Whole Transcriptome Data Analysis Reveals Prognostic Signature Genes for Overall Survival Prediction in Diffuse Large B Cell Lymphoma

MicroRNAs in Diffuse Large B-Cell Lymphoma (DLBCL): Biomarkers with Prognostic Potential

Background/Objectives: The heterogeneity of diffuse large B-cell lymphoma (DLBCL) based on differences in both genetic and epigenetic factors contributes to the dynamics of tumor growth and efficacy of cytoreductive therapy, as well as considerably affecting disease prognosis. This study aimed to detect microRNAs (miRNAs) capable of improving prognostic accuracy in DLBCL patients. Methods: We performed miRNA sequencing in bone marrow (BM) samples collected from DLBCL patients. Next, the expression levels of miRNAs in lymph node (LN) samples (n = 43) and BM samples (n = 70) were analyzed by real-time RT-PCR in the group of DLBCL patients. Results: It was found that the expression levels of miRNA-10b, -100, -125a, -125b, -126, -143, -23a and let-7a were statistically significantly reduced in the group of DLBCL patients who had a poor prognosis compared to DLBCL patients with a favorable prognosis (p < 0.05). Kaplan-Meier survival analysis demonstrated that the upregulated expression of miRNA-23a, miRNA-125a, and miRNA-100 was associated with better overall survival in DLBCL patients. A statistically significant elevation in the expression levels of miRNA-151a, miRNA-148b and miRNA-192 in the BM samples was observed for DLBCL patients both with and without BM involvement compared to BM samples from non-cancerous blood disease (NCBD) patients (p < 0.05). Statistically significant upregulation of PD-L1, TIMP1, TOP2A, and TP53 was observed in BM samples from DLBCL patients with and without BM involvement in comparison with BM samples from NCBD patients (p < 0.05). Conclusions: miRNA-23a, miRNA-125a, and miRNA-100 were shown to be potential prognostically significant biomarkers in DLBCL patients. Changes in expression levels of miRNA-151a, miRNA-148b, miRNA-192, PD-L1, TIMP1, TOP2A, and TP53 reflect alterations in the BM without morphological or immunophenotypic signs of a DLBCL-related BM pathology.

Development and Validation of a Novel Four Gene-Pairs Signature for Predicting Prognosis in DLBCL Patients

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin's lymphoma. Because individual clinical outcomes of DLBCL in response to standard therapy differ widely, new treatment strategies are being investigated to improve therapeutic efficacy. In this study, we identified a novel signature for stratification of DLBCL useful for prognosis prediction and treatment selection. First, 408 prognostic gene sets were selected from approximately 2500 DLBCL samples in public databases, from which four gene-pair signatures consisting of seven prognostic genes were identified by Cox regression analysis. Then, the risk score was calculated based on these gene-pairs and we validated the risk score as a prognostic predictor for DLBCL patient outcomes. This risk score demonstrated independent predictive performance even when combined with other clinical parameters and molecular subtypes. Evaluating external DLBCL cohorts, we demonstrated that the risk-scoring model based the four gene-pair signatures leads to stable predictive performance, compared with nine existing predictive models. Finally, high-risk DLBCL showed high resistance to DNA damage caused by anticancer drugs, suggesting that this characteristic is responsible for the unfavorable prognosis of high-risk DLBCL patients. These results provide a novel index for classifying the biological characteristics of DLBCL and clearly indicate the importance of genetic analyses in the treatment of DLBCL.

Machine-learning-based classification of diffuse large B-cell lymphoma patients by a 7-mRNA signature enriched with immune infiltration and cell cycle

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) exhibits remarkable heterogeneity but still remains undiagnosed in identifying the subpopulation of DLBCL to predict the prognosis and guide clinical treatment.

METHODS

Molecular subgroups were identified in gene expression data from GSE10846 by a consensus clustering algorithm. And gene set enrichment analysis, immune infiltration, and the proposed cell cycle algorithm were applied to explore the biological functions of different subtypes. Meanwhile, univariate and multivariate Cox regression analyses were used to evaluate independent prognostic factors of DLBCL. Finally, the prognostic model, including some key genes screened by Lasso regression, Random Forest algorithm, and point-biserial correlation, was constructed by an optimal classifier from seven machine learning algorithms and validated by another three external datasets (GSE34171, GSE87371, GSE31312).

RESULTS

Comprehensive genomic analysis of 1,143 DLBCL samples identify 2 molecularly, prognostically relevant subtypes: immune-enriched (IME) and cell-cycle-enriched (CCE). Then a new predictive model including seven key genes (SERPING1, TIMP2, NME1, DCTPP1, RFC4, POLE2, and SNRPD1) was developed with high prediction accuracy (88.6%) and strong predictive power (AUC = 0.973) based on the Support Vector Machine (SVM) algorithm in 414 patients from GSE10846. The predictive power was similar in another three testing sets (HR > 1.400, p < 0.05).

CONCLUSION

This model could evaluate survival independently with strong predictive power compared with other clinical risk factors. Our study constructed a reliable model to predict two new subtypes of DLBCL patients, which could guide the implementation of individualized treatment.

Genetic and transcriptomic analyses of diffuse large B-cell lymphoma patients with poor outcomes within two years of diagnosis

Despite the improvements in clinical outcomes for DLBCL, a significant proportion of patients still face challenges with refractory/relapsed (R/R) disease after receiving first-line R-CHOP treatment. To further elucidate the underlying mechanism of R/R disease and to develop methods for identifying patients at risk of early disease progression, we integrated clinical, genetic and transcriptomic data derived from 2805 R-CHOP-treated patients from seven independent cohorts. Among these, 887 patients exhibited R/R disease within two years (poor outcome), and 1918 patients remained in remission at two years (good outcome). Our analysis identified four preferentially mutated genes (TP53, MYD88, SPEN, MYC) in the untreated (diagnostic) tumor samples from patients with poor outcomes. Furthermore, transcriptomic analysis revealed a distinct gene expression pattern linked to poor outcomes, affecting pathways involved in cell adhesion/migration, T-cell activation/regulation, PI3K, and NF-κB signaling. Moreover, we developed and validated a 24-gene expression score as an independent prognostic predictor for treatment outcomes. This score also demonstrated efficacy in further stratifying high-risk patients when integrated with existing genetic or cell-of-origin subtypes, including the unclassified cases in these models. Finally, based on these findings, we developed an online analysis tool ( https://lymphprog.serve.scilifelab.se/app/lymphprog ) that can be used for prognostic prediction for DLBCL patients.

A novel lipid metabolism-based risk model associated with immunosuppressive mechanisms in diffuse large B-cell lymphoma

BACKGROUND

The molecular diversity exhibited by diffuse large B-cell lymphoma (DLBCL) is a significant obstacle facing current precision therapies. However, scoring using the International Prognostic Index (IPI) is inadequate when fully predicting the development of DLBCL. Reprogramming lipid metabolism is crucial for DLBCL carcinogenesis and expansion, while a predictive approach derived from lipid metabolism-associated genes (LMAGs) has not yet been recognized for DLBCL.

METHODS

Gene expression profiles of DLBCL were generated using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The LASSO Cox regression was used to construct an effective predictive risk-scoring model for DLBCL patients. The Kaplan-Meier survival assessment was employed to compare a given risk score with the IPI score and its impact on the survival of DLBCL patients. Functional enrichment examination was performed utilizing the KEGG pathway. After identifying hub genes via single-sample GSEA (ssGSEA), immunohistochemical staining and immunofluorescence were performed on lymph node samples from control and DLBCL patients to confirm these identified genes.

RESULTS

Sixteen lipid metabolism- and survival-associated genes were identified to construct a prognostic risk-scoring approach. This model demonstrated robust performance over various datasets and emerged as an autonomous risk factor for predicting the development of DLBCL patients. The risk score could significantly distinguish the development of DLBCL patients from the low-risk and elevated-risk IPI classes. Results from the inhibitory immune-related pathways and lower immune scores suggested an immunosuppressive phenotype within the elevated-risk group. Three hub genes, MECR, ARSK, and RAN, were identified to be negatively correlated with activated CD8 T cells and natural killer T cells in the elevated-risk score class. Ultimately, it was determined that these three genes were expressed by lymphoma cells but not by T cells in clinical samples from DLBCL patients.

CONCLUSION

The risk level model derived from 16 lipid metabolism-associated genes represents a prognostic biomarker for DLBCL that is novel, robust, and may have an immunosuppressive role. It can compensate for the limitations of the IPI score in predicting overall survival and has potential clinical application value.