| ID | Sequence | Length | GC content |
|---|---|---|---|
| ATP6V0E2-AS1:3 | CUUGGCUACGCAGGCAACACUGAGCCCCAGUUCAUUAUUCCUUCAUGUA… | 739 nt | 0.4330 |
| ATP6V0E2-AS1:4 | CUUCACCCCACAUGGAGCCCUAACAAUGAAAAUUUACUUGUGUGUAUAU… | 3797 nt | 0.5407 |
| ATP6V0E2-AS1:5 | GGUGGAGUUUCACUCUUGUCGCCCAGGCUGGAGUGCAGUGGCGUGAUCU… | 1879 nt | 0.4476 |
| ATP6V0E2-AS1:6 | CAGCCAGCCACUACCUACACUUCCACGGGGUGCCCUGGACACACACGAC… | 222 nt | 0.6036 |
| ATP6V0E2-AS1:7 | CGGAGGCUCAGAGAGGGAGUGUAGGGGGAGGAUGUCAAUGGGAGGCCCC… | 451 nt | 0.5698 |
| ATP6V0E2-AS1:8 | GAUUGGCUGAGCAGGGCCGCGGACAGAGAGAAAUGCCGAGGAAUAGGGG… | 3069 nt | 0.5806 |
| ATP6V0E2-AS1:9 | CGCGGACAGAGAGAAAUGCCGAGGAAUAGGGGCGCGGCCGCCCGGUUUC… | 2974 nt | 0.5847 |
No relevant information is available at the moment.
A study in human peripheral blood demonstrated that the ATP6V0E2-AS1 is a long non-coding RNA with expression significantly negatively correlated with chronological age (ρ = -0.521) [Chen et al. DOI:10.1016/J.Fsigen.2025.103419]. This biomarker was among 20 candidate lncRNAs used to construct age prediction models via machine learning, with the best-performing XGBoost model achieving a mean absolute error of 8.04 years in the test cohort and similar performance in external validation datasets.