Qiang Chen; Lutong Xu; Jing Hu; Tonglian Wang; Kang Zhang; Hongbo Zhao; Yuanyue Li; Tao Shou
Volume 21, Issue 3 , 2019, Pages 1-10
Abstract
Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer with very poor 5-year overall survival (OS) rate. It is histopathologically difficult to predict clinical outcome in early-stage LUAD. Identifying reliable prognostic biomarker is absolutely critical ...
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Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer with very poor 5-year overall survival (OS) rate. It is histopathologically difficult to predict clinical outcome in early-stage LUAD. Identifying reliable prognostic biomarker is absolutely critical to benefit from early additional treatment for early-stage LUAD patients.Objectives: The purpose of the current study was to identify critical genes as prognostic biomarkers in early-stage LUAD using gene expression profiles based on the microarray.Methods: In this bioinformatics-based cross-study, gene expression profiles from early-stage LUAD, including GSE10072 and GSE19804 genes were integrated using bioinformatics methods, including differentially expressed gene analysis (DEGA), Kyoto En- cyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network construction. Subse- quently, the survival analysis of key genes was performed using The Cancer Genome Atlas (TCGA) database and was validated using online Gene Expression Profiling Interactive Analysis (GEPIA) database.Results: A total of 89 up-regulated and 214 down-regulated genes were identified in early-stage LUAD, and the functional changes of 303 differentially expressed genes (DEGs) were mainly related to cell cycle. A PPI network was established by online STRING database with 207 nodes and 775 edges. Centrality analysis showed that CDKN3 and UBE2C genes were identified as key genes implicated in early-stage LUAD. Survival analysis revealed that low mRNA expressions of CDKN3 and UBE2C were significantly associated with longer OS of early-stage LUAD patients.Conclusions: This cross-study found key dysregulated genes involved in early-stage LUAD, which might provide insights into the pathogenesis of early-stage LUAD, and identified UBE2C and CDKN3 might serve as potential diagnostic and prognostic biomarkers and therapeutic targets for early-stage LUAD.
Tonglian Wang; Jing Hu; Lutong Xu; Hongbo Zhao; Yuanyue Li; Tao Shou; Xueshan Xia; Qiang Chen
Volume 20, Issue 3 , 2018, Pages 1-9
Abstract
Background: Non-small cell lung cancer (NSCLC) is the most common type of lung Neoplasms, which accounts for about 85% of all lung cancer types. However, critical biological pathways and key genes implicated in NSCLC remain ambiguous. Objectives: The present study aimed at identifying the critical biological ...
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Background: Non-small cell lung cancer (NSCLC) is the most common type of lung Neoplasms, which accounts for about 85% of all lung cancer types. However, critical biological pathways and key genes implicated in NSCLC remain ambiguous. Objectives: The present study aimed at identifying the critical biological pathways and key genes implicated in NSCLC, and provid- ing insight into the molecular mechanism underlying NSCLC. Methods: In this case-control bioinformatics study, the researchers used four microarray data of NSCLC from public gene expres- sion omnibus (GEO) database at the national center for biotechnology information (NCBI) website. The microarray data came fromstudies of American, Spanish, and Taiwanese NSCLC patients, and in total contained 190 NSCLC tissue and 180 normal lung tissue. A standardized- microarray preprocessing and gene set enrichment analysis (GSEA) were used to analyze each microarray data and obtained significantly regulated pathways. Venn analysis was used to identify the common significantly regulated biological path- ways. Protein and protein interaction (PPI) network analysis was used to identify the key genes within common significantly reg- ulated pathways. The PPI information was retrieved from the STRING database, and Cytoscape software was used to construct and visualize the PPI network. Results: Through integrating GSEA results of four microarray data, finally, the researchers identified 22 common up-regulated and 85 common down-regulated pathways. Many genes within 107 common significantly regulated pathways were significantly en- riched within cell cycle pathway (P value of 2.58e-79) and focal adhesion pathway (P value of 2.44e-81). The PPI network showed thatup-regulated CDK1 (P value = 1.33e-18 and logFC = 1.41) and down-regulated PIK3R1 (P value = 5.09e-22 and logFC = -1.13) genes shared the most abundant edges, and were associated with NSCLC. Conclusions: This cross-sectional study showed increased concordance between gene expression profiling data. These identified pathways and genes provide some insight into the molecular mechanisms of NSCLC, and the genes may serve as candidate diagnos-tic and therapeutic targets of NSCLC.
