The Long Noncoding RNA <i>NEAT1</i> Promotes Sarcoma Metastasis by Regulating RNA Splicing Pathways
Bibliographic record
Abstract
Abstract Soft-tissue sarcomas (STS) are rare malignancies showing lineage differentiation toward diverse mesenchymal tissues. Half of all high-grade STSs develop lung metastasis with a median survival of 15 months. Here, we used a genetically engineered mouse model that mimics undifferentiated pleomorphic sarcoma (UPS) to study the molecular mechanisms driving metastasis. High-grade sarcomas were generated with Cre recombinase technology using mice with conditional mutations in Kras and Trp53 (KP) genes. After amputation of the limb bearing the primary tumor, mice were followed for the development of lung metastasis. Using RNA-sequencing of matched primary KP tumors and lung metastases, we found that the long noncoding RNA (lncRNA) Nuclear Enriched Abundant Transcript 1 (Neat1) is significantly upregulated in lung metastases. Furthermore, NEAT1 RNA ISH of human UPS showed that NEAT1 is upregulated within a subset of lung metastases compared with paired primary UPS. Remarkably, CRISPR/Cas9-mediated knockout of Neat1 suppressed the ability of KP tumor cells to colonize the lungs. To gain insight into the underlying mechanisms by which the lncRNA Neat1 promotes sarcoma metastasis, we pulled down Neat1 RNA and used mass spectrometry to identify interacting proteins. Interestingly, most Neat1 interacting proteins are involved in RNA splicing regulation. In particular, KH-Type Splicing Regulatory Protein (KHSRP) interacts with Neat1 and is associated with poor prognosis of human STS. Moreover, depletion of KHSRP suppressed the ability of KP tumor cells to colonize the lungs. Collectively, these results suggest that Neat1 and its interacting proteins, which regulate RNA splicing, are involved in mediating sarcoma metastasis. Implications: Understanding that lncRNA NEAT1 promotes sarcoma metastasis, at least in part, through interacting with the RNA splicing regulator KHSRP may translate into new therapeutic approaches for sarcoma.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".