Prediction of Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer From Gene Expression Profiling Using Cox-Boost Regression Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Backgrounds: Distant metastasis in breast cancer patients contributes to increased breast cancer mortality, highlighting the urgent need for effective predictive strategies. Understanding metastasis mechanisms and identifying relevant biomarkers are crucial for improving patient outcomes and informing targeted therapies. This study employed a high-dimensional regression model to identify biomarkers linked to distant metastasis-free survival in breast cancer patients, with the goal of enhancing prognostic accuracy and guiding clinical decisions. Methods: We utilized the publicly available breast cancer dataset (GSE2034), which includes gene expression profiles for 22 283 genes across 286 samples. To identify relevant genes, we applied Cox-Boost regression and a random forest (RF) model. We then explored the association between the selected genes and metastasis-free survival outcomes using quantile regression, chosen for its ability to assess the impact of these genes across different survival quantiles ( P < .05). This approach complements the Cox-Boost model by providing a more detailed understanding of gene-survival relationships at various points in the survival distribution, thereby strengthening the robustness of our findings. Results: We identified 222 significant transcripts using univariate Cox regression models. By applying Cox-Boost, both with and without adjustment for ER+/− status, we identified 7 genes associated with time-to-relapse/metastasis in breast cancer patients: SNU13, CLINT1, ACBD3, NEK2, COL2A1, WFDC1, and RACGAP1. A similar approach was used for ER-positive patients. Patients were classified as high or low risk for metastasis based on the median prognostic index calculated from the identified genes ( P < .001). The top-ranked genes associated with high/low risk groups using RF were RACGAP1, NEK2, CCNA2, DTL, ACBD3, ARL6IP5, WFDC1, and PDCD4. Conclusions: We identified eleven key genes, including SNU13, CLINT1, ACBD3, NEK2, COL2A1, WFDC1, and RACGAP1, as well as CCNA2, DTL, ARL6IP5, and PDCD4, that are related to the risk of distant metastasis and may be used as biomarkers to predict distant metastasis of 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