Excavation and validation of the active components and mechanisms of longan against colorectal cancer
Bibliographic record
Abstract
• In this study, we created a database on the composition of longan. • Prediction of the main components of longan for treating colorectal cancer. • The target TP53 is closely associated with the survival and prognosis of colorectal cancer. • Targets IL-6 and GAPDH can serve as biomarkers for diagnosing CRC. • Providing a rationale for a potential component of longan to combat CRC. Colorectal cancer (CRC) is the fourth most common malignant tumor worldwide and remains a leading cause of cancer-related mortality. Dimocarpus longan Lour., a member of the Dimocarpus genus in the Sapindaceae family, has been traditionally used as a tonic to alleviate deficiencies and enhance cognitive function. In addition, its extracts exhibit therapeutic potential against various malignant tumors. However, no studies have reported the potential role of longan in CRC treatment. Metabolomics was used to detect secondary metabolites of longan, while network pharmacology was used to explore its active components and potential mechanisms of action against CRC. Finally, bioinformatics analysis, molecular docking, and cellular experiments were conducted to further validate the findings. This study used metabolomics integrated with network pharmacology to qualitatively and quantitatively analyze the flesh of five developmental stages of ‘Shixia’ eye fruit. The potential active ingredients, core targets, and mechanisms of action of longan against CRC were identified, and the expression of core targets in CRC was validated at multiple levels using cBioPortal, CCLE database, Protein Atlas database, and GEPIA database. Use Kaplan Meier Plotter database and pROC software package to analyze the prognostic value and diagnostic significance of core targets. Molecular docking validation was performed using AutoDock Vina 1.2.2. Finally, the inhibitory effect of key compounds on HCT116 cells and their impact on EGFR and P53 protein expression were verified through MTT and Western blot experiments. Luteolin, laricitrin, naringenin, hispidulin and diosmetin were hypothesized to be the key active compounds, while TP53, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), interleukin 6 (IL-6), epidermal growth factor receptor (EGFR), and AKT1 were identified as the probable key targets. TP53 is closely associated with CRC survival and prognosis, while IL-6 and GAPDH are potential diagnostic biomarkers for the disease. Molecular docking analysis revealed that TP53 and EGFR demonstrated strong binding affinity with lignans and geraniol, the key active compounds of longan. Cellular experiments indicated a significant reduction in cell viability following treatment, along with a decrease in EGFR levels and an upregulation of TP53 expression in HCT116 cells. Overall, longan exerts its anti-CRC effects through multiple targets, active components, and signaling pathways. These findings provide valuable insights into the potential therapeutic applications of longan as a traditional Chinese medicine resource. This study used ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) to qualitatively and quantitatively analyze the secondary metabolites of longan, and explored its active ingredients and mechanism of action for treating CRC through network pharmacology integrated cell experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".