Liu Jing, Wang Huiyu, Huang Fang, Hang Zhiqiang, Xu Pan, Wang Runjie, Xu Junying
Department of Oncology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, 299 Qingyang Road, Liangxi District, Wuxi, 214000, Jiangsu Province, China.
Discov Oncol. 2025 Jun 2;16(1):986. doi: 10.1007/s12672-025-02477-4.
This study aimed to identify prognostic genes associated with immunosenescence in gastric carcinoma (GC) and to elucidate their mechanisms to provide new ideas for the clinical treatment of GC.
According to single cell data, clustering and annotation were conducted to acquire key cells. Then, differentially expressed genes (DEGs) in key cells (KC-DEGs) and TCGA-GC (GC-DEGs) were obtained, and took their intersection with CS-RGs to obtain candidate genes. Afterwards, prognostic genes were identified by regression analyses. Following this, the risk model was constructed, and the high-risk and low-risk groups were obtained. Next, a nomogram based on independent prognostic factors was constructed for predicting survival in GC. Finally, to further explore the mechanisms associated with the risk groups, immune microenvironment analysis was performed.
T cells were used as key cells. Subsequently, AXL, PIM1, STK40, CXCL1, IFNG and SERPINE1 were identified as prognostic genes. The risk model and nomogram had favourable predictive capability in survival of GC patients. Surprisingly, 17 differential immune cells had higher levels of infiltration in the high-risk group, a result that was further confirmed in tumor purity. Notably, there was mostly a positive correlation between them and prognostic genes. Then, both tumor mutation burden (TMB) and microsatellite instability (MSI) were lower in the high-risk group, suggested the high-risk group might be associated with lower treatment benefit.
6 prognostic genes were identified, providing novel concepts in prognosis and therapy for GC.
本研究旨在鉴定与胃癌(GC)免疫衰老相关的预后基因,并阐明其机制,为GC的临床治疗提供新思路。
根据单细胞数据进行聚类和注释以获取关键细胞。然后,获得关键细胞中的差异表达基因(KC-DEGs)和TCGA-GC中的差异表达基因(GC-DEGs),并将它们与CS-RGs取交集以获得候选基因。之后,通过回归分析鉴定预后基因。随后,构建风险模型,得到高风险组和低风险组。接下来,构建基于独立预后因素的列线图以预测GC患者的生存情况。最后,为进一步探索与风险组相关的机制,进行免疫微环境分析。
将T细胞用作关键细胞。随后,鉴定出AXL、PIM1、STK40、CXCL1、IFNG和SERPINE1为预后基因。风险模型和列线图对GC患者的生存具有良好的预测能力。令人惊讶的是,17种差异免疫细胞在高风险组中的浸润水平更高,这一结果在肿瘤纯度分析中得到进一步证实。值得注意的是,它们与预后基因之间大多呈正相关。然后,高风险组的肿瘤突变负荷(TMB)和微卫星不稳定性(MSI)均较低,提示高风险组可能与较低的治疗获益相关。
鉴定出6个预后基因,为GC的预后和治疗提供了新的概念。