Identification of structural key genes of mutual information gene networks of brain tumor

Identifying genes associated with specific diseases plays an important role in the pathological study, diagnosis, and treatment of diseases. In this paper, we propose a new method to identify key genes for any specific disease-the mutual information gene network (MIGN)-structural key gene (SKG). Considering brain tumors as an example, we identified four types of 37 genes that have varying behaviors in MIGNs of normal cells and different grades of tumor cells, called SKGs, which are closely related to brain tumors. Using SKGs and K-means clustering algorithm for testing, the test accuracy rate was approximately 94.56%. MIGN-SKG effectively identifies a subset of genes that may be markers of disease progression or therapeutic targets for the disease. The key innovation of MIGN-SKG is that it is unrestricted by differentially expressed genes and it directly identifies key genes from the perspective of changes in genetic relationships during disease progression. It can identify potential key genes for any specific disease as well as other dynamic biological systems.(c) 2022 Published by Elsevier B.V.

Automated summary

This paper proposes a new method, MIGN-SKG, for identifying key genes for specific diseases. Using brain tumors as an example, the method effectively identifies genes closely related to brain tumors and achieves a high accuracy rate in testing. The key innovation of this method is that it directly identifies key genes from the perspective of changes in genetic relationships, regardless of differentially expressed genes.