微生物微环境的组成是影响肿瘤疾病发展的重要因素。然而，由于目前技术水平的限制，我们仍然无法全面研究和阐明微生物对肿瘤的影响的深度和广度，特别是微生物是否对癌症有影响。因此，本研究旨在基于细菌脂多糖（LPS）相关基因，利用生物信息学方法对前列腺微生物组在胃癌（GC）中的作用和机制进行深入研究。通过在毒素基因组学数据库（CTD）中进行比较，我们可以找到并筛选出与细菌LPS相关的基因。在研究中，我们使用Venn图和Lasso分析获得差异表达的与LPS相关的关键基因（LRHG）。然后，为了建立预后风险评分模型和基于LRHG特征的柱状图，我们使用单变量和多变量Cox回归分析进行建模和组成。此外，我们还对TMB、MSI、KRAS突变体和TIDE分数等免疫疗法的临床作用进行了深入研究。我们在数据库中筛选出9个与LPS相关的关键基因。我们基于LRHG构建了预后风险评分和柱状图，表明低风险评分对患者有保护作用。我们特别发现，通过TIDE分数评估，低风险评分对免疫疗法是有益的。基于LPS相关的关键基因，我们建立了一个LRHG签名，可以帮助预测GC患者的免疫疗法和预后。细菌脂多糖相关基因还可以作为预测GC患者无进展生存的生物标志物。© 2023. Springer Nature Limited.

The composition of microbial microenvironment is an important factor affecting the development of tumor diseases. However, due to the limitations of current technological levels, we are still unable to fully study and elucidate the depth and breadth of the impact of microorganisms on tumors, especially whether microorganisms have an impact on cancer. Therefore, the purpose of this study is to conduct in-depth research on the role and mechanism of prostate microbiome in gastric cancer (GC) based on the related genes of bacterial lipopolysaccharide (LPS) by using bioinformatics methods. Through comparison in the Toxin Genomics Database (CTD), we can find and screen out the bacterial LPS related genes. In the study, Venn plots and lasso analysis were used to obtain differentially expressed LPS related hub genes (LRHG). Afterwards, in order to establish a prognostic risk score model and column chart in LRHG features, we used univariate and multivariate Cox regression analysis for modeling and composition. In addition, we also conducted in-depth research on the clinical role of immunotherapy with TMB, MSI, KRAS mutants, and TIDE scores. We screened 9 LRHGs in the database. We constructed a prognostic risk score and column chart based on LRHG, indicating that low risk scores have a protective effect on patients. We particularly found that low risk scores are beneficial for immunotherapy through TIDE score evaluation. Based on LPS related hub genes, we established a LRHG signature, which can help predict immunotherapy and prognosis for GC patients. Bacterial lipopolysaccharide related genes can also be biomarkers to predict progression free survival in GC patients.© 2023. Springer Nature Limited.