Long noncoding RNA RP11-909N17.2 promotes proliferation, invasion, and migration of hepatocellular carcinoma by regulating microRNA-767-3p
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related deaths worldwide, especially in developing countries. Although advances in surgical procedures and targeted medicine have improved the overall survival of patients with HCC, the prognosis is poor. Hence, there is a need to identify novel therapeutic targets for HCC. Here, we report that the expression of RP11-909N17.2, a novel, long, noncoding RNA (lncRNA), is dysregulated in patients with HCC and cell lines. Additionally, this study demonstrated that RP11-909N17.2 facilitates the proliferation and invasion of HCC cells by binding to miRNA-767-3p, a tumor-suppressive microRNA (miRNA). Small integral membrane protein 7 (SMIM7) was identified as the downstream target of miRNA-767-3p. The expression of SMIM7 was upregulated in HCC clinical samples and cell lines. Moreover, SMIM7 was involved in the proliferation and invasion of HCC cells. Furthermore, SMIM7 inhibited the apoptosis of HCC cells, which indicated the oncogenic role of SMIM7 in HCC. The findings of this study suggest that the lncRNA-miRNA-mRNA regulatory axis, which regulates the pathogenesis of HCC, can be a potential novel diagnostic and therapeutic target for HCC.
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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 | 0.001 |
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".