EMP3: A promising biomarker for tumor prognosis and targeted cancer therapy
Bibliographic record
Abstract
Epithelial membrane protein 3 (EMP3) belongs to the peripheral myelin protein 22 kDa (PMP22) gene family, characterized by four transmembrane domains and widespread expression across various human tissues and organs. Other members of the PMP22 family, including EMP1, EMP2, and PMP22, have been linked to various cancers, such as glioblastoma, laryngeal cancer, nasopharyngeal cancer, gastric cancer, breast cancer, and endometrial cancer. However, few studies report on the function and relevance of EMP3 in tumorigenicity. Given the significant structural similarities among members of the PMP22 family, there are likely potential functional similarities as well. Previous studies have established the regulatory role of EMP3 in immune cells like T cells and macrophages. Additionally, EMP3 is found to be involved in critical signaling pathways, including HER-2/PI3K/Akt, MAPK/ERK, and TGF-beta/Smad. Furthermore, EMP3 is associated with cell cycle regulation, cellular proliferation, and apoptosis. Hence, it is likely that EMP3 participates in cancer development through these aforementioned pathways and mechanisms. This review aims to systematically examine and summarize the structure and function of EMP3 and its association to various cancers. EMP3 is expected to emerge as a significant biological marker for tumor prognosis and a potential target in cancer therapeutics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".