BACKGROUND: Squamous cell lung cancer is the main cause of cancer-associated mortality. The discovery of promising prognostic biomarkers for predicting the survival of patients with squamous cell lung cancer remains a challenge.

MATERIAL AND METHODS: Gene expression profiles of GSE33479 and GSE51855, including 42 squamous cell lung cancer tissues and 17 normal tissues, from the GEO database were assessed to find common differentially expressed genes (DEGs) via the GEO2R online tool and Venn diagram software. Then, gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analyses were conducted. The key protein-protein interaction (PPI) network within those common DEGs was subsequently illustrated through a combination of Search Tool for Retrieval of Interacting Genes (STRING) and Cytoscape software. Finally, core genes associated with survival and levels of immune infiltration were demonstrated by the Kaplan-Meier plotter and Tumor Immune Estimation Resource (TIMER) online database, respectively.

RESULTS: In total, 483 DEGs were involved, including 216 upregulated genes enriched in “cell division”, “DNA replication”, and “DNA repair pathway” and 267 downregulated genes enriched in “cell adhesion”, “oxidation-reduction process”, and “cell-cell signaling”. The 75 core genes were selected by Molecular Complex Detection applied in Cytoscape. Four genes – MND1, FOXM1, CDC6, and POLE2 – were found to be significantly associated with survival. Further analysis of the KEEG pathway and TIMER database revealed that only POLE2 was enriched in “DNA replication” and its higher expression was negatively associated with survival and immune infiltration.

CONCLUSIONS: Higher expression of POLE2 is a prognosis-related biomarker for worse survival and is negatively associated with immune infiltration in squamous cell lung cancer.

Keywords: Biological Markers, Biomarkers, Tumor, Carcinoma, Squamous Cell, Computational Biology, DNA Polymerase II, Databases, Genetic, Gene Expression Profiling, gene ontology, Gene Regulatory Networks, Protein Interaction Mapping, Protein Interaction Maps, Software