Investigation of MACC1 Gene Expression in Head and Neck Cancer and Cancer Stem Cells
Bibliographic record
Abstract
PURPOSE: By investigating the MACC1 gene (metastasis-associated in colon cancer 1) in cancer stem cells (CSC) resistant to chemotherapy and in cancer stem cells (CSC) resistant to chemotherapy and in cancer cells (CS) sensitive to chemotherapy we determineda steady expression in both types of cells in head and neck cancer. In conformity with the result we examined if this gene could be a competitor gene for chemotherapy. According to literature, the MACC1 gene shows a clear expression in head and neck cancer cells [1]. Here we examined MACC1 expression in CSC and investigated it as a possible biomarker. METHODS: Our experiments were performed in the UT -SCC -74 in primary head and neck cancer cell line. We examined the MACC -1 gene expression by Real Time PCR from both isolated CSC and CS. RESULTS: Expression of MACC -1 gene of cancer stem cells showed an two-fold increase compared with cancer cells. Based on the positive expression of MACC1 in both CS and CSC, this gene may serve as a potential biomarker in head and neck cancer. By comparing the results of this study with the novel features of MACC1, two important hypotheses could be examined. The first hypothesis is that MACC1 is a possible transcripton factor in colon cancer, which influences a high expression of CSC in head and neck and affects the expression of three biomarkers of the CSC control group biomarkers. The second hypothesisis is that the positive expression of MACC1 in patients with a malignant prognosis of tongue cancer, which belongs to head and neck cancer types, operates a faster development of CSC to cancer cells.
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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) | 0.002 | 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".