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Record W4394071937 · doi:10.6084/m9.figshare.20987831

MiR-200c regulates invasion, proliferation and EMT of anaplastic thyroid cancer cells by targeting parathyroid hormone like hormone

2022· dataset· en· W4394071937 on OpenAlexaff
Yan Zhang, Yuanyuan Duan, Chenguang Wu, Wen Peng, Wenyu Chen, Li Wang, Zhaoqun Deng

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMcMaster UniversityUniversity of Calgary
Fundersnot available
KeywordsAnaplastic thyroid cancerParathyroid hormoneHormoneCancer researchThyroidThyroid cancerInternal medicineMedicineOncologyBiologyCalcium

Abstract

fetched live from OpenAlex

This study aimed to explore the specific effect of miR-200c in anaplastic thyroid cancer (ATC). Hth74 and ARO cell lines were used. Proliferation, invasion, and colony formation activities of Hth74 and ARO cell lines affected by miR-200c were studied. Expression of epithelial-to-mesenchymal transition (EMT) markers (E-cadherin, N-cadherin, Slug, and Snail) in the Hth74 and ARO cell lines were validated by western blot and qRT-PCR. In addition, the regulation of the parathyroid hormone-like hormone (PTHLH) by miR-200c was assessed. Overexpression of miR-200c inhibited the invasion, proliferation, and colony formation of the ATC cell lines, whereas its downregulation achieved the opposite results. PTHLH was found to be regulated negatively by miR-200c through a miR-200c binding site within the 3′-UTR of PTHLH. miR-200c repressed the proliferation, invasion, and EMT process of cells in ATC cell lines by targeting PTHLH post-transcriptionally, which indicates that miR-200c may be a potential target for the treatment of ATC.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.012

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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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