UNASSIGNED:胃癌仍然是世界上最致命的肿瘤疾病之一。尽管有一些改进,胃癌患者的预后仍无法准确预测。
UNASSIGNED:基于单细胞测序数据,我们对胃癌患者和正常组织进行了详细分析,以确定单核细胞在胃癌进展中的作用。WCGA促进了我们在TCGA中搜索等级相关基因。然后,根据单核细胞的标记基因和细胞分化基因,我们确定了单核细胞的促癌基因。基于LASSO回归,我们使用TCGA数据库建立了预后模型。通过PCA验证了模型的准确性,ROC曲线,生存分析和预后分析。最后,我们通过观察药物敏感性评估模型在临床诊断和治疗中的意义,不同风险组患者的免疫微环境和免疫检查点表达。
未经证实:单核细胞在肿瘤微环境中分化较差。它主要在两个方面起到促进癌症的作用。一种是通过与其他肿瘤基质细胞相互作用间接促进肿瘤进展。另一种是通过MIF和TNF通路直接连接肿瘤细胞发挥促瘤作用。前者在这两个方面更为重要。共获得292个单核细胞促瘤基因,最终将12个基因纳入预后模型的构建。多种验证方法表明,我们的模型具有准确的预测能力。药敏分析可为临床患者用药提供指导。免疫微环境和免疫检查点的结果也表明了高危患者预后不良的原因。
未经批准:总而言之,我们为胃癌患者提供了12个基因的风险评分公式和列线图,以帮助临床药物治疗和预后预测。该模型具有较好的准确性和临床意义。
UNASSIGNED: Gastric cancer is still one of the most lethal tumor diseases in the world. Despite some improvements, the prognosis of patients with gastric cancer is still not accurately predicted.
UNASSIGNED: Based on single cell sequencing data, we conducted a detailed analysis of gastric cancer patients and normal tissues to determine the role of monocytes in the progression of gastric cancer. WCGA facilitated our search for Grade-related genes in TCGA. Then, according to the marker genes and cell differentiation genes of monocytes, we determined the cancer-promoting genes of monocytes. Based on LASSO regression, we established a prognostic model using TCGA database. The accuracy of the model was verified by PCA, ROC curve, survival analysis and prognostic analysis. Finally, we evaluated the significance of the model in clinical diagnosis and treatment by observing drug sensitivity, immune microenvironment and immune checkpoint expression in patients with different risk groups.
UNASSIGNED: Monocytes were poorly differentiated in tumor microenvironment. It mainly played a role in promoting cancer in two ways. One was to promote tumor progression indirectly by interacting with other tumor stromal cells. The other was to directly connect with tumor cells through the MIF and TNF pathway to play a tumor-promoting role. The former was more important in these two ways. A total of 292 monocyte tumor-promoting genes were obtained, and 12 genes were finally included in the construction of the prognosis model. A variety of validation methods showed that our model had an accurate prediction ability. Drug sensitivity analysis could provide guidance for clinical medication of patients. The results of immune microenvironment and immune checkpoint also indicated the reasons for poor prognosis of high-risk patients.
UNASSIGNED: In conclusion, we provided a 12-gene risk score formula and nomogram for gastric cancer patients to assist clinical drug therapy and prognosis prediction. This model had good accuracy and clinical significance.