Large Scale Identification of Osteosarcoma Pathogenic Genes by Multiple Extreme Learning Machine

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.

AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma

“…Although it accounts for 0.2% of human malignant solid tumors, the mortality and disability rates are very high. Osteosarcoma is the most common type of bone malignancy in the elderly and children, and the second peak of its morbidity is over 60 years old [ 2 , 3 , 4 ]. At present, the five-year survival rate of osteosarcoma patients in China is about 60%, and the ten-year survival rate decreases to only 30%.…”