肺腺癌中双硫死亡通路相关基因的鉴定及预后模型的建立

Identification of disulfidptosis pathway-related genes and construction of prognostic model in lung adenocarcinoma

目的建立肺腺癌(LUAD)与双硫死亡(DS)通路相关的基因(DPRGs)预后模型,阐明其潜在的生物学机制。方法LUAD相关基因测序及临床信息源于公共数据库。使用基因集变异分析(GSVA)结果与癌症基因组图谱(TCGA)数据集中mRNA表达量的相关性筛选DS通路中显著活跃的基因。基于最小绝对收缩和选择算子(LASSO)分析和随机森林(RF)算法筛选出DPRGs,使用多因素Cox回归分析构建风险评分(RS)模型,并通过基因表达综合数据库(GEO)进行验证。根据RS中位数将样本分为高、低风险组并进行分析。建立7个DPRGs的蛋白质-蛋白质互作(PPI)网络,发现与其他蛋白互作关系最多的蛋白是乳酸脱氢酶A(LDHA),并进一步研究其功能及表达情况。结果本研究筛选共得到7个DPRGs:SLC2A1、LDHA、SNAI2、ACO2、FGF12、ANP32B和ST13,由以上基因构建的预后模型验证效能较高。Kaplan-Meier生存分析结果显示,4个数据集中,高风险组LUAD患者的总生存时间与低风险组比较,差异有统计学意义(P<0.05)。高、低风险组差异表达基因富集分析发现,差异基因于p53信号通路、细胞周期等通路富集。实时定量聚合酶链反应(qRT-PCR)及免疫组织化学法结果表明,与正常组织相比,LUAD组织中LDHA表达水平升高。结论基于DPRGs建立的预测模型能有效预测患者预后,可能为LUAD患者的治疗和预后提供思路。

Objective To establish a prognostic model for lung adenocarcinoma(LUAD)based on genes associated with the disulfidptosis(DS)pathway,and to elucidate its potential biological mechanisms.Methods LUAD-related gene sequencing and clinical information were sourced from public databases.The correlation between results of gene set variation analysis(GSVA)and mRNA expression in The Cancer Genome Atlas(TCGA)dataset was used to screen genes that were significantly active in the disulfur death(DS)pathway.The Least Absolute Shrinkage and Selection Operator(LASSO)analysis and Random Forest(RF)algorithm were employed to screen out DS pathway prognosis-related genes(DPRGs)and multivariate Cox regression analysis was used to construct risk score(RS)model,which was validated using external GEO datasets.The samples were divided into high and low-risk groups based on the median score of RS.A protein-protein interaction(PPI)network corresponding to 7 DPRGs was established,with LDHA identified as the protein with the most interactions,thereby further investigating its function and expression patterns.Results In this study,7 DPRGs were screened,including SLC2A1,LDHA,SNAI2 and ACO2,FGF12,ANP32B and ST13.The prognostic model constructed based on these genes exhibited high validation efficiency.Kaplan-Meier survival analysis revealed significant differences in overall survival of patients between high-risk group and low-risk group in four datasets.Differential expression gene enrichment analysis between the high-risk and low-risk groups showed that these genes were enriched in pathways such as the p53 signaling pathway and cell cycle.Results of real-time quantitative polymerase chain reaction(qRT-PCR)and immunohistochemistry indicated that LDHA expression levels were elevated in LUAD tissue compared to normal tissues.Conclusion The LUAD model established based on DPRGs can effectively predict patients′prognosis,potentially offering insights into the treatment and prognosis of LUAD patients.