Aberrant serum lipid levels predict clinical outcomes in patients with neuroblastoma
Bibliographic record
Abstract
This study aimed to explore how serum lipid levels are related to clinical features and outcomes in neuroblastoma(NB). We analyzed data from 169 patients diagnosed with NB, focusing on their serum lipid levels and how these relate to disease characteristics and prognosis. We found that older age (>18 months), high-risk disease features (e.g. elevated LDH, NSE, serum ferritin), genetic abnormalities (such as MYCN amplification, 1p36/11q23 LOH, PHOX2B expression), tumor size ≥10 cm, bone marrow metastasis, and multiple organ involvement were linked to abnormal lipid profiles, including elevated total cholesterol (TC), triglycerides (TG), LDL-C, and lipid ratios (TC/HDL-C, TG/HDL-C), along with reduced HDL-C. Interestingly, high TG and TG/HDL-C levels correlated with increased regulatory T cells (Tregs), which play a role in immune response. Patients with unfavorable lipid profiles-particularly high TC, TG, LDL-C, and lipid ratios or low HDL-C-had worse 5-year event-free survival (EFS) rates. This was especially true for high-risk patients. In statistical models, the TC/HDL-C ratio emerged as an independent predictor of poorer EFS. To assess lipid dynamics during treatment, we monitored patients at multiple time points and observed decreasing TC, TG, LDL-C, TC/HDL-C, and TG/HDL-C levels, alongside increasing HDL-C, suggesting treatment-induced improvement in lipid metabolism. In patients with disease progression or relapse, lipid levels (TC, LDL-C, HDL-C) were significantly elevated at the time of the event compared to post-chemotherapy levels.In summary, dysregulation of serum lipids is common in NB and lipid profiles are closely linked to NB progression and may serve as potential biomarkers for prognosis and treatment monitoring.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".