Abstract 3913: New insights from transgenic mouse models of PyMT-induced breast cancer: identifying novel long non-coding RNA biomarkers
Bibliographic record
Abstract
Abstract Metastatic breast cancer with complex molecular mechanisms of progression accounts for most cancer related deaths in women. To improve diagnosis and drug development, it is important to identify novel biomarkers and critical molecular pathways involved in tumor initiation and progression. Here, we profiled and analyzed the expression of lncRNAs from three distinct stages of tumor initiation and progression (hyperplasia, adenoma, and carcinoma) in mammary tumors. We performed RNAseq on tumor and mammary epithelial cells derived from ROSAmT/mG; MMTV-PyMT mice and ROSAmT/mG; non-cancer mice derived from the same strain, respectively. We identified 1913 differentially expressed protein coding genes and 324 lncRNAs in breast cancer cells of all stages compared with normal mammary epithelial cells. Pearson correlation analysis correlated 93 differentially expressed lncRNAs with protein coding genes, providing a comprehensive lncRNA-coding gene co-expression network. Among them, we focused on Gm19303 which was paired with the differentially expressed protein coding gene, Trps1, and identified its human counterpart as LINC00536. Both LINC00536 and human TRPS1 are exclusively overexpressed in breast cancer and correlate with poor patient prognosis from the TCGA and GTEx databases. Single cell RNAseq data from the Atlas of Human breast cancers further confirmed that TRPS1 is upregulated in human breast cancer compared to normal human mammary tissue with highest expression in ER+ subgroup. In summary, our study explored the potential role of lncRNAs in breast cancer initiation and progression, presented the lncRNA-protein coding genes co-expression profiles, and identified human LINC00536/TRPS1 as a potential diagnostic and therapeutic target for breast cancer. Citation Format: Rui Zhang, Jiarong Li, Dunarel Badescu, Jiannis Ragoussis, Richard Kremer. New insights from transgenic mouse models of PyMT-induced breast cancer: identifying novel long non-coding RNA biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3913.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".