Correlation between SPARC, TGFβ1, Endoglin and angiogenesis mechanism in lung cancer.
Bibliographic record
Abstract
To study the relationship between Secreted protein, acidic and rich in cysteine (SPARC), Transforming growth factorβ1 (TGFβ1), Endoglin and angiogenesis in lung cancer, 40 cases of lung cancer specimens and 40 adjacent normal lung tissues specimens were collected and 10 cases from each were selected for preparation of tissue chip. CD34 (endothelial cell marker), Endoglin human α-Smooth muscle actin, and (α-SMA) markers were performed by immunohistochemical staining, and the immuno-phenotype and the relationship between different morphologies of the microvascular wall components were evaluated. The expression of SPARC mRNA and protein, TGFβ1 mRNA and protein and Endoglin in the remaining 30 cases of lung cancer were detected by immunohistochemistry and in-situ hybridization. The result shows that the positive rates of SPARC, TGFβ1 and Endoglin in lung cancer tissues were significantly higher than those in adjacent normal lung tissues (P less than 0.05). The expression of SPARC and TGFβ1 was negatively correlated with lung cancer. When the positive expression of SPARC increased, the micro-vessel density (MVD) marked by Endoglin decreased gradually; while the positive expression of TGFβ1 increased, MVD increased gradually, and SPARC, TGFβ1 and MVD were correlated (P less than0.05). High SPARC mRNA expression in lung cancer tissues could inhibit the progression of lung cancer, while high TGFβ1 mRNA expression can promote the progression of lung cancer and participate in the metastasis of lung cancer. To sum up, the angiogenesis of lung cancer may be related to the interaction of SPARC, TGFβ1 and Endoglin.
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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".