Hu Jia-Li, Huang Mei-Jin, Halina Halike, Qiao Kun, Wang Zhi-Yuan, Lu Jia-Jie, Yin Cheng-Liang, Gao Feng
Department of Gastroenterology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830001, Xinjiang Uygur Autonomous Region, China.
Xinjiang Clinical Research Center for Digestive Disease, Urumqi 830001, Xinjiang Uygur Autonomous Region, China.
World J Gastrointest Oncol. 2024 Mar 15;16(3):945-967. doi: 10.4251/wjgo.v16.i3.945.
Gastric cancer (GC) is a highly aggressive malignancy with a heterogeneous nature, which makes prognosis prediction and treatment determination difficult. Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours. Inflammation also affects the prognosis of GC patients. Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis. However, the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear.
To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients.
In this study, the mRNA expression profiles and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) database (GSE66229). An inflammatory-related gene prognostic signature model was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database. GC patients from the GSE26253 cohort were used for validation. Univariate and multivariate Cox analyses were used to determine the independent prognostic factors, and a prognostic nomogram was established. The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram. The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram. Gene set enrichment analysis was performed to explore the potential regulatory pathways involved. The relationship between tumour immune infiltration status and risk score was analysed Tumour Immune Estimation Resource and CIBERSORT. Finally, we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents.
A prognostic model consisting of three inflammatory-related genes (MRPS17, GUF1, and PDK4) was constructed. Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients. According to the risk score, GC patients were stratified into high- and low-risk groups, and patients in the high-risk group had significantly worse prognoses according to age, sex, TNM stage and Lauren type. Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging. Finally, the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group, indicating a link between inflammation-related genes and drug sensitivity.
In conclusion, we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.
胃癌（GC）是一种具有高度侵袭性的异质性恶性肿瘤，这使得预后预测和治疗决策变得困难。炎症现在被认为是癌症的标志之一，在肿瘤的病因学和持续生长中起重要作用。炎症也影响GC患者的预后。最近的报告表明，一些炎症相关生物标志物可用于预测肿瘤预后。然而，炎症相关生物标志物在预测GC患者预后中的重要性仍不清楚。
研究炎症相关生物标志物对GC患者预后的预测作用。
在本研究中，从基因表达综合数据库（GEO）（GSE66229）获取GC患者的mRNA表达谱和相应的临床信息。基于GEO数据库，使用最小绝对收缩和选择算子Cox回归模型构建炎症相关基因预后特征模型。来自GSE26253队列的GC患者用于验证。采用单因素和多因素Cox分析确定独立预后因素，并建立预后列线图。利用校准曲线和基于受试者工作特征分析的曲线下面积评估列线图的预测价值。绘制决策曲线分析结果以量化和评估列线图的临床价值。进行基因集富集分析以探索潜在的调控途径。使用肿瘤免疫估计资源（Tumour Immune Estimation Resource）和CIBERSORT分析肿瘤免疫浸润状态与风险评分之间的关系。最后，我们分析了风险评分与患者对常用化疗和靶向治疗药物敏感性之间的关联。
构建了一个由三个炎症相关基因（MRPS17、GUF1和PDK4）组成的预后模型。独立预后分析显示，风险评分是GC患者的一个独立预后因素。根据风险评分，GC患者被分为高风险组和低风险组，高风险组患者在年龄、性别、TNM分期和Lauren分型方面的预后明显更差。共识聚类确定了三种炎症亚型，它们比传统的分级和分期更能准确预测GC预后。最后，研究表明低风险组患者比高风险组患者对某些药物更敏感，这表明炎症相关基因与药物敏感性之间存在联系。
总之，我们建立了一种新的三基因预后特征，可能有助于预测GC患者的预后并实现治疗决策的个性化。
