Graph Neural Networks have achieved remarkable success across various domains but suffer from over-smoothing and high computational cost in deep architectures. Knowledge distillation offers a solution by transferring knowledge from a deep teacher to a lightweight student model, yet most existing approaches depend on labeled data, limiting their applicability in real-world unlabeled scenarios. To address this, we propose DisGCL, a contrastive learning-based distillation framework for deep GNNs without labels. DisGCL enables the student to inherit knowledge from the teacher while extracting discriminative features via self-supervision. Extensive experiments show that DisGCL outperforms state-of-the-art distillation methods in unlabeled settings, achieving improved generalization with lower complexity and resource demand.