Abstract LB-306: Long non-coding RNA NEAT1 promotes lung metastasis of soft tissue sarcoma by regulating RNA splicing pathways
Bibliographic record
Abstract
Soft tissue sarcomas (STS) are malignant tumors from diverse mesenchymal tissues. About 40% STS patients develop fatal lung metastasis with a median survival of 15 months. The mechanisms driving the development of lung metastasis in sarcoma patients are poorly understood. Therefore, our lab has developed a genetically engineered mouse model (GEMM) of high-grade primary STS with conditional mutations in Kras and Trp53 (KP) where 40% of mice tumors develop lung metastasis. This KP model recapitulates human patients with Undifferentiated Pleomorphic Sarcoma (UPS), one of the most common subtypes of STS diagnosed in adults. RNA sequencing (RNA-Seq) was performed on paired primary and lung metastases in KP mouse sarcomas and determined that the expression of the long non-coding RNA (lncRNA) Neat1 is increased in lung metastases compared to paired primary tumors. Real time PCR (qPCR) in 22 paired KP mouse primary sarcomas and lung metastases further confirmed that Neat1 is significantly upregulated in some lung metastases. In addition, RNA-Seq and qPCR data from 10 pairs of human primary sarcomas and matched lung metastases also showed that NEAT1 levels are increased in lung metastases. Furthermore, NEAT1 RNA in situ hybridization (ISH) on tissue microarrays (TMAs) of human primary UPS and lung metastases determined that the expression of NEAT1 is upregulated in lung metastases. Next, CRISPR/Cas9 technology was applied to delete Neat1 in primary mouse sarcoma cells and loss of expression of Neat1 was confirmed by qPCR and northern blot in knockout (KO) clones. In addition, loss of Neat1 significantly reduced lung metastasis in vivo following tail vein injection of these modified cells into nude mice. Furthermore, RNA pull down assay with mass spectrometry analysis determined Neat1 interacting proteins, such as Khsrp, were mainly involved in RNA splicing pathways which was also shown to be dysregulated in lung metastases and Neat1 KO cells. Finally, CRISPR/Cas9 mediated knockout of Khsrp significantly reduced lung metastasis in vivo following tail vein injection of these modified cells into nude mice. Overall, these results suggest that upregulation of Neat1 promotes lung metastasis of soft tissue sarcoma through regulating RNA splicing pathways and NEAT1 is a potential target to prevent or treat lung metastasis in sarcoma patients.Citation Format: Jianguo Huang, Eric Xu, Mohit Sachdeva, Timothy Robinson, Xiaodi Qin, Dadong Zhang, Kouros Owzar, Nalan Gokgoz, Andrew Seto, Irene Andrulis, Jay Wunder, Tomoyo Okada, Simuel Singer, Alexander Lazar, Brian Rubin, David G. Kirsch. Long non-coding RNA NEAT1 promotes lung metastasis of soft tissue sarcoma by regulating RNA splicing pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-306.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".