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Large Scale Identification of Osteosarcoma Pathogenic Genes by Multiple Extreme Learning Machine
Zhipeng Zhao1, Jijun Shi2, Guang Zhao3
1Department of Basic Medical Sciences, Taizhou University, Taizhou, China.
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.
Insights
Identifying osteosarcoma pathogenic genes is crucial for developing new treatments. A novel computational method, RELM, effectively identifies these genes, improving upon traditional methods for better osteosarcoma research.
Area of Science:
- Oncology
- Genetics
- Bioinformatics
Background:
- Osteosarcoma, a bone cancer primarily affecting individuals under 20, has a stagnant 5-year survival rate despite current chemotherapy and surgery treatments.
- The disease exhibits significant multigenomic heterogeneity, complicating the identification of effective therapeutic targets.
- Understanding the genetic factors driving osteosarcoma is essential for advancing treatment strategies, particularly those targeting the tumor immune microenvironment.
Purpose of the Study:
- To develop a novel, efficient computational method for identifying osteosarcoma pathogenic genes.
- To overcome the limitations of traditional gene identification methods, such as high cost and time consumption.
- To lay the groundwork for future research into osteosarcoma immune microenvironment-related treatments.
Main Methods:
- Utilized known osteosarcoma-related genes and advanced computer algorithms.
- Developed a novel method named RELM (relying on extreme learning machines).
- Fused multiple extreme learning machines (ELM) within the RELM framework.
Main Results:
- The RELM method demonstrated high reliability in identifying osteosarcoma pathogenic genes.
- Achieved an Area Under the Curve (AUC) of 0.91 in 10-cross validation.
- Obtained an Area Under the Precision-Recall Curve (AUPR) of 0.88 in 10-cross validation.
Conclusions:
- The RELM method offers a reliable and efficient approach to identifying osteosarcoma pathogenic genes.
- This computational strategy surpasses traditional methods in terms of cost and time efficiency.
- The findings provide a foundation for further exploration of gene-targeted therapies and immune microenvironment treatments for osteosarcoma.
