Uses of Vascular Endothelial Growth Factor C as a Lung Adenocarcinoma Prognostic Biomarker
Bibliographic record
Abstract
Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer and a leading cause of death worldwide. Vascular endothelial growth factor C (VEGF-C) has been identified as a prognosis prediction marker for LUAD. However, VEGF-C protein expression does not appear to significantly relate to LUAD patient survival in several studies. Methods: We carried out a bioinformatic analysis to review the effect of VEGF-C mRNA expression on LUAD patient outcomes. GEPIA, UALCAN, TCGAportal, OncoLnc, LCE, GeneMANIA, Metascape, ImmuCellAI, and GSCA online databases were utilized. The expression levels of VEGF-C mRNA between normal tissue and LUAD tissue, overall survival (OS) analysis, function analysis, tumor microenvironment and drug sensitivity were conducted in the current study. Results: We found that the expression level of VEGF-C mRNA was significantly lower in LUAD than normal tissue. Low expression of VEGF-C mRNA was also associated with better OS. VEGF-C expression was correlated with both NF1 and TP53 mutation status. No relationship was observed between VEGF-C and Tr1 or CD4 T-cell infiltrate scores. Additionally, VEGF-C was associated with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance. The sensitivity of 5-fluorouracil was positively correlated with VEGF-C, and the sensitivity of TGX221 was negatively correlated with VEGF-C. The activity of BI-2536 and BRD-A94377914 was positively correlated with VEGF-C. Conclusion: Novel LUAD prognostic biomarkers such as VEGF-C mRNA may aid diagnosis and treatment, and may help identify optimal LUAD populations for therapeutic treatments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".