Potassium Channel Candidate Genes Predict the Development of Secondary Lymphedema Following Breast Cancer Surgery
Bibliographic record
Abstract
BACKGROUND: Potassium (K) channels play an important role in lymph pump activity, lymph formation, lymph transport, and the functions of lymph nodes. No studies have examined the relationship between K channel candidate genes and the development of secondary lymphedema (LE). OBJECTIVE: The study purpose was to evaluate for differences in genotypic characteristics in women who did (n = 155) or did not (n = 387) develop upper extremity LE following breast cancer treatment based on an analysis of single-nucleotide polymorphisms (SNPs) and haplotypes in 10 K channel genes. METHODS: Upper extremity LE was diagnosed using bioimpedance resistance ratios. Logistic regression analyses were used to identify those SNPs and haplotypes that were associated with LE while controlling for relevant demographic, clinical, and genomic characteristics. RESULTS: Patients with LE had a higher body mass index, had a higher number of lymph nodes removed, had more advanced disease, received adjuvant chemotherapy, received radiation therapy, and were less likely to have undergone a sentinel lymph node biopsy. One SNP in a voltage-gated K channel gene (KCNA1 rs4766311), four in two inward-rectifying K channel genes (KCNJ3 rs1037091 and KCNJ6 rs2211845, rs991985, rs2836019), and one in a two-pore K channel gene (KCNK3 rs1662988) were associated with LE. DISCUSSION: These preliminary findings suggest that K channel genes play a role in the development of secondary LE.
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 | 0.002 |
| 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) | 0.003 | 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".