Integrative Genomic and in Silico Analysis Reveals Mitochondrially Encoded Cytochrome C Oxidase III (MT—CO3) Overexpression and Potential Neem-Derived Inhibitors in Breast Cancer
Bibliographic record
Abstract
BACKGROUND: The increasing global incidence of breast cancer calls for the identification of new therapeutic targets and the assessment of possible neem-derived inhibitors by means of computational modeling and integrated genomic research. METHODS: Originally looking at 59,424 genes throughout 42 samples, we investigated gene expression data from The Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) dataset. We chose 286 genes for thorough investigation following strict screening for consistent expression. R's limma package was used in differential expression analysis. The leading candidate's protein modeling was done with Swiss-ADME and Discovery Studio. Molecular docking studies, including 132 neem compounds, were conducted utilizing AutoDock Vina. RESULTS: ) inside the structure of MT-CO3. Docking investigations showed five interesting neem-derived inhibitors: 7-benzoylnimbocinol, nimolicinol, melianodiol, isonimocinolide, and stigmasterol. Strong binding affinities ranging from -9.2 to -11.5 kcal/mol and diverse interactions with MT-CO3, mostly involving the residues Phe214, Arg221, and Trp58, these molecules displayed. With hydrophobic interactions dominant across all chemicals, fragment contribution analysis revealed that scaffold percentage greatly influences binding effectiveness. Stigmasterol revealed greater drug-likeness (QED = 0.79) despite minimal interaction variety, while 7-benzoylnimbocinol presented the best-balanced physicochemical profile. CONCLUSION: Connecting traditional medicine with current genomics and computational biology, this work proposes a methodology for structure-guided drug design and development using neem-derived chemicals and finds MT-CO3 as a potential therapeutic target for breast cancer.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".