< The research team. Front row, center: Professor Ji Min Lee from the Graduate School of Medical Science and Engineering; to her right, Professor Hyun-woo Kim from the Department of Chemistry. To Professor Lee >
A single preoperative blood sample may help improve the prediction of recurrence or metastasis in patients with colorectal cancer. A joint research team from KAIST, Gangnam Severance Hospital, and Asan Medical Center has shown that, as colorectal cancer advances, the network of relationships among circulating amino acids (a kind of metabolic map) undergoes systematic remodeling. Building on this finding, the researchers developed an analytical method that showed higher predictive performance than a CEA-only model and models based solely on individual amino acid levels.
KAIST (President Choongsik Bae) announced on July 22 that a joint research team led by Professor Ji Min Lee from the Graduate School of Medical Science and Engineering and Professor Hyunwoo Kim from the Department of Chemistry, in collaboration with researchers at Gangnam Severance Hospital and Asan Medical Center, has developed a new framework for analyzing networks of circulating amino acids, which reflect the body's metabolic state. Using this framework, the team showed that the circulating amino acid network undergoes stage-dependent remodeling that reflects systemic metabolic reprogramming. The team then used these network-derived features to develop a new analytical strategy for predicting recurrence or metastasis.
Cancer cells require large amounts of nutrients to grow and proliferate. Amino acids are not only the building blocks of proteins but also essential for energy production and DNA synthesis, making them critical to cancer cell survival and growth. Colorectal cancer, in particular, is characterized by pronounced changes in amino acid metabolism.
These changes are not confined to tumor tissue; they also appear in the bloodstream. As a result, blood amino acids have drawn attention as an important metabolic biomarker reflecting the body's overall metabolic state. Until now, however, research has focused mainly on the concentrations of individual amino acids, leaving the question of how amino acids are interconnected and change together largely unexplored.
The team used fluorine-19 nuclear magnetic resonance (¹⁹F NMR) spectroscopy to simultaneously quantify 18 circulating amino acids in a small serum sample. By analyzing not only the relative abundance of each amino acid but also the relationships among them as a network, the researchers showed that the circulating amino acid network is progressively remodeled as colorectal cancer advances.
The researchers interpreted this remodeling as evidence of systemic metabolic reprogramming (broad changes in metabolism associated with tumor progression).
As colorectal cancer progressed, the proportion of branched-chain amino acids (BCAAs) such as valine and leucine, which play key roles in muscle and energy metabolism, decreased, while the proportion of glycine and serine, which cancer cells need to synthesize DNA and proliferate rapidly, increased. This shift suggests that systemic amino acid utilization changes with advancing disease.
Glycine proved particularly notable. Although glycine is actively used by rapidly proliferating cancer cells, its relative abundance in the blood increased rather than decreased. The team also observed the emergence of a glycine-centered interaction pattern, providing further evidence of systemic metabolic remodeling during colorectal cancer progression.
The team then applied the pairwise amino acid interaction features into machine-learning models designed to identify patients with recurrence or metastasis.
In nested cross-validation, the correlation-based model showed higher predictive performance than a CEA-only model, while the combined model incorporating CEA, individual amino acid levels, and interaction-derived features achieved the highest overall performance. It also outperformed a model based solely on individual amino acid levels.
< FIGURE 1 Workflow of 19 F NMR-based serum amino acid profiling and data analysis. Serum samples collected from CRC patients were subjected to fluorine-labeling-based amino acid detection followed by 19 F NMR spectroscopy. Quantified amino acid data were processed using ratio-based normalization and multivariate analyses to characterize stage-specific metabolic patterns and construct machine learning models for prognostic risk prediction. >
The findings suggest that examining how amino acids interact and change together, rather than considering their levels alone, provides a more informative picture of cancer progression. The study is the first to show that the interaction network among circulating amino acids could serve as a blood-based metabolic biomarker.
"We hope this will lead to new precision medicine technologies that can predict recurrence risk more accurately using a blood sample alone and help establish personalized treatment strategies," said Professor Ji Min Lee.
The research began with an interdisciplinary idea proposed through KAIST's Master's and PhD Venture Research Program. Graduate students in medical science and chemistry jointly conceived an interdisciplinary approach to studying cancer progression through networks of circulating amino acids. The proposal was selected for support and ultimately led to publication in the internationally renowned journal Advanced Science.
"This research embodies the spirit of KAIST by showing how students' creative ideas and interdisciplinary collaboration can open new possibilities," said KAIST President Choongsik Bae. He added that KAIST will continue to foster an environment in which students and researchers can freely pursue challenges across disciplinary boundaries and support creative interdisciplinary research that produces innovative technologies contributing to public health and quality of life.
The study's co-first authors are Ji-Yeon Lee, a student in the integrated master's and doctoral program at the Graduate School of Medical Science and Engineering, and Dr. Jumi Kim, a postdoctoral researcher in the Department of Chemistry. Professors Ji Min Lee and Hyunwoo Kim of KAIST and Professor Eun Jung Park, affiliated with Gangnam Severance Hospital and Asan Medical Center, served as co-corresponding authors. The findings were published online in Advanced Science, which has a Journal Impact Factor of 14.1, on June 9, 2026.
Paper title: Circulating Amino Acid Network Remodeling Reveals Systemic Metabolic Reprogramming Predictive of Colorectal Cancer Recurrence and Metastasis
DOI: 10.1002/advs.76044
This research was supported by the Samsung Research Funding & Incubation Center of Samsung Electronics, the National Research Foundation of Korea, a Faculty Research Grant from the Department of Surgery at Asan Medical Center, and grants from the Asan Institute for Life Sciences, among others.
