This study aims to identify potential microRNAs (miRNAs) contribute to liver fibrosis progression and investigate how the miRNA is involved. We recruited totally 58 patients. Magnetic resonance imaging (MRI) was employed to detect fibrosis. Classification of liver fibrosis was carried out by Ishak scoring system. Cell viability was tested using cell counting kit-8. Measurements of mRNA and protein expressions were conducted using real-time quantitative polymerase chain reaction and western blotting. Luciferase reporter assay was recruited for determination of miR-29b-3p targets. We found that relative enhancement (RE) values were reduced with the increases of fibrosis stages and was negatively associated with Ishak scores. In comparison with patients without liver fibrosis, miR-29b-3p level was remarkably reduced in those with liver fibrosis. Its level was found to be positively associated with RE values. Transforming growth factor beta 1 (TGF-β1)-induced hepatic stellate cell (HSC) activation significantly decreased miR-29b-3p expression. However, miR-29b-3p overexpression repressed TGF-β1-induced collagen I protein and alpha-smooth muscle actin (α-SMA) expression. As expected, its overexpression also reduced cell viability. We found that miR-29b-3p directly bind to signal transducer and activator of transcription 3 (STAT3) and suppressed its expression. Our study demonstrates that low expression of miR-29b-3p may contribute to the progression of liver fibrosis by suppressing STAT3.

Purpose. To develop and validate a radiomic nomogram based on texture features from out-of-phase T1W images and clinical biomarkers in prediction of liver fibrosis. Materials and Methods. Patients clinically diagnosed with chronic liver fibrosis who underwent liver biopsy and noncontrast MRI were enrolled. All patients were assigned to the nonsignificant fibrosis group with fibrosis stage <2 and the significant fibrosis group with stage ≥2. Texture parameters were extracted from out-of-phase T1-weighted (T1W) images and calculated using the Artificial Intelligent Kit (AK). Boruta and LASSO regressions were used for feature selection and a multivariable logistic regression was used for construction of a combinational model integrating radiomics and clinical biomarkers. The performance of the models was assessed by using the receiver operator curve (ROC) and decision curve. Results. ROC analysis of the radiomics model that included the most discriminative features showed AUCs of the training and test groups were 0.80 and 0.78. A combinational model integrating RADscore and fibrosis 4 index was established. ROC analysis of the training and test groups showed good to excellent performance with AUC of 0.93 and 0.86. Decision curves showed the combinational model added more net benefit than radiomic and clinical models alone. Conclusions. The study presents a combinational model that incorporates RADscore and clinical biomarkers, which is promising in classification of liver fibrosis.