Expression and Clinical Significance of Insulin-Like Growth Factor 1 in Lung Cancer Tissues and Perioperative Circulation from Patients with Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
OBJECTIVE: We explored the role of insulin-like growth factor 1 (igf-1) in the development of lung cancer. METHODS: We used immunohistochemistry to measure the expression of igf-1 and igf-1 receptor (igf-1r) in specimens of tissue and perioperative circulation from 80 patients with primary non-small-cell lung cancer (nsclc) and from 45 patients with benign pulmonary lesions (bpls). Correlations of those measurements with clinicopathologic characteristics and clinical follow-up were analyzed. Circulating igf-1 was measured before and after surgery in all patients. RESULTS: Compared with bpl specimens, nsclc specimens showed overexpression of igf-1and igf-1r (p < 0.001). The expression levels of igf-1 and igf-1r were significantly associated with advanced-stage disease (p = 0.034 and 0.029 respectively) and lymph node metastasis (p = 0.012 and 0.017 respectively), and expression of igf-1 correlated with tumour differentiation and tumour diameter (p = 0.011 and 0.021 respectively). Specimens positive for igf-1 or igf-1r were significantly correlated with shorter patient survival (p = 0.0012 and 0.0016 respectively). After surgery, circulating igf-1 was significantly elevated in patients with bpl (p = 0.0346) and significantly lower in patients with nsclc (p = 0.0030), especially in those with advanced-stage disease, a larger tumour size, regional lymphoid node metastasis, or lesser differentiation (p = 0.0092, 0.0051, 0.0131, and p < 0.001 respectively). CONCLUSIONS: In nsclc, igf-1 and igf-1r are upregulated, and expression of those factors is correlated with tumour progression and prognosis in nsclc patients. Radical resection of nsclc can directly influence the serum concentration of igf-1. Autocrine/paracrine igf-1 might be playing an important role in the development of lung cancer.
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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 | 0.001 |
| 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".