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<p>Recent advances in <i>Medical Oncology</i> highlight the integration of bulk and single-cell transcriptomics to reveal glycolytic heterogeneity in colorectal cancer. Translating these discoveries into reliable clinical tools requires rigorous methods, transparent validation, and equity-minded implementation. This communication proposes a standards-first roadmap for reproducible and globally relevant biomarker development. It identifies major technical pitfalls such as batch-effect over-correction and normalization bias, and recommends the application of internationally recognized frameworks—TRIPOD + AI, PROBAST + AI, and DECIDE-AI—to ensure transparency, calibration, and staged clinical evaluation. Orthogonal validation using metabolic imaging and spectroscopy is emphasized to confirm biological realism beyond transcriptomic data. The roadmap concludes with strategies for global equity, including LMIC-inclusive trial design, FAIR data standards, and cost-aware clinical surrogates. This structured approach bridges discovery science with practical implementation, aligning precision oncology with reproducibility, accountability, and global accessibility.</p>

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From glycolytic signatures to patients: A translational roadmap for reproducible, equitable deployment of multi-omics and AI in colorectal cancer

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • M. Vijayasimha

摘要

Recent advances in Medical Oncology highlight the integration of bulk and single-cell transcriptomics to reveal glycolytic heterogeneity in colorectal cancer. Translating these discoveries into reliable clinical tools requires rigorous methods, transparent validation, and equity-minded implementation. This communication proposes a standards-first roadmap for reproducible and globally relevant biomarker development. It identifies major technical pitfalls such as batch-effect over-correction and normalization bias, and recommends the application of internationally recognized frameworks—TRIPOD + AI, PROBAST + AI, and DECIDE-AI—to ensure transparency, calibration, and staged clinical evaluation. Orthogonal validation using metabolic imaging and spectroscopy is emphasized to confirm biological realism beyond transcriptomic data. The roadmap concludes with strategies for global equity, including LMIC-inclusive trial design, FAIR data standards, and cost-aware clinical surrogates. This structured approach bridges discovery science with practical implementation, aligning precision oncology with reproducibility, accountability, and global accessibility.