Serum miRNA Signatures in Cancer Cachexia Depend on Systemic Inflammation
Bibliographic record
Abstract
Cancer cachexia is a complex syndrome marked by involuntary weight and muscle loss, often driven by systemic inflammation. This multicenter, longitudinal observational study investigated circulating microRNA (miRNA) profiles in patients with unresectable locally advanced or metastatic colorectal cancer, comparing those with and without cachexia and inflammation. A total of 168 patients were categorized into four groups based on cachexia and C-reactive protein (CRP) levels. Cachexia was defined using the 2011 consensus criteria, incorporating weight loss, low BMI, and sarcopenia. Patients with both cachexia and systemic inflammation exhibited significantly distinct miRNA profiles as well as poorer overall survival (HR 2.10, p < 0.001) compared to patients with neither condition. No significant differences were observed in patients lacking either cachexia or inflammation or both. Inflammatory cachexia emerged as a biologically distinct entity, with 82 differentially expressed miRNAs. The miR-320-family, miR-6087, miR-4488, miR-29a-3p, miR-194-5p, and miR-10a-5p were most altered, several of which are linked to muscle mass, metabolism, lipid, and protein synthesis. These findings highlight the pivotal role of systemic inflammation in cancer cachexia and support its inclusion in diagnostic criteria. Moreover, circulating miRNAs may serve as promising biomarkers for identifying cachexia in cancer patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".