Tumor-based gene expression biomarkers to predict survival following curative intent resection for stage I lung adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: Prognostic biomarkers are needed in clinical setting to predict outcome after resection for early-stage lung adenocarcinoma. The goal of this study is to validate tumor-based single-gene expression biomarkers with demonstrated prognostic value in order to move them along the clinical translation pipeline. METHODS: Prognostic genes were selected from the literature and the best candidates measured by quantitative real-time polymerase chain reaction (qPCR) in tumors of 233 patients with stage I adenocarcinoma. Significant prognostic genes were then validated in an independent set of 210 patients matching the first set in terms of histology, stage, and clinical data. RESULTS: Eleven genes with demonstrated prognostic value were selected from the literature. Complementary analyses in public databases and our own microarray dataset led to the investigation of six genes associated with good (BTG2, SELENBP1 and NFIB) or poor outcome (RRM1, EZH2 and FOXM1). In the first set of patients, EZH2 and RRM1 were significantly associated with better survival on top of age, sex and pathological stage (EZH2 p = 3.2e-02, RRM1 p = 5.9e-04). The prognostic values of EZH2 and RRM1 were not replicated in the second set of patients. A trend was observed for both genes in the joint analyses (n = 443) with higher expression associated with worse outcome. CONCLUSION: Adenocarcinoma-specific mRNA expression levels of EZH2 and RRM1 are associated with poor post-surgical survival in the first set of patients, but not replicated in a clinically and pathologically matched independent validation set. This study highlights challenges associated with clinical translation of prognostic biomarkers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".