Abstract 3504: ARS2 and paraspeckle interactions: A nexus for lncRNA mediated drug resistance and cancer progression in hepatocellular carcinoma
Bibliographic record
Abstract
Abstract Paraspeckles, elaborate sub-nuclear assemblies composed of the lncRNA NEAT1-2 and a cohort of RNA-binding proteins, have a noted association with cancer progression and drug resistance. The specific mechanisms, however, have remained obscure. Our investigation into Hepatocellular Carcinoma (HCC) reveals a critical interaction: the sequestration of ARS2—an RNA-binding protein vital for the suppression of mRNA transcripts and the maturation of miRNAs—within paraspeckles. This sequestration leads to increased levels of NEAT1-2, derails miRNA processing, and diminishes the gene-silencing function of ARS2, thereby enhancing the expression of oncogenic genes. This discovery positions the ARS2-paraspeckle axis as a pivotal factor in cancer biology and a promising target for future treatments. Citation Format: Gobi Thillainadesan, Hon Sing Leong, Omar Alawamry. ARS2 and paraspeckle interactions: A nexus for lncRNA mediated drug resistance and cancer progression in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3504.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".