KEGnet: A Knowledge-Enhanced Graph Attention Framework for Gene Biomarker Discovery and Prognosis Prediction in Cancer

Accurate prognosis prediction is pivotal for personalized cancer treatment. While pre-treatment gene expression data offers significant potential, existing predictive models are predominantly data-driven and often neglect critical biological prior knowledge, such as gene-gene interactions and adjacent tissue expression patterns, thereby limiting interpretability and generalizability. To address this, we propose KEGnet, a knowledge-enhanced graph attention framework that systematically incorporates biological priors into both feature selection and model prediction. KEGnet comprises two core components: (1) a knowledge-guided feature screening module (KGATV2) that leverages protein-protein interaction (PPI) network and tumor-normal expression contrasts to identify task-specific gene signatures; and (2) a prediction pipeline that integrates a stacked graph attention network and XGBoost, unified via logistic regression. Applied to key prognostic tasks in two major cancers, the breast cancer (BC) signature PNAC50 identified by KEGnet demonstrated superior performance to traditional signatures (OncotypeDX, PAM50, and HER2DX) in predicting pathological complete response (pCR) across six public datasets $(\mathrm{n}=1,316)$. Furthermore, leveraging prior lung adenocarcinoma (LUAD) signatures, KEGnet delivered more robust predictions of recurrence risk in a private LUAD dataset ($\mathbf{n}=\mathbf{1 1 9}$) compared to conventional approaches. Notably, KEGnet also demonstrates superior clinical and biological interpretability. Altogether, by fusing expression data with biological knowledge, KEGnet not only enhances prediction performance but also facilitates the discovery of high-impact gene signature biomarkers with potential.