Prognostic significance of CXCR4 and mTOR expression in diffuse large B-cell lymphoma patients
Bibliographic record
Abstract
Background: The aim of this study was to investigate the prognostic role of mammalian target of Rapamycin (mTOR) and C-X-C chemokine receptor type 4 (CXCR4) in diffuse large-B-cell lymphoma (DLBCL) patients.Patients and methods: This retrospective study was collected data from 64 de novo DLBCL patients, who received standardized R-CHOP therapy at two oncology centers. CXCR4 and mTOR expressions were assessed by immunohistochemistry.Results: Out of the 64 DLBCL patients, 40 patients were positive for CXCR4 (62.5%) and 35 patients for mTOR (54.7%) expressions. CXCR4 expression was positively correlated with mTOR expression (r = 0.7; p < .001). While mTOR expression was significantly associated with high lactate dehydrogenase level (p = .03) and number of extranodal sites one or more (p =.02), CXCR4 expression was significantly associated with high IPI score (p < .001) and ECOG PS (p = .005). Furthermore, theexpression levels of mTOR and CXCR4 were significantly associated with older ages and poor response to treatment (p = .04, <.001 and .04, .03, respectively). After a median Follow up of 22 months, mean ± SD overall survival (OS) was 65.391 ± 4.705. Kaplan–Meier analysis showed that patients positive for mTOR and CXCR4 expression had shorter DFS (p = .01 & .02) and OS (p = .02 & .04). Multivariate analysis showed that CXCR4 and mTOR positivity is an independent prognostic factor for significantly poorer DFS (p = .03, and .02 respectively) but not for OS (p = .09 and .08 respectively) in the DLBCL pateints.Conclusion: Our results indicate that the expression of CXCR4 and mTOR may be poor prognostic biomarkers in DLBCL.
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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) | 0.000 | 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".