A novel super-enhancer-related risk model for predicting prognosis and guiding personalized treatment in hepatocellular carcinoma

Background Our research endeavored to develop a robust predictive signature grounded in super-enhancer-related genes (SERGs), with the dual objectives of forecasting survival outcomes and evaluating the tumor immune microenvironment (TiME) in hepatocellular carcinoma (HCC). Methods HCC RNA-sequencing data were retrieved from The Cancer Genome Atlas (TCGA), and 365 patients were randomly assigned to training or testing sets in 1:1 ratio. SERGs of HCC were downloaded from Super-Enhancer Database (SEdb). On the basis of training set, a SERGs signature was identified, and its prognostic value was confirmed by internal and external validation (GSE14520) sets. We subsequently examined the model for potential functional enrichment and the degree of tumor immune infiltration. Additionally, we carried out in vitro experiments to delve into the biological functions of CBX2 gene. Results An SE-related prognostic model including CBX2, TPX2, EFNA3, DNASE1L3 and SOCS2 was established and validated. According to this risk model, patients in the high-risk group had a significantly worse prognosis, and their immune cell infiltration was significantly different from that of low-risk group. Moreover, the high-risk group exhibited a significant enrichment of tumor-associated pathological pathways. The SERGs signature can generally be utilized to screen HCC patients who are likely to respond to immunotherapy, as there is a positive correlation between the risk score and the Tumor Immune Dysfunction and Exclusion (TIDE) score. Furthermore, the downregulation of the CBX2 gene expression was found to inhibit HCC cell viability, migration, and cell cycle progression, while simultaneously promoting apoptosis. Conclusions We developed a novel HCC prognostic model utilizing SERGs, indicating that patients with high-risk score not only face a poorer prognosis but also may exhibit a diminished therapeutic response to immune checkpoint inhibitors (ICIs). This model is designed to tailor personalized treatment strategies to the individual needs of each patient, thereby improving the overall clinical outcomes for HCC patients. Furthermore, CBX2 is a promising candidate for therapeutic intervention in HCC.

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