Mutational and Expression Profile of ZNF217, ZNF750, ZNF703 Zinc Finger Genes in Kenyan Women Diagnosed with Breast Cancer
Bibliographic record
Abstract
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.
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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 | 0.001 |
| 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".