Gene expression profiling of ductal carcinomas in situ and invasive breast tumors.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
UNLABELLED: Comparative and functional genomics are powerful tools to advance the understanding of the molecular basis of cancer. It is believed that genes are epigenetically regulated and, thus, each tumor type and stage will be characterized by a gene expression fingerprint. In this study we identified genes that are differentially expressed in ductal carcinoma in situ and invasive ductal carcinoma of the breast. To isolate genes that are associated with progression of breast cancer we performed differential display and subtractive cloning procedures using matched RNA from normal and tumor tissue. cDNA microarray analysis generated gene expression profiles typical of the transition from in situ to invasive breast cancer when we used mRNA extracted from a case of low- to intermediate-grade DCIS and a case of high-grade DCIS/IDC. cDNAs from these samples were the probes in a cDNA microarray hybridization to 9183 unique cDNAs representing 8507 genes. Signals from both transcriptomes were obtained for 8083 genes, and the balanced differential expression values between pure DCIS and DCIS/invasive tumors revealed 303 distinct cDNAs with a ratio of > 2. Interferon inducible genes were found to be expressed at the highest level in the pure DCIS sample. Genes most abundantly expressed in the invasive tumor were immunoglobulin heavy constant gamma 3 and calgranulin B. Further analysis of RNA and protein expression in breast tumor cell lines and patient tissue samples revealed that: IGFBP-rP1 is down-regulated in invasive tumors whereas cyclin I protein is regulated by ubiquitination and is associated with ER-negative breast cancers. CONCLUSION: The known and novel genes discussed here represent targets for molecular characterization during breast cancer development as well as for designing novel strategies for diagnosis and treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it