Exploring CDKN1A Upregulation Mechanisms: Insights into Cell Cycle Arrest Induced by NC2603 Curcumin Analog in MCF-7 Breast Cancer Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it