Serum LAMC2 enhances the prognostic value of a multi-parametric panel in non-small cell lung cancer
Bibliographic record
Abstract
BACKGROUND: Non-small cell lung cancer (NSCLC) lacks reliable serological biomarkers for predicting patients' survival and response to treatment. The present study examined the capability of serum LAMC2 and four known tumour markers for disease prognosis and patients' risk stratification. METHODS: LAMC2, CA 125, CEA, CYFRA 21-1 and SCC levels were retrospectively measured in sera obtained from 127 patients diagnosed with NSCLC by commercial immunoassays. Prognostic performance of the markers was compared with established clinical parameters and multivariate models were constructed to assess the prognostic complementarity of variables. RESULTS: LAMC2 showed significant prognostic ability for overall survival (hazards ratio: 1.607, 95% confidence interval: 1.268-2.037, P<0.0001) in the full cohort. LAMC2 and CYFRA 21-1 combination enhanced prognostic models based on common clinical parameters (c-index: 0.81 vs 0.72, P=0.00018), further enabling stratification of patients into clear risk groups. A bootstrap-based cross-validation analysis was supportive of our findings. Combination of LAMC2 and CA 125 showed similar performance. CONCLUSIONS: Our preliminary study proposes LAMC2 as a novel NSCLC prognostic factor. LAMC2 combined with CA 125 and CYFRA 21-1 could aid in clinical prediction of NSCLC patients' overall survival and inform clinical practice. Larger studies are necessary to unravel LAMC2's full potential as a new NSCLC biomarker.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".