Identification of Antisense RNA NRAS-AS and Its Preliminary Exploration of the Anticancer Regulatory Mechanism
Bibliographic record
Abstract
OBJECTIVE: To explore the influence of NRAS-AS on the proliferation, apoptosis, cell cycle, migration, and invasion ability of HCC cells, as well as its underlying mechanisms. METHODS: A double-stranded cDNA library for liver cancer cells was constructed, and identified NRAS-AS through High-throughput sequencing, bioinformatics, chain-specific fluorescent quantitative PCR, and RACE. NRAS-AS's effects on HepG2 and SMMC-7721 cells and gene expression were evaluated. Additionally, the study analyzed the influence of NRAS-AS overexpression on tumor formation in nude mice. Immunohistochemistry and Western blotting were used to detect NRAS protein levels in clinical samples. RT-qPCR examined NRAS-AS and NRAS gene expression in HCC and adjacent tissues. RESULTS: NRAS-AS overexpression suppresses HCC cell proliferation and invasion, induces cell cycle alterations in HepG2 and SMMC-7721 cells, and enhances apoptosis. NRAS-AS interference promoted liver cancer invasion, inhibited apoptosis, and influences the cell cycle. Nude mice overexpressing NRAS-AS showed smaller tumors. NRAS-AS expression in liver cancer patients correlated with clinical factors. RT-qPCR revealed an inverse correlation between NRAS-AS and NRAS gene expression in liver cancer and adjacent tissues. IHC analysis revealed reduced NRAS protein expression in HepG2 and SMMC-7721 cells following NRAS-AS overexpression. The impact of AZA treatment on antisense NRAS-AS and sense NRAS gene expression in liver cancer cells was observed, and antisense. CONCLUSION: Reduced NRAS-AS expression is frequently observed in HCC and is inversely related to NRAS gene expression, suggesting a role in HCC pathogenesis through NRAS regulation. Targeting antisense RNA NRAS-AS could hold promise as a therapeutic target and diagnostic biomarker for HCC.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".