Introduction of hub genes and herbal treatment of breast cancer through bioinformatics
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Bibliographic record
Abstract
Background: Breast cancer (BC) is a prevalent form of endocrine cancer that affects women globally, and their incidence and mortality rates are predicted to rise significantly in the coming years.As a result, breast cancer continues to pose a significant health issue and is a top priority for biomedical research.Methods: We used bioinformatics and reverse pharmacology techniques to identify herbal medicines that could be effective in treating breast cancer.To do this, we analyzed 121 genes from a dataset (GSE42568) containing both cancer and normal samples.Through this analysis, we identified differentially expressed genes (DEGs) and then used the protein-protein interaction (PPI) network to identify 19 hub genes.To pinpoint hub genes, we utilized the widely-used bioinformatics tool, Search Tool for Reciprocal Genes (STRING).To conduct a more detailed analysis, subnetworks were identified using the molecular complex detection (MCODE) algorithm.Results: The hub genes identified in our research are involved in various functions, including positive regulation of cold-induced thermogenesis, patched binding, and the Peroxisome Proliferator-Activated Receptor (PPAR) signaling pathway, as revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.We understood that the herbs Ginkgo biloba seeds, Polygoni Cuspidati Rhizoma Et Radix, Smilacis Glabrae Rhizoma, Capsici Fructus, Cyathulae Radix, Puerariae Flos, and Ardisiae Japonicae Herba can target hub genes such as PPARG, CCNB1, CAV1, CDH1, ADIPOQ, LEP, IGF1, LPL, DGAT2, ACSL1, and PCK1.Using nine ingredients, these herbs were identified as key in targeting hub genes.This study provides insights into potential therapeutic targets and drugs for treating breast cancer.
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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 | 0.000 | 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