Complement and Fungal Dysbiosis as Prognostic Markers and Potential Targets in PDAC Treatment
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is still hampered by a dismal prognosis. A better understanding of the tumor microenvironment within the pancreas and of the factors affecting its composition is of utmost importance for developing new diagnostic and treatment tools. In this context, the complement system plays a prominent role. Not only has it been shown to shape a T cell-mediated immune response, but it also directly affects proliferation and apoptosis of the tumor cells, influencing angiogenesis, metastatic spread and therapeutic resistance. This makes complement proteins appealing not only as early biomarkers of PDAC development, but also as therapeutic targets. Fungal dysbiosis is currently the new kid on the block in tumorigenesis with cancer-associated mycobiomes extracted from several cancer types. For PDAC, colonization with the yeast Malassezia seems to promote cancer progression, already in precursor lesions. One responsible mechanism appears to be complement activation via the lectin pathway. In the present article, we review the role of the complement system in tumorigenesis, presenting observations that propose it as the missing link between fungal dysbiosis and PDAC development. We also present the results of a small pilot study supporting the crucial interplay between the complement system and Malassezia colonization in PDAC pathogenesis.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".