Integrative Analysis of NSCLC Identifies LINC01234 as an Oncogenic lncRNA that Interacts with HNRNPA2B1 and Regulates miR-106b Biogenesis
Bibliographic record
Abstract
The discovery of long noncoding RNAs (lncRNAs) has increased our understanding of the development and progression of many cancers, but their contributions to non-small cell lung cancer (NSCLC) remain poorly understood. Here, we profiled lncRNA expression in NSCLC and investigated in detail the molecular function of one upregulated lncRNA, LINC01234. LINC01234 was overexpressed in NSCLC compared with normal lung tissue and correlated positively with poor prognosis. Downregulation of LINC01234 impaired cell proliferation in vitro and tumor growth in vivo. RNA pull-down/mass spectrometry experiments showed that LINC01234 interacted with the RNA-binding protein heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1), which, in turn, led to the recruitment of DiGeorge syndrome critical region gene 8 (DGCR8), a subunit of the microRNA (miRNA) microprocessor complex. Accordingly, depletion of either LINC01234 or HNRNPA2B1 reduced the processing of several miRNA precursors, including primary microRNA (pri-miR)-106b. miR-106b-5p enhanced NSCLC cell growth by downregulating cryptochrome 2 (CRY2), thereby increasing c-Myc expression. Finally, we found that activated c-Myc binds to the LINC01234 promoter to increase its transcription, creating a c-Myc-LINC01234-HNRNPA2B1-miR-106b-5p-CRY2-c-Myc positive-feedback loop. We identified numerous lncRNAs with dysregulated expression in NSCLC and demonstrated a novel oncogenic axis involving LINC01234, HNRNPA2B1, miR-106b-5p, CRY2, and c-Myc. Components of this axis may be potential novel targets for NSCLC.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".