Urokinase or urokinase-type plasminogen activator (uPA) (PLAU) : Machine learning discoveries of 2nd order synergy in Meningiomas
Bibliographic record
Abstract
Background : Urokinase, encoded by PLAU, was originally isolated from human urine, and it is also present in the blood and in the extracellular matrix of many tissues. The primary physiological substrate of this enzyme is plasminogen, which is an inactive form (zymogen) of plasmin. Further, activation of plasmin instigates a proteolytic cascade that participates in thrombolysis or extracellular matrix degradation. It has been implicated in atherosclerotic aneurysm formation, Alzheimer disease, radioresistant oesophageal cancer, extravillous trophoblast cell invasion in pregnancy, prostate cancer, coal miners' pneumoconiosis, ErbB2-positive breast cancer, Quebec platelet disorder, colon cancer, chemoresistance of bladder cancer, glioblastoma multiforme, keratinocyte wound healing, psoriasis and basal cell carcinoma, Gaucher disease, obesity-induced type 2 diabetes, hepatocellulcar carcinoma, bone marrow stromal cells, hypertension, hyperglycemia, SARS-CoV-2 and COVID-19 infection, cervical cancer, laryngeal squamous cell carcinoma, pulpitis and aging and aging-related diseases, to name a few. However, its role in meningiomas has not yet been deter- mined completely. Meningiomas are the most common intracranial primary neoplasm in adults. Patel et al. [1] analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) using clinical, gene expression, and sequencing data and using unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. Further, these groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Issue : Increasing evidence point to the fact that meningioma classification and grading, that is based on histopathology does not always accurately predict tumor aggressiveness and recurrence behaviour and knowledge of the underlying biology of the treatment resistant meningiomas and the impact of genetic alterations in these tumors, is lacking. At the current stage more genomic studies are required to unravel the role of other genes and their interations with other genetic factors. Resolution : In a recently published work Sinha [2], a frame work of a search engine was developed which can rank combinations of factors (genes/proteins) in a signaling pathway. Adapting this search engine to the Meningioma dataset, i present here 2nd order combinations of PLAU, some of which have been known to exist via wet lab experiments, but many are yet to be tested. The reveals combinations might help oncologists/biologists test possible hypotheses that might be the causing factors in meningioma. Further, in my limited grasp, if proven true, the combinations revealed by the search engine might pave way for development of gene based therapies aimed at resolving pathological issues related to meningiomas.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".