ArticleOncoTargets and therapy2017
Identification of key genes and molecular mechanisms associated with dedifferentiated liposarcoma based on bioinformatic methods.
Article in OncoTargets and therapy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 11 citations in OpenAlex.
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- Prognostic value of ESR2 expression on adjuvant chemotherapy in completely resected NSCLC.PloS one · 2020Article
- Differentially expressed gene profile and relevant pathways of the traditional Chinese medicine cinobufotalin on MCF‑7 breast cancer cells.Molecular medicine reports · 2019Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDedifferentiated liposarcoma (DDLPS) is one of the most deadly types of soft tissue sarcoma. To date, there have been few studies dedicated to elucidating the molecular mechanisms behind the disease; therefore, the molecular mechanisms behind this malignancy remain largely unknown. MATERIALS AND
methodsMicroarray profiles of 46 DDLPS samples and nine normal fat controls were extracted from Gene Expression Omnibus (GEO). Quality control for these microarray profiles was performed before analysis. Hierarchical clustering and principal component analysis were used to distinguish the general differences in gene expression between DDLPS samples and the normal fat controls. Differentially expressed genes (DEGs) were identified using the Limma package in R. Next, the enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained using the online tool DAVID (http://david.abcc.ncifcrf.gov/). A protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Furthermore, the hub genes within the PPI network were identified.
resultsAll 55 microarray profiles were confirmed to be of high quality. The gene expression pattern of DDLPS samples was significantly different from that of normal fat controls. In total, 700 DEGs were identified, and 83 enriched GO terms and three KEGG pathways were obtained. Specifically, within the DEGs of DDLPS samples, several pathways were identified as being significantly enriched, including the PPAR signaling pathway, cell cycle pathway, and pyruvate metabolism pathway. Furthermore, the dysregulated PPI network of DDLPS was constructed, and 14 hub genes were identified. Characteristic of DDLPS, the genes
conclusionThis study used bioinformatics to comprehensively mine DDLPS microarray data in order to obtain a deeper understanding of the molecular mechanism of DDLPS.
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