scholarly journals Multiple functional SNPs in differentially expressed genes modify risk and survival of non-small cell lung cancer in chinese female non-smokers

Oncotarget ◽  
2017 ◽  
Vol 8 (12) ◽  
pp. 18924-18934 ◽  
Author(s):  
Xue Fang ◽  
Zhihua Yin ◽  
Xuelian Li ◽  
Lingzi Xia ◽  
Xiaowei Quan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Differentially Expressed Genes ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Differentially Expressed ◽  
Small Cell Lung ◽  
Functional Snps

scholarly journals Identification of miRNAs and differentially expressed genes in early phase non-small cell lung cancer

2016 ◽  
Vol 35 (4) ◽  
pp. 2171-2176 ◽  
Author(s):  
WEN TIAN ◽  
JIE LIU ◽  
BAOJING PEI ◽  
XIAOBO WANG ◽  
YU GUO ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Differentially Expressed Genes ◽  
Cell Lung Cancer ◽  
Early Phase ◽  
Small Cell ◽  
Differentially Expressed ◽  
Small Cell Lung

scholarly journals Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis

2021 ◽  
Vol 20 ◽  
pp. 153303382110602
Author(s):  
Ke Gong ◽  
Huiling Zhou ◽  
Haidan Liu ◽  
Ting Xie ◽  
Yong Luo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Differentially Expressed Genes ◽  
Cell Lung Cancer ◽  
Prognostic Value ◽  
Small Cell ◽  
Differentially Expressed ◽  
Small Cell Lung ◽  
Diagnosis And Prognosis

Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer affecting humans. However, appropriate biomarkers for diagnosis and prognosis have not yet been established. Here, we evaluated the gene expression profiles of patients with NSCLC to identify novel biomarkers. Methods: Three datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes were analyzed. Venn diagram software was applied to screen differentially expressed genes, and gene ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape was used to analyze protein-protein interactions (PPI) and Kaplan–Meier Plotter was used to evaluate the survival rates. Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas (THPA) were used to analyze protein expression. Quantitative real-time polymerase (qPCR) chain reaction was used to verify gene expression. Results: We identified 595 differentially expressed genes shared by the three datasets. The PPI network of these differentially expressed genes had 202 nodes and 743 edges. Survival analysis identified 10 hub genes with the highest connectivity, 9 of which ( CDC20, CCNB2, BUB1, CCNB1, CCNA2, KIF11, TOP2A, NDC80, and ASPM) were related to poor overall survival in patients with NSCLC. In cell experiments, CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated, and among different types of NSCLC, these four genes showed highest expression in large cell lung cancer. The highest prognostic value was detected for patients who had successfully undergone surgery and for those who had not received chemotherapy. Notably, CCNB1 and CCNA2 showed good prognostic value for patients who had not received radiotherapy. Conclusion: CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated in patients with NSCLC. These genes may be meaningful diagnostic biomarkers and could facilitate the development of targeted therapies.

scholarly journals Identification of differentially expressed genes in small and non-small cell lung cancer based on meta-analysis of mRNA

Heliyon ◽  
2019 ◽  
Vol 5 (6) ◽  
pp. e01707 ◽  
Author(s):  
Nitesh Shriwash ◽  
Prithvi Singh ◽  
Shweta Arora ◽  
Syed Mansoor Ali ◽  
Sher Ali ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Differentially Expressed Genes ◽  
Cell Lung Cancer ◽  
Meta Analysis ◽  
Small Cell ◽  
Differentially Expressed ◽  
Small Cell Lung

scholarly journals Screening prognostic markers for non-small cell lung cancer based on data mining and bioinformatics analysis

2020 ◽  
Author(s):  
Bin Han ◽  
Kaushik Chandra Aman ◽  
Dongqing Wei ◽  
Shulin Zhang ◽  
Minjie Meng
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Small Cell Lung Cancer ◽  
Differentially Expressed Genes ◽  
Cell Lung Cancer ◽  
Poor Prognosis ◽  
Prognostic Markers ◽  
Small Cell ◽  
Differentially Expressed ◽  
Small Cell Lung

Abstract Background At present, non-small cell lung cancer has a high morbidity and mortality, and the recurrence and metastasis situation is serious. It is impossible to accurately predict the prognosis of cancer patients clinically. Biomarker is a kind of biomolecule with wide application prospects, and its potential in cancer prognosis is gradually revealed, and it is expected to be applied clinically. Results We integrated four gene expression profiles (GSE19188, GSE19804, GSE101929 and GSE18842) from the GEO database and screened the commonly differentially expressed genes using the GEO2R online tool. We screened 952 commonly differentially expressed genes. Gene ontology analysis showed that CDEGs were mainly enriched in biological processes such as cell adherin, angiogenesis and positive regulation of angiogenesis, and KEGG pathways such as ECM-receptor interaction and cell adherin molecules (CAMs). Up-regulation of G2 and S phase-expressed protein 1(GTSE1) expression is associated with poor prognosis of lung adenocarcinoma(LADE) and lung squamous cell carcinoma(LUSC). Up-regulation of Neuromedin-U(NMU) expression, down-regulation of Proto-oncogene c-Fos(FOS) and Cyclin-dependent kinase inhibitor 1C(CDKN1C) is only associated with poor prognosis of LADE. Conclusions We believe that GTSE1, NMU, FOS, and CDKN1C have potential application value as prognostic markers for lung adenocarcinoma, and are of great significance for lung adeno carcinoma efficacy evaluation and relapse monitoring. At the same time, GTSE1 may also be used as a new target for cancer treatment New ways.

scholarly journals Identification of differentially expressed genes in non-small cell lung cancer

Aging ◽  
2019 ◽  
Vol 11 (23) ◽  
pp. 11170-11185 ◽  
Author(s):  
Ke Wang ◽  
Ruo Chen ◽  
Zhuan Feng ◽  
Yu-Meng Zhu ◽  
Xiu-Xuan Sun ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Differentially Expressed Genes ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Differentially Expressed ◽  
Small Cell Lung
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