据估计，超过 50% 的肿瘤中 TP53 基因发生突变，大多数肿瘤表现出异常的 TP53 信号通路。然而，TP53突变相关的LncRNA在肝细胞癌（HCC）中的探索仍然不完整。本研究旨在鉴定此类LncRNA并提高肝癌患者的预后准确性。使用R中的“limma”包鉴定差异基因表达。通过单变量Cox回归分析鉴定与预后相关的LncRNA，同时使用多变量构建预后模型考克斯回归分析。使用 Kaplan-Meier 曲线进行生存分析。通过ROC分析评估预后模型的精度。随后，通过TIDE数据库在TCGA数据集上执行肿瘤免疫功能障碍和排除（TIDE）算法。使用反卷积技术从 NCI 癌症研究数据共享中获得了 24 种免疫细胞浸润的分数。特定基因编码的蛋白表达水平是通过TPCA数据库获得的。在这项研究中，我们鉴定了8​​5个与TP53突变相关的LncRNA，并开发了相应的签名，称为TP53MLncSig。 Kaplan-Meier 分析显示，与低风险患者 (74.2%) 相比，高风险患者 (46.9%) 的 3 年生存率较低。通过计算 ROC 曲线下面积进一步评估预后 TP53MLncSig 的准确性。该分析得出的 5 年 ROC 得分为 0.793，证实了其有效性。此外，发现 TP53MLncSig 得分较高与免疫检查点阻断剂 (ICB) 治疗的反应率增加相关 (p = .005)。具有高风险分类的患者表现出较低水平的P53蛋白表达和较高水平的基因组不稳定性。本研究旨在鉴定和验证与TP53突变相关的LncRNA。我们构建了一个预后模型，可以预测 HCC 患者的化疗敏感性和对 ICB 治疗的反应。这种新方法揭示了 LncRNA 在 TP53 突变中的作用，并为分析患者预后和治疗选择提供了宝贵的资源。

The TP53 gene is estimated to be mutated in over 50% of tumors, with the majority of tumors exhibiting abnormal TP53 signaling pathways. However, the exploration of TP53 mutation-related LncRNAs in Hepatocellular carcinoma (HCC) remains incomplete. This study aims to identify such LncRNAs and enhance the prognostic accuracy for Hepatoma patients.Differential gene expression was identified using the "limma" package in R. Prognosis-related LncRNAs were identified via univariate Cox regression analysis, while a prognostic model was crafted using multivariate Cox regression analysis. Survival analysis was conducted using Kaplan-Meier curves. The precision of the prognostic model was assessed through ROC analysis. Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were executed on the TCGA dataset via the TIDE database. Fractions of 24 types of immune cell infiltration were obtained from NCI Cancer Research Data Commons using deconvolution techniques. The protein expression levels encoded by specific genes were obtained through the TPCA database.In this research, we have identified 85 LncRNAs associated with TP53 mutations and developed a corresponding signature referred to as TP53MLncSig. Kaplan-Meier analysis revealed a lower 3-year survival rate in high-risk patients (46.9%) compared to low-risk patients (74.2%). The accuracy of the prognostic TP53MLncSig was further evaluated by calculating the area under the ROC curve. The analysis yielded a 5-year ROC score of 0.793, confirming its effectiveness. Furthermore, a higher score for TP53MLncSig was found to be associated with an increased response rate to immune checkpoint blocker (ICB) therapy (p = .005). Patients possessing high-risk classification exhibited lower levels of P53 protein expression and higher levels of genomic instability.The present study aimed to identify and validate LncRNAs associated with TP53 mutations. We constructed a prognostic model that can predict chemosensitivity and response to ICB therapy in HCC patients. This novel approach sheds light on the role of LncRNAs in TP53 mutation and provides valuable resources for analyzing patient prognosis and treatment selection.