Clinical significance of circulating neutrophils and lymphocyte subsets in newly diagnosed patients with diffuse large B-cell lymphoma

Prognostic relevance of bone marrow immune cell fractions in newly diagnosed B-cell non-Hodgkin lymphoma patients

INTRODUCTION

Non-Hodgkin lymphomas (NHLs) are the most common hematological malignancies worldwide. Among these, B-cell lymphomas (B-NHLs) are the second leading cause of death in hematologic neoplasms.

MATERIAL AND METHODS

In this study, a detailed immunophenotypic analysis of lymphocytes in the bone marrow aspirate (BMA) of 75 patients with four different subtypes of B-NHLs was performed at diagnosis. The samples were analyzed by flow cytometry (FC) using a stain-lyse-no wash technique and a comprehensive six-color antibody panel.

RESULTS

Our data showed a different trend in the percentage values of the distinct lymphocyte subsets, which did not seem to correlate with a worse prognosis, except for B cells in diffuse large B-cell lymphoma (DLBCL), which were significantly higher in stage IV than in stages II and III. ROC curve analysis showed that the B-cell percentage value could be used to predict the stage of the disease. Total lymphocytes and B cells were greater in lymphomas that presented a lower percentage of disease progression, specifically mantle cell lymphoma (MCL) and marginal zone lymphoma (MZL). In contrast, natural killer (NK) and T cells showed higher values in DLBCL and follicular lymphoma (FL), which progressed more frequently. Interestingly, in DLBCL patients with higher percentage values of double positive (DPT) and helper T cells (Th), we observed a good prognosis. Specifically, univariate Cox regression analyses indicated that a higher value of Th cells at diagnosis was a better prognostic predictor in patients with DLBCL.

CONCLUSIONS

These preliminary findings encourage us to further investigate the role of lymphocyte subpopulations in B-cell NHL.

Diagnostic value of baseline 18 F-FDG PET/CT and peripheral blood inflammatory markers for aggressive lymphoma in non-Hodgkin's lymphoma

PURPOSE

This study aims to investigate the diagnostic value of baseline F18 fluorodeoxyglucose (FDG) PET/computed tomography (CT) parameters and peripheral blood inflammatory markers in aggressive lymphoma of non-Hodgkin lymphoma (NHL) and the correlation between peripheral blood inflammatory markers and maximum standardized uptake value (SUV max ).

PATIENTS AND METHODS

We conducted a retrospective analysis including 121 patients with NHL. Patients were divided into aggressive lymphoma group and indolent lymphoma group. Mann-Whitney U test, chi-square test and multivariate stepwise logistic regression were used to analyse. Subsequently, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance. Additionally, Spearman correlation analysis was utilized to explore the correlation between peripheral blood inflammatory markers and SUV max .

RESULTS

Leptin mass criterion uptake value (SUL) max , SUV max , SUV avg , SUV peak , focal SUV max /liver SUV max , focal SUV max / mediastinal SUV max , SUL avg , SUL peak , systemic immune-inflammation (SII), neutrophil ratio, total lesion glycolysis (TLG), neutrophils versus lymphocyte ratio (NLR), plateletto-lymphocyte ratio (PLR), lactate dehydrogenase (LDH) and lymphocyle ratio between two groups were statistically significant ( P  < 0.05). SUV max was an independent influencing factor, and the area under the ROC curve was 0.862. There was a positive correlation between the PLR and SUV max ( r  = 0.239; P  = 0.008).

CONCLUSION

PET/CT parameters and peripheral blood inflammatory markers have certain value in the diagnosis of aggressive lymphoma in NHL, among which SUV max is an independent influencing marker and is positively correlated with PLR.

Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors

BACKGROUND

New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (CRF), severely impacts the quality of life of lymphoma survivors. However, clinical diagnosis and treatment of CRF are inadequate and require enhancement.

OBJECTIVE

The main objective of this study is to construct machine learning-based CRF prediction models for lymphoma survivors to help healthcare professionals accurately identify the CRF population and better personalize treatment and care for patients.

METHODS

A cross-sectional study in China recruited lymphoma patients from June 2023 to March 2024, dividing them into two datasets for model construction and external validation. Six machine learning algorithms were used in this study: Logistic Regression (LR), Random Forest, Single Hidden Layer Neural Network, Support Vector Machine, eXtreme Gradient Boosting, and Light Gradient Boosting Machine (LightGBM). Performance metrics like the area under the receiver operating characteristic (AUROC) and calibration curves were compared. The clinical applicability was assessed by decision curve, and Shapley additive explanations was employed to explain variable significance.

RESULTS

CRF incidence was 40.7 % (dataset I) and 44.8 % (dataset II). LightGBM showed strong performance in training and internal validation. LR excelled in external validation with the highest AUROC and best calibration. Pain, total protein, physical function, and sleep disturbance were important predictors of CRF.

CONCLUSION

The study presents a machine learning-based CRF prediction model for lymphoma patients, offering dynamic, data-driven assessments that could enhance the development of automated CRF screening tools for personalized management in clinical practice.

Multifaceted roles of neutrophils in tumor microenvironment

Neutrophils, the most abundant leukocyte population in circulation, play a crucial role in detecting and responding to foreign cells, such as pathogens and tumor cells. However, the impact of neutrophils on cancer pathogenesis has been overlooked because of their short lifespan, terminal differentiation, and limited transcriptional activity. Within the tumor microenvironment (TME), neutrophils can be influenced by tumor cells or other stromal cells to acquire either protumor or antitumor properties via the cytokine environment. Despite progress in neutrophil-related research, a comprehensive understanding of tissue-specific neutrophil diversity and adaptability in the TME is still lacking, which poses a significant obstacle to the development of neutrophil-based cancer therapies. This review evaluated the current studies on the dual roles of neutrophils in cancer progression, emphasizing their importance in predicting clinical outcomes, and explored various approaches for targeting neutrophils in cancer treatment, including their potential synergy with cancer immunotherapy.

Cuproptosis-related lncRNA signature as a prognostic tool and therapeutic target in diffuse large B cell lymphoma

Cuproptosis is a newly defined form of programmed cell death that relies on mitochondria respiration. Long noncoding RNAs (lncRNAs) play crucial roles in tumorigenesis and metastasis. However, whether cuproptosis-related lncRNAs are involved in the pathogenesis of diffuse large B cell lymphoma (DLBCL) remains unclear. This study aimed to identify the prognostic signatures of cuproptosis-related lncRNAs in DLBCL and investigate their potential molecular functions. RNA-Seq data and clinical information for DLBCL were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Cuproptosis-related lncRNAs were screened out through Pearson correlation analysis. Utilizing univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis, we identified seven cuproptosis-related lncRNAs and developed a risk prediction model to evaluate its prognostic value across multiple groups. GO and KEGG functional analyses, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. Additionally, drug sensitivity analysis identified drugs with potential efficacy in DLBCL. Finally, the protein-protein interaction (PPI) network were constructed based on the weighted gene co-expression network analysis (WGCNA). We identified a set of seven cuproptosis-related lncRNAs including LINC00294, RNF139-AS1, LINC00654, WWC2-AS2, LINC00661, LINC01165 and LINC01398, based on which we constructed a risk model for DLBCL. The high-risk group was associated with shorter survival time than the low-risk group, and the signature-based risk score demonstrated superior prognostic ability for DLBCL patients compared to traditional clinical features. By analyzing the immune landscapes between two groups, we found that immunosuppressive cell types were significantly increased in high-risk DLBCL group. Moreover, functional enrichment analysis highlighted the association of differentially expressed genes with metabolic, inflammatory and immune-related pathways in DLBCL patients. We also found that the high-risk group showed more sensitivity to vinorelbine and pyrimethamine. A cuproptosis-related lncRNA signature was established to predict the prognosis and provide insights into potential therapeutic strategies for DLBCL patients.

A Novel Inflammatory-Nutritional Prognostic Scoring System for Patients with Diffuse Large B Cell Lymphoma

Purpose

This study aimed to examine the predictive ability of inflammatory and nutritional markers and further establish a novel inflammatory nutritional prognostic scoring (INPS) system.

Patients and Methods

We collected clinicopathological and baseline laboratory data of 352 patients with DLBCL between April 2010 and January 2023 at the First affiliated hospital of Ningbo University. Eligible patients were randomly divided into training and validation cohorts (n = 281 and 71, respectively) in an 8:2 ratio. We used the least absolute shrinkage and selection operator (LASSO) Cox regression model to determine the most important factors among the eight inflammatory-nutritional variables. The impact of INPS on OS was evaluated using the Kaplan-Meier curve and the Log rank test. A prognostic nomogram was developed based on the multivariate Cox regression method. Then, we used the concordance index (C-index), calibration plot, and time-dependent receiver operating characteristic (ROC) analysis to evaluate the prognostic performance and predictive accuracy of the nomogram.

Results

Seven inflammatory-nutritional biomarkers, including neutrophil-lymphocyte ratio (NLR), prognostic nutritional index (PNI), body mass index (BMI), monocyte-lymphocyte ratio (MLR), prealbumin, C reactive protein, and D-dimer were selected using the LASSO Cox analysis to construct INPS, In the multivariate analysis, IPI-High-intermediate group, IPI-High group, high INPS were independently associated with OS, respectively. The prognostic nomogram for overall survival consisting of the above two indicators showed excellent discrimination. The C-index for the nomogram was 0.94 and 0.95 in the training and validation cohorts. The time-dependent ROC curves showed that the predictive accuracy of the nomogram for OS was better than that of the NCCN-IPI system.

Conclusion

The INPS based on seven inflammatory-nutritional indexes was a reliable and convenient predictor of outcomes in DLBCL patients.

A novel telomere-related genes model for predicting prognosis and treatment responsiveness in diffuse large B-cell lymphoma

Diffuse large B cell lymphoma (DLBCL) is a highly heterogeneous disease with diverse clinical and molecular features. Telomere maintenance is widely present in tumors, but there is a lack of relevant reports on the role of telomere-related genes (TRGs) in DLBCL. In this study, we used consensus clustering based on TRGs expression to identify two molecular clusters with distinct prognoses and immune cell infiltration. We developed a TRGs scoring model using univariate Cox regression and LASSO regression in the GSE10846 training cohort. DLBCL patients in the high-risk group had a worse prognosis than those in the low-risk group, as revealed by Kaplan-Meier curves. The scoring model was validated in the GSE10846 testing cohort and GSE87371 cohort, respectively. The high-risk group was characterized by elevated infiltration of activated DCs, CD56 dim natural killer cells, myeloid-derived suppressor cells, monocytes, and plasmacytoid DCs, along with reduced infiltration of activated CD4 T cells, Type 2 T helper cells, γδ T cells, NK cells, and neutrophils. Overexpression of immune checkpoints, such as PDCD1, CD274, and LAG3, was observed in the high-risk group. Furthermore, high-risk DLBCL patients exhibited increased sensitivity to bortezomib, rapamycin, AZD6244, and BMS.536924, while low-risk DLBCL patients showed sensitivity to cisplatin and ABT.263. Using RT-qPCR, we found that three protective model genes, namely TCEAL7, EPHA4, and ELOVL4, were down-regulated in DLBCL tissues compared with control tissues. In conclusion, our novel TRGs-based model has great predictive value for the prognosis of DLBCL patients and provides a promising direction for treatment optimization.

An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers

Background: Systemic inflammatory indicators are clinically significant in guiding diffuse large B-cell lymphoma (DLBCL) prognosis. However, which inflammatory markers are the best predictors of DLBCL prognosis is still unclear. In this study, we aimed to create a nomogram based on the best inflammatory markers and clinical indicators to predict the overall survival of patients with DLBCL. Patients and methods: We analyzed data from 423 DLBCL patients from two institutions and divided them into a training set, an internal validation set, and an external validation set (n = 228, 97, and 98, respectively). The least absolute shrinkage and selection operator and Cox regression analysis were used to develop nomograms. We assessed model fit using the Akaike information criterion and Bayesian information criterion. The concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the nomogram's predictive performance and clinical net benefit and compared with the International Prognostic Index (IPI) and National Comprehensive Cancer Network (NCCN)-IPI. Results: The inclusion variables for the nomogram model were age, Eastern Cooperative Oncology Group performance status, lactate dehydrogenase level, the systemic immune-inflammation index (SII), the prognostic nutritional index (PNI), and β-2 microglobulin (β-2 MG) level. In the training cohort, the nomogram showed better goodness of fit than the IPI and NCCN-IPI. The C-index of the nomogram (0.804, 95% CI: 0.751-0.857) outperformed the IPI (0.690, 95% CI: 0.629-0.751) and NCCN-IPI (0.691, 95% CI: 0.632-0.750). The calibration curve, ROC curve, and DCA curve analysis showed that the nomogram has satisfactory predictive power and clinical utility. Similar results were found in the validation cohort. Conclusion: The nomogram integrated with the clinical characteristics and inflammatory markers is beneficial to predict the prognosis of patients with DLBCL.