PDF(Pubmed)
Abstract：
Parthanatos is a novel programmatic form of cell death based on DNA damage and PARP-1 dependency. Nevertheless, its specific role in the context of gastric cancer (GC) remains uncertain.
In this study, we integrated multi-omics algorithms to investigate the molecular characteristics of parthanatos in GC. A series of bioinformatics algorithms were utilized to explore clinical heterogeneity of GC and further predict the clinical outcomes.
Firstly, we conducted a comprehensive analysis of the omics features of parthanatos in various human tumors, including genomic mutations, transcriptome expression, and prognostic relevance. We successfully identified 7 cell types within the GC microenvironment: myeloid cell, epithelial cell, T cell, stromal cell, proliferative cell, B cell, and NK cell. When compared to adjacent non-tumor tissues, single-cell sequencing results from GC tissues revealed elevated scores for the parthanatos pathway across multiple cell types. Spatial transcriptomics, for the first time, unveiled the spatial distribution characteristics of parthanatos signaling. GC patients with different parthanatos signals often exhibited distinct immune microenvironment and metabolic reprogramming features, leading to different clinical outcomes. The integration of parthanatos signaling and clinical indicators enabled the creation of novel survival curves that accurately assess patients\' survival times and statuses.
In this study, the molecular characteristics of parthanatos\' unicellular and spatial transcriptomics in GC were revealed for the first time. Our model based on parthanatos signals can be used to distinguish individual heterogeneity and predict clinical outcomes in patients with GC.
In this study, we integrated multi-omics algorithms to investigate the molecular characteristics of parthanatos in GC. A series of bioinformatics algorithms were utilized to explore clinical heterogeneity of GC and further predict the clinical outcomes.
Firstly, we conducted a comprehensive analysis of the omics features of parthanatos in various human tumors, including genomic mutations, transcriptome expression, and prognostic relevance. We successfully identified 7 cell types within the GC microenvironment: myeloid cell, epithelial cell, T cell, stromal cell, proliferative cell, B cell, and NK cell. When compared to adjacent non-tumor tissues, single-cell sequencing results from GC tissues revealed elevated scores for the parthanatos pathway across multiple cell types. Spatial transcriptomics, for the first time, unveiled the spatial distribution characteristics of parthanatos signaling. GC patients with different parthanatos signals often exhibited distinct immune microenvironment and metabolic reprogramming features, leading to different clinical outcomes. The integration of parthanatos signaling and clinical indicators enabled the creation of novel survival curves that accurately assess patients\' survival times and statuses.
In this study, the molecular characteristics of parthanatos\' unicellular and spatial transcriptomics in GC were revealed for the first time. Our model based on parthanatos signals can be used to distinguish individual heterogeneity and predict clinical outcomes in patients with GC.
摘要：
背景：Parthanatos是一种基于DNA损伤和PARP-1依赖性的新型程序性细胞死亡形式。然而，其在胃癌（GC）中的具体作用仍不确定。
方法：在本研究中，我们集成了多组学算法来研究GC中parthanatos的分子特征。利用一系列生物信息学算法来探索GC的临床异质性并进一步预测临床结果。
结果：首先，我们对各种人类肿瘤中parthanatos的组学特征进行了全面分析，包括基因组突变,转录组表达，和预后相关性。我们成功地鉴定了GC微环境中的7种细胞类型：骨髓细胞，上皮细胞，T细胞,基质细胞,增殖细胞，B细胞,NK细胞与邻近的非肿瘤组织相比，来自GC组织的单细胞测序结果显示,在多种细胞类型中,parthanatos途径的得分升高.空间转录组学，第一次,揭示了parthanatos信号的空间分布特征。具有不同parthanatos信号的GC患者通常表现出不同的免疫微环境和代谢重编程特征，导致不同的临床结果。parthanatos信号和临床指标的整合使得能够创建新的生存曲线，准确评估患者的生存时间和状态。
结论：在这项研究中，首次揭示了GC中parthanatos单细胞和空间转录组学的分子特征。我们基于parthanatos信号的模型可用于区分个体异质性并预测GC患者的临床结局。
方法：在本研究中，我们集成了多组学算法来研究GC中parthanatos的分子特征。利用一系列生物信息学算法来探索GC的临床异质性并进一步预测临床结果。
结果：首先，我们对各种人类肿瘤中parthanatos的组学特征进行了全面分析，包括基因组突变,转录组表达，和预后相关性。我们成功地鉴定了GC微环境中的7种细胞类型：骨髓细胞，上皮细胞，T细胞,基质细胞,增殖细胞，B细胞,NK细胞与邻近的非肿瘤组织相比，来自GC组织的单细胞测序结果显示,在多种细胞类型中,parthanatos途径的得分升高.空间转录组学，第一次,揭示了parthanatos信号的空间分布特征。具有不同parthanatos信号的GC患者通常表现出不同的免疫微环境和代谢重编程特征，导致不同的临床结果。parthanatos信号和临床指标的整合使得能够创建新的生存曲线，准确评估患者的生存时间和状态。
结论：在这项研究中，首次揭示了GC中parthanatos单细胞和空间转录组学的分子特征。我们基于parthanatos信号的模型可用于区分个体异质性并预测GC患者的临床结局。
