Abstract
At present, the main treatment methods of osteosarcoma are chemotherapy and surgery. Its 5-year survival rate has not been significantly improved in the past decades. Osteosarcoma has extremely complex multigenomic heterogeneity and lacks universally applicable signal blocking targets. Osteosarcoma is often found in adolescents or children under the age of 20, so it is very important to explore its genetic pathogenic factors. We used known osteosarcoma-related genes and computer algorithms to find more osteosarcoma pathogenic genes, laying the foundation for the treatment of osteosarcoma immune microenvironment-related treatments, so as to carry out further explorations on these genes. It is a traditional method to identify osteosarcoma related genes by collecting clinical samples, measuring gene expressions by RNA-seq technology and comparing differentially expressed gene. The high cost and time consumption make it difficult to carry out research on a large scale. In this paper, we developed a novel method “RELM” which fuses multiple extreme learning machines (ELM) to identify osteosarcoma pathogenic genes. The AUC and AUPR of RELM are 0.91 and 0.88, respectively, in 10-cross validation, which illustrates the reliability of RELM.
Highlights
Osteosarcoma is the most common malignant bone tumor in clinic (Marko et al, 2016), which is mostly seen in children and adolescents
Whole-exome and whole-genome sequencing analysis of the germline DNA of patients with osteosarcoma showed that the prevalence of pathogenic variants in genes associated with known cancer susceptibility syndromes was higher than expected (Gianferante et al, 2017)
Using the idea of random forest for reference, we fused multiple Extreme Learning Machines (ELM) to build a model through the known osteosarcoma related genes to predict more genes potentially associated with osteosarcoma
Summary
Osteosarcoma is the most common malignant bone tumor in clinic (Marko et al, 2016), which is mostly seen in children and adolescents. Various molecular changes and genomes closely related to the occurrence and progress of osteosarcoma have been identified These changes include gene amplification, deletion and germline mutation, overexpression and RTK activation, abnormal cell proliferation, metastasis, apoptosis, drug tolerance genes and miRNAs (Saraf et al, 2018). The main causes of osteosarcoma are the inactivation of tumor suppressor gene expression and the abnormal doubling of oncogenes (Orr and Compton, 2013) Common oncogenes, such as avian cell homolog Myc, purine / pyrimidine exonuclease 1 (APEX1), action associated vascular endothelial growth factor A (VEGFA) and RecQ protein analog 4 (RecQL4). These amplified genes are closely related to the biological processes of osteosarcoma cell proliferation, growth and angiogenesis. Using the idea of random forest for reference, we fused multiple Extreme Learning Machines (ELM) to build a model through the known osteosarcoma related genes to predict more genes potentially associated with osteosarcoma
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