Proteomic Profiling of a Mouse Model for Ovarian Granulosa Cell Tumor Identifies VCP as a Highly Sensitive Serum Tumor Marker in Several Human Cancers
Bibliographic record
Abstract
The initial aim of this study was to identify novel serum diagnostic markers for the human ovarian granulosa cell tumor (GCT), a tumor that represents up to 5% of all ovarian cancers. To circumvent the paucity of human tissues available for analyses, we used the Ctnnb1(tm1Mmt/+);Pten(tm1Hwu/tmiHwu);Amhr2(tm3(cre)Bhr/+) transgenic mouse model, which features the constitutive activation of CTNNB1 signaling combined with the loss of Pten in granulosa cells and develops GCTs that mimic aggressive forms of the human disease. Proteomic profiling by mass spectrometry showed that vinculin, enolase 1, several heat shock proteins, and valosin containing protein (VCP) were more abundantly secreted by cultured mouse GCT cells compared to primary cultured GC. Among these proteins, only VCP was present in significantly increased levels in the preoperative serum of GCT cancer patients compared to normal subjects. To determine the specificity of VCP, serum levels were also measured in ovarian carcinoma, non-Hodgkin's lymphoma and breast, colon, pancreatic, lung, and prostate cancer patients. Increased serum VCP levels were observed in the majority of cancer cases, with the exception of patients with lung or prostate cancer. Moreover, serum VCP levels were increased in some GCT, ovarian carcinoma, breast cancer, and colon cancer patients who did not otherwise display increased levels of widely used serum tumor markers for their cancer type (e.g. inhibin A, inhibin B, CA125, CEA, or CA15.3). These results demonstrate the potential use of VCP as highly sensitive serum marker for GCT as well as several other human cancers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".