Lin Feizhi, Chen Xiaojiang, Liang Chengcai, Zhang Ruopeng, Chen Guoming, Zheng Ziqi, Huang Bowen, Wei Chengzhi, Zhao Zhoukai, Zhang Feiyang, Chen Zewei, Ruan Shenghang, Chen Yongming, Nie Runcong, Li Yuangfang, Zhao Baiwei
State Key Laboratory of Oncology in South China, Department of Gastric Surgery, Sun Yat-Sen University Cancer Center, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, China.
Front Pharmacol. 2024 Dec 18;15:1477363. doi: 10.3389/fphar.2024.1477363. eCollection 2024.
Programmed cell death (PCD) critically influences the tumor microenvironment (TME) and is intricately linked to tumor progression and patient prognosis. This study aimed to develop a novel prognostic indicator and marker of drug sensitivity in patients with gastric cancer (GC) based on PCD.
We analyzed genes associated with 14 distinct PCD patterns using bulk transcriptome data and clinical information from TCGA-STAD for model construction with univariate Cox regression and LASSO regression analyses. Microarray data from GSE62254, GSE15459, and GSE26901 were used for validation. Single-cell transcriptome data from GSE183904 were analyzed to explore the relationship between TME and the newly constructed model, named PCD index (PCDI). Drug sensitivity comparisons were made between patients with high and low PCDI scores.
We developed a novel twelve-gene signature called PCDI. Upon validation, GC patients with higher PCDI scores had poorer prognoses. A high-performance nomogram integrating the PCDI with clinical features was also established. Additionally, single-cell transcriptome data analysis suggested that PCDI might be linked to critical components of the TME. Patients with high PCDI scores exhibited resistance to standard adjuvant chemotherapy and immunotherapy but might benefit from targeted treatments with NU7441, Dasatinib, and JQ1.
The novel PCDI model shows significant potential in predicting clinical prognosis and drug sensitivity of GC, thereby facilitating personalized treatment strategies for patients with GC.
程序性细胞死亡（PCD）对肿瘤微环境（TME）具有关键影响，并与肿瘤进展和患者预后密切相关。本研究旨在基于PCD开发一种用于胃癌（GC）患者的新型预后指标和药物敏感性标志物。
我们使用来自TCGA-STAD的批量转录组数据和临床信息，通过单变量Cox回归和LASSO回归分析，分析了与14种不同PCD模式相关的基因，以构建模型。来自GSE62254、GSE15459和GSE26901的微阵列数据用于验证。分析来自GSE183904的单细胞转录组数据，以探索TME与新构建的名为PCD指数（PCDI）的模型之间的关系。对PCDI评分高和低的患者进行药物敏感性比较。
我们开发了一种名为PCDI的新型十二基因特征。经验证，PCDI评分较高的GC患者预后较差。还建立了一个将PCDI与临床特征相结合的高性能列线图。此外，单细胞转录组数据分析表明，PCDI可能与TME的关键成分有关。PCDI评分高的患者对标准辅助化疗和免疫治疗表现出耐药性，但可能从使用NU7441、达沙替尼和JQ1的靶向治疗中获益。
新型PCDI模型在预测GC的临床预后和药物敏感性方面显示出巨大潜力，从而有助于为GC患者制定个性化治疗策略。
