• [Advanced Science] Circulating Amino Acid Network Remodeling RevealsSystemic Metabolic Reprogramming Predictive of ColorectalCan
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  • 2026-07-31 09:27:13|
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[Title]

Circulating Amino Acid Network Remodeling RevealsSystemic Metabolic Reprogramming Predictive of ColorectalCancer Recurrence and Metastasis.


[Corresponding Author]
* Ji Min Lee (jimin.lee@kaist.ac.kr)
Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
* Hyunwoo Kim (hwkim@kaist.edu)
Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
* Eun Jung Park (kendoej@naver.com)
Division of Colon and Rectal Surgery, Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.

[Journal] 
Advanced Science. 2026 Jun 9
DOI: 10.1002/advs.76044

[Abstract]
Characterizing the dynamics of systemic metabolic pathways during cancer progression could enable the development ofprognostic tools and therapeutic strategies. However, achieving this goal requires analytical platforms optimized for liquidbiospecimens together with interpretive frameworks capable of identifying robust serum-based biomarkers. Here, we establish anetwork-based metabolic profiling framework using 1 9 F NMR–based serum amino acid analysis to characterize systemic metabolicremodeling during colorectal cancer (CRC) progression. Using an analytical protocol optimized for clinical serum samples, wequantified circulating amino acids from 152 CRC patients and implemented a ratio-based normalization strategy to mitigatecohort variability in concentration-based approaches. We systematically evaluated both individual amino acid changes andcorrelation structures across tumor stages. Advanced-stage CRC exhibited a distinct metabolic shift characterized by decreasedvaline and increased glycine levels. Correlation network analysis further revealed stage-dependent remodeling of circulatingamino acid interactions, leading to the emergence of a glycine-centered metabolic architecture. Importantly, machine-learningmodels integrating individual amino acid levels with network-derived features significantly improved the prediction of recurrenceor metastasis compared with models using either feature type alone and outperformed conventional biomarker carcinoembryonicantigen (AUROC = 0.806). These findings highlight the remodeling of circulating amino acid network as a promising strategy forprognostic stratification and postoperative monitoring in CRC.
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