Purpose: Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values. Methods: Microarray datasets from the GEO (GSE54129) and TCGA-stomach adenocarcinoma (STAD) datasets were applied for common differentially coexpressed genes using differential gene expression analysis and weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis and protein-protein interaction (PPI) network analysis of differentially coexpressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, GSEA was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated. Results: Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell−cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and SERPINE1 was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that ECM receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8+ T cell and T cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment in GC. Conclusions: Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.