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Record W4309449430 · doi:10.3390/cancers14225687

Discovery of Novel Lin28 Inhibitors to Suppress Cancer Cell Stemness

2022· article· en· W4309449430 on OpenAlexafffund
Mariia Radaeva, Chia-Hao Ho, Ning Xie, Sijie Zhang, Joseph Lee, Liangliang Liu, Nada Lallous, Artem Cherkasov, Xuesen Dong

Bibliographic record

VenueCancers · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchU.S. Department of Defense
KeywordsLIN28SOX2CD44microRNACancer researchCancer stem cellCancerIn silicoCancer cellHomeobox protein NANOGStem cellComputational biologyChemistryBiologyBioinformaticsCellCell biologyTranscription factorBiochemistryInduced pluripotent stem cellGeneticsGene

Abstract

fetched live from OpenAlex

Lin28 is a pluripotency factor that regulates cancer cell stem-like phenotypes to promote cancer development and therapy-resistant tumor progression. It acts through its cold shock domain and zinc knuckle domain (ZKD) to interact with the Let-7 pre-microRNA and block Let-7 biosynthesis. Chemical inhibition of Lin28 from interacting with Let-7 presents a therapeutic strategy for cancer therapy. Herein, we present the computer-aided development of small molecules by in silico screening 18 million compounds from the ZINC20 library, followed by the biological validation of 163 predicted compounds to confirm 15 new Lin28 inhibitors. We report three lead compounds, Ln7, Ln15, and Ln115, that target the ZKD of both Lin28A and Lin28B isoforms and block Lin28 from binding Let-7. They restore Let-7 expression and suppress tumor oncogenes such as SOX2 in cancer cells and show strong inhibitory effects on cancer cell stem-like phenotypes. However, minimal impacts of these compounds were observed on Lin28-negative cells, confirming the on-target effects of these compounds. We conclude from this study the discovery of several new Lin28 inhibitors as promising candidate compounds that warrant further drug development into potential anticancer therapies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.095
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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 teacher head, 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

Citations30
Published2022
Admission routes2
Has abstractyes

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