B7-H4 Is a Novel Membrane-Bound Protein and a Candidate Serum and Tissue Biomarker for Ovarian Cancer
Bibliographic record
Abstract
Using cDNA database mining strategies and real-time quantitative reverse transcription-PCR, we identified B7-H4 as a novel gene that is overexpressed in ovarian and breast cancer tissues when compared with normal tissues. The gene encodes a protein of 282 amino acids with a signal sequence, an immunoglobulin domain, and a COOH-terminal hydrophobic transmembrane domain. Immunohistochemistry experiments show plasma membrane staining in serous ovarian and breast cancer, confirming the tissue specificity and cell surface localization. We have developed a sensitive dual monoclonal antibody sandwich ELISA to analyze the level of B7-H4 protein in >2,500 serum samples, ascites fluids, and tissue lysates. High levels of B7-H4 protein were detected in ovarian cancer tissue lysates when compared with normal tissues. B7-H4 was present at low levels in all sera but showed an elevated level in serum samples from ovarian cancer patients when compared with healthy controls or women with benign gynecologic diseases. The median B7-H4 concentration in endometrioid and serous histotypes was higher than in mucinous histotypes, consistent with results of immunohistochemical staining. The multivariate logistic regression analysis of B7-H4 and CA125 measured in the same sample set resulted in an area under the curve (AUC) of 0.86 for all stages and 0.86 for stage I/II patients, which was significantly higher than the AUC for either marker alone. In early-stage patients, the sensitivity at 97% specificity increased from 52% for CA125 alone to 65% when used in combination with B7-H4. We conclude that B7-H4 is a promising new biomarker for ovarian carcinoma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".