The effects of timing of fine needle aspiration biopsies on gene expression profiles in breast cancers
Bibliographic record
Abstract
BACKGROUND: DNA microarray analysis has great potential to become an important clinical tool to individualize prognostication and treatment for breast cancer patients. However, with any emerging technology, there are many variables one must consider before bringing the technology to the bedside. There are already concerted efforts to standardize protocols and to improve reproducibility of DNA microarray. Our study examines one variable that is often overlooked, the timing of tissue acquisition, which may have a significant impact on the outcomes of DNA microarray analyses especially in studies that compare microarray data based on biospecimens taken in vivo and ex vivo. METHODS: From 16 patients, we obtained paired fine needle aspiration biopsies (FNABs) of breast cancers taken before (PRE) and after (POST) their surgeries and compared the microarray data to determine the genes that were differentially expressed between the FNABs taken at the two time points. qRT-PCR was used to validate our findings. To examine effects of longer exposure to hypoxia on gene expression, we also compared the gene expression profiles of 10 breast cancers from clinical tissue bank. RESULTS: Using hierarchical clustering analysis, 12 genes were found to be differentially expressed between the FNABs taken before and after surgical removal. Remarkably, most of the genes were linked to FOS in an early hypoxia pathway. The gene expression of FOS also increased with longer exposure to hypoxia. CONCLUSION: Our study demonstrated that the timing of fine needle aspiration biopsies can be a confounding factor in microarray data analyses in breast cancer. We have shown that FOS-related genes, which have been implicated in early hypoxia as well as the development of breast cancers, were differentially expressed before and after surgery. Therefore, it is important that future studies take timing of tissue acquisition into account.
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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 | 0.001 | 0.004 |
| 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".