Open AccessA risk score model for the prediction of osteosarcoma metastasis
Author(s) -
Dong Siqi,
Huo Hongjun,
Mao Yu,
Li Xin,
Dong Lixin
Publication year - 2019
Publication title -
febs open bio
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.718
H-Index - 31
ISSN - 2211-5463
DOI - 10.1002/2211-5463.12592
Subject(s) - osteosarcoma , kegg , metastasis , malignancy , gene , lasso (programming language) , logistic regression , medicine , computational biology , cancer , bioinformatics , gene expression , computer science , biology , gene ontology , cancer research , genetics , world wide web
Osteosarcoma is the most common primary solid malignancy of the bone, and its high mortality usually correlates with early metastasis. In this study, we developed a risk score model to help predict metastasis at the time of diagnosis. We downloaded and mined four expression profile datasets associated with osteosarcoma metastasis from the Gene Expression Omnibus. After data normalization, we performed LASSO logistic regression analysis together with 10‐fold cross validation using the GSE21257 dataset. A combination of eight genes ( RAB 1 , CLEC 3B , FCGBP , RNASE 3 , MDL 1 , ALOX 5 AP , VMO 1 and ALPK 3 ) were identified as being associated with osteosarcoma metastasis. These genes were put into a gene risk score model, and the prediction efficiency of the model was then validated using three independent datasets ( GSE33383 , GSE66673 , and GSE49003 ) by plotting receiver operating characteristic curves. The expression levels of the eight genes in all datasets were shown as heatmaps, and gene ontology gene annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed. These eight genes play a role in cancer‐related biological processes, such as apoptosis and biosynthetic processes. Our results may aid in elucidating the possible mechanisms of osteosarcoma metastasis, and may help to facilitate the individual management of patients with osteosarcoma after treatment.