Prognostic Gene Expression Signature for Squamous Cell Carcinoma of Lung
Bibliographic record
Abstract
PURPOSE: This study aimed to identify and validate a gene expression signature for squamous cell carcinoma of the lung (SQCC). EXPERIMENTAL DESIGN: A published microarray dataset from 129 SQCC patients was used as a training set to identify the minimal gene set prognostic signature. This was selected using the MAximizing R Square Algorithm (MARSA), a novel heuristic signature optimization procedure based on goodness-of-fit (R square). The signature was tested internally by leave-one-out-cross-validation (LOOCV), and then externally in three independent public lung cancer microarray datasets: two datasets of non-small cell lung cancer (NSCLC) and one of adenocarcinoma (ADC) only. Quantitative-PCR (qPCR) was used to validate the signature in a fourth independent SQCC cohort. RESULTS: A 12-gene signature that passed the internal LOOCV validation was identified. The signature was independently prognostic for SQCC in two NSCLC datasets (total n = 223) but not in ADC. The lack of prognostic significance in ADC was confirmed in the Director's Challenge ADC dataset (n = 442). The prognostic significance of the signature was validated further by qPCR in another independent cohort containing 62 SQCC samples (hazard ratio, 3.76; 95% confidence interval, 1.10-12.87; P = 0.035). CONCLUSIONS: We identified a novel 12-gene prognostic signature specific for SQCC and showed the effectiveness of MARSA to identify prognostic gene expression signatures.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".