Molecular profiling of lung squamous carcinogenesis
Bibliographic record
Abstract
Molecular profiling of from pre-invasive bronchial lesions may identify promising new biomarkers for early detection and targets for chemoprevention/treatment of lung cancer. mRNA expression was analyzed (Agilent microarrays) from fresh frozen human bronchial biopsies (N=122, 77 patients) at successive morphological stages of lung squamous carcinogenesis. Modules of co-expressed genes were identified among the 7739 stage associated genes, selected using a linear mixed-effects model adjusting for smoking status, gender and history of cancer as fixed effects and patient as a random effect, Genes expression alterations were grouped based on their behavior across 4 successive molecular steps. Four different gene-expression patterns were observed: gene modules primarily up-regulated among the early or late steps, up-regulated in a linearly across the 4 steps, as well as down- then up-regulated (biphasic). Modules that changed in the early step were characterized by genes involved in cell function/maintenance, cell growth/proliferation and cell-to-cell signaling. Modules with linear up-regulation included genes involved in cell cycle, cellular assembly, DNA replication/repair. At the later stages, the transition to high-grade dysplasia, the most significant gene expression changes included immune/inflammatory response genes. These data provide a platform for future in depth investigation into the precise molecular mechanisms and pathways involved in lung carcinogenesis. The significant modification of inflammatory/immune response genes in association with high-grade lesions suggests a critical role of the surrounding microenvironment at this critical stage of carcinogenesis and raises the possibility of new chemopreventive approaches.
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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 | 0.001 |
| 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".