Investigation of the expression of RIF1 gene on head and neck, pancreatic and brain cancer and cancer stem cells
Bibliographic record
Abstract
PURPOSE: Recent studies have shown that cancer stem cells are resistant to chemotherapy. The aim of this study was to compare RIF1 gene expression in head and neck, pancreatic cancer and glioma cell lines and the cancer stem cells isolated from these cell lines. METHODS: UT-SCC-74 from Turku University and UT-SCC-74B primary tumor metastasis and neck cancer cell lines, YKG-1 glioma cancer cell line from RIKEN, pancreatic cancer cell lines and ASPC-1 cells from ATCC were grown in cell culture. To isolate cancer stem cells, ALDH-1 for UT-SCC-74 and UT-SCC-74B cell line, CD-133 for YKG-1 cell line and CD-24 for ASPC-1 cell line, were used as markers of cancer stem cells. RNA isolation was performed for both cancer lines and cancer stem cells. RNAs were converted to cDNA. RIF1 gene expression was performed by qRT-PCR analysis. RIF1 gene expression was compared with cancer cell lines and cancer stem cells isolated from these cell lines. The possible effect of RIF1 gene was evaluated. RESULTS: In the pancreatic cells, RIF1 gene expression in the stem cell-positive cell line was 256 time that seen in the stem cell-negative cell line. CONCLUSION: Considering the importance of RIF1 in NHEJ and of NHEJ in pancreatic cancer, RIF1 may be one of the genes that plays an important role in the diagnoses and therapeutic treatment of pancreatic cancer. The results of head and neck and brain cancers are inconclusive and further studies are required to elucidate the connection between RIF1 gene and these other types of cancers.
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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 | 0.012 |
| 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".