Dataset Information

Identification of differentially expressed protein-coding genes in lung adenocarcinomas.

ABSTRACT: Lung adenocarcinoma accounts for a high proportion of lung cancers. Though efforts have been made to develop new and effective treatments for this disease, the mortality rate remains high. Gene expression microarrays facilitate the study of lung cancer at the molecular level. The present study aimed to detect differentially expressed protein-coding genes to identify novel biomarkers and therapeutic targets for lung adenocarcinoma. Aberrations in gene expression in lung adenocarcinoma were determined by analysis of mRNA microarray datasets from the Gene Expression Omnibus database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) networks and statistical analysis were used to identify the biological functions of the differentially expressed genes (DEGs). The results of the bioinformatics analysis were subsequently validated using reverse transcription-quantitative PCR. A total of 303 DEGs were identified in lung adenocarcinomas, and they were enriched in a number of cancer-associated GO terms and KEGG pathways. DNA topoisomerase 2? (TOP2A), cell division cycle protein homolog 20 (CDC20), mitotic checkpoint serine/threonine protein kinase BUB1 (BUB1) and mitotic spindle assembly checkpoint protein MAD2A (MAD2L1) exhibited the highest degree of interaction in the PPI network. Survival analysis performed using Kaplan-Meier curves and Cox regression indicated that these four genes were all significantly associated with the survival of patients with lung adenocarcinomas. In conclusion, TOP2A, CDC20, BUB1 and MAD2L1 may be key protein-coding genes that may serve as biomarkers and therapeutic targets in lung adenocarcinomas.

SUBMITTER: Wang L

PROVIDER: S-EPMC6966171 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Identification of differentially expressed protein-coding genes in lung adenocarcinomas.

Wang Luyao L Li Shicheng S Wang Yuanyong Y Tang Zhenxue Z Liu Chaolong C Jiao Wenjie W Liu Jia J

Experimental and therapeutic medicine 20191206 2

Lung adenocarcinoma accounts for a high proportion of lung cancers. Though efforts have been made to develop new and effective treatments for this disease, the mortality rate remains high. Gene expression microarrays facilitate the study of lung cancer at the molecular level. The present study aimed to detect differentially expressed protein-coding genes to identify novel biomarkers and therapeutic targets for lung adenocarcinoma. Aberrations in gene expression in lung adenocarcinoma were determ ...[more]

PMID: 32010276

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Project description:Background Lung cancer is one of the most common cancers and the primary cause of cancer-related deaths in the world. The 5-year survival of lung cancer patients is lower than 15%. As a common subtype of lung cancer, lung adenocarcinoma still has a high morbidity and mortality, although many strategies have been made, such as surgical operation, chemotherapy, targeted therapy. The use of gene expression microarray has provided a feasible and effective approach for the study on lung cancer. However, the biomarkers and potential therapeutic targets of lung adenocarcinomas are still not completely identified. Our study is aimed to find biomarkers and therapeutic targets of lung adenocarcinomas by identifying the key protein-coding gene in lung adenocarcinomas by bioinformatical approaches. Methods We selected and obtained messenger RNA microarray datasets from Gene Expression Omnibus database to identify differentially expressed genes between lung adenocarcinomas and normal lung tissue. The differentially expressed genes were clarified by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the protein-protein interaction (PPI) network and statistical analyses. Subsequently, quantitative real-time PCR was used to verify the results of bioinformatic analysis. Results We obtained 1,264, 896 and 408 differentially expressed genes from GSE32863, GSE43458 and GSE63459, respectively. The 242 common differentially expressed genes in three datasets were related to cell adhesion molecules, ECM-receptor interaction, leukocyte transendothelial migration according to KEGG analysis. GO analysis showed that these common differentially expressed genes were enriched in tumor-related functions. ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T and KIAA0101 have the strongest protein-protein interaction relationships based on protein-protein interaction networks. Survival analysis showed that these nine genes were closely related to the survival of lung adenocarcinomas. The further qRT-PCR assays indicated that seven key genes (ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T) display differential profile between clinical lung adenocarcinoma specimens and their matched normal tissues. Conclusions ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T may be key protein coding genes in lung adenocarcinoma, and deserve further study to verify their feasibility and effectiveness as biomarkers and therapeutic targets for lung adenocarcinomas.

Project description:Background: Rabbit is a good model for genetic and medical studies in other livestock species. The rabbit shows low adipose tissue deposition, and the phenomena indicates that there is some specificity of adipose deposition during the rabbit growth. However, little is known about genes that regulate the growth of adipose tissue in rabbits.Materials and methods: Deep RNA-seq and comprehensive bioinformatics analyses were used to characterize the genes of rabbit visceral adipose tissue (VAT) at 35, 85 and 120 days after birth. Differentially expressed genes (DEGs) were identified at the three growth stages by DESeq. To explore the function of the candidate genes, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. Six DEGs were randomly selected, and their expression profiles were validated by q-PCR.Results: A total of 20,303 known transcripts and 99,199 new transcripts from 8 RNA sequencing libraries were identified, and 34 differentially expressed genes (DEGs) were screened. GO enrichment and KEGG pathway analyses revealed that the DEGs were mainly involved in lipid metabolism regulation including acylglycerol metabolic process and mobilization, and decomposition of lipids to generate ATP in adipocytes and fatty acid metabolism, included LOC100342322 and LOC100342572. In addition, 133 protein-coding genes that play a role in adipose growth and development were screened, including acyl-CoA synthetase long-chain family member 5 (ACSL5) and fatty acid-binding protein 2 (FABP2). The validation results of six DEGs by q-PCR showed similar trends with the results of RNA-seq.Conclusion: In summary, this study provides the first report of the coding genes profiles of rabbit adipose tissue during different growth stages. These data allow for the identification of candidate genes for subsequent studies on rabbit genetics and regulation of adipose cells, and provide an animal model for studying obesity in humans.

Dataset Information

Identification of differentially expressed protein-coding genes in lung adenocarcinomas.

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Identification of differentially expressed protein-coding genes in lung adenocarcinomas.

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