面向信息系统设计的癌症阶段生物标志物识别

Cancer is an exceptionally complex and highly heterogeneous disease with dynamic changes. Its occurrence and development are accompanied by a large number of gene mutations and functional disorders. Identifying biomarkers related to cancer stages is crucial for understanding the pathogenic and developmental mechanisms of cancer. However, the existing research on cancer biomarker recognition often treat individual genes as isolated nodes and usually only focus on the binary classification of cancer, ignoring the significant differences among different stages of cancer. To overcome the above issues, we first construct a RRN (regression residual network) for each cancer stage, and then analyze the nodes and edges of RRN in each stage. After that, the multi-source data mining is conducted in biological pathways, and the entire process of cancer evolution is characterized along with stages. By doing this, both biomarkers for cancer binary classification and multi-stage classification are obtained, and they are validated on GSE10072 and GSE42171 datasets, respectively. The experimental results show that the obtained biomarkers ALDOA and NME1 achieve competitive accuracy compared with existing methods by using only two genes for lung adenocarcinoma, and the biomarkers consisting of 17 edges achieve the improved accuracy by 14.86% by comparing with existing methods in terms of multi-stage classification.