver transplantation. Fer-1 reduces gut microbiota pathogenicity, and lowers iron absorption and improves fat metabolism of recipients, thereby protecting steatotic liver grafts.

Single-cell sequencing combined with spatial transcriptomics reveals that the IRF7 gene in M1 macrophages inhibits the occurrence of pancreatic cancer by regulating lipid metabolism-related mechanisms.

AIM: The main focus of this study is to explore the molecular mechanism of IRF7 regulation on RPS18 transcription in M1-type macrophages in pancreatic adenocarcinoma (PAAD) tissue, as well as the transfer of RPS18 by IRF7 via exosomes to PAAD cells and the regulation of ILF3 expression. METHODS: By utilising single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data from the Gene Expression Omnibus database, we identified distinct cell types with significant expression differences in PAAD tissue. Among these cell types, we identified those closely associated with lipid metabolism. The differentially expressed genes within these cell types were analysed, and target genes relevant to prognosis were identified. Flow cytometry was employed to assess the expression levels of target genes in M1 and M2 macrophages. Cell lines with target gene knockout were constructed using CRISPR/Cas9 editing technology, and cell lines with target gene knockdown and overexpression were established using lentiviral vectors. Additionally, a co-culture model of exosomes derived from M1 macrophages with PAAD cells was developed. The impact of M1 macrophage-derived exosomes on the lipid metabolism of PAAD cells in the model was evaluated through metabolomics analysis. The effects of M1 macrophage-derived exosomes on the viability, proliferation, division, migration and apoptosis of PAAD cells were assessed using MTT assay, flow cytometry, EdU assay, wound healing assay, Transwell assay and TUNEL staining. Furthermore, a mouse PAAD orthotopic implantation model was established, and bioluminescence imaging was utilised to assess the influence of M1 macrophage-derived exosomes on the intratumoural formation capacity of PAAD cells, as well as measuring tumour weight and volume. The expression of proliferation-associated proteins in tumour tissues was examined using immunohistochemistry. RESULTS: Through combined analysis of scRNA-seq and ST technologies, we discovered a close association between M1 macrophages in PAAD samples and lipid metabolism signals, as well as a negative correlation between M1 macrophages and cancer cells. The construction of a prognostic risk score model identified RPS18 and IRF7 as two prognostically relevant genes in M1 macrophages, exhibiting negative and positive correlations, respectively. Mechanistically, it was found that IRF7 in M1 macrophages can inhibit the transcription of RPS18, reducing the transfer of RPS18 to PAAD cells via exosomes, consequently affecting the expression of ILF3 in PAAD cells. IRF7/RPS18 in M1 macrophages can also suppress lipid metabolism, cell viability, proliferation, migration, invasion and intratumoural formation capacity of PAAD cells, while promoting cell apoptosis. CONCLUSION: Overexpression of IRF7 in M1 macrophages may inhibit RPS18 transcription, reduce the transfer of RPS18 from M1 macrophage-derived exosomes to PAAD cells, thereby suppressing ILF3 expression in PAAD cells, inhibiting the lipid metabolism pathway, and curtailing the viability, proliferation, migration, invasion of PAAD cells, as well as enhancing cell apoptosis, ultimately inhibiting tumour formation in PAAD cells in vivo. Targeting IRF7/RPS18 in M1 macrophages could represent a promising immunotherapeutic approach for PAAD in the future.

Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort study.

Background: With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS. Methods: We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449). Findings: From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83-0.88) in the training cohort, and 0.89 (95% CI 0.86-0.92), 0.76 (95% CI 0.73-0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model. Interpretation: Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes. Funding: This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).