研究表明，液-液相分离 (LLPS) 相关基因与前列腺癌 (PCa) 的进展密切相关。然而，PCa 中与 LLPS 相关的长非编码 RNA (lncRNA) 之间的相互作用仍然难以捉摸。因此，我们构建了基于PCa中LLPS相关LncRNA的预测模型，以探讨其与PCa的预后和药物治疗的关系。我们从相分离蛋白数据库中获得了TCGA和LLPS基因的临床和测序数据。通过分析前列腺癌中LLPS相关基因和lncRNA的差异表达，并利用泊松相关性，我们鉴定了LLPS相关lncRNA。通过预后相关分析发现预后 LLPS-lncRNA，并将其纳入 Cox 模型中以计算回归系数。对患者进行评分并分为高风险组和低风险组。将独立的预后因素整合到带有风险和格里森评分的预后列线图中。我们还进行了药物敏感性分析、GSEA，并通过功能实验验证了关键 lncRNA 的影响。我们的研究确定了 5 种具有预后重要性的 LLPS 相关 lncRNA。并发现这些风险组之间的生化复发率和生存结果存在显着差异，低风险组表现出更好的预后指标。此外，我们的预测列线图证明了强大的预测准确性和显着的临床实用性。此外，我们的模型在预测患者对各种传统治疗药物的敏感性方面表现出有前途的能力，从而凸显了其在个性化治疗策略中的潜力。 GSEA 表明，这些 lncRNA 可能通过影响细胞周期等途径来影响 PCa 预后和对治疗药物的敏感性。 AC009812.4的敲低可以抑制PCa细胞的增殖、迁移和侵袭能力，并且与癌旁组织相比，AC009812.4在PCa组织中的表达显着更高。我们的研究揭示了与LLPS相关的lncRNA在PCa和癌中的预后意义。建立了一个具有出色预测准确性的模型。这些 lncRNA 可能会通过细胞周期等途径影响 PCa 的进展以及对治疗药物的敏感性。© 2024。作者。

Studies have indicated a close association between genes linked to liquid-liquid phase separation (LLPS) and the progression of prostate cancer (PCa). However, the interplay among long non-coding RNAs (lncRNAs) linked to LLPS in PCa remains elusive. Therefore, we constructed a prediction model based on LLPS-related LncRNA in PCa to explore its relationship with the prognosis and drug treatment of PCa.We obtained clinical and sequencing data from TCGA and LLPS genes from the Phase Separation Protein Database. By analyzing the differential expression of LLPS-related genes and lncRNAs in prostate cancer, and using Poisson correlation, we identified LLPS-related lncRNAs. Prognostic LLPS-lncRNAs were found through prognostic correlation analysis and included in a Cox model to compute regression coefficients. Patients were scored and divided into high- and low-risk groups. Independent prognostic factors were integrated into a prognostic nomogram with risk and Gleason scores. We also conducted drug sensitivity analyses, GSEA, and validated the impact of key lncRNAs through functional experiments.Our study identified five LLPS-associated lncRNAs that are of prognostic importance. And found notable disparities in biochemical recurrence rates and survival outcomes between these risk groups, with the low-risk cohort exhibiting superior prognostic indicators. Moreover, our prediction nomogram demonstrated robust predictive accuracy and significant clinical utility. Furthermore, our model exhibited promising capabilities in forecasting patient sensitivity to various conventional therapeutic drugs, thereby highlighting its potential in personalized treatment strategies. GSEA showed that these lncRNAs may influence PCa prognosis and sensitivity to therapeutic agents by affecting pathways such as cell cycle. Knockdown of AC009812.4 could inhibit the ability of PCa cells to proliferate, migrate and invade, and compare to paracancerous tissue, AC009812.4 in PCa tissue has significantly higher expression.Our research uncovers the prognostic significance of lncRNAs associated with LLPS in PCa and established a model exhibiting excellent predictive accuracy for prognosis. Those lncRNAs may influence progress of PCa as well as sensitivity to therapy drugs through pathways such as cell cycle.© 2024. The Author(s).