Department of Day Surgery, People's Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, China.
BMC Bioinformatics. 2023 Apr 15;24(1):147. doi: 10.1186/s12859-023-05283-3.
Gastric cancer (GC) is one of the most common causes of cancer-related fatalities worldwide, and its progression is associated with RNA modifications. Here, using RNA modification-related genes (RNAMRGs), we aimed to construct a prognostic model for patients with GC.
Based on RNAMRGs, RNA modification scores (RNAMSs) were obtained for GC samples from The Cancer Genome Atlas and were divided into high- and low-RNAMS groups. Differential analysis and weighted correlation network analysis were performed for the differential expressed genes (DEGs) to obtain the key genes. Next, univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were performed to obtain the model. According to the model risk score, samples were divided into high- and low-risk groups. Enrichment analysis and immunoassays were performed for the DEGs in these groups. Four external datasets from Gene Expression Omnibus data base were used to test the accuracy of the predictive model.
We identified SELP and CST2 as key DEGs, which were used to generate the predictive model. The high-risk group had a worse prognosis compared to the low-risk group (p < 0.05). Enrichment analysis and immunoassays revealed that 144 DEGs related to immune cell infiltration were associated with the Wnt signaling pathway and included hub genes such as ELN. Overall mutation levels, tumor mutation burden, and microsatellite instability were lower, but tumor immune dysfunction and exclusion scores were greater (p < 0.05) in the high-risk group than in the low-risk group. The validation results showed that the prediction model score can accurately predict the prognosis of GC patients. Finally, a nomogram was constructed using the risk score combined with the clinicopathological characteristics of patients with GC.
This risk score from the prediction model related to the tumor microenvironment and immunotherapy could accurately predict the overall survival of GC patients.
胃癌（GC）是全球癌症相关死亡的最常见原因之一，其进展与 RNA 修饰有关。在这里，我们使用 RNA 修饰相关基因（RNAMRGs），旨在为 GC 患者构建一个预后模型。
基于 RNAMRGs，我们从癌症基因组图谱（The Cancer Genome Atlas）获得了 GC 样本的 RNA 修饰评分（RNAMS），并将其分为高和低 RNAMS 组。对差异表达基因（DEGs）进行差异分析和加权相关网络分析，以获得关键基因。接下来，进行单变量 Cox 回归、最小绝对收缩和选择算子（least absolute shrinkage and selection operator）和多变量 Cox 回归分析，以获得模型。根据模型风险评分，将样本分为高风险组和低风险组。对这些组中的 DEGs 进行富集分析和免疫检测。从基因表达 Omnibus 数据库（Gene Expression Omnibus data base）中使用四个外部数据集来测试预测模型的准确性。
我们确定 SELP 和 CST2 为关键 DEGs，用于生成预测模型。与低风险组相比，高风险组的预后更差（p<0.05）。富集分析和免疫检测显示，与 Wnt 信号通路相关的 144 个与免疫细胞浸润相关的 DEG 包括 ELN 等核心基因。高风险组的总体突变水平、肿瘤突变负担和微卫星不稳定性较低，但肿瘤免疫功能障碍和排斥评分较高（p<0.05）。验证结果表明，预测模型评分可以准确预测 GC 患者的预后。最后，使用风险评分结合 GC 患者的临床病理特征构建了列线图。
该预测模型相关的肿瘤微环境和免疫治疗风险评分可以准确预测 GC 患者的总生存期。
