Puai Medical College, Shaoyang University, The First Affiliated Hospital of Shaoyang University, Shaoyang, Hunan, China.
Department of Immunology, School of Basic Medicine, Central South University, Changsha, Hunan, China.
Aging (Albany NY). 2024 Jun 6;16(11):9709-9726. doi: 10.18632/aging.205895.
Gastric cancer (GC), the third most lethal cancer worldwide, is often diagnosed at an advanced stage, leaving limited therapeutic options. Given the diverse outcomes among GC patients with similar AJCC/UICC-TNM characteristics, there is a pressing need for more reliable prognostic tools. Recent advances in targeted therapy and immunotherapy have underscored this necessity. In this context, our study focused on a novel stress response state of T cells, termed T, identified across multiple cancers, which is associated with resistance to immunotherapy. We aimed to develop a predictive gene signature for the T phenotype within the tumor microenvironment (TME) of GC patients. By categorizing GC patients into high and low T groups based on the infiltration states of TME T cells, we observed significant differences in clinical prognosis and characteristics between the groups. Through a multi-step bioinformatics approach, we established an eight-gene signature based on genes differentially expressed between these groups. We conducted functional validations for the signature gene in GC cells. This gene signature effectively stratifies GC patients into high and low-risk categories, demonstrating robustness in predicting clinical outcomes. Furthermore, these risk groups exhibited distinct immune profiles, somatic mutations, and drug susceptibilities, highlighting the potential of our gene signature to enhance personalized treatment strategies in clinical practice.
胃癌（GC）是全球第三大致命癌症，通常在晚期诊断，治疗选择有限。鉴于具有相似 AJCC/UICC-TNM 特征的 GC 患者的预后结果存在差异，因此迫切需要更可靠的预后工具。靶向治疗和免疫治疗的最新进展凸显了这一需求。在这种情况下，我们的研究集中在一种称为 T 的 T 细胞应激反应状态上，这种状态在多种癌症中都有发现，与免疫治疗耐药性有关。我们旨在为 GC 患者的肿瘤微环境（TME）中的 T 表型开发一个预测性基因特征。通过根据 TME T 细胞的浸润状态将 GC 患者分为高 T 和低 T 组，我们观察到两组之间在临床预后和特征方面存在显著差异。通过多步生物信息学方法，我们基于这些组之间差异表达的基因建立了一个由八个基因组成的特征。我们对 GC 细胞中特征基因进行了功能验证。该基因特征可有效地将 GC 患者分为高风险和低风险类别，在预测临床结局方面具有稳健性。此外，这些风险组表现出不同的免疫特征、体细胞突变和药物敏感性，这突出了我们的基因特征在增强临床实践中个性化治疗策略方面的潜力。
