Identification of neutrophil-derived proteases and angiotensin II as biomarkers of cancer cachexia
Bibliographic record
Abstract
BACKGROUND: Cachexia is a metabolic disorder characterised by muscle wasting, diminished response to anti-cancer treatments and poor quality of life. Our objective was to identify blood-based biomarkers of cachexia in advanced cancer patients. Hence, we characterised the plasma cytokine and blood cell mRNA profiles of patients grouped in three cohorts: patients with cachexia, pre-cachexia (no cachexia but high CRP levels: ⩾ 5 mg l⁻¹) and no cachexia (no cachexia and CRP: < 5 mg l⁻¹). METHODS: A total of 122 newly diagnosed cancer patients with seven cancer types were studied prior to their initial therapy. Plasma levels of 22 cytokines were quantified using the bio-plex technology. mRNAs isolated from whole blood and expression profiles were determined by the chip array technology and Ingenuity Pathway Analysis (IPA) software. RESULTS: In comparison with non-cachectic individuals, both pre-cachectic and cachectic patients showed an increase (⩾ 1.5-folds) in mRNA expression of neutrophil-derived proteases (NDPs) and significantly elevated angiotensin II (Ang II) (P = 0.005 and P = 0.02, respectively), TGFβ1 (P = 0.042 and P < 0.0001, respectively) and CRP (both P < 0.0001) in the plasma. Moreover, cachectic patients displayed a significant increase in IL-6 (P = 0.005), IL-8 (P = 0.001) and absolute neutrophil counts (P = 0.007). CONCLUSIONS: Ang II, TGFβ1, CRP and NDP are blood biomarkers for cancer cachexia. These findings contribute to early diagnosis and prevention of cachexia.
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) | 0.001 | 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".