Correlations between Carcinoembryonic Antigen, Epidermal Growth Factor and Leptin in Patients with Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Objectives:Carcinoembryonic antigen (CEA), epidermal growth factor (EGF) and leptin have been reported to be intimately intertwined in lung carcinogenesis.However, few studies have simultaneously examined these proteins in lung cancer and whether a correlation exist among them remains unclear. Here, we compared the levels of CEA, EGF and leptin in non-small-cell lung cancer (NSCLC) patients and controls and evaluated the possible associations among them. Methods:97 patients ranged from 30 to 83 years of age were studied. Serum CEA, EGF and leptin levels were determined following a standard protocol. The relationships between these proteins and clinicopathological factors were evaluated by Wilcoxon rank sum or Kruskal-Wallis H test. Spearman rank-correlation were used to determine the correlations among CEA, EGF and leptin. Co-expression of these proteins in NSCLC tissues was examined by immunofluorescence. Results: Serum CEA and leptin levels in NSCLC patients were significantly higher compared to controls (both P = 0.000), but no statistically significant difference was found for EGF. CEA and EGF were not associated with the tumor-related factors, but leptin was strongly correlated with sex (P = 0.005). Significant correlations among these proteins were found when the patients were categorized into subgroups. Co-expresstion of these proteins was significantly enhanced with lung carcinogenesis. Conclusions:CEA, EGF and leptin may interplay and play vital roles in the pathogenesis of NSCLC. Besides CEA, the leptin levels were also significantly higher in NSCLC patients than in controls. Determination of preoperative leptin levels may prove useful for screening and predicting NSCLC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".