Deregulation of a Cis-Acting lncRNA in Non-small Cell Lung Cancer May Control HMGA1 Expression
Bibliographic record
Abstract
Background Long non-coding RNAs (lncRNAs) have long been implicated in cancer-associated phenotypes. Recently, a class of lncRNAs, known as cis -acting, have been shown to regulate the expression of neighboring protein-coding genes and may represent undiscovered therapeutic action points. The chromatin architecture modification gene HMGA1 has recently been described to be aberrantly expressed in lung adenocarcinoma (LUAD). However, the mechanisms mediating the expression of HMGA1 in LUAD remain unknown. Here we investigate the deregulation of a putative cis -acting lncRNA in LUAD, and its effect on the oncogene HMGA1 . Methods LncRNA expression was determined from RNA-sequencing data of tumor and matched non-malignant tissues from 36 LUAD patients. Transcripts with significantly deregulated expression were identified and validated in a secondary LUAD RNA-seq dataset (TCGA). SiRNA-mediated knockdown of a candidate cis -acting lncRNA was performed in BEAS-2B cells. Quantitative real-time PCR was used to observe the effects of lncRNA knockdown on the expression of HMGA1. Results We identified the lncRNA RP11.513I15.6, which we refer to as HMGA1- lnc , neighboring HMGA1 to be significantly downregulated in both LUAD cohorts. Conversely, we found HMGA1 significantly overexpressed in LUAD and anticorrelated with HMGA1- lnc . In vitro experiments demonstrated siRNA-mediated inhibition of HMGA1- lnc in immortalized non-malignant lung epithelial cells resulted in a significant increase in HMGA1 gene expression. Conclusion Our results suggest that HMGA1- lnc is a novel cis -acting lncRNA that negatively regulates HMGA1 gene expression in lung cells. Further characterization of this regulatory mechanism may advance our understanding of the maintenance of lung cancer phenotypes and uncover a novel therapeutic intervention point for tumors driven by HMGA1 .
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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) | 0.000 | 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) | 0.000 | 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".