Primary tissue metabolic fingerprinting for efficient diagnosis of lymph node metastasis and metabolic reprogramming mechanisms in colorectal cancer.

Accurate detection of lymph node metastasis (LNM) is critical for colorectal cancer (CRC) staging and treatment planning, yet current histopathological assessment based on lymph nodes remains labor-intensive and operator-dependent. Here, we developed a tissue metabolic fingerprinting platform leveraging label-free ferric nanoparticle-enhanced laser desorption/ionization mass spectrometry (FELDI-MS) to directly acquire colorectal cancer tissue metabolic fingerprints (CRC-TMFs) from 276 primary CRC tissue samples (138 non-metastatic/LNM-, 138 metastatic/LNM+). Based on CRC-TMFs, we constructed a machine learning-based diagnostic model for LNM detection, achieving area under the curve (AUC) of 0.914. Furthermore, metabolic profiling revealed cysteine deficiency in LNM + tissues, concomitant with upregulation of glutamate-cysteine ligase catalytic subunit (GCLC), which catalyzes the rate-limiting step in glutathione biosynthesis from cysteine. Functional validation demonstrated that GCLC knockdown inhibited CRC cell proliferation and migration, underscoring its role in metastatic reprogramming. Our work not only introduces a rapid, operator-independent tool for precise LNM assessment but also highlights dysregulated cysteine-GCLC-glutathione metabolism as a key feature of metastatic reprogramming in CRC.