Deciphering the circular RNAs landscape in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Abstract Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive loss of motor neurons, with most cases lacking a clear genetic basis. Emerging evidence highlights the involvement of non-coding RNAs, particularly circular RNAs (circRNAs), in disease onset and progression. In this study, we investigated circRNAs implicated in ALS and related motor neuron diseases (MNDs). First, we conducted a systematic review to identify ALS-associated circRNAs, followed by in silico analyses of 15 selected candidates. Our results revealed that hsa_circ_0000099, hsa_circ_0001017, hsa_circ_004846, and hsa_circ_0034880 regulate a high number of ALS-related genes through miRNA sponging. Pathway enrichment analysis indicated that hsa_circ_0000099 is particularly involved in unfolded protein response, oxidative stress, cell cycle regulation, and apoptosis. Protein–RNA interaction analysis further showed that ALS-related circRNAs can sponge 20 RNA-binding proteins, with FMR1, ELAVL1, EIF4A3, and SRSF1 exhibiting the highest number of interactions. Additionally, molecular docking analysis demonstrated that FUS mutations significantly alter its binding affinity to hsa_circ_0000567 and hsa_circ_0060762. RNA-seq data from ALS patients confirmed significant alterations in the expression of host genes of ALS-related circRNAs and hub proteins across affected tissues, including the spinal cord and multiple brain regions. Collectively, these findings support circRNAs as active contributors to ALS pathogenesis. In particular, the host gene of hsa_circ_0000099, which is expressed in the spinal cord and hippocampus, emerges as a potential biomarker for tissue-specific impairment in ALS. Moreover, its regulatory role in key ALS-related cellular pathways underscores its promise as a candidate for biomarker development and therapeutic targeting. Further experimental validation is warranted to confirm its role in ALS pathology. Graphical abstract
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".