Immune Biomarkers in Blood from Sarcoma Patients: A Pilot Study
Bibliographic record
Abstract
The main role of the host immune system is to identify and eliminate cancer cells, which is a complex process, but it is not a fail-safe mechanism. Many sarcoma patients succumb to this disease despite treatments rendered. The aim of this pilot study was to compare the levels of CD4+ T-cells, T-regulatory (Treg) cells, and cytokines such as tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), interleukin-17A (IL-17A), and transforming growth factor-beta-1 (TGF-β1) in peripheral blood leukocytes of sarcoma patients and healthy controls. For gene expression studies, total ribonucleic acid (RNA) was extracted from peripheral blood leukocytes and genes that were differentially regulated in peripheral blood leukocytes of sarcoma patients compared with healthy controls were determined using a commercial T-helper cell differentiation quantitative polymerase chain reaction (qPCR) array. Flow cytometer analysis was performed on blood samples from 26 sarcoma patients and 10 healthy controls to identify the levels of CD4+ T-cells and T-reg cells. The level of cytokines in plasma and culture supernatant were quantified using commercial enzyme-linked immunosorbent assay (ELISA) kits. A marked reduction in the percentage of CD4+ T-cells (p = 0.037) and levels of TNF-α (p = 0.004) and IFN-γ (0.010) was observed in sarcoma patients. Gene expression analysis showed five genes (homeobox A10 (HOXA10), GATA binding protein 3 (GATA3), prostaglandin D2 receptor 2 (PTGDR2), thymocyte selection associated high mobility group box (TOX), and C-C motif chemokine receptor 3 (CCR3)) were dysregulated (p < 0.05) in sarcoma patients. This study suggests that T-helper-1 immune responses are reduced in sarcoma patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".