Microarray Analysis Identifies Pathways In Progression of Early Stage Lung Adenocarcinoma: The Importance of Focal Adhesion and ECM-Receptor Interactions
Bibliographic record
Abstract
Recurrence after lung cancer surgery is high, even among Non-Small Cell Lung Cancer (NSCLC) adenocarcinoma patients diagnosed early as Stage I, where there has been no spread to lymph nodes.Understanding the biological underpinnings of aggressivity and recurrence in this subset of tumours may enable the identiication of patients who would beneit from adjuvant therapy.The purpose of this study was to identify differentially expressed molecular biomarkers that might underlie recurrence of Stage I tumours by comparing gene expression in later-stage tumours with those expressed in early-stage tumours.Gene expression in tissue biopsy samples from ive Stage I and ive Stage II/III NSCLC adenocarcinoma patients was analysed using an oligonucleotide microarray containing 17,000 probes printed in duplicate.Analyses were performed on total RNA isolated from tumour tissue of each patient using universal human RNA as a reference.Compared to normal tissues, the transcriptome of Stage I NSCLC adenocarcinomas showed enrichment in general pathways in cancer, whereas in Stage II/III more speciic cancer pathways such as focal adhesion and ECM-receptor interaction pathways were enriched and components of the PPAR signalling pathway were depleted.Relative to early-stage NSCLC, Stage II/III adenocarcinomas showed up-regulation of genes of the basic transcriptional and translational machinery, particularly the "cancer testis antigen" PASD1 transcription factor.The actin cytoskeleton re-organisation and interleukin-6 pathways were also up-regulated whereas there was a generalized down-regulation of immune effectors and genes involved in immune system development.This small-scale transcriptome study provides important information about the pathways and molecules likely to be involved in the more metastatic propensity of those Stage I NSCLC adenocarcinomas that recur.
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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".