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Prior studies, however, had two major limitations: (1) coarse-grained defect prediction results and (2) weak long-term dependencies modeling. As a result, developers need to review the prediction results to figure out which function or even which line of code produced the issue. In this study, we present OdegVul, a novel statement-level defect prediction model, to address these concerns. To capture both semantic and structural relationships between statements, a statement representation framework combining deep learning and graph neural networks is designed. Then the long-term dependencies between statements are encoded as a partial differential equation of a graph neural network. Through the experiment of 32 releases of 9 open-source Java projects, we found that semantic and structural dependencies are crucial to statement-level defect prediction. OdegVul outperforms other state-of-the-art (SOTA) predictors and achieves reasonable performance in cross-project statement-level defect prediction scenarios. The finer granularity of predicting results reduces the developer\u2019s workforce in reviewing the prediction results and increases the practicality of the defect prediction model. 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