Differential protein-coding gene and long noncoding RNA expression in smoking-related lung squamous cell carcinoma

Abstract

Background: Cigarette smoking is one of the greatest preventable risk factors for developing cancer, and most cases of lung squamous cell carcinoma (lung SCC) are associated with smoking. The pathogenesis mechanism of tumor progress is unclear. This study aimed to identify biomarkers in smoking-related lung cancer, including protein-coding gene, long noncoding RNA, and transcription factors.

Methods: We selected and obtained messenger RNA microarray datasets and clinical data from the Gene Expression Omnibus database to identify gene expression altered by cigarette smoking. Integrated bioinformatic analysis was used to clarify biological functions of the identified genes, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the construction of a protein-protein interaction network, transcription factor, and statistical analyses. Subsequent quantitative real-time PCR was utilized to verify these bioinformatic analyses.

Results: Five hundred and ninety-eight differentially expressed genes and 21 long noncoding RNA were identified in smoking-related lung SCC. GO and KEGG pathway analysis showed that identified genes were enriched in the cancer-related functions and pathways. The protein-protein interaction network revealed seven hub genes identified in lung SCC. Several transcription factors and their binding sites were predicted. The results of real-time quantitative PCR revealed that AURKA and BIRC5 were significantly upregulated and LINC00094 was downregulated in the tumor tissues of smoking patients. Further statistical analysis indicated that dysregulation of AURKA, BIRC5, and LINC00094 indicated poor prognosis in lung SCC.

Conclusion: Protein-coding genes AURKA, BIRC5, and LINC00094 could be biomarkers or therapeutic targets for smoking-related lung SCC.

Keywords: Biomarkers; gene expression profiling; lncRNA; smoking-related lung SCC.

Figure 1

A heat map of differentially expressed gene analysis between smoking‐related lung squamous cell carcinoma and normal tissues in GSE43346 and GSE50081: 419 upregulated and 179 downregulated genes. Red, upregulation; green, downregulation.

Figure 2

The top eight terms of biological processes of (a) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and (b) Gene Ontology (GO) enrichment analysis in differentially expressed genes.

Figure 3

Protein–protein interaction network of hub genes obtained using Cytoscape software. (a) There is a strong interaction between MCM5, CDC6, RRM2, CCNB2, AURKA, and BIRC5. (b) PIK3CA does not interact with any of the genes.

Figure 4

Kaplan–Meier plots for (a) AURKA, (b) BIRC5, and (c) LINC00094 in lung squamous cell carcinoma. Log‐rank P values and hazard ratios (HR, 95% confidence intervals in parentheses) are shown.

Figure 5

Figures were derived from gene expression data in the ONCOMINE database comparing expression levels in normal (left plot) and cancer tissues (right plot) in (a) AURKA and (b) BIRC5. The Y‐axis represents the median intensity, 10th, and 90th percentile data.

Figure 6

Verification of messenger RNA expression levels of differentially expressed genes and long noncoding RNA between lung squamous cell carcinoma and normal tissues through quantitative real‐time PCR. (a) AURKA, (b) BIRC5, (c) LINC00094. **P < 0.01; ***P < 0.001.

Figure 7

Transcription factors (TFs) and their hub gene binding sites. (a) TFs of BIRC5 are p53, STAT3, NF‐kappaB, NF‐kappaB1, Egr‐1, HNF‐4alpha2, HNF‐ 4alpha1, Sp1, p300, and IRF‐1. (b) TFs of AURKA are p53, NF‐kappaB, and NFkappaB1.