Biomedical networks are critical for representing complex biological systems, and network curvature is a key structural property that captures topological features not highlighted by traditional graph metrics. This study introduces a Graph Neural Network (GNN)-based approach for detecting communities in cancer-specific Gene Co-expression Networks (GCNs), using Ollivier-Ricci curvature as an integral feature. The inclusion of curvature has shown to enhance the detection of biologically significant communities, improve network modularity, and enable finer partitioning. These preliminary results indicate that curvature-based analyses can offer new insights into the organization of gene co-expression networks, aiding in the understanding of biological modularity, disease mechanisms, and functional interactions.