LATS2 and FAT4 as key candidate genes of hippo pathway associated with the risk and progression of breast cancer: an in-silico approach
Bibliographic record
Abstract
BACKGROUND: The 2020 cancer report states that breast cancer remains a significant cause of death for females, despite the use of various strategies for early detection and treatment. However, there are still gaps in the fight against this disease. Researchers are exploring the hippo pathway, one of eight significant pathways involved in cancer progression, for potential biomarkers to use in personalized therapeutics. METHODS: The current study used bioinformatic tools such as DEGs analysis, Methsurv, Km Plotter to generate data that can predict molecular biomarkers associated with hippo pathway in breast cancer development and treatment. The protein-protein interaction pathway was generated using the STRING database to find associations of hippo pathway genes with other dysregulated genes in breast cancer datasets. A disease enrichment study was also done to explore the potential of the hippo pathway in various aspects. RESULTS: LATS2 and FAT4 genes of the hippo pathway have shown an interesting association with overall survival, hypermethylation, genetic alterations, and decreased expression levels in the breast cancer cohort. Our findings suggest that both of these genes are associated with breast cancer progression and diagnosis and can be utilized as predictive biomarkers by oncologists for personalized therapy in patients.
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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 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".