Neurodegenerative disease pathways are perturbed in patients with cancer who self‐report cognitive changes and anxiety: A pathway impact analysis
Bibliographic record
Abstract
BACKGROUND: Cancer-related cognitive impairment (CRCI) and anxiety co-occur in patients with cancer. Little is known about mechanisms for the co-occurrence of these two symptoms. The purposes of this secondary analysis were to evaluate for perturbed pathways associated with the co-occurrence of self-reported CRCI and anxiety in patients with low versus high levels of these two symptoms and to identify potential mechanisms for the co-occurrence of CRCI and anxiety using biological processes common across any perturbed neurodegenerative disease pathways. METHODS: Patients completed the Attentional Function Index and the Spielberger State-Trait Anxiety Inventory six times over two cycles of chemotherapy. Based on findings from a previous latent profile analysis, patients were grouped into none versus both high levels of these symptoms. Gene expression was quantified, and pathway impact analyses were performed. Signaling pathways for evaluation were defined with the Kyoto Encyclopedia of Genes and Genomes database. RESULTS: A total of 451 patients had data available for analysis. Approximately 85.0% of patients were in the none class and 15.0% were in the both high class. Pathway impact analyses identified five perturbed pathways related to neurodegenerative diseases (i.e., amyotrophic lateral sclerosis, Huntington disease, Parkinson disease, prion disease, and pathways of neurodegeneration-multiple diseases). Apoptosis, mitochondrial dysfunction, oxidative stress, and endoplasmic reticulum stress were common biological processes across these pathways. CONCLUSIONS: This study is the first to describe perturbations in neurodegenerative disease pathways associated with CRCI and anxiety in patients receiving chemotherapy. These findings provide new insights into potential targets for the development of mechanistically based interventions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".