Investigating the Antitumor Activities of Curcumae Rhizoma by Building HepG2 Tumor-bearing Nude Mice Models and Exploring its Anticancer Mechanism by Observing Glycoprotein Expression of Tumor Tissues Using Lectin Microarray Technology
Bibliographic record
Abstract
Background: Curcumae Rhizoma (CR) comes from Curcuma genus, functional breaking blood stasis, detumescence and acesodyne for treatment of Zhengjia accumulation, amenorrhea, traumatic injury and bruising pain. Modern pharmacological studies have shown that the main monomer compositions of Curcumae Rhizoma, such as curcumol, β-elemene, curcumin, have good anti-tumor effects. However, the mechanisms are not clear yet. Previous studies have revealed that the associated glycoprotein showed significant differences with normal people after the development of hepatocellular carcinoma (HCC). Methods: In this study, the anticancer activities of CR extract were investigated by constructing HepG2 tumor bearing nude mice models. Furthermore, glycan profiles of tumor tissues were thoroughly characterized by lectin microarrays, a high-throughput technique, in an effort to explore the anticancer mechanism of Curcumae Rhizoma. Results: It is indicated that CR extract might inhibit the tumor proliferation in tumor-bearing model and the potential mechanisms might be CR treatment altered protein glycosylation of tumor cell, which plays an important role in the pathogenesis and progression of HCC. In detail, fucosylation (identified by PSA and UEA-I), bisecting GlcNAc or multianternnary (identified by PHA-E+L) and terminal GalNAc (identified by BPL) decreased, while sialylation (identified by WGA and SNA), high-mannose (identified by ConA) and T-antigen/TN-antigen/sialyl-T antigen (identified by ACA) increased in CR treatment group compared to model group. Similar phenomenon also occurred in two positive groups, western medicine cyclophosphamide (CTX) and Chinese medicine monomer β-elemene administration groups, especially in β-elemene administration one. The liver contains various receptors on sinusoidal and hepatocyte surfaces, and many proteins that bind to these receptors reply on carbohydrate moieties during the development of HCC. Conclusion: In this point of view, a search for the biological significance of glycosylation expression and its function after Chinese Medicine administration in HCC may open a new direction in glycobiology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".