BackgroundThe metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI.MethodsGenes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein–protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription–quantitative real‐time PCR (RT‐qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes.ResultsThis study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune‐related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells.ConclusionWe distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.

中文翻译：

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利用胆汁酸基因建立预测胃肠道癌症患者免疫力和生存的预后模型

背景异常胆汁酸（BA）的代谢与胃肠道（GI）癌的发生和发展有关。然而，目前对胃肠道中BAs代谢分子机制的研究缺乏。方法从癌症基因组图谱(TCGA)数据库、基因表达综合(GEO)数据库和分子特征数据库等公共数据库中挖掘参与BAs代谢的基因（MSigDB）。 ConsensusClusterPlus 用于对 GI 分子亚型进行分类。为了开发预测胃肠道预后的 RiskScore 模型，对蛋白质-蛋白质相互作用 (PPI) 网络中的基因进行单变量 Cox 分析，然后使用 Lasso 回归和逐步回归来完善模型并确定关键的预后基因。使用ESTIMATE算法和富集分析评估不同风险组的胃肠道患者的肿瘤免疫微环境。进行逆转录定量PCR（RT-qPCR）、Transwell实验和伤口愈合实验来验证模型基因的表达和功能。结果本研究定义了三种分子亚型（C1、C2和C3）。具体而言，C1 预后最好，而 C3 预后最差，免疫检查点基因表达水平和 TIDE 评分较高。我们选择了显着影响胃肠道预后的九个关键基因（AXIN2、ATOH1、CHST13、PNMA2、GYG2、MAGEA3、SNCG、HEYL 和 RASSF10），并使用它们相应地开发了 RiskScore 模型。结合列线图的验证结果，模型的预测被证明是准确的。高风险评分组的肿瘤和免疫相关通路显着丰富。与正常胃粘膜上皮细胞相比，胃癌细胞中9个基因的mRNA水平存在差异。抑制PNMA2可抑制癌细胞的迁移和侵袭。结论我们根据BAs代谢相关基因区分了三种不同预后的GI分子亚型，并开发了RiskScore模型，为GI患者的诊断和治疗做出了贡献。