Exploring CDKN1A Upregulation Mechanisms: Insights into Cell Cycle Arrest Induced by NC2603 Curcumin Analog in MCF-7 Breast Cancer Cells
Bibliographic record
Abstract
Breast cancer stands out as one of the most prevalent malignancies worldwide, necessitating a nuanced understanding of its molecular underpinnings for effective treatment. Hormone receptors in breast cancer cells substantially influence treatment strategies, dictating therapeutic approaches in clinical settings, serving as a guide for drug development, and aiming to enhance treatment specificity and efficacy. Natural compounds, such as curcumin, offer a diverse array of chemical structures with promising therapeutic potential. Despite curcumin’s benefits, challenges like poor solubility and rapid metabolism have spurred the exploration of analogs. Here, we evaluated the efficacy of the curcumin analog NC2603 to induce cell cycle arrest in MCF-7 breast cancer cells and explored its molecular mechanisms. Our findings reveal potent inhibition of cell viability (IC50 = 5.6 μM) and greater specificity than doxorubicin toward MCF-7 vs. non-cancer HaCaT cells. Transcriptome analysis identified 12,055 modulated genes, most notably upregulation of GADD45A and downregulation of ESR1, implicating CDKN1A-mediated regulation of proliferation and cell cycle genes. We hypothesize that the curcumin analog by inducing GADD45A expression and repressing ESR1, triggers the expression of CDKN1A, which in turn downregulates the expression of many important genes of proliferation and the cell cycle. These insights advance our understanding of curcumin analogs’ therapeutic potential, highlighting not just their role in treatment, but also the molecular pathways involved in their activity toward breast cancer cells.
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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".