Bone Marrow Tumor Microenvironment of Obese Hodgkin Lymphoma Patients: implications of insulin axis
Bibliographic record
Abstract
Abstract Background Excessive adiposity, or obesity, has been associated with cancer promotion, including an increased risk for developing Hodgkin Lymphoma (HL). However, the association between obesity and survival in HL can be somewhat paradoxical and may indeed influence prognosis. Examining the bone marrow (BM) cytokine profile in HL patients could provide insights into the mechanisms underlying the altered association between excess adiposity and HL. The BM is an important site for hematopoiesis and can be influenced by various factors, including disease processes and systemic metabolic changes associated with obesity. Methods From our cohort, we analyzed interstitial marrow fluid (IMF) from BM aspirates of 16 HL patients at diagnosis and 11 control subjects. Participants were then matched by sex, age, and Body mass index (BMI) for inclusion in our discovery protein array analysis (n = 8 HL and n = 8 donors). We validated our findings in the total sample by measuring adipokine-related molecules using ELISA. Adiposity was measured through abdominal circumference measurement and BMI. Gene expression analysis was conducted through RT-qPCR. Activated signaling pathways were analyzed using HL cell line (L428 cells). Statistical analyses were performed using SPSS and GraphPad. Results The IMF of HL patients presented downregulation of interleukins (IL-1α/β, IL-6sR, IL-12), chemokines (CCL2, CCL3, CCL16), IGF-axis mediators (IGFBP-1, IGFBP-2, IGFBP-3, IGF-1sR), sTNFRII, TGFβ1, leptin, osteoprotegerin (OPG), and Fas compared to healthy donors and after controlling for adiposity status. Interestingly, HL overweight/obese subjects showed up-regulation of OPG and lymphotactin in IMF. The results were confirmed by quantification of cytokines, where we observed lower levels of insulin growth factor binding protein IGFBP-3 and higher levels of OPG levels in HL patients. The high-molecular weight (HMW) and total of adiponectin levels were high in HL BM. We further demonstrate that LEPR, TGFβ1, and IGFBP3 transcripts were upregulated in fractionated BMAd from HL compared to controls, while IFG2R was upregulated in SC. Finally, we observed a possible modulation of L428 cells through IGFBP-3 in an IGF-1-dependent manner, which could be reflected in the BM TME of HL disease. Conclusions Our data supports a role for the insulin axis in the BM microenvironment of obese HL patients, particularly through the regulation of insulin ligand-binding proteins.
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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 | 0.000 |
| 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".