Loss of Parkinson’s susceptibility gene LRRK2 promotes carcinogen-induced lung tumorigenesis
Bibliographic record
Abstract
Pathological links between neurodegenerative disease and cancer are emerging. LRRK2 overactivity contributes to Parkinson's disease, whereas our previous analyses of public cancer patient data revealed that decreased LRRK2 expression is associated with lung adenocarcinoma (LUAD). The clinical and functional relevance of LRRK2 repression in LUAD is unknown. Here, we investigated associations between LRRK2 expression and clinicopathological variables in LUAD patient data and asked whether LRRK2 knockout promotes murine lung tumorigenesis. In patients, reduced LRRK2 was significantly associated with ongoing smoking and worse survival, as well as signatures of less differentiated LUAD, altered surfactant metabolism and immunosuppression. We identified shared transcriptional signals between LRRK2-low LUAD and postnatal alveolarization in mice, suggesting aberrant activation of a developmental program of alveolar growth and differentiation in these tumors. In a carcinogen-induced murine lung cancer model, multiplex IHC confirmed that LRRK2 was expressed in alveolar type II (AT2) cells, a main LUAD cell-of-origin, while its loss perturbed AT2 cell morphology. LRRK2 knockout in this model significantly increased tumor initiation and size, demonstrating that loss of LRRK2, a key Parkinson's gene, promotes lung tumorigenesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".