Integrating Meta-Analysis of Microarray Data and Targeted Proteomics for Biomarker Identification: Application in Breast Cancer
Bibliographic record
Abstract
The development of signature biomarkers has gained considerable attention in the past decade. Although the most well-known examples of biomarker panels stem from gene expression studies, proteomic panels are becoming more relevant, with the advent of targeted mass spectrometry-based methodologies. At the same time, the development of multigene prognostic classifiers for early stage breast cancer patients has resulted in a wealth of publicly available gene expression data from thousands of breast cancer specimens. In the present study, we integrated transcriptome and proteome-based platforms to identify genes and proteins related to patient survival. Candidate biomarker proteins have been identified in a previously generated breast cancer tissue extract proteome. A mass-spectrometry-based assay was then developed for the simultaneous quantification of these 20 proteins in breast cancer tissue extracts. We quantified the relative expression levels of the 20 potential biomarkers in a cohort of 96 tissue samples from patients with early stage breast cancer. We identified two proteins, KPNA2 and CDK1, which showed potential to discriminate between estrogen receptor positive patients of high and low risk of disease recurrence. The role of these proteins in breast cancer prognosis warrants further investigation.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".