Identifying prognostic biomarkers of non-small cell lung cancer by transcriptome analysis

BACKGROUND: Prognostic biomarkers are promising targets for cancer prevention and treatment. OBJECTIVE: We try to filtrate survival-related genes for non-small cell lung cancer (NSCLC) via transcriptome analysis. METHODS: Transcriptome data and clinical information of Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), mainly subtypes of NSCLC, were obtained from The Cancer Genome Atlas (TCGA) program. Differentially expressed genes (DEGs) analyzed by DESeq2 package were regarded as candidate genes. For survival analysis, univariate and multivariate Cox regression were applied to select biomarkers for overall survival (OS) and progression-free survival (PFS), where univariate analysis was for preliminary filtration and multivariate analysis considering age, gender, TNM parameters and clinical stage was for ultimate determination. Gene ontology (GO) analysis and pathway enrichment were used for biological annotation. RESULTS: We ultimately acquired a series of genes closely related to prognosis. For LUAD, we determined 314 OS-related genes and 275 PFS-related genes, while 54 OS-related genes and 78 PFS-related genes were chosen for LUSC. The final biological analysis indicated important function of proliferative signaling in LUAD but for LUSC, only cornification process had statistical meaning. CONCLUSIONS: We strictly determined prognostic genes of NSCLC, which would contribute to its carcinogenesis investigation and therapeutic methods improvement.