Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer and is frequently diagnosed at advanced stages with metastasis, contributing to its poor prognosis. Identifying key metastasis-related biomarkers is critical for improving early diagnosis and therapeutic targeting. Methods: We analyzed four GEO microarray datasets (GSE32863, GSE27262, GSE40275, and GSE33356) and TCGA data to identify differentially expressed genes (DEGs) in LUAD. Functional enrichment of DEGs was analyzed using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and a Cancer Hallmark Enrichment Plot. Hub gene analysis was conducted using Cytoscape. Hub genes were evaluated for their expression, prognostic significance (via the Kaplan–Meier plotter), and clinical correlation using additional platforms (TCGA, Lung Cancer Explorer, TNMplot, and the Human Protein Atlas). Results: A total of 333 consistently dysregulated DEGs were identified, enriched in pathways related to metastasis, including angiogenesis, immune escape, and ECM interaction. Ten hub genes (AURKA, TOP2A, CCNB2, CENPF, MCM4, TPX2, KIF20A, ASPM, MELK, and NEK2) were identified through network analysis. Among these, MCM4 showed strong upregulation in LUAD and was significantly associated with poor overall survival. Notably, MCM4 expression also correlated with post-progression survival and markers of invasiveness. Immunohistochemistry and transcriptomic analyses confirmed MCM4 overexpression at both mRNA and protein levels. Additionally, MCM4 expression was positively correlated with various matrix metalloproteinases, supporting its role in promoting tumor invasiveness. Conclusions: MCM4 is a novel potential biomarker for LUAD metastasis and prognosis. Its consistent upregulation, association with metastatic markers, and clinical significance suggest it may serve as a candidate target for diagnostic use or therapeutic intervention in advanced LUAD.

Background: Lung cancer continues to be one of the most fatal malignancies globally. Uncovering differentially expressed genes (DEGs) is crucial for advancing our understanding of tumor mechanisms and discovering new therapeutic targets. This study sought to identify key genes linked to prognosis and immune infiltration in lung cancer through the analysis of public gene expression datasets. Methods: We examined three microarray datasets from the Gene Expression Omnibus (GSE10072, GSE33356, and GSE18842) to detect DEGs between tumor and normal lung tissues. Functional enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to interpret the biological relevance of these genes. Protein–protein interaction (PPI) networks were constructed via STRING and visualized using Cytoscape to screen for central hub genes. The prognostic implications of the hub genes were investigated using Kaplan–Meier Plotter and TIMER2.0 based on data from The Cancer Genome Atlas (TCGA). PECAM1 expression levels and its relationship with immune cell infiltration were further explored using UCSC Xena. Results: A total of 477 DEGs were consistently identified across all three datasets. Among the top 10 down-regulated hub genes, PECAM1 was significantly reduced in tumor tissues. Lower PECAM1 expression was positively associated with better first-progression survival (FPS) in lung cancer patients. This gene was particularly suppressed in lung adenocarcinoma (LUAD) and showed strong correlations with immune cell infiltration. Co-expression analysis revealed that genes linked to PECAM1 are involved in immune-related pathways. Conclusions: Our findings highlight PECAM1 as a potential prognostic biomarker in lung cancer, especially in LUAD. Its association with immune infiltration and patient survival supports its possible utility in early detection and as a candidate for immunotherapy development.