Abstract 4709: NXF2 is a Candidate Biomarker of Mechanical Stress in Breast Cancer
Bibliographic record
Abstract
Abstract Mechanical stress has been implicated as an important factor influencing tumour cell biology. Due to the nature of early stages of breast cancer and its ductal environment and associated stromal changes, origins in ductal changes in gland tension are likely to occur and the associated mechanical forces may contribute to the progression of breast cancer. To identify specific changes in gene expression that might serve as biomarkers of mechanical stress in breast cancer, we applied static tensile forces (0.65 pN/μm2) to breast cancer cell lines via collagen-coated magnetite beads, profiled gene expression changes by Microarray chips, validation by RT-PCR and immunoblotting. Our results showed that mechanical stretch forces altered cell signaling in both MDA-MB-231 and MDA-MB-468 breast cancer cell lines by array chip analysis and 7.24.% genes (4.67% up and 2.57% down regulation) were regulated by mechanical stress in 16822 profiling genes in MDA-MB-231 cell line and 5.79% genes (2.22% up and 3.57% down regulated) was regulated in MDA-MB-468 cell lines. The 0.48% (82/16822) up regulation and the 0.125% (21/16822) down regulation was in complete concordance with Microarray in both cell lines. The validation of the gene expression with significant changes of >1.5-fold in MDA-MB-231 cell line, RT-PCR revealed the up-regulation of LCE2D and NXF2 and down-regulation of PIGH and RAPD1B and confirmed the force-induced genes identified by Microarray. Mechanical stress also increased 2.75 fold NXF2 protein expression level with confirmation of immunoblotting. There were no significant expression changes found relating to hypoxia stress control gene NDRG1, which changed the highest gene in hypoxia control chip. We conclude that mechanical stress promotes early breast cancer progression mediated by multiple signal transduction pathways and that NXF2 is a potential mechanical stress marker in breast cancer progression. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4709.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".