Mutational and Expression Profile of ZNF217, ZNF750, ZNF703 Zinc Finger Genes in Kenya Women diagnosed with Breast Cancer
Bibliographic record
Abstract
Objectives To characterize the somatic mutational spectrum and transcriptomic expression of the zinc-finger genes ZNF217, ZNF703, and ZNF750 in Kenyan women with breast cancer, and to explore their associations with clinicopathologic features. Methods Whole-exome sequencing and RNA-sequencing were performed on paired tumor and adjacent normal tissues from 23 consented patients treated at two Kenyan referral hospitals. Variants were called with Mutect2 using a study-specific panel of normal; functional consequences were annotated with VEP. After featureCounts quantification, differential expression was analyzed in DESeq2 (fold-change ≥ 1.5, p < 0.05). Two-sample t-tests (mutations) and ANOVA (expression) evaluated relationships with HER2 status and clinical stage. Results A total of 358 somatic mutations were detected: 170 in ZNF217, 24 in ZNF703 and 164 in ZNF750. Single-nucleotide substitutions (319 SNPs) dominated, with C→T and A→G changes most common; 27 deletions and 2 insertions were also observed. Frameshift events in ZNF217 and ZNF703 introduced premature stop codons predicted to truncate protein function. All three genes were significantly up-regulated in tumors versus normal (ZNF217 p = 0.0004), with the greatest expression in HER2-positive tumors and in stages 2–3 disease. Mutation burden for each gene did not differ by HER2 status (p > 0.56) or by stage (p > 0.32). Conclusions Kenyan breast tumors harbor frequent, functionally relevant mutations and marked over-expression of ZNF217, ZNF703, and ZNF750. These alterations, especially the pronounced up-regulation of ZNF217, highlight the trio’s potential as diagnostic or prognostic biomarkers and warrant larger studies to validate their clinical utility and suitability as therapeutic targets in sub-Saharan African populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".