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Record W4408805959 · doi:10.14740/wjon2514

Comprehensive Investigation of a Tyrosine Kinase Inhibitor-Resistant Gene <i>Zeste White 10</i> in Hepatocellular Carcinoma

2025· article· en· W4408805959 on OpenAlexvenueno aff
Huang Qing, Guangxie Zhang, Quanping Su, Qian Lin, Shanshan Li, Han He, Yong Tang, Yi‐Wu Dang, Gang Chen, Rong‐Rong He

Bibliographic record

VenueWorld Journal of Oncology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHepatocellular carcinomaTyrosine-kinase inhibitorCancer researchGeneOncologyInternal medicineGeneticsCancerBiology

Abstract

fetched live from OpenAlex

Background: Tyrosine kinase inhibitors (TKIs) are first-line therapies for hepatocellular carcinoma (HCC), but the drug resistance restricts the long-term clinical outcomes. This study aimed to investigate the expression patterns and possible clinical significance of a TKI-resistant gene zeste white 10 (ZW10) in HCC. Methods: Clustered regularly interspaced short palindromic repeats (CRISPR) screening was conducted to obtain TKI-resistant genes. Pan-cancer analysis was employed to analyze the expression landscape of the critical TKI-resistant gene ZW10. Transcriptional expression data for ZW10 were obtained from 76 centers, including 3,312 HCC samples and 2,703 noncancerous tissues. A summary receiver operating characteristic (SROC) curve was built to evaluate ZW10 expression characteristics in HCC. Unpaired two-sample Wilcoxon method was conducted to analyze ZW10 expression levels in HCC of various etiologies. Univariate Cox method was employed to assess the prognostic value of ZW10. Moreover, the gene function within HCC cell lines, the TKI treatment responses, key pathways, and tumor microenvironment of ZW10 were bioinformatically investigated. Drug prediction and molecular docking techniques were used to explore the potency of ZW10 as a novel therapeutic target. Results: The abundance of small guide RNA (sgRNA) corresponding to ZW10 gene was decreased in the whole genome CRISPR knockout library (LogFC = -1.19), indicating that ZW10 may participate in TKI resistance. The differential expression landscape of ZW10 was found in various malignancies including HCC, which was associated with poorer prognosis. Pooled standardized mean difference (SMD) of ZW10 mRNA expression was 0.47 (95% confidence interval (CI): 0.32 - 0.63), the area under SROC was 0.76 (95% CI: 0.72 - 0.79), the sensitivity was 0.63 (95% CI: 0.53 - 0.72), and the specificity was 0.77 (95% CI: 0.67 - 0.84). ZW10 was investigated significant for the growth of HCC cells. Nucleocytoplasmic transport was the possible pathway that ZW10 involved. High level of ZW10 was reversely associated with TKI responses and the abundance of immune cell infiltration. Mocetinostat and capecitabine were predicted to be the potential inhibitors targeting ZW10 with a minimum binding energy of -8.2 and -7.1 kcal/mol, respectively. Conclusions: ZW10 is considered a TKI-resistant and tumor-supportive gene, which is also a promising novel prognostic biomarker for HCC or a therapeutic target for overcoming TKI resistance.

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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.258
Teacher spread0.239 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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