A 6-tsRNA signature for early detection, treatment response monitoring, and prognosis prediction in diffuse large B cell lymphoma

Abstract

Diffuse large B-cell lymphoma (DLBCL) presents considerable clinical challenges due to its aggressive nature and diverse clinical progression. New molecular biomarkers are urgently needed for outcome prediction. We analyzed blood samples from DLBCL patients and healthy individuals using short, non-coding RNA sequencing. A classifier based on six tsRNAs was developed through random forest and primary component analysis. This classifier, established using Cox proportional hazards modeling with repeated 10-fold cross-validation on an internal cohort of 100 samples analyzed using RT-qPCR, effectively identified high-risk patients with significantly lower overall survival compared to low-risk patients (Hazard ratio: 6.657, 95%CI 2.827-15.68, P = 0.0006). Validation in an external cohort of 160 samples using RT-qPCR confirmed the classifier's robust performance. High-risk status was strongly associated with disease histological subtype, stage, and International Prognostic Index scores. Integration of the classifier into the IPI model enhanced the precision and consistency of prognostic predictions. A dynamic study revealed that patients experiencing a 1.06-fold decrease after one therapy cycle (early molecular response) exhibited better treatment outcomes and prognosis. Furthermore, the 6-tsRNA signature accurately differentiated healthy individuals from DLBCL (AUC 0.882, 95%CI 0.826-0.939). These findings underscore the potential of the identified 6-tsRNA profile as a biomarker for monitoring treatment effectiveness and predicting DLBCL outcomes.

Fig. 1. Identification of tsRNA signatures in serum of DLBCL patients.

A The relative proportion of eight major sncRNA categories in DLBCL patients and healthy controls. B The relative expression level of tsRNA between DLBCL patients and healthy individuals, the expression level was presented as means ± SEM. C The heat map indicates the differences in tsRNA expression profiling between DLBCL patients and healthy individuals, and six tsRNA samples selected in (D) were highlighted. D Schematic overview of tsRNA feature selection. E The association between the clinical characteristics of DLBCL patients and individual tsRNAs, 6-tsRNA classifiers, and risk scores (RS) in the internal validation cohort (n = 100), with each column representing a sample. F Comparison of the distribution of risk scores between DLBCL histological subtypes in the internal validation cohort (n = 100) and external validation cohort (n = 160). G Comparison of RS in disease stages of DLBCL patients. H Comparison of the distribution of RS in risk group-based IPI scoring systems in DLBCL patients.

Fig. 2. Risk scores of overall survival based on the 6-tsRNA classifiers in DLBCL patients.

A Risk score and patient survival status are classified by the 6-tsRNA classifiers in the internal validation cohort (n = 100). B Kaplan–Meier estimated the overall survival (OS) of different risk groups based on 6-tsRNA classifiers’ risk scores in the internal validation cohort (n = 100). C, D Kaplan–Meier estimated the overall survival of different risk groups based on RS in GCB and non-GCB subgroups of the internal validation cohort (n = 100). E The 6-tsRNA classifiers classified the risk score and DLBCL patient survival status in the external validation cohort (n = 160). F In the external validation cohort (n = 160), Kaplan–Meier estimated the overall survival of different risk groups based on 6-tsRNA classifier risk scores. G, H Kaplan–Meier estimated the overall survival of different risk groups based on RS in GCB and non-GCB subgroups of the external validation cohort (n = 160). I The risk score and patient survival status were classified by the 6-tsRNA classifiers in the whole cohort (internal + external validation cohort, n = 260). J Kaplan–Meier estimated the overall survival of different risk groups based on 6-tsRNA classifier risk scores across the whole cohort (internal + external validation cohort, n = 260).

Fig. 3. Kaplan–Meier estimated the overall survival of traditional IPI classification and IPI + RS in different cohorts.

A–C Kaplan–Meier estimated the overall survival of traditional IPI classification in the internal validation cohort (n = 100), the external validation cohort (n = 160), and the whole cohort (internal + external validation cohort, n = 260). D–F Kaplan–Meier estimated the overall survival of IPI + RS classification in the internal validation cohort (n = 100), the external validation cohort (n = 160), and the whole cohort (the internal + external validation cohort, n = 260). G–I ROC curve of IPI, IPI + RS in the internal validation cohort (n = 100), the external validation cohort (n = 160) and the whole cohort (internal + external validation cohort, n = 260).

Fig. 4. Dynamics of individual tsRNAs and RS during therapy.

A–F The relative expression of tsRNA-Leu-AAG, tsRNA-Gln-CTG, tsRNA-Pro-CGG, tsRNA-Leu-CAG, tsRNA-Lys-CTT, and tsRNA-Cys-GCA at pre-treatment and cycle 2, day 1. G Comparison of interim response assessment in DLBCL patients with the early molecular response (EMR) and non-early molecular response (No-EMR) at cycle 2, day 1. H Kaplan–Meier estimated the overall survival of different groups based on EMR in the internal validation cohort.

Fig. 5. Differential expression analysis of serum tsRNAs in DLBCL patients.

A–F The relative expression of tsRNA-Leu-AAG, tsRNA-Gln-CTG, tsRNA-Pro-CGG, tsRNA-Leu-CAG, tsRNA-Lys-CTT, and tsRNA-Cys-GCA between patients and healthy individuals. G Comparison of RS between newly diagnosed DLBCL patients and healthy individuals. H ROC curves of individual tsRNAs and RS in serum samples from DLBCL patients versus healthy individuals.