A prognostic gene signature for gastric cancer and the immune infiltration-associated mechanism underlying the signature gene, PLG

Hui Shi; Jiangling Duan; Zhangming Chen; Mengqi Huang; Wenxiu Han; Rui Kong; Xiuyin Guan; Zhen Qi; Shuang Zheng; Ming Lu

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A prognostic gene signature for gastric cancer and the immune infiltration-associated mechanism underlying the signature gene, PLG

Hui Shi ^[1] ; Jiangling Duan ^[1] ; Zhangming Chen ^[2] ; Mengqi Huang ^[3] ; Wenxiu Han ^[2] ; Rui Kong ^[1] ; Xiuyin Guan ^[1] ; Zhen Qi ^[1] ; Shuang Zheng ^[2] ; Ming Lu ^[1]
1. [1] Anhui Medical University
  
  Anhui Medical University
  
  China
2. [2] First Affiliated Hospital of Anhui Medical University
  
  First Affiliated Hospital of Anhui Medical University
  
  China
3. [3] Department of Pathology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
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Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 25, Nº. 4 (April), 2023, págs. 995-1010
Idioma: inglés
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Resumen
- Background 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.
  
  Methods 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.
  
  Results 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.
  
  Conclusion 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.