Zhenyu Zhao | Search PubMed

PubMed2025

Hepatocyte-Enriched miRNA-193b-3p Promotes Hepatitis B Virus Replication by Dual Activation of Viral Core Promoter Activity and Autophagy Induction by Targeting IGF-1R.

Hepatitis B virus (HBV) infection is a principal cause of severe liver disease in humans and is associated with increased levels of specific serum or intracellular microRNAs (miRNAs). Among these, miR-193b-3p is a liver-enriched miRNA; however, its role in HBV replication remains unknown. This study aimed to investigate the influence of chronic HBV infection on miR-193b-3p levels in the peripheral blood and liver tissues of patients with chronic hepatitis B (CHB), evaluate the effect of miR-193b-3p on HBV replication both in vitro and in vivo, and elucidate the potential underlying mechanisms. We showed that hepatic miR-193b-3p levels in patients with CHB were significantly elevated compared with those in healthy controls. Ectopic expression of miR-193b-3p significantly enhanced HBV replication and transcription in different hepatoma cell lines. Furthermore, we identified IGF-1R as a direct target through which miR-193b-3p regulates HBV replication. Mechanistically, miR-193b-3p increased HBV core promoter activity via the IGF-1R/FXRα axis, thereby enhancing HBV transcription. Additionally, miR-193b-3p increased IGF-1R/Akt/MDM2/p53 signaling-mediated autophagy induction, which in turn facilitated increased HBV post-transcriptional activity. Collectively, hepatocyte-enriched miR-193b-3p exerts a proviral effect on HBV replication through dual synergistic mechanisms, offering novel insights into its role in HBV replication and potential therapeutic implications in CHB infection.

Humans↗

PubMed2020

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

BACKGROUND: Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. METHOD: RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). RESULTS: Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = - 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. CONCLUSION: In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Adenocarcinoma of Lung↗