RAC1, a Potential Diagnostic and Prognostic Marker for Diffuse Large B Cell Lymphoma
Bibliographic record
Abstract
The gene changes for diagnosis and prognosis of diffuse large B cell lymphoma (DLBCL) still remain unclear. RAC1 was reported to be asso;ciated with the B cell receptor signal pathway, but its relations with DLBCL have not yet been systematically explored. In this study, we have conducted molecular, bioinformatics and clinical analyses by using publicly available data from The Cancer Genome Atlas (TCGA). Wilcoxon signed-rank test and logistic regression were performed to evaluate the association between RAC1 and clinical features in patients. Kaplan-Meier and Cox regression methods were used to examine the impacts of RAC1 expression level on overall survival, and a nomogram was performed to illustrate the correlation between RAC1 and the risk of DLBCL. Our results revealed that the expression level of RAC1 in DLBCL was higher than that in normal tissues or lymphadenitis. High-level expression of RAC1 was significantly associated with clinical stage, as well as being an independent factor affecting overall survival. RAC1 was negatively correlated with Bruton's tyrosine kinase (BTK). The association between RAC1 gene expression and the risk of DLBCL was presented in a nomogram. In conclusion, RAC1 expression patterns may be used to predict the development and prognosis of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".