Transcriptomic Microenvironment of Lung Adenocarcinoma
Bibliographic record
Abstract
Abstract Background: Tissues surrounding tumors are increasingly studied to understand the biology of cancer development and identify biomarkers. Methods: A unique geographic tissue sampling collection was obtained from patients that underwent curative lobectomy for stage I pulmonary adenocarcinoma. Tumor and nontumor lung samples located at 0, 2, 4, and 6 cm away from the tumor were collected. Whole-genome gene expression profiling was performed on all samples (n = 5 specimens × 12 patients = 60). Analyses were carried out to identify genes differentially expressed in the tumor compared with adjacent nontumor lung tissues at different distances from the tumor as well as to identify stable and transient genes in nontumor tissues with respect to tumor proximity. Results: The magnitude of gene expression changes between tumor and nontumor sites was similar with increasing distance from the tumor. A total of 482 up- and 843 downregulated genes were found in tumors, including 312 and 566 that were consistently differentially expressed across nontumor sites. Twenty-nine genes induced and 34 knocked-down in tumors were also identified. Tumor proximity analyses revealed 15,700 stable genes in nontumor lung tissues. Gene expression changes across nontumor sites were subtle and not statistically significant. Conclusions: This study describes the transcriptomic microenvironment of lung adenocarcinoma and adjacent nontumor lung tissues collected at standardized distances relative to the tumor. Impact: This study provides further insights about the molecular transitions that occur from normal tissue to lung adenocarcinoma and is an important step to develop biomarkers in nonmalignant lung tissues. Cancer Epidemiol Biomarkers Prev; 26(3); 389–96. ©2016 AACR.
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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 | 0.001 | 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".