Liu Xing, Ou Jianghong
Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Department of Integrated Chinese and Western Medicine, Changsha Central Hospital, Nanhua University, Changsha, 410000, China.
Heliyon. 2024 Feb 13;10(4):e26013. doi: 10.1016/j.heliyon.2024.e26013. eCollection 2024 Feb 29.
Gastric cancer (GC) is a malignancy known for its high fatality rate. Disulfidptosis, a potentially innovative therapeutic strategy for cancer treatment, has been proposed. Nevertheless, the specific involvement of disulfidptosis in the context of GC remains uncertain.
The mRNA expression profiles were obtained from the TCGA and GEO databases. Univariate and LASSO Cox regression analyses were employed to identify differentially expressed genes and develop a risk model for disulfidptosis-related genes. The performance of the model was evaluated using Kaplan-Meier curve, ROC curve, and nomogram. Univariate and multivariate Cox regression analyses were conducted to determine if the risk model could serve as an independent prognostic factor. The biological function of the identified genes was assessed through GO, KEGG, and GSEA analyses. The prediction of drug response was conducted employing the package "pRRophetic". Furthermore, gene expression was determined using qRT-PCR.
An eight-gene signature were identified and utilized to categorize patients into low- and high-risk groups. Survival, receiver operating characteristic (ROC) curve, and Cox analyses provided clarification that these eight hub genes served as a favorable independent prognostic factor for patients with GC. A nomogram was constructed by integrating clinical parameters with the risk signatures, demonstrating high precision in predicting 1-, 3-, and 5-year survival rates. Additionally, drug sensitivity was different in the high-risk and low-risk groups, and the expression of three genes was verified by qRT-PCR.
The prognostic risk model developed in this study demonstrates the potential to accurately forecast the prognosis of patients with GC.
胃癌（GC）是一种以高死亡率著称的恶性肿瘤。已提出二硫化物诱导的细胞程序性坏死这一潜在的创新癌症治疗策略。然而，二硫化物诱导的细胞程序性坏死在胃癌中的具体作用仍不明确。
从TCGA和GEO数据库获取mRNA表达谱。采用单因素和LASSO Cox回归分析来鉴定差异表达基因，并建立二硫化物诱导的细胞程序性坏死相关基因的风险模型。使用Kaplan-Meier曲线、ROC曲线和列线图评估模型的性能。进行单因素和多因素Cox回归分析，以确定风险模型是否可作为独立的预后因素。通过GO、KEGG和GSEA分析评估所鉴定基因的生物学功能。使用“pRRophetic”软件包进行药物反应预测。此外，采用qRT-PCR测定基因表达。
鉴定出一个八基因特征，并用于将患者分为低风险和高风险组。生存分析、受试者工作特征（ROC）曲线和Cox分析表明，这八个核心基因是胃癌患者良好的独立预后因素。通过将临床参数与风险特征相结合构建了列线图，在预测1年、3年和5年生存率方面显示出高精度。此外，高风险和低风险组的药物敏感性不同，并且通过qRT-PCR验证了三个基因的表达。
本研究建立的预后风险模型显示出准确预测胃癌患者预后的潜力。
