Proteomic Profiling of Limited-Stage Follicular Lymphoma Reveals Differentially Expressed Proteins Linked to Disease Progression Post-Radiation Therapy
Bibliographic record
Abstract
Follicular lymphoma (FL) is the most common indolent lymphoma. Despite a generally favorable prognosis and long-term survival for many patients, FL remains incurable, with disease progression occurring in approximately half of limited-stage FL cases. In this study, we employed high-throughput mass spectrometry-based proteomics to explore the differential protein expression in diagnostic lymphoma biopsies from 26 limited-stage FL patients. Of these, 9 patients experienced subsequent disease progression (sp-FL), while 17 did not (np-FL). A total of 1940 proteins were identified, with 78 showing significant differential expression between progressing and non-progressing cases. Unsupervised clustering analyses were able to separate the two patient groups based on these differential protein profiles. Notably, proteins involved in metabolism, immune regulation, and apoptosis were downregulated in sp-FL samples. Among the identified proteins, caspase 4 and 8 (CASP4 and CASP8) were further evaluated. The low expression of CASP4 in the diagnostic lymphoma tissue correlated with shorter progression-free survival (PFS) (p < 0.001), primarily with this difference apparent in the expression profiles in the intrafollicular areas (p = 0.015). Similarly, low CASP8 expression was associated with inferior PFS (p = 0.031). Interestingly, addressing the expression pattern for advanced-stage FL patients, the low protein expression of both CASP4 and CASP8 was also found to be associated with progressing cases, suggesting their potential role in disease pathogenesis independent of the disease stage. With further research, the expression pattern of CASP4 and CASP8 may enable the early prediction of disease progression in FL patients, which may ultimately improve patient stratification and allow for more individualized treatment strategies.
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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 | 0.001 | 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".