Isobaric Tags for Relative and Absolute Quantitation in Proteomic Analysis of Potential Biomarkers in Invasive Cancer, Ductal Carcinoma In Situ, and Mammary Fibroadenoma
Bibliographic record
Abstract
OBJECTIVES: Breast malignancy is a serious threat to women's health around the world. Following the rapid progress in the field of cancer diagnostics and identification of pathological markers, breast tumor treatment methods have been greatly improved. However, for invasive, ductal carcinomas and mammary fibroadenoma, there is an urgent demand for better breast tumor-linked biomarkers. The current study was designed to identify diagnostic and/or therapeutic protein biomarkers for breast tumors. METHODS: iTRAQ) proteomic analysis was employed to characterize differentially expressed proteins for potential biomarkers in IBC, DCIS, and fibroadenomas by comparisons with their matched adjacent tissues and/or normal breast tissues. The public databases Metascape and String were used for bioinformatic analyses. RESULTS: Using the proteomics approach, we identified differentially expressed proteins in tissues of different breast tumors compared to normal/adjacent breast tissues, including 100 in IBC, 52 in DCIS, and 44 in fibroadenoma. Among the 100 IBC differentially expressed proteins, 37 were found to be specific to this type of cancer only. Additionally, four proteins were specifically expressed in DCIS and four in fibroadenoma. Compared to corresponding adjacent tissues and normal breast tissues, 18 step-changing proteins were differentially expressed in IBC, 14 in DCIS, and 13 in fibroadenoma, respectively. Compared to DCIS and normal breast tissues, 65 proteins were differentially expressed in IBC with growing levels of malignancy. CONCLUSIONS: The identified potential protein biomarkers may be used as diagnostic and/or therapeutic targets in breast tumors.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".