L1 Cell Adhesion Molecule Promotes Tumorigenicity and Metastatic Potential in Non–Small Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Non-small cell lung cancer (NSCLC) is a highly metastatic cancer with limited treatment options, thus requiring development of novel targeted therapies. Our group previously identified L1 cell adhesion molecule (L1CAM) expression as a member of a prognostic multigene expression signature for NSCLC patients. However, there is little information on the biologic function of L1CAM in lung cancer cells. This study investigates the functional and prognostic role of L1CAM in NSCLC. EXPERIMENTAL DESIGN: Cox proportional hazards regression analysis was done on four independent published mRNA expression datasets of primary NSCLCs. L1CAM expression was suppressed by short-hairpin RNA (shRNA)-mediated silencing in human NSCLC cell lines. Effects were assessed by examining in vitro migration and invasion, in vivo tumorigenicity in mice, and metastatic potential using an orthotopic xenograft rat model of lung cancer. RESULTS: L1CAM is an independent prognostic marker in resected NSCLC patients, with overexpression strongly associated with worse prognosis. L1CAM downregulation significantly decreased cell motility and invasiveness in lung cancer cells and reduced tumor formation and growth in mice. Cells with L1CAM downregulation were deficient in constitutive extracellular signal-regulated kinase (Erk) activation. Orthotopic studies showed that L1CAM suppression in highly metastatic lung cancer cells significantly decreases spread to distant organs, including bone and kidney. CONCLUSION: L1CAM is a novel prometastatic gene in NSCLC, and its downregulation may effectively suppress NSCLC tumor growth and metastasis. Targeted inhibition of L1CAM may be a novel therapy for NSCLC.
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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".