Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang, China.
Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang, China.
Ann Med. 2024 Dec;56(1):2426758. doi: 10.1080/07853890.2024.2426758. Epub 2024 Nov 11.
Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear.
In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data.
We identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated.
The NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.
癌症的特征是其抵抗细胞死亡的能力，新出现的证据表明非凋亡调控的细胞死亡（RCD）、肿瘤进展和治疗反应之间存在潜在的相关性。然而，胃癌（GC）中非凋亡 RCD 相关基因（NRG）的预后意义及其与免疫反应的关系尚不清楚。
本研究从癌症基因组图谱和基因表达综合数据库中获取了 GC 患者的 RNA-seq 基因表达和临床信息。Cox 和 LASSO 回归分析用于构建 NRG 特征。此外，我们基于 ResNet50 开发了一种深度学习模型，用于从数字病理学幻灯片预测 NRG 特征。使用实时定量 PCR 和单细胞 RNA 测序数据验证特征基因的表达。
我们确定了 13 个 NRG 作为预测 GC 患者预后的特征基因。高风险组 NRG 评分较高，总生存率较短，免疫抑制性细胞浸润增加，免疫功能障碍。此外，NRG 特征与 GC 患者的化疗药物反应性和免疫治疗效果之间存在关联。此外，深度学习模型通过利用形态学差异，有效地根据 NRG 特征对 GC 患者进行分层，为 GC 患者的分类提供了有前景的结果。验证实验表明，SERPINE1 在 GC 中表达水平显著上调，而 GPX3 和 APOD 的表达水平显著下调。
NRG 特征及其深度学习模型具有重要的临床意义，强调了基于 GC 亚分型的个体化治疗策略的重要性。这些发现为指导 GC 的临床决策和治疗方法提供了有价值的见解。
