SUMOylation plays a crucial role in numerous cellular biological and pathophysiological processes associated with human disease; however, the mechanisms regulating the genes involved in SUMOylation remain unclear. In the present study, E2F transcription factor 4 (E2F4) was identified as an E2F member related to hepatocellular carcinoma (HCC) progression by public database analysis. It was found that E2F4 promoted the proliferation and invasiveness of HCC cells via SUMOylation using Soft agar and Transwell migration assays. Mechanistically, it was demonstrated that E2F4 upregulated the transcript and protein expression levels of baculoviral IAP repeat containing 5, cell division cycle associated 8 and DNA topoisomerase II α using western blotting. Furthermore, the interaction between E2F4 with lin‑9 DREAM multi‑vulva class B core complex component (LIN9) was explored by co‑immunoprecipitation, immunofluorescence co‑localization and bimolecular fluorescence complementation assays. Moreover, it was demonstrated that E2F4 promoted the progression of HCC cells via LIN9. Rescue experiments revealed that LIN9 facilitated the SUMOylation and proliferation of HCC cells, which was prevented by knocking down E2F4 expression. In conclusion, the findings of the present study indicated that E2F4 plays a major role in the proliferation of HCC cells and may be a potential therapeutic target in the future.

Artificial intelligence (AI) technologies are increasingly being applied in the field of pediatric surgery. Utilizing machine learning (ML) to analyze clinical case data, we can develop models for disease diagnosis and prognosis prediction. This study aims to explore whether AI can effectively process massive amounts of medical data, extract key information, and assist doctors in aspects such as disease diagnosis, surgical plan selection, and prognosis assessment. The protocol of this study was registered with PROSPERO (CRD420251184780). We searched PubMed, Web of Science, and Scopus for studies published between February 2016 and June 2025 focusing on AI applications in pediatric appendicitis, intussusception, Hirschsprung’s disease (HD), necrotizing enterocolitis (NEC), and biliary atresia (BA). PRISMA guidelines and Synthesis Without Meta-analysis (SWiM) guidelines were used. Models integrating multimodal data (such as clinical data, laboratory markers, and imaging) generally outperformed those utilizing single data sources. Some models performed at a level comparable to or exceeding that of experienced specialists in diagnosis, improving the diagnostic accuracy of junior physicians. Most included studies were retrospective with single-center designs, resulting in a generally high risk of bias. Current research has demonstrated AI’s potential to improve diagnostic accuracy, optimize treatment decisions, and enhance patient outcomes, while improvements are needed in areas such as bias risk control, model interpretability, and data quality. More high-quality, multicenter prospective studies are required to fully realize the comprehensive clinical translation of AI technology in pediatric surgery.