The Association of CXCR3 and Renal Cell Carcinoma Metastasis
Bibliographic record
Abstract
PURPOSE: Renal cell carcinoma expresses CXCR3 but the function of CXCR3 in renal cell carcinoma has not been clarified. We explored the function of CXCR3 in renal cell carcinoma and investigated CXCR3 regulating factors. MATERIALS AND METHODS: We obtained 56 clinical samples of clear cell renal cell carcinoma and corresponding normal renal tissue samples from the surgical specimens of Japanese patients who underwent radical nephrectomy at Chiba University Hospital between 2000 and 2011. As renal cell carcinoma cell lines, we used 786-O, ACHN and Caki-1. The expression profiles of CXCR3 and its splice variants were examined. For functional analyses 786-O and interferon-γ inducible 10 kDa protein or IP-10 (CXCL10) were selected as representatives. RESULTS: CXCR3 and its ligands were abundant in renal cell carcinoma samples compared to corresponding normal kidney samples. The CXCR3-A-to-CXCR3-B ratio was 1.5 times higher in renal cell carcinoma samples than in normal kidney samples. CXCL10 treatment induced 786-O cell migration and invasion, and these effects were inhibited by neutralizing antibody. Phosphorylated RhoA and pro/active matrix metalloproteinase-9 expression was up-regulated by CXCL10 treatment. In clinical samples CXCR3 and CXCR3-A expression was significantly higher in metastatic than in nonmetastatic carcinoma samples. Finally, the expression of CXCR3-A and HIF-1α correlated significantly in clinical samples. In 786-O treatment with CoCl2 up-regulated CXCR3 and HIF-1α expression 4.5 and 2.2-fold, respectively. CONCLUSIONS: We determined the association of CXCR3 and renal cell carcinoma metastasis. CXCR3 expression may be regulated by hypoxia.
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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) | 0.003 | 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".