ObjectivePolyunsaturated fatty acids (PUFAs) have attracted increasing attention for their role in liver cancer development. The objective of this study is to develop a prognosis prediction model for patients with liver cancer based on PUFA‐related metabolic gene characteristics.MethodTranscriptome data and clinical data were obtained from public databases, while gene sets related to PUFAs were acquired from the gene set enrichment analysis (GSEA) database. Univariate Cox analysis was conducted on the training set, followed by LASSO logistic regression and multivariate Cox analysis on genes with p < .05. Subsequently, the stepwise Akaike information criterion method was employed to construct the model. The high‐ and low‐risk groups were divided based on the median score, and the model's survival prediction ability, diagnostic efficiency, and risk score distribution of clinical features were validated. The above procedures were also validated in the validation set. Immune infiltration levels were evaluated using four algorithms, and the immunotherapeutic potential of different groups was explored. Significant enrichment pathways among different groups were selected based on the GSEA algorithm, and mutation analyses were conducted. Nomogram prognostic models were constructed by incorporating clinical factors and risk scores using univariate and multivariate Cox regression analysis, validated through calibration curves and clinical decision curves. Additionally, sensitivity analysis of drugs was performed to screen potential targeted drugs.ResultsWe constructed a prognostic model comprising eight genes (PLA2G12A, CYP2C8, ABCCI, CD74, CCR7, P2RY4, P2RY6, and YY1). Validation across multiple datasets indicated the model's favorable prognostic prediction ability and diagnostic efficiency, with poorer grading and staging observed in the high‐risk group. Variations in mutation status and pathway enrichment were noted among different groups. Incorporating Stage, Grade, T.Stage, and RiskScore into the nomogram prognostic model demonstrated good accuracy and clinical decision benefits. Multiple immune analyses suggested greater benefits from immunotherapy in the low‐risk group. We predicted multiple targeted drugs, providing a basis for drug development.ConclusionOur study's multifactorial prognostic model across multiple datasets demonstrates good applicability, offering a reliable tool for personalized therapy. Immunological and mutation‐related analyses provide theoretical foundations for further research. Drug predictions offer important insights for future drug development and treatment strategies. Overall, this study provides comprehensive insights into tumor prognosis assessment and personalized treatment planning.

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基于多不饱和脂肪酸相关基因的肝细胞癌患者预后模型

目的多不饱和脂肪酸（PUFA）因其在肝癌发生中的作用而受到越来越多的关注。本研究的目的是建立基于PUFA相关代谢基因特征的肝癌患者预后预测模型。方法转录组数据和临床数据从公共数据库获得，而PUFA相关基因集则从基因集富集中获得。分析（GSEA）数据库。对训练集进行单变量Cox分析，然后对具有以下特征的基因进行LASSO逻辑回归和多变量Cox分析：p< .05。随后，采用逐步Akaike信息准则方法构建模型。根据中位评分划分高危组和低危组，验证模型的生存预测能力、诊断效率和临床特征的风险评分分布。上述过程也在验证集中得到了验证。使用四种算法评估免疫浸润水平，并探讨不同组的免疫治疗潜力。基于GSEA算法选择不同群体之间的显着富集路径，并进行突变分析。使用单变量和多变量 Cox 回归分析将临床因素和风险评分纳入列线图预后模型，并通过校准曲线和临床决策曲线进行验证。此外，还进行药物敏感性分析，以筛选潜在的靶向药物。结果我们构建了包含8个基因（PLA2G12A、CYP2C8、ABCCI、CD74、CCR7、P2RY4、P2RY6和YY1）的预后模型。多个数据集的验证表明该模型具有良好的预后预测能力和诊断效率，但在高风险组中观察到较差的分级和分期。不同群体之间的突变状态和途径富集存在差异。将分期、等级、T.Stage 和 RiskScore 纳入列线图预后模型显示出良好的准确性和临床决策优势。多项免疫分析表明，低风险人群从免疫治疗中获益更大。我们预测了多种靶向药物，为药物开发提供了基础。结论我们的研究跨多个数据集的多因素预后模型表现出良好的适用性，为个性化治疗提供了可靠的工具。免疫学和突变相关分析为进一步研究提供理论基础。药物预测为未来的药物开发和治疗策略提供了重要的见解。总体而言，这项研究为肿瘤预后评估和个性化治疗计划提供了全面的见解。