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Record W4415740574 · doi:10.7554/elife.108076

Mutational and Expression Profile of ZNF217, ZNF750, ZNF703 Zinc Finger Genes in Kenyan Women Diagnosed with Breast Cancer

2025· article· W4415740574 on OpenAlexaff
Michael Kitoi, John Gitau, Godfrey Wagutu, Kennedy Mwangi, Florence Ng’ong’a, Francis Makokha

Bibliographic record

VenueeLife · 2025
Typearticle
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsFrameshift mutationGeneBreast cancerTranscriptomeSomatic cellMutationGene expressionGenomeCarcinogenesis

Abstract

fetched live from OpenAlex

Objective To characterize the mutational landscape and expression profiles of ZNF217, ZNF703, and ZNF750, and assess their clinical relevance in breast cancer patients from Kenya. Methods Whole-exome sequencing (WES) and RNA sequencing (RNA-Seq) data from 23 paired tumor–normal samples were analyzed in a Linux-based environment. Somatic mutations were identified using MuTect2 following alignment to the hg38 reference genome and annotation with VEP. Variants were classified by type, coding consequence, and protein position, and mapped to functional domains. Recurrent mutations were identified, and comparisons were made with The Cancer Genome Atlas (TCGA). Gene expression was quantified using STAR and featureCounts, normalized with DESeq2, and analyzed using paired statistical tests with multiple testing correction. Principal component analysis (PCA) and regression analyses were performed to assess expression patterns and clinical associations. Results ZNF217 and ZNF750 exhibited high mutational burdens, whereas ZNF703 showed a lower mutation frequency. Mutations were predominantly single nucleotide variants, with missense and synonymous variants as the major classes. Variants were distributed across protein sequences, with limited domain enrichment and no clear hotspot clustering. Recurrent mutations were gene-specific and infrequent. Comparison with TCGA data showed concordant mutation prevalence for ZNF217, low frequency for ZNF703, and absence of ZNF750 mutations. All three genes were significantly upregulated in tumors compared to matched normal tissues (ZNF217: p = 0.00068; ZNF703: p = 0.00475; ZNF750: p = 0.00366). Tumor expression exceeded normal expression in 74% of cases for ZNF217, 64% for ZNF703, and 83% for ZNF750. PCA demonstrated partial separation between tumor and normal samples. ZNF703 expression was positively associated with body mass index (β = 0.194, p = 0.025), and ZNF750 expression was higher in estrogen receptor–positive tumors (β = 1.050, p = 0.005). Conclusion ZNF217, ZNF703, and ZNF750 display distinct mutation and expression profiles in breast cancer, with evidence of cohort-specific variation. These findings highlight gene-specific mechanisms of dysregulation and emphasize the value of integrating genomic and transcriptomic analyses.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.004
GPT teacher head0.247
Teacher spread0.243 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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