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

ABSTRACT: 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 w

SUBMITTER: Zhang H 

PROVIDER: S-EPMC12813119 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature