Gene-expression Profiling in Non-small Cell Lung Cancer with Invasion of Mediastinal Lymph Nodes for Prognosis Evaluation.
Bibliographic record
Abstract
BACKGROUND/AIM: The aim of the study was to determine the pathways and expression profile of the genes that might predict response to neoadjuvant chemotherapy in patients with stage IIIA non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: We evaluated, by microarray, the gene-expression profile of tumoral mediastinal lymph node samples surgically removed from 27 patients with stage IIIA NSCLC before neoadjuvant chemotherapy treatment. Depending on the response to the induction treatment, the patients were divided in two groups: group A: patients whose disease evolved, stabilized or who had minor response to chemotherapy, and group B: patients whose disease stabilized or had major response to chemotherapy. RESULTS: The microarray experiments identified 1,127 genes with a modified expression in the tumoral tissue compared to normal tissue with p≤0.05 and 44 genes with p≤0.01. The identified up-regulated genes between tumoral versus normal tissue included collagen, type I, alpha 1 (COL1A1), inhibin beta A (INHBA) and thioredoxin interacting protein (TXNIP). Pathways identified with a false-discovery rate of <0.005 included: cytokine pathways, focal adhesion or extracellular matrix receptor interaction. CONCLUSION: Our approach identified important characteristics of NSCLC and pointed-out molecular differences between sub-groups of patients based on their response to therapy.
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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 | 0.000 | 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) | 0.001 | 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".