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Record W4393042454 · doi:10.14740/wjon1754

DNA Damage-Induced Apoptosis Suppressor Triggers Progression and Stemness of Glioma by Enhancing Lymphoid Enhancer-Binding Factor 1 Expression

2024· article· en· W4393042454 on OpenAlexvenueno aff
You Lin Chen, Yi Liu, Yan Xu, An Qiang Yang, Gui Jie Chen, Jin Xing, Hong Wei Su, Li Shang Liao

Bibliographic record

VenueWorld Journal of Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsnot available
FundersSichuan Province Science and Technology Support Program
KeywordsDownregulation and upregulationGliomaGene knockdownSmall hairpin RNACancer researchBiologyCell growthCarcinogenesisMolecular biologyApoptosisCell biologyCancerGeneGenetics

Abstract

fetched live from OpenAlex

Background: DNA damage-induced apoptosis suppressor (DDIAS) has recently been discovered to induce cancer progression, but its functions and mechanisms in glioma have not been well studied. Methods: DDIAS expression in glioma tissues was analyzed by the Gene Expression Profiling Interactive Analysis server (GEPIA) and the Gene Expression database of Normal and Tumor tissue 2 (GENT2) databases. The role of DDIAS in glioma progression was studied by short hairpin RNA (shRNA) targeting DDIAS. The effects of DDIAS on glioma cell viability, cell proliferation, invasion, migration, and tumor sphere formation were determined by cell counting kit-8 (CCK-8), EdU, Transwell, tumor spheroid formation, extreme limiting dilution analysis assays in vitro and xenograft model construction in vivo . In addition, RNA sequencing and further functional experiments were used to analyze the DDIAS regulatory mechanism in glioma. Results: We found that DDIAS was highly expressed in glioma and that upregulated DDIAS indicated poor prognosis. Functionally, DDIAS knockdown inhibited glioma cell viability, cell proliferation, invasion and migration in vitro and tumor growth in vivo . In addition, lymphoid enhancer-binding factor 1 (LEF1) was identified as the downstream effector of DDIAS by RNA sequencing. DDIAS downregulation inhibited LEF1 mRNA and protein expression. The expression of DDIAS and LEF1 was positively correlated, and LEF1 overexpression rescued the inhibitory phenotype induced by DDIAS downregulation. We further showed that DDIAS downregulation inhibited cyclin A1, vimentin and the stemness-related factor CD133 and decreased the sphere formation capability, but these features were rescued by upregulation of LEF1. Conclusion: Taken together, these findings suggest that DDIAS promotes glioma progression and stemness by inducing LEF1 expression, proving that DDIAS may be a potential target for the treatment of glioma. World J Oncol. 2024;15(2):209-222 doi: https://doi.org/10.14740/wjon1754

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.301
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

Citations1
Published2024
Admission routes1
Has abstractyes

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