Cancer upregulated gene 2, a novel oncogene, enhances migration and drug resistance of colon cancer cells via STAT1 activation
Bibliographic record
Abstract
Cancer upregulated gene (CUG) 2, as a novel oncogene, has been predominantly detected in various cancer tissues, such as ovary, liver, lung and colon. We recently showed that CUG2 elevates STAT1 activity, leading to resistance to infection by oncolytic vesicular stomatitis virus. To investigate a possible role for CUG2-induced activation of STAT1 in oncogenesis, we first established a colon cancer cell line stably expressing CUG2 (Colon26L5-CUG2). Colon26L5-CUG2 exhibited higher levels not only in phosphorylation of STAT1, but also phosphorylation of Jak1/Tyk2 compared to that of the control (Colon26L5-Vec) cell line. Inhibition of Akt or ERK activity reduced phosphorylation of STAT1 in Colon26L5-CUG2 cells whereas inhibition of p38 MAPK did not significantly decrease levels of STAT1 phosphorylation, indicating that cell proliferation signals may be involved in CUG2-mediated activation of STAT1. Suppression of STAT1 expression diminished cell migration and wound healing compared to the control cells. In addition, since CUG2 expression conferred resistance to DNA damage caused by doxorubicin treatment, we investigated whether STAT1 is involved in resistance to doxorubicin-induced cell death. We found that STAT1 was not activated in Colon26L5-Vec cells while phosphorylated STAT1 was maintained in Colon26L5-CUG2 cells during doxorubicin treatment. Furthermore, suppression of STAT1 expression sensitized Colon26L5-CUG2 cells to doxorubicin-induced apoptosis whereas the control cells exhibited resistance to doxorubicin. Taken together, our results suggest that CUG2 enhances metastasis and drug resistance through STAT1 activation, which eventually contributes to tumor progression.
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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".