Direct bilirubin levels are prognostic in non-small cell lung cancer.

We investigated the prognostic value of serum bilirubin levels in stage I-II non-small cell lung cancer (NSCLC) patients and evaluated the relationship between bilirubin levels and response to first-line platinum-based chemotherapy. We divided 634 NSCLC patients from a single hospital in China into retrospective training (n = 307) and prospective validation (n = 327) cohorts. X-tile was used to identify the optimal serum bilirubin cutoff value for sorting retrospective cohort patients into low and high overall survival (OS) groups. TNM stage and serum bilirubin levels were associated with OS on univariate analysis. Direct bilirubin (DBIL) levels were correlated with tumor progression and response to first-line platinum-based chemotherapy, and were associated with OS after adjusting for TNM stage. Our findings indicate a DBIL-based prognostic nomogram is more accurate than the TNM staging system in predicting clinical outcomes, and that the DBIL level is an independent predictor of OS in NSCLC. Thus, an index that combines DBIL with TNM stage may better predict patient outcomes than TNM stage alone.

Integrated analysis reveals key genes with prognostic value in lung adenocarcinoma.

BACKGROUND: Lung cancer is one of the most common malignant tumors. Despite advances in lung cancer therapies, prognosis of non-small-cell lung cancer is still unfavorable. The aim of this study was to identify the prognostic value of key genes in lung tumorigenesis. METHODS: Differentially expressed genes (DEGs) were screened out by GEO2R from three Gene Expression Omnibus cohorts. Common DEGs were selected for Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology enrichment analysis. Protein- protein interaction networks were constructed by the STRING database and visualized by Cytoscape software. Hub genes, filtered from the CytoHubba, were validated using the Gene Expression Profiling Interactive Analysis database, and their genomic alterations were identified by performing the cBioportal. Finally, overall survival analysis of hub genes was performed using Kaplan-Meier Plotter. RESULTS: From three datasets, 169 DEGs (70 upregulated and 99 downregulated) were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that upregulated DEGs were significantly enriched in cell cycle, p53 pathway, and extracellular matrix-receptor interactions; the downregulated DEGs were significantly enriched in PPAR pathway and tyrosine metabolism. The protein-protein interaction network consisted of 71 nodes and 305 edges, including 49 upregulated and 22 downregulated genes. The hub genes, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, were overexpressed compared with the normal group by Gene Expression Profiling Interactive Analysis analysis, and associated with reduced overall survival in lung cancer patients. In the genomic alterations analysis, two hotspot mutations (S2021C/F and E314K/V) were identified in Pfam protein domains. CONCLUSION: DEGs, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, might be potential biomarkers for the prognosis and treatment of lung adenocarcinoma.