Overexpression of Long Noncoding RNA <i>HOTAIR</i> Is a Unique Epigenetic Characteristic of Myxopapillary Ependymoma
Bibliographic record
Abstract
Ependymomas are a heterogeneous group of central nervous system tumors. Despite the recent advances, there are no specific biomarkers for ependymomas. In this study, we explored the role of homeobox (HOX) genes and long noncoding RNA (LncRNA) HOTAIR in ependymomas along the neural axis. Bioinformatics analysis was performed on publicly available gene expression data. Quantitative RT-PCR was used to determine the mRNA expression level among different groups of ependymomas. RNA in situ hybridization (ISH) with probes specific to HOTAIR was performed on tumor tissue microarray (TMA) constructed with 19 ependymomas formalin-fixed paraffin-embedded tissue. Gene expression analysis revealed higher expression of posterior HOX genes and HOTAIR in myxopapillary ependymoma (MPE), in comparison to other spinal and intracranial ependymoma. qRT-PCR confirmed the high HOXD10 expression in spinal MPEs. There was a significant upregulation of HOTAIR expression in spinal MPE and elevated HOTAIR expressions were further confirmed by RNA ISH on the TMA. Intriguingly, HOXD10 and HOTAIR expressions were not elevated in nonependymoma spinal tumors. Our collective results suggest an important role for the lncRNA HOTAIR and posterior HOX genes in the tumorigenesis of spinal MPE. HOTAIR may also serve as a potential diagnostic marker for spinal MPE.
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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".