Role of lncRNA FAM83H antisense RNA1 (FAM83H-AS1) in the progression of non-small cell lung cancer by regulating the miR-545-3p/heparan sulfate 6-O-sulfotransferase (HS6ST2) axis
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) are crucial regulators of cancer pathogenesis and are potentially useful diagnostic and prognostic biomarker tools. FAM83H antisense RNA1 (FAM83H-AS1) has been reported to be a vital regulator of different cancers; however, little attention has been paid to its significance in lung cancer. Non-tumorigenic lung cell line BEAS-2B and adenocarcinoma lung cancer cell lines NCI-H1299 and HCC827 were used in the present study. In addition, RNA immunoprecipitation, Western blotting, quantitative reverse transcription-PCR (qRT-PCR), and luciferase reporter assays were used to dissect the role of FAM83H-AS1 in lung cancer progression. The results revealed that FAM83H-AS1 is highly expressed in lung cancer tissues, and its knockdown inhibits lung cancer cell invasion and proliferation reducing tumor growth in vivo. Besides, we found that FAM83H-AS1 targets miR-545-3p, and a negative correlation exists between their expression in lung cancer tissues. Simultaneously, miR-545-3p negatively regulates heparan sulfate 6-O-sulfotransferase (HS6ST2). Moreover, inhibition of miR-545-3p promoted HS6ST2 protein expression and lung cancer cell invasion. FAM83H-AS1 favors non-small cell lung cancer by targeting the miR-545-3p/HS6ST2 axis, supporting the possibility of developing FAM83H-AS1 as a target for NSCLC intervention.
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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".