In silico investigation of heparanase-correlated genes in breast cancer subtypes
Bibliographic record
Abstract
OBJECTIVE: To investigate the possible genes that may be related to the mechanisms that modulate heparanase-1. METHODS: The analysis was conducted at Universidade Federal de São Paulo, on the data provided by: The Cancer Genome Atlas, University of California Santa Cruz Genome Browser, Kyoto Encyclopedia of Genes and Genomes Pathway Database, Database for Annotation, Visualization and Integrated Discovery Bioinformatics Database and the softwares cBioPortal and Ingenuity Pathway Analysis. RESULTS: Using messenger RNA expression pattern of different molecular subtypes of breast cancer, we proposed that heparinase-1 was co-related with its progression. In addition, genes that were analyzed presented co-expression with heparanase-1. The results that showed that heparanase-1 co-expressed with phosphoinositide 3-kinase adapter protein 1, sialic acid-binding immunoglobulin-like lectin 7, and leukocyte-associated immunoglobulin-like receptor 1 are directed related with immune system evasion during breast cancer progression. Furthermore, cathepsin L was co-expressed with heparanase-1 and transformed inactive heparanase-1 form into active heparanase-1, triggering extracellular matrix remodeling, which contributes to enhanced tumor-host interaction of the tumor. CONCLUSION: The signaling pathway analysis using bioinformatics tools gives supporting evidence of possible mechanisms related to breast cancer development. Evasion genes of the immune system co-expressed with heparanase-1, a enzyme related with tumor progression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".