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Medical Science Monitor Basic Research


eISSN: 1643-3750

Comprehensive Bioinformatic Analysis Genes Associated to the Prognosis of Liposarcoma

Jianwei Liu, Rong Li, Xiwen Liao, Weiping Jiang

Department of Osteology, The Third Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China (mainland)

Med Sci Monit 2018; 24: MOL7329-7339

DOI: 10.12659/MSM.913043

Available online:

Published: 2018-10-14


BACKGROUND: Liposarcoma is the most common type of soft tissue sarcoma, but its molecular mechanism is poorly defined. This study aimed to identify genes crucial to the pathogenesis of liposarcoma and to explore their functions, related pathways, and prognostic value.
MATERIAL AND METHODS: Differentially expressed genes (DEGs) in the GSE59568 dataset were screened. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to investigate the DEGs at the functional level. Protein-protein interaction (PPI) networks and module analysis were applied to identify hub genes from among the DEGs. The GSE30929 dataset was used to validate the relationship between hub genes and the distant recurrence-free survival (DRFS) of liposarcoma patients using Cox model analysis.
RESULTS: A total of 1111 DEGs were identified. GO and KEGG pathway analysis indicated that the DEGs were mainly associated with lipopolysaccharides and pathways in cancer. The PPI network and module analysis identified 10 hub genes from the DEG network. The Cox model identified 3 genes (NIP7, RPL10L, and MCM2) significantly associated with DRFS. The risk score calculated by the Cox model of the NIP7-RPL10L-MCM2 signature could largely predict the 1-, 3-, and 5-year DRFS of liposarcoma patients, and the prognostic value was even higher for subtypes of liposarcoma.
CONCLUSIONS: This study identified genes that might play critical roles in liposarcoma pathogenesis as well as a 3-gene-based signature that could be used as a candidate prognostic biomarker for patients with liposarcoma.

Keywords: Computational Biology, Genes, vif, Liposarcoma, Microarray Analysis