An EcDNA gene-based risk model and functional verification of a key ec-lncRNA AC016394.2 for prostate cancer

Abstract

Abstract Background Prostate cancer(PCa) ranks among the most frequently diagnosed malignancies in men. The progression and heterogeneity of tumors pose significant challenges to clinical prognosis and treatment strategies. Recently, extrachromosomal DNA(ecDNA) has emerged as a critical player in cancer biology, influencing tumor progression, metastasis, and resistance to therapy. Oncogenes and regulatory sequences carried on ecDNA(ecDNA genes) can significantly alter the biological characteristics of tumors and their clinical outcomes. Methods In this study, we obtained ecDNA genes specifically expressed in PCa from the ECGA database. To construct a prognostic risk model for Biochemical Recurrence-Free Survival (BRFS), the two most common types of ecDNA genes which are protein-coding genes and long non-coding RNAs, were analyzed using Cox regression and LASSO regression techniques. Through KEGG/GO pathway enrichment analysis, we identified relevant pathways and analyzed the immune cell infiltration status. Functional assays, such as colony formation, CCK-8, migration, and invasion assays, were employed to assess the cellular functions of a key lncRNA AC016394.2. Results Our analysis identified six key ecDNA lncRNAs(ec-lncRNAs), including the ec-lncRNA AC016394.2, with significant prognostic value in PCa. By employing our risk scoring model, patients were classified into high-risk and low-risk groups, revealing significant differences in their BRFS outcomes. The model demonstrated strong predictive accuracy and clinical relevance. The 1/3/5-year AUC of the model is close to 0.8, which is higher than most common clinical indicators such as Gleason score and TM staging. KEGG and GO pathway enrichment analyses revealed that the high-risk group was predominantly enriched in immune-related pathways. Additionally, immune cell infiltration analysis demonstrated notable differences in the distribution of specific immune cell populations between the high-risk and low-risk groups. Knockdown of AC016394.2 inhibited PCa cell proliferation, migration, and invasion. Conclusions This study presents a novel ecDNA gene-based prognostic risk model for PCa, highlighting the functional importance of ec-lncRNA AC016394.2. These findings offer valuable insights into the biological role of ec-lncRNAs, highlighting their potential as targets for precision oncology and therapeutic intervention.

Keywords

• EcDNA gene
• TCGA
• Prostate cancer
• Prognostic model
• AC016394.2