Achieving a trade-off between biological utility and patient privacy remains a key challenge for secure data sharing when applying transcriptomic clinical datasets to artificial intelligence in precision oncology. Here, we introduce the first benchmarking study tailored to high-dimensional clinical transcriptomic cancer data, comparing synthetic data generation methods across three clinical cancer trials. Our framework, SynOmicBench, combines standardized preprocessing with multidimensional evaluation, prioritizing downstream biological validation alongside statistical fidelity and attack-based privacy assessment. Results indicate that no single method dominated all dimensions, with Gaussian Copula achieving the most balanced performance, followed by Avatar, demonstrating that metric-based similarity alone is insufficient to ensure preservation of higher-order molecular dependencies. Synthetic data consistently reproduced biomedical signal directionality but with attenuated effect sizes and inter-replicate variability, supporting hypothesis generation when multi-seed synthesis is adopted. Collectively, this framework provides a reproducible decision-support tool for method selection and promotes biologically informed, privacy-aware adoption of synthetic data in precision oncology.