Introduction of hub genes and herbal treatment of breast cancer through bioinformatics
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.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".