Expression of Lysophosphatidic Acid Receptors and Local Invasiveness and Metastasis in Chinese Pancreatic Cancers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The present study evaluated the potential role of lysophosphatidic acid receptors (lpars) in processes leading to local invasiveness and metastasis in Chinese pancreatic carcinoma. METHODS: Real-time reverse-transcriptase polymerase chain reaction and Western blot analysis were used to detect expression of lpars in tumour and adjacent non-tumour tissues from patients with surgically resected pancreatic carcinoma. Surgical specimens from 50 patients were examined for relative expression of each receptor's messenger rna (mrna) and protein. Findings were analyzed for correlations with tumour size, pathologic classification, clinical stage, and infiltration of capsule and lymphonodi. RESULTS: Increased levels of mrna of lpars (lpar1 ≈ lpar3 < lpar2) were found in the pancreatic cancer tissues examined. Low levels of transcripts for lpar1, lpar2, and lpar3 receptors were detectable in adjacent non-tumour tissues. The difference in lpar1 protein expression between tumour and adjacent non-tumour tissues does not seem significant, but the signals of lpar2 expression in pancreatic cancer tumour tissues were significantly amplified compared with those in adjacent non-tumour tissues. Tumour and adjacent non-tumour tissues both weakly expressed lpar3 protein with no statistical difference. However, expression of lpar1, lpar2, and lpar3 showed an obvious correlation with infiltration of capsule cells, surrounding lymphonodi, and specific histopathologic features. CONCLUSIONS: Lysophosphatidic acid receptor is a promising indicator for pancreatic cancer, and our findings suggested that lpar2 might be a potential target for clinical treatment of pancreatic 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".