Prediction of Recurrence and Survival for Triple-Negative Breast Cancer (TNBC) by a Protein Signature in Tissue Samples
Bibliographic record
Abstract
To date, there is no available targeted therapy for patients who are diagnosed with triple-negative breast cancers (TNBC). The aim of this study was to identify a new specific target for specific treatments. Frozen primary tumors were collected from 83 adjuvant therapy-naive TNBC patients. These samples were used for global proteome profiling by iTRAQ-OFFGEL-LC-MS/MS approach in two series: a training cohort (n = 42) and a test set (n = 41). Patients who remains free of local or distant metastasis for a minimum of 5 years after surgery were classified in the no-relapse group; the others were in the relapse group. OPLS and Kaplan-Meier analyses were performed to select candidate markers, which were validated by immunohistochemistry. Three proteins were identified in the training set and validated in the test set by Kaplan-Meier method and immunohistochemistry (IHC): TrpRS as a good prognostic markers and DP and TSP1 as bad prognostic markers. We propose the establishment of an IHC test to calculate the score of TrpRS, DP, and TSP1 in TNBC tumors to evaluate the degree of aggressiveness of the tumors. Finally, we propose that DP and TSP1 could provide therapeutic targets for specific treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".