IncRNA ZFAS1 contributes to the radioresistance of nasopharyngeal carcinoma cells by sponging hsa-miR-7-5p to upregulate ENO2
Bibliographic record
Abstract
Previous research revealed that lncRNA ZFAS1 could promote nasopharyngeal carcinoma (NPC) by inhibiting its downstream target axis. However, the association between ZFAS1 and radioresistant NPC cells is unclear. This study aimed to explore the roles of ZFAS1 in the radioresistance of NPC. Bioinformatics analysis was conducted to identify the significant factors (ENO2 and miR-7-5p) that contributed to the radioresistance of NPC cells. After performing qRT-PCR analysis, we found that the expression of ZFAS1 and ENO2 was upregulated in NPC cells but that the miR-7-5p expression was downregulated in the same samples. Apart from that, we noticed that ZFAS1 inhibition enhanced the sensitivity of NPC cells to radiation therapy by repressing cell proliferation and promoting cell apoptosis. Subsequently, we found that ZFAS1 could sponge miR-7-5p to upregulate ENO2, which was the target of miR-7-5p. Experimental results also indicated that the suppression of miR-7-5p inhibited the sensitivity of NPC cells to radiation therapy, thereby suppressing ENO2 expression. Overall, our findings suggested that ZFAS1 contributed to the radioresistance of NPC cells by regulating the miR-7-5p/ENO2 axis and that ZFAS1 might be a potential therapeutic target for addressing the radioresistance of NPC 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.014 |
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".