Cardiac atrophy, dysfunction, and metabolic impairments: a cancer-induced heart failure phenotype
Bibliographic record
Abstract
ABSTRACT Muscle atrophy and weakness are prevalent features of cancer. While extensive research has characterized skeletal muscle wasting in cancer cachexia, limited studies have investigated how cardiac structure and function are affected by therapy-naïve cancer. In cell-based models of orthotopic, syngeneic epithelial ovarian cancer (EOC) and pancreatic ductal adenocarcinoma (PDAC), and a patient-derived pancreatic xenograft model (PDX), we evaluated cardiac structure, function, and metabolism. Tumor-bearing mice showed cardiac atrophy and intrinsic systolic and diastolic dysfunction; associated with hypotension and exercise intolerance. In hearts of ovarian tumor-bearing mice, fatty acid-supported mitochondrial respiration decreased and carbohydrate-supported respiration increased, establishing a substrate shift in cardiac metabolism that is characteristic of heart failure. EOC decreased cytoskeletal and cardioprotective gene expression, which was paralleled by downregulation of transcription factors that regulate cardiomyocyte size and function. PDX tumors altered myosin heavy chain isoform expression – a molecular phenotype observed in heart failure. Markers of autophagy and ubiquitin-proteasome system were upregulated with cancer, providing evidence of catabolic signaling that promotes cardiac wasting. Together, metabolic stress, cardiac gene dysregulation, and upregulation of catabolic pathways contribute to cardiac atrophy and failure during cancer. Finally, we demonstrate that pathological cardiac remodeling is induced by human cancer, providing translational evidence of cancer-induced cardiomyopathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".