Bulk RNA sequencing dataset of Claudin-low breast cancer cell lines with Neuropilin-1 knockdown
Bibliographic record
Abstract
Triple-negative breast cancers (TNBC) are a particularly aggressive breast cancer subtype with poor prognosis and high relapse rates. Due to a lack of identified targeted therapies, chemotherapy currently remains as the primary treatment for TNBC. Approximately 25-39% of TNBC are claudin-low breast cancers, which are mainly defined by low expression of cell-cell adhesion proteins and enrichment of mesenchymal signatures. Functional studies have demonstrated the potential role of the transmembrane-coreceptor, Neuropilin-1 (NRP1) in regulating the progression of these tumours. However, there have been no high-throughput studies to date that comprehensively investigate NRP1-modulated cell-signalling across multiple claudin-low cell lines. Therefore, we treated HS578T, MDA-MB-231 and SUM159PT claudin-low cell lines with either a non-targeting (NT) control or two NRP1-targeting small-interfering RNA (siRNA) or short-hairpin RNA (shRNA) sequences and followed this with bulk-RNA sequencing. We present this comprehensive transcriptomic dataset which provides a valuable resource for understanding both the transcriptomic landscape of claudin-low breast cancer and NRP1-regulated signalling pathways. Therefore, paving the way for future studies of its potential as a therapeutic target.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".