Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China.
Department of Translational Medicine, Geneplus-Beijing Institute, Beijing, 102205, China.
Gastric Cancer. 2023 Nov;26(6):891-903. doi: 10.1007/s10120-023-01416-y. Epub 2023 Aug 6.
Gastric cancer patients responded differently to the same treatment strategy and had various prognoses for the lack of biomarkers to guide the therapy choice.
RNA data of a local gastric cancer cohort with 103 patients were processed and used to explore potential treatment guiding factors. Cluster analysis was performed by non-negative matrix factorization. The expression level of collagen-related genes was evaluated by ssGSEA named collagen score (CS). Data from TCGA, ACRG, and an immune therapy cohort were utilized to explore prognosis and efficacy. Prognostic predictive power of CS was assessed using the nomogram.
In our study, local RNA data were processed by cluster analysis, and it was found that cluster 2 contained a worse tumor infiltration status. The GSEA result showed that collagen-related pathways were differentially activated in two clusters. In TCGA and ACRG cohorts, the CS can be used as an independent prognostic factor (TCGA OS: p = 0.018, HR = 3.5; ACRG OS: p = 0.014, HR = 4.88). An immunotherapy cohort showed that the patients with higher CS had a significantly worse ORR (p = 0.0025). The high CS group contained several cell death pathways down-regulated and contained the worse tumor microenvironment. The nomogram demonstrated the survival prediction capability of collagen score.
CS was verified as an independent prognostic factor and potentially reflected the therapeutic effect of immunotherapy. The CS could provide a new way to evaluate the clinical prognosis and response information helping develop the collagen-targeted treatment.
由于缺乏指导治疗选择的生物标志物，胃癌患者对相同的治疗策略反应不同，预后也各不相同。
对 103 例局部胃癌患者的 RNA 数据进行处理，以探索潜在的治疗指导因素。采用非负矩阵分解进行聚类分析。通过 ssGSEA 评估胶原相关基因的表达水平，命名为胶原评分（CS）。利用 TCGA、ACRG 和免疫治疗队列的数据来探讨预后和疗效。使用列线图评估 CS 的预后预测能力。
在本研究中，通过聚类分析对局部 RNA 数据进行处理，发现第 2 组包含更差的肿瘤浸润状态。GSEA 结果表明，两个聚类中胶原相关途径的活性存在差异。在 TCGA 和 ACRG 队列中，CS 可以作为独立的预后因素（TCGA OS：p=0.018，HR=3.5；ACRG OS：p=0.014，HR=4.88）。免疫治疗队列显示，CS 较高的患者客观缓解率（ORR）显著更差（p=0.0025）。高 CS 组包含几个细胞死亡途径下调，并且肿瘤微环境更差。列线图显示了胶原评分的生存预测能力。
CS 被验证为一个独立的预后因素，并且可能反映了免疫治疗的疗效。CS 可以为评估临床预后和反应信息提供一种新的方法，有助于开发针对胶原的治疗方法。
