Proteomic analyses of serous and endometrioid epithelial ovarian cancers – Cases studies – Molecular insights of a possible histological etiology of serous ovarian cancer
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
PURPOSE: Epithelial ovarian carcinogenesis may occur de novo on the surface of ovarian mesothelial epithelial cells or from cells originating in other organs. Foreign Müllerian cell intrusion into the ovarian environment has been hypothesized to explain the latter scenario. In this study, MALDI MS profiling technology was used to provide molecular insights regarding these potentially different mechanisms. EXPERIMENTAL DESIGN: Using MALDI MS profiling, the molecular disease signatures were established in their anatomical context. MALDI MS profiling was used on serous and endometrioid cancer biopsies to investigate cases of epithelial ovarian cancer. We then applied bioinformatic methods and identification strategies on the LC-MS/MS analyses of extracts from digested formalin-fixed, paraffin-embedded tissues. Extracts from selected regions (i.e. serous ovarian adenocarcinoma, fallopian tube serous adenocarcinoma, endometrioid ovarian cancer, benign endometrium, and benign ovarian tissues) were performed, and peptide digests were subjected to LC-MS/MS analysis. RESULTS: Comparison of the proteins identified from benign endometrium or three ovarian cancer types (i.e. serous ovarian adenocarcinoma, endometrioid ovarian adenocarcinoma, and serous fallopian tube adenocarcinoma) provided new evidence of a possible correlation between the fallopian tubes and serous ovarian adenocarcinoma. Here, we propose a workflow consisting of the comparison of multiple tissues in their anatomical context in an individual patient. CONCLUSION AND CLINICAL RELEVANCE: The present study provides new insights into the molecular similarities between these two tissues and an assessment of highly specific markers for an individualized patient diagnosis and care.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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