Shi Hui, Duan Jiangling, Chen Zhangming, Huang Mengqi, Han Wenxiu, Kong Rui, Guan Xiuyin, Qi Zhen, Zheng Shuang, Lu Ming
Department of Immunology, School of Basic Medical Sciences, Anhui Medical University, No.81, Mei Shan Road, Hefei, 230032, Anhui, China.
Department of Gastrointestinal Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Clin Transl Oncol. 2023 Apr;25(4):995-1010. doi: 10.1007/s12094-022-03003-6. Epub 2022 Nov 14.
Globally, gastric cancer (GC) is a common and lethal solid malignant tumor. Identifying the molecular signature and its functions can provide mechanistic insights into GC development and new methods for targeted therapy.
Differentially expressed genes (DEGs) and prognostic genes (from univariate Cox regression analysis) were overlapped to obtain prognostic DEGs. Subsequently, molecular modules and the functions of these prognostic DEGs were identified by Metascape and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Set Enrichment Analysis (GSEA) enrichment analyses, respectively. Protein-protein interaction (PPI) networks of up- and down-regulated prognostic DEGs in GC were analyzed using the MCC algorithm of the Cytohubba plug-in in Cytoscape. The prognostic gene signature was defined on hub genes of the PPI networks by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. Furthermore, the expressional level of PLG in our clinical GC samples was validated by quantitative PCR (qPCR), western blotting, and immunohistochemistry (IHC). Subsequently, the PLG expression-correlation analysis was performed to assess the role of PLG in GC progression. Immune infiltration analysis was performed by single-sample gene set enrichment analysis (ssGSEA) to assess the inhibitory effect of PLG on immune infiltration.
Firstly, Up- and down-regulated prognostic DEGs and hub genes in protein-protein interaction (PPI) networks in GC were identified. A prognostic five-gene signature (i.e., PLG, SPARC, FGB, SERPINE1, and KLHL41) was identified. Among the five genes, the relationship between plasminogen (PLG) and GC remains largely unclear. Moreover, the functions of PLG-correlated genes in GC, like 'fibrinolysis', 'hemostasis', 'ion channel complex', and 'transporter complex' were identified. In addition, PLG expression correlated negatively with the infiltration of almost all immune cell types. Interestingly, the expression of PLG was significantly and highly correlated with that of CD160, an immune checkpoint inhibitor.
Our findings defined a new five-gene signature for predicting GC prognosis, but more validation is required to assess the effects and mechanism of the five genes, especially PLG, for the development of new GC therapies.
在全球范围内，胃癌（GC）是一种常见且致命的实体恶性肿瘤。识别分子特征及其功能可为胃癌的发生发展提供机制性见解，并为靶向治疗提供新方法。
将差异表达基因（DEGs）和预后基因（来自单变量Cox回归分析）进行重叠，以获得预后DEGs。随后，分别通过Metascape和基因本体论（GO）/京都基因与基因组百科全书（KEGG）/基因集富集分析（GSEA）富集分析来识别这些预后DEGs的分子模块及其功能。使用Cytoscape中Cytohubba插件的MCC算法分析胃癌中上调和下调的预后DEGs的蛋白质-蛋白质相互作用（PPI）网络。通过最小绝对收缩和选择算子（LASSO）-Cox回归分析在PPI网络的枢纽基因上定义预后基因特征。此外，通过定量PCR（qPCR）、蛋白质印迹和免疫组织化学（IHC）验证了我们临床胃癌样本中PLG的表达水平。随后，进行PLG表达相关性分析以评估PLG在胃癌进展中的作用。通过单样本基因集富集分析（ssGSEA）进行免疫浸润分析，以评估PLG对免疫浸润的抑制作用。
首先，识别出胃癌中蛋白质-蛋白质相互作用（PPI）网络中的上调和下调预后DEGs以及枢纽基因。确定了一个预后五基因特征（即PLG、SPARC、FGB、SERPINE1和KLHL41）。在这五个基因中，纤溶酶原（PLG）与胃癌之间的关系在很大程度上仍不清楚。此外，还确定了PLG相关基因在胃癌中的功能，如“纤维蛋白溶解”、“止血”、“离子通道复合物”和“转运体复合物”。此外，PLG表达与几乎所有免疫细胞类型的浸润呈负相关。有趣的是，PLG的表达与免疫检查点抑制剂CD160的表达显著且高度相关。
我们的研究结果定义了一种用于预测胃癌预后的新的五基因特征，但需要更多的验证来评估这五个基因，特别是PLG，在胃癌新疗法开发中的作用和机制。
