Long Non-Coding RNA Profile in Genetic Symptomatic and Presymptomatic Frontotemporal Dementia: A GENFI Study
Bibliographic record
Abstract
Background: Long non-coding RNAs (lncRNAs) play crucial roles in gene regulation and are implicated in neurodegenerative diseases, including frontotemporal dementia (FTD). However, their expression patterns and potential as biomarkers in genetic FTD involving Chromosome 9 Open Reading Frame (C9ORF72), Microtubule Associated Protein Tau (MAPT), and Progranulin (GRN) genes are not well understood. Objective: This study aimed to profile the expression levels of lncRNAs in peripheral blood mononuclear cells collected within the GENetic Frontotemporal dementia Initiative (GENFI). Methods: Fifty-three lncRNAs were analyzed with the OpenArray Custom panel, in 131 patients with mutations in C9ORF72, MAPT, and GRN, including 68 symptomatic mutation carriers (SMC) and 63 presymptomatic mutation carriers (PMC), compared with 40 non-carrier controls (NC). Results: Thirty-eight lncRNAs were detectable; the relative expression of NEAT1 and NORAD was significantly higher in C9ORF72 SMC as compared with NC. GAS5 expression was instead significantly lower in the GRN group versus NC. MAPT carriers showed no significant deregulations. No significant differences were observed in PMC. Disease duration did not correlate with lncRNA expression. Conclusions: NEAT1 and NORAD are upregulated in C9ORF72 SMC and GAS5 levels are downregulated in GRN SMC, underlining lncRNAs' relevance in FTD and their potential for biomarker development. Further validation and mechanistic studies are crucial for clinical implications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".