Long Noncoding RNA NIHCOLE Promotes Ligation Efficiency of DNA Double-Strand Breaks in Hepatocellular Carcinoma
Bibliographic record
Abstract
Abstract Long noncoding RNAs (lncRNA) are emerging as key players in cancer as parts of poorly understood molecular mechanisms. Here, we investigated lncRNAs that play a role in hepatocellular carcinoma (HCC) and identified NIHCOLE, a novel lncRNA induced in HCC with oncogenic potential and a role in the ligation efficiency of DNA double-stranded breaks (DSB). NIHCOLE expression was associated with poor prognosis and survival of HCC patients. Depletion of NIHCOLE from HCC cells led to impaired proliferation and increased apoptosis. NIHCOLE deficiency led to accumulation of DNA damage due to a specific decrease in the activity of the nonhomologous end-joining (NHEJ) pathway of DSB repair. DNA damage induction in NIHCOLE-depleted cells further decreased HCC cell growth. NIHCOLE was associated with DSB markers and recruited several molecules of the Ku70/Ku80 heterodimer. Further, NIHCOLE putative structural domains supported stable multimeric complexes formed by several NHEJ factors including Ku70/80, APLF, XRCC4, and DNA ligase IV. NHEJ reconstitution assays showed that NIHCOLE promoted the ligation efficiency of blunt-ended DSBs. Collectively, these data show that NIHCOLE serves as a scaffold and facilitator of NHEJ machinery and confers an advantage to HCC cells, which could be exploited as a targetable vulnerability. Significance: This study characterizes the role of lncRNA NIHCOLE in DNA repair and cellular fitness in HCC, thus implicating it as a therapeutic target. See related commentary by Barcena-Varela and Lujambio, p. 4899
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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) | 0.002 | 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".