Biology of Mesothelin and Clinical Implications: A Review of Existing Literature
Bibliographic record
Abstract
Since its discovery in 1992, mesothelin (MSLN) has generated significant interest as a therapeutic target. A number of characteristics make it ideal for this purpose. First, it is not expressed on the parenchyma of any vital organs. Second, it is differentially expressed on a number of cancer types that have relatively poor prognosis and lack effective systemic options. Third, it is expressed on the cell membrane making it accessible to large molecule targeted therapies. However, unlike other drug targets that have been exploited for therapeutic benefit, the precise function of MSLN, why it is expressed in certain cancers, and its biological role have not been clearly elucidated. Here the existing literature on the cellular function and expression patterns of MSLN across tumor types is reviewed in order to gain further understanding of this intriguing molecule. In doing so, we conclude that there remains significant ambiguity surrounding its function and role in cellular and tumor biology. Furthermore, the expression of MSLN and its relation of prognosis seems to depend on the type of tumor. Finally, the unified mechanism by which MSLN acts as a protein that conveys tumor aggressiveness remains elusive. What is clear is that there is much yet to be discovered in this realm and doing so may have large implications for treatment of otherwise lethal malignancies.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".