A Haplotype Analysis of <i>HER-2</i> Gene Polymorphisms: Association with Breast Cancer Risk, HER-2 Protein Expression in the Tumor, and Disease Recurrence in Korea
Bibliographic record
Abstract
PURPOSE: A single-nucleotide polymorphism (SNP) in codon 655 of HER-2 has been extensively studied with inconclusive results. The purpose of this study was to investigate the association between common variants of HER-2 and breast cancer risk, HER-2 expression, and survival using a haplotype-based stepwise approach. EXPERIMENTAL DESIGN: Twenty-nine SNPs listed in the National Center for Biotechnology Information database were screened to identify novel polymorphisms of HER-2 gene in 90 healthy Korean women. Six of 29 SNPs were polymorphic and had greater than 10% of minor allele frequencies. Using these six SNPs, linkage disequilibrium and haplotype patterns were characterized. We tested association between the haplotypes and breast cancer in a large case-control study (n=1,039 cases and 995 controls). Six-hundred two breast cancer patients with follow-up at least 24 months were analyzed for outcome in relation to haplotype. Expression of HER-2 protein was determined by immunohistochemistry in 1,094 cases of invasive breast cancer. RESULTS: All six SNPs showed a strong linkage disequilibrium pattern and were considered to belong to one haplotype block. Two haplotype-tagging SNPs (I655V and P1170A) for three common haplotypes (>5%) were genotyped in cases and controls. The haplotypes and individual SNPs were not associated with breast cancer risk. In patients with at least one copy of haplotype I (the most common haplotype), HER-2 expression was 1.5 times higher (P = 0.009) and the prognosis was worse (P = 0.032) compared with patients without having that haplotype. CONCLUSIONS: Our results suggest that the currently identified genetic polymorphisms of HER-2 are not associated with an increased risk of breast cancer in Korean women, whereas one haplotype does affect protein expression of the tumor and disease outcome.
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 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 | 0.000 | 0.000 |
| 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".