基于二硫化相关lncRNA构建肝细胞癌预后模型

Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Disulfidptosis-Related lncRNA

摘要: 背景：二硫化血症是一种新的代谢调节性细胞死亡，其特征是异常的二硫化物应激和肌动蛋白网络崩溃。然而，二硫化相关lncRNAs在肝细胞癌(HCC)中的预测潜力仍在研究之中。研究方法：采用皮尔逊相关分析、单变量分析、最小绝对缩减和选择算子分析等方法获得二硫化相关lncRNA，并进行多变量Cox回归分析，构建二硫化相关lncRNA模型。此外，我们还通过共识聚类分析将HCC患者分为亚型，进一步研究了免疫微环境的差异，检查了免疫检查点，评估了免疫状态，确定了潜在的疾病治疗药物，为HCC治疗提供了新的思路和选择。研究结果：共获得了4个与二硫化相关的lncRNAs (GIHCG, MKLN1-AS, AL031985.3, AL442125.2)，并建立了预后模型，我们发现高风险组患者的总生存率(OS)更低，ssGSEA分析显示免疫细胞浸润与风险评分呈正相关。低危患者对吉非替尼敏感，高危患者对博来霉素、吉西他滨、顺铂和依托泊苷敏感。聚类分析确定了两种不同的亚型，它们具有不同的预后和免疫浸润。这有助于我们为HCC患者确定更精确的靶向疗法。结论：我们建立了一个与二硫化相关的lncRNA模型，该模型可用于预测HCC患者的预后、肿瘤突变负荷、免疫细胞浸润情况以及对药物治疗的反应。

Abstract: Background: Disulfidptosis is a novel metabolically regulated cell death characterized by aberrant disulfide stress and actin network collapse. However, the predictive potential of disulfidptosis-related lncRNAs in hepatocellular carcinoma (HCC) is still under investigation. Methods: Pearson correlation analysis, univariate analysis, least absolute shrinkage, and selection operator analysis were used to obtain disulfidptosis-related lncRNAs, and conducted multivariate Cox regression analysis to construct a model of disulfidptosis-related lncRNAs. The correlation between risk score and pathway enrichment analysis, immune infiltrating cells, immune checkpoint genes, and anticancer drug sensitivity was analyzed. In addition, we classified HCC patients into subtypes by consensus clustering analysis, further investigated the differences in the immune microenvironment, examined immune checkpoints, assessed immune status, identified potential disease therapeutic drugs, and provided new ideas and options for HCC treatment. Results: A total of 4 disulfidptosis-related lncRNAs (GIHCG, MKLN1-AS, AL031985.3, AL442125.2) were obtained and prognostic models were developed, and we found that patients in the high-risk group had worse overall survival (OS). ssGSEA analysis showed a positive correlation between immune cell infiltration and risk score. Low-risk patients were sensitive to gefitinib and high-risk patients were sensitive to bleomycin, gemcitabine, cisplatin, and etoposide. Cluster analysis identified two distinct subtypes with different prognoses and immune infiltration. This helps us to identify more precise targeted therapies for HCC patients. Conclusions: We established a disulfidptosis-related lncRNA model that can be exploited to predict the prognosis, tumor mutational burden, immune cell infiltration landscape, and response to drug therapy in HCC patients.