Shao Shuai, Sun Yang, Zhao Dongmei, Tian Yu, Yang Yifan, Luo Nan
General Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
Cardiology, The Second Hospital of Dalian Medical University, Dalian, China.
PeerJ. 2024 Jan 31;12:e16868. doi: 10.7717/peerj.16868. eCollection 2024.
Ubiquitination is crucial for the growth of cancer. However, the role of ubiquitination-related genes (URGs) in stomach adenocarcinoma (STAD) remains unclear. Differentially expressed URGs (DE-URGs) were examined in the whole TCGA-STAD dataset, and the prognosis-related genes were discovered from the The Cancer Genome Atlas (TCGA) training set. Prognostic genes were discovered using selection operator regression analysis and absolute least shrinkage (LASSO). A multivariate Cox analysis was further employed, and a polygene-based risk assessment system was established. Signatures were verified using the Gene Expression Omnibus (GEO) database record GSE84433 and the TCGA test set. Using the MEXPRESS dataset, a detailed analysis of gene expression and methylation was carried out. Using the DAVID database, DE-URG function and pathway enrichment was examined. The identified 163 DE-URGs were significantly associated with pathways related to protein ubiquitination, cell cycle, and cancer. A prognostic signature based on 13 DE-URGs was constructed, classifying patients into two risk groups. Compared to low-risk patients, people at high risk had considerably shorter survival times. Cox regression analyses considered prognostic parameters independent of age and risk score and were used to generate nomograms. Calibration curves show good agreement between nomogram predictions and observations. Furthermore, the results of the MEXPRESS analysis indicated that 13 prognostic DE-URGs had an intricate methylation profile. The enhanced Random Forest-based model showed greater efficacy in predicting prognosis, mutation, and immune infiltration. The validation, including CCK8, EdU, Transwell, and co-culture Transwell, proved that RNF144A was a potent oncogene in STAD and could facilitate the migration of M2 macrophages. In this research, we have created a genetic model based on URGs that can appropriately gauge a patient's prognosis and immunotherapy response, providing clinicians with a reliable tool for prognostic assessment and supporting clinical treatment decisions.
泛素化对癌症的发展至关重要。然而，泛素化相关基因（URGs）在胃腺癌（STAD）中的作用仍不清楚。在整个TCGA-STAD数据集中检测了差异表达的URGs（DE-URGs），并从癌症基因组图谱（TCGA）训练集中发现了与预后相关的基因。使用选择算子回归分析和绝对最小收缩（LASSO）发现预后基因。进一步进行多变量Cox分析，并建立了基于多基因的风险评估系统。使用基因表达综合数据库（GEO）记录GSE84433和TCGA测试集对特征进行验证。使用MEXPRESS数据集对基因表达和甲基化进行了详细分析。使用DAVID数据库检查DE-URG功能和通路富集情况。鉴定出的163个DE-URGs与蛋白质泛素化、细胞周期和癌症相关的通路显著相关。构建了基于13个DE-URGs的预后特征，将患者分为两个风险组。与低风险患者相比，高风险患者的生存时间明显更短。Cox回归分析将预后参数视为独立于年龄和风险评分，并用于生成列线图。校准曲线显示列线图预测与观察结果之间具有良好的一致性。此外，MEXPRESS分析结果表明，13个预后DE-URGs具有复杂的甲基化谱。增强的基于随机森林的模型在预测预后、突变和免疫浸润方面显示出更高的效能。包括CCK8、EdU、Transwell和共培养Transwell在内的验证证明，RNF144A是STAD中的一种强效癌基因，可促进M2巨噬细胞的迁移。在本研究中，我们创建了一个基于URGs的遗传模型，该模型可以适当地评估患者的预后和免疫治疗反应，为临床医生提供可靠的预后评估工具，并支持临床治疗决策。
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