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Record W3036806878 · doi:10.1158/1541-7786.mcr-19-1170

The Long Noncoding RNA <i>NEAT1</i> Promotes Sarcoma Metastasis by Regulating RNA Splicing Pathways

2020· article· en· W3036806878 on OpenAlexaff
Jianguo Huang, Mohit Sachdeva, Eric S. Xu, Timothy J. Robinson, Lixia Luo, Yan Ma, Nerissa T. Williams, Omar Lopez, Lisa D. Cervia, Fan Yuan, Xiaodi Qin, Dadong Zhang, Kouros Owzar, Nalan Gökgöz, Andrew Seto, Tomoyo Okada, Samuel Singer, Irene L. Andrulis, Jay S. Wunder, Alexander J. Lazar, Brian P. Rubin, Krista Pipho, Stephano S. Mello, Jimena Giudice, David G. Kirsch

Bibliographic record

VenueMolecular Cancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsUniversity of TorontoLunenfeld-Tanenbaum Research Institute
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Institutes of HealthAmerican Heart Association
KeywordsBiologyCancer researchRNAMetastasisRNA splicingLong non-coding RNAKRASSarcomaDownregulation and upregulationRNA-binding proteinAlternative splicingNon-coding RNAGeneCancerExonGeneticsMutationPathologyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.049
GPT teacher head0.338
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2020
Admission routes1
Has abstractyes

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