ArticleTranslational lung cancer research2019
Screening of potential biomarkers and their predictive value in early stage non-small cell lung cancer: a bioinformatics analysis.
Article in Translational lung cancer research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 35 citations in OpenAlex.
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- Screening and Validation of Significant Genes with Poor Prognosis in Pathologic Stage-I Lung Adenocarcinoma.Journal of oncology · 2022Article
- Upregulated mitosis-associated genes CENPE, CENPF, and DLGAP5 predict poor prognosis and chemotherapy resistance of Acute Myeloid Leukemia.Cancer biomarkers : section A of Disease markers · 2022Article
- Gene Identification and Potential Drug Therapy for Drug-Resistant Melanoma with Bioinformatics and Deep Learning Technology.Disease markers · 2022Article
- Decreased APOC1 expression inhibited cancer progression and was associated with better prognosis and immune microenvironment in esophageal cancer.American journal of cancer research · 2022Article
- NEDD4L-induced ubiquitination mediating UBE2T degradation inhibits progression of lung adenocarcinoma via PI3K-AKT signaling.Cancer cell international · 2021Article
- Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas.Bioscience reports · 2021Article
- High-Throughput Screen of Natural Compounds and Biomarkers for NSCLC Treatment by Differential Expression and Weighted Gene Coexpression Network Analysis (WGCNA).BioMed research international · 2021Article
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4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-small cell lung cancer (NSCLC) remains the first leading cause of death in malignancies worldwide. Despite the early screening of NSCLC by low-dose spiral computed tomography (CT) in high-risk individuals caused a 20% reduction in the mortality, there still exists imperative needs for the identification of novel biomarkers for the diagnosis and treatment of lung cancer.
methodsmRNA microarray datasets GSE19188, GSE33532, and GSE44077 were searched, and the differentially expressed genes (DEGs) were obtained using GEO2R. Functional and pathway enrichment analyses were performed for the DEGs using DAVID database. Protein-protein interaction (PPI) network was plotted with STRING and visualized by Cytoscape. Module analysis of the PPI network was done through MCODE. The overall survival (OS) analysis of genes from MCODE was performed with the Kaplan Meier-plotter.
resultsA total of 221 DEGs were obtained, which were mainly enriched in the terms related to cell division, cell proliferation, and signal transduction. A PPI network was constructed, consisting of 221 nodes and 739 edges. A significant module including 27 genes was identified in the PPI network. Elevated expression of these genes was associated with poor OS of NSCLC patients, including UBE2T, UNF2, CDKN3, ANLN, CCNB2, and CKAP2L. The enriched functions and pathways included protein binding, ATP binding, cell cycle, and p53 signaling pathway.
conclusionsThe DEGs in NSCLC have the potential to become useful targets for the diagnosis and treatment of NSCLC.
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