胃癌（GC）个性化治疗发展的主要障碍是肿瘤内、患者内和患者间普遍存在的异质性。虽然病理分期和组织学亚型诊断可以大致预测预后，但GC的异质性很少被考虑。肿瘤微环境（TME）的一个重要组成部分，细胞外基质（ECM）与肿瘤和免疫细胞广泛互动，为研究GC的异质性提供了可能的代理。然而，ECM由众多蛋白质组成，目前没有合适的模型来筛选与肿瘤生长和预后有关的ECM相关基因。我们构建了患者来源的肿瘤异种移植（PDTX）模型，以获得稳定的ECM相关转录组标志，提高GC预后预测和治疗设计的准确性。我们收集了122例原发性GC肿瘤组织，构建PDTX模型，并研究了其发生率与GC预后的关系。进行了PDTX起源肿瘤的转录组分析，并应用最小绝对收缩和选择算子（LASSO）Cox回归分析提取预后的ECM标志，并建立PDTX肿瘞性相关基因（PTG）得分。利用两个独立队列验证了PTG得分的预测能力。最后，我们结合PTG得分、年龄和病理分期信息建立了一个稳定的GC预后预测诺模图。我们发现，即使在相同的病理分期下，PDTX肿瘛建议GC患者预后差。PDTX起源的GC组织和相应的正常对照组的转录组分析鉴定了383个差异表达基因，富集了ECM相关基因。利用PTG得分建立的可靠预后预测模型在两个验证队列中表现出良好性能。高PTG得分与M2极化巨噬细胞和癌相关成纤维细胞浸润相关。最后，将PTG得分与年龄和TNM分期结合使用，比仅使用年龄或TNM分期更为有效的预后模型。我们发现，ECM相关标志可能会促进PDTX肿瘤发生，并预示GC预后不良。基于PTG得分建立了可行的生存预测模型，与免疫细胞浸润相关。与患者年龄和病理TNM分期相结合，PTG得分可以成为GC预后预测的新方法。 © 2023. BioMed Central Ltd., part of Springer Nature.

A major obstacle to the development of personalized therapies for gastric cancer (GC) is the prevalent heterogeneity at the intra-tumor, intra-patient, and inter-patient levels. Although the pathological stage and histological subtype diagnosis can approximately predict prognosis, GC heterogeneity is rarely considered. The extracellular matrix (ECM), a major component of the tumor microenvironment (TME), extensively interacts with tumor and immune cells, providing a possible proxy to investigate GC heterogeneity. However, ECM consists of numerous protein components, and there are no suitable models to screen ECM-related genes contributing to tumor growth and prognosis. We constructed patient-derived tumor xenograft (PDTX) models to obtain robust ECM-related transcriptomic signatures to improve GC prognosis prediction and therapy design.One hundred twenty two primary GC tumor tissues were collected to construct PDTX models. The tumorigenesis rate and its relationship with GC prognosis were investigated. Transcriptome profiling was performed for PDTX-originating tumors, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to extract prognostic ECM signatures and establish PDTX tumorigenicity-related gene (PTG) scores. The predictive ability of the PTG score was validated using two independent cohorts. Finally, we combined PTG score, age, and pathological stage information to establish a robust nomogram for GC prognosis prediction.We found that PDTX tumorigenicity indicated a poor prognosis in patients with GC, even at the same pathological stage. Transcriptome profiling of PDTX-originating GC tissues and corresponding normal controls identified 383 differentially expressed genes, with enrichment of ECM-related genes. A robust prognosis prediction model using the PTG score showed robust performance in two validation cohorts. A high PTG score was associated with elevated M2 polarized macrophage and cancer-associated fibroblast infiltration. Finally, combining the PTG score with age and TNM stage resulted in a more effective prognostic model than age or TNM stage alone.We found that ECM-related signatures may contribute to PDTX tumorigenesis and indicate a poor prognosis in GC. A feasible survival prediction model was built based on the PTG score, which was associated with immune cell infiltration. Together with patient ages and pathological TNM stages, PTG score could be a new approach for GC prognosis prediction.© 2023. BioMed Central Ltd., part of Springer Nature.