Dataset Information

Identification of differential protein-coding gene expressions in early phase lung adenocarcinoma.

ABSTRACT: BACKGROUND:The diagnosis of early phase lung adenocarcinoma (LADC) is associated with therapeutic strategy, effect, and survival time. However, the sensitive biomarkers of early phase LADC are still unclear. This study aimed to identify protein-coding genes that can be used as biomarkers of early stage LADC. METHODS:Gene microarray analysis was performed to identify key hub genes that show different expression in lung adenocarcinoma compared to normal tissues. The microarray data of lung adenocarcinoma in stages IA, IB, IIA, IIB, and normal tissues (GSE10072) were downloaded from a free online database, Gene Expression Omnibus (GEO). RESULTS:A total of 572 differentially expressed genes (DEGs) were identified between early phase lung adenocarcinoma and normal tissues using R software. Database for?Annotation, Visualization and Integrated?Discovery online tools were used to obtain Gene Ontology analysis and the Kyoto Encyclopedia of Genes and Genomes was used to analyze DEGs. Cytoscape software was used to express the protein-protein interaction network. We found that some cancer-related Gene Ontology terms and pathways (e.g. cell adhesion, cell surface receptor signaling pathway, PI3K-Akt signaling pathway) were significantly enriched in DEGs. CONCLUSION:Protein-coding genes JUN, FYN, CAV1, and SFN may play vital roles in the progress of early-stage lung adenocarcinoma. Consequently, through bioinformatics analysis, the key genes could be established to provide more potential references for the therapeutic targets of lung adenocarcinoma.

SUBMITTER: Zhou LN

PROVIDER: S-EPMC5792719 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of differential protein-coding gene expressions in early phase lung adenocarcinoma.

Zhou Li-Na LN Li Shi-Cheng SC Li Xue-Ying XY Ge Hong H Li Hong-Mei HM

Thoracic cancer 20171220 2

<h4>Background</h4>The diagnosis of early phase lung adenocarcinoma (LADC) is associated with therapeutic strategy, effect, and survival time. However, the sensitive biomarkers of early phase LADC are still unclear. This study aimed to identify protein-coding genes that can be used as biomarkers of early stage LADC.<h4>Methods</h4>Gene microarray analysis was performed to identify key hub genes that show different expression in lung adenocarcinoma compared to normal tissues. The microarray data ...[more]

PMID: 29266838

Similar Datasets

Project description:BackgroundThe acquisition of invasive tumor cell behavior is considered to be the cornerstone of the metastasis cascade. Thus, genetic markers associated with invasiveness can be stratified according to patient prognosis. In this study, we aimed to identify an invasive genetic trait and study its biological relevance in lung adenocarcinoma.Methods250 TCGA patients with lung adenocarcinoma were used as the training set, and the remaining 250 TCGA patients, 500 ALL TCGA patients, 226 patients with GSE31210, 83 patients with GSE30219, and 127 patients with GSE50081 were used as the verification data sets. Subtype classification of all TCGA lung adenocarcinoma samples was based on invasion-associated genes using the R package ConsensusClusterPlus. Kaplan-Meier curves, LASSO (least absolute contraction and selection operator) method, and univariate and multivariate Cox analysis were used to develop a molecular model for predicting survival.ResultsAs a consequence, two molecular subtypes for LUAD were first identified from all TCGA all data sets which were significant on survival time. C1 subtype with poor prognosis has higher clinical characteristics of malignancy, higher mutation frequency of KRAS and TP53, and a lower expression of immune regulatory molecules. 2463 differentially expressed invasion genes between C1 and C2 subtypes were obtained, including 580 upregulation genes and 1883 downregulation genes. Functional enrichment analysis found that upregulated genes were associated with the development of tumor pathways, while downregulated genes were more associated with immunity. Furthermore, 5-invasion gene signature was constructed based on 2463 genes, which was validated in four data sets. This signature divided patients into high-risk and low-risk groups, and the LUDA survival rate of the high-risk group is significantly lower than that of the low-risk group. Multivariate Cox analysis revealed that this gene signature was an independent prognostic factor for LUDA. Compared with other existing models, our model has a higher AUC.ConclusionIn this study, two subtypes were identified. In addition, we developed a 5-gene signature prognostic risk model, which has a good AUC in the training set and independent validation set and is a model with independent clinical characteristics. Therefore, we recommend using this classifier as a molecular diagnostic test to assess the prognostic risk of patients with LUDA.

Project description:To investigate the differentiated whole genome expression profiling of gastric high- and low-grade intraepithelial neoplasia and early-stage adenocarcinoma.Gastric specimens from an upper magnifying chromoendoscopic targeted biopsy were collected from March 2010 to May 2013. Whole genome expression profiling was performed on 19 low-grade intraepithelial neoplasia (LGIN), 20 high-grade intraepithelial neoplasia (HGIN), 19 early-stage adenocarcinoma (EGC), and 19 chronic gastritis tissue samples using Agilent 4 × 44K Whole Human Genome microarrays. Differentially expressed genes between different types of lesions were identified using an unpaired t-test and corrected with the Benjamini and Hochberg false discovery rate algorithm. A gene ontology (GO) enrichment analysis was performed using the GeneSpring software GX 12.6. The differentially expressed gene was verified using a real-time TaqMan® PCR assay with independent tissue samples, including 26 LGIN, 15 HGIN, 14 EGC, and 20 chronic gastritis. The expression of G0S2 were further validated by immunohistochemical staining (IHC) in 24 LGIN, 40 HGIN, 30 EGC and 61 chronic gastritis specimens.The gene expression patterns of LGIN and HGIN tissues were distinct. There were 2521 significantly differentially expressed transcripts in HGIN, with 951 upregulated and 1570 downregulated. A GO enrichment analysis demonstrated that the most striking overexpressed transcripts in HGIN compared with LGIN were in the category of metabolism, defense response, and nuclear factor ?B (NF-?B) cascade. While the vast majority of transcripts had barely altered expression in HGIN and EGC tissues, only 38 transcripts were upregulated in EGC. A GO enrichment analysis revealed that the alterations of the immune response were most prominent in the progression from HGIN to EGC. It is worth noting that, compared with LGIN, 289 transcripts were expressed at higher levels both in HGIN and EGC. A characteristic gene, G0/G1 switch 2 (G0S2) was one of the 289 transcripts and related to metabolism, the immune response, and the NF-?B cascade, and its expression was validated in independent samples through real-time TaqMan® PCR and immunohistochemical staining. In real-time PCR analysis, the expression of G0S2 was elevated both in HGIN and EGC compared with that in LGIN (P < 0.01 and P < 0.001, respectively). In IHC analysis, G0S2 immunoreactivity was detected in the cytoplasmic of neoplastic cells, but was undetectable in chronic gastritis cells. The G0S2 expression in HGIN was higher than that of LGIN (P = 0.012, ? (2) = 6.28) and EGC (P = 0.008, ? (2) = 6.94).A clear biological distinction between gastric high- and low-grade intraepithelial neoplasia was identified, and provides molecular evidence for clinical application.

Dataset Information

Identification of differential protein-coding gene expressions in early phase lung adenocarcinoma.

Publications

Identification of differential protein-coding gene expressions in early phase lung adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets