Identification of an individualized RNA binding protein-based prognostic signature for diffuse large B-cell lymphoma

Epstein-Barr Virus miR-BARTs 7 and 9 modulate viral cycle, cell proliferation, and proteomic profiles in Burkitt lymphoma

The Epstein-Barr Virus (EBV) encodes viral microRNAs (miRs) that have been implicated in the pathogenesis of nasopharyngeal and gastric carcinomas, yet their potential roles in lymphomas remain to be fully elucidated. This study evaluated the impact of CRISPR/Cas9-mediated knockdown of EBV miRs BART-7 and BART-9 in EBV-positive Burkitt lymphoma cells Akata. As anticipated, the Akata cells subjected to CRISPR/Cas9-mediated knockdown of either EBV BART-7 or BART-9 exhibited a significant reduction in the expression of these viral miRs compared to cells with wild-type (wt) EBV genomes. This outcome effectively validates the experimental model employed in this study. Knocking down either BART-7 or BART-9 resulted in a notable reduction in cell viability and proliferation rates, alongside an elevation in the expression of EBV lytic genes. Global proteomic analysis revealed that the knockdown of EBV BART-7 significantly decreased the expression of ubiquitin/proteasome proteins while concurrently increasing RNA binding proteins (RBPs). Conversely, BART-9 knockdown reduced proteins associated with oxidoreductase activity, particularly those involved in fatty acid metabolism. Our findings unveil previously undiscovered EBV miRs BARTs 7 and 9 roles in cellular pathways relevant to both viral biology and lymphomagenesis.

Analyzing the involvement of diverse cell death-related genes in diffuse large B-cell lymphoma using bioinformatics techniques

Diffuse large B-cell lymphoma (DLBCL) stands as the most prevalent subtype of non-Hodgkin's lymphoma and exhibits significant heterogeneity. Various forms of programmed cell death (PCD) have been established to have close associations with tumor onset and progression. To this end, this study has compiled 16 PCD-related genes. The investigation delved into genes linked with prognosis, constructing risk models through consecutive application of univariate Cox regression analysis and Lasso-Cox regression analysis. Furthermore, we employed RT-qPCR to validate the mRNA expression levels of certain diagnosis-related genes. Subsequently, the models underwent validation through KM survival curves and ROC curves, respectively. Additionally, nomogram models were formulated employing prognosis-related genes and risk scores. Lastly, disparities in immune cell infiltration abundance and the expression of immune checkpoint-associated genes between high- and low-risk groups, as classified by risk models, were explored. These findings contribute to a more comprehensive understanding of the role played by the 16 PCD-associated genes in DLBCL, shedding light on potential novel therapeutic strategies for the condition.

Should we use nomograms for risk predictions in diffuse large B cell lymphoma patients? A systematic review

Models based on risk stratification are increasingly reported for Diffuse large B cell lymphoma (DLBCL). Due to a rising interest in nomograms for cancer patients, we aimed to review and critically appraise prognostic models based on nomograms in DLBCL patients. A literature search in PubMed/Embase identified 59 articles that proposed prognostic models for DLBCL by combining parameters of interest (e.g., clinical, laboratory, immunohistochemical, and genetic) between January 2000 and 2024. Of them, 40 studies proposed different gene expression signatures and incorporated them into nomogram-based prognostic models. Although most studies assessed discrimination and calibration when developing the model, many lacked external validation. Current nomogram-based models for DLBCL are mainly developed from publicly available databases, lack external validation, and have no applicability in clinical practice. However, they may be helpful in individual patient counseling, although careful considerations should be made regarding model development due to possible limitations when choosing nomograms for prognostication.

SurvIAE: Survival prediction with Interpretable Autoencoders from Diffuse Large B-Cells Lymphoma gene expression data

BACKGROUND

In Diffuse Large B-Cell Lymphoma (DLBCL), several methodologies are emerging to derive novel biomarkers to be incorporated in the risk assessment. We realized a pipeline that relies on autoencoders (AE) and Explainable Artificial Intelligence (XAI) to stratify prognosis and derive a gene-based signature.

METHODS

AE was exploited to learn an unsupervised representation of the gene expression (GE) from three publicly available datasets, each with its own technology. Multi-layer perceptron (MLP) was used to classify prognosis from latent representation. GE data were preprocessed as normalized, scaled, and standardized. Four different AE architectures (Large, Medium, Small and Extra Small) were compared to find the most suitable for GE data. The joint AE-MLP classified patients on six different outcomes: overall survival at 12, 36, 60 months and progression-free survival (PFS) at 12, 36, 60 months. XAI techniques were used to derive a gene-based signature aimed at refining the Revised International Prognostic Index (R-IPI) risk, which was validated in a fourth independent publicly available dataset. We named our tool SurvIAE: Survival prediction with Interpretable AE.

RESULTS

From the latent space of AEs, we observed that scaled and standardized data reduced the batch effect. SurvIAE models outperformed R-IPI with Matthews Correlation Coefficient up to 0.42 vs. 0.18 for the validation-set (PFS36) and to 0.30 vs. 0.19 for the test-set (PFS60). We selected the SurvIAE-Small-PFS36 as the best model and, from its gene signature, we stratified patients in three risk groups: R-IPI Poor patients with High levels of GAB1, R-IPI Poor patients with Low levels of GAB1 or R-IPI Good/Very Good patients with Low levels of GPR132, and R-IPI Good/Very Good patients with High levels of GPR132.

CONCLUSIONS

SurvIAE showed the potential to derive a gene signature with translational purpose in DLBCL. The pipeline was made publicly available and can be reused for other pathologies.

Prognostic value and gene regulatory network of CMSS1 in hepatocellular carcinoma

BACKGROUND

Cms1 ribosomal small subunit homolog (CMSS1) is an RNA-binding protein that may play an important role in tumorigenesis and development.

OBJECTIVE

RNA-seq data from the GEPIA database and the UALCAN database were used to analyze the expression of CMSS1 in liver hepatocellular carcinoma (LIHC) and its relationship with the clinicopathological features of the patients.

METHODS

LinkedOmics was used to identify genes associated with CMSS1 expression and to identify miRNAs and transcription factors significantly associated with CMSS1 by GSEA.

RESULTS

The expression level of CMSS1 in hepatocellular carcinoma tissues was significantly higher than that in normal tissues. In addition, the expression level of CMSS1 in advanced tumors was significantly higher than that in early tumors. The expression level of CMSS1 was higher in TP53-mutated tumors than in non-TP53-mutated tumors. CMSS1 expression levels were strongly correlated with disease-free survival (DFS) and overall survival (OS) in patients with LIHC, and high CMSS1 expression predicted poorer OS (P< 0.01) and DFS (P< 0.01). Meanwhile, our results suggested that CMSS1 is associated with the composition of the immune microenvironment of LIHC.

CONCLUSIONS

The present study suggests that CMSS1 is a potential molecular marker for the diagnosis and prognostic of LIHC.

Emerging roles for heterogeneous ribonuclear proteins in normal and malignant B cells

Heterogeneous nuclear ribonucleoproteins (hnRNPs) are among the most abundantly expressed RNA binding proteins in the cell and play major roles in all facets of RNA metabolism. hnRNPs are increasingly appreciated as essential for mammalian B cell development by regulating the carefully ordered expression of specific genes. Due to this tight regulation of the hnRNP-RNA network, it is no surprise that a growing number of genes encoding hnRNPs have been causally associated with the onset or progression of many cancers, including B cell neoplasms. Here we discuss our current understanding of hnRNP-driven regulation in normal, perturbed, and malignant B cells, and the most recent and emerging therapeutic innovations aimed at targeting the hnRNP-RNA network in lymphoma.

Identification of three small nucleolar RNAs (snoRNAs) as potential prognostic markers in diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a non-Hodgkin lymphoma with high mortality rates. Small nucleolar RNAs (snoRNAs) are tumor-specific biological markers, but there are few studies on the role of snoRNAs in DLBCL.

MATERIALS AND METHODS

Survival-related snoRNAs were selected to construct a specific snoRNA-based signature via computational analyses (Cox regression and independent prognostic analyses) to predict the prognosis of DLBCL patients. To assist in clinical applications, a nomogram was built by combining the risk model and other independent prognostic factors. Pathway analysis, gene ontology analysis, transcription factor enrichment, protein-protein interactions, and single nucleotide variant analysis were used to explore the potential biological mechanisms of co-expressed genes.

RESULTS

Twelve prognosis-correlated snoRNAs were selected from the DLBCL patient cohort of microarray profiles, and a three-snoRNA signature consisting of SNORD1A, SNORA60, and SNORA66 was constructed. DLBCL patients could be divided into high-risk and low-risk cohorts using the risk model, and the high-risk group and activated B cell-like (ABC) type DLBCL were linked with disappointing survival. In addition, SNORD1A co-expressed genes were inseparably linked to the biological functions of the ribosome and mitochondria. Potential transcriptional regulatory networks have also been identified. MYC and RPL10A were the most mutated SNORD1A co-expressed genes in DLBCL.

CONCLUSION

Put together, our findings explored the potential biological effects of snoRNAs in DLBCL, and provided a new predictor for DLBCL prediction.

An Autophagy-Related Gene Signature can Better Predict Prognosis and Resistance in Diffuse Large B-Cell Lymphoma

Background: Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous disease, and about 30%–40% of patients will develop relapsed/refractory DLBCL. In this study, we aimed to develop a gene signature to predict survival outcomes of DLBCL patients based on the autophagy-related genes (ARGs).

Methods: We sequentially used the univariate, least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses to build a gene signature. The Kaplan–Meier curve and the area under the receiver operating characteristic curve (AUC) were performed to estimate the prognostic capability of the gene signature. GSEA analysis, ESTIMATE and ssGSEA algorithms, and one-class logistic regression were performed to analyze differences in pathways, immune response, and tumor stemness between the high- and low-risk groups.

Results: Both in the training cohort and validation cohorts, high-risk patients had inferior overall survival compared with low-risk patients. The nomogram consisted of the autophagy-related gene signature, and clinical factors had better discrimination of survival outcomes, and it also had a favorable consistency between the predicted and actual survival. GSEA analysis found that patients in the high-risk group were associated with the activation of doxorubicin resistance, NF-κB, cell cycle, and DNA replication pathways. The results of ESTIMATE, ssGSEA, and mRNAsi showed that the high-risk group exhibited lower immune cell infiltration and immune activation responses and had higher similarity to cancer stem cells.

Conclusion: We proposed a novel and reliable autophagy-related gene signature that was capable of predicting the survival and resistance of patients with DLBCL and could guide individualized treatment in future.