Patients with ulcerative colitis (UC) have a higher risk of developing colorectal cancer (CRC), however, the metabolic shifts during the UC‐to‐CRC transition remain elusive. In this study, an AOM‐DSS‐induced three‐stage colitis‐associated colorectal cancer (CAC) model is constructed and targeted metabolomics analysis and pathway enrichment are performed, uncovering the metabolic changes in this transition. Spatial metabolic trajectories in the “normal‐to‐normal adjacent tissue (NAT)‐to‐tumor” transition, and temporal metabolic trajectories in the “colitis‐to‐dysplasia‐to‐carcinoma” transition are identified through K‐means clustering of 74 spatially and 77 temporally differential metabolites, respectively. The findings reveal two distinct metabolic profile categories during the inflammation‐to‐cancer progression: those with consistent changes, either increasing (e.g., kynurenic acid, xanthurenic acid) or decreasing (e.g., long‐chain fatty acids, LCFAs), and those enriched at specific disease stages (e.g., serotonin). Further analysis of metabolites with consistent temporal trends identifies eicosapentaenoic acid (EPA) as a key metabolite, potentially exerting anti‐inflammatory and anti‐cancer effects by inhibiting insulin‐like growth factor binding protein 5 (IGFBP5). This study reveals novel metabolic mechanisms underlying the transition from UC to CAC and suggests potential targets to delay the progression.

Saikosaponin d (SSd) is one of the main active ingredients in Radix Bupleuri. In our study, network pharmacology databases and metabolomics were used in combination to explore the new targets and reveal the in-depth mechanism of SSd. A total of 35 potential targets were chosen through database searching (HIT and TCMID), literature mining, or chemical similarity predicting (Pubchem). Out of these obtained targets, Neuropilin-1 (NRP-1) was selected for further research based on the degree of molecular docking scores and novelty. Cell viability and wound healing assays demonstrated that SSd combined with NRP-1 knockdown could significantly enhance the damage of HepG2. Metabolomics analysis was then performed to explore the underlying mechanism. The overall difference between groups was quantitatively evaluated by the metabolite deregulation score (MDS). Results showed that NRP-1 knockdown exhibited the lowest MDS, which demonstrated that the metabolic profile experienced the slightest interference. However, SSd alone, or NRP-1 knockdown in combination with SSd, were both significantly influenced. Differential metabolites mainly involved short- or long-chain carnitines and phospholipids. Further metabolic pathway analysis revealed that disturbed lipid transportation and phospholipid metabolism probably contributed to the enhanced anti-hepatoma effect by NRP-1 knockdown in combination with SSd. Taken together, in this study, we provided possible interaction mechanisms between SSd and its predicted target NRP-1.