Expression of NOTCH3 exon 16 differentiates Diffuse Large B-cell Lymphoma into molecular subtypes and is associated with prognosis

Role of RNA Splicing Mutations in Diffuse Large B Cell Lymphoma

Ribonucleic acid splicing is a crucial process to create a mature mRNA molecule by removing introns and ligating exons. This is a highly regulated process, but any alteration in splicing factors, splicing sites, or auxiliary components affects the final products of the gene. In diffuse large B-cell lymphoma, splicing mutations such as mutant splice sites, aberrant alternative splicing, exon skipping, and intron retention are detected. The alteration affects tumor suppression, DNA repair, cell cycle, cell differentiation, cell proliferation, and apoptosis. As a result, malignant transformation, cancer progression, and metastasis occurred in B cells at the germinal center. B-cell lymphoma 7 protein family member A (BCL7A), cluster of differentiation 79B (CD79B), myeloid differentiation primary response gene 88 (MYD88), tumor protein P53 (TP53), signal transducer and activator of transcription (STAT), serum- and glucose-regulated kinase 1 (SGK1), Pou class 2 associating factor 1 (POU2AF1), and neurogenic locus notch homolog protein 1 (NOTCH) are the most common genes affected by splicing mutations in diffuse large B cell lymphoma.

The implications of alternative pre-mRNA splicing in cell signal transduction

Cells produce multiple mRNAs through alternative splicing, which ensures proteome diversity. Because most human genes undergo alternative splicing, key components of signal transduction pathways are no exception. Cells regulate various signal transduction pathways, including those associated with cell proliferation, development, differentiation, migration, and apoptosis. Since proteins produced through alternative splicing can exhibit diverse biological functions, splicing regulatory mechanisms affect all signal transduction pathways. Studies have demonstrated that proteins generated by the selective combination of exons encoding important domains can enhance or attenuate signal transduction and can stably and precisely regulate various signal transduction pathways. However, aberrant splicing regulation via genetic mutation or abnormal expression of splicing factors negatively affects signal transduction pathways and is associated with the onset and progression of various diseases, including cancer. In this review, we describe the effects of alternative splicing regulation on major signal transduction pathways and highlight the significance of alternative splicing.

Role of Notch Receptors in Hematologic Malignancies

Notch receptors are single-pass transmembrane proteins that play a critical role in cell fate decisions and have been implicated in the regulation of many developmental processes. The human Notch family comprises of four receptors (Notch 1 to 4) and five ligands. Their signaling can regulate extremely basic cellular processes such as differentiation, proliferation and death. Notch is also involved in hematopoiesis and angiogenesis, and increasing evidence suggests that these genes are involved and frequently deregulated in several human malignancies, contributing to cell autonomous activities that may be either oncogenic or tumor suppressive. It was recently proposed that Notch signaling could play an active role in promoting and sustaining a broad spectrum of lymphoid malignancies as well as mutations in Notch family members that are present in several disorders of T- and B-cells, which could be responsible for altering the related signaling. Therefore, different Notch pathway molecules could be considered as potential therapeutic targets for hematological cancers. In this review, we will summarize and discuss compelling evidence pointing to Notch receptors as pleiotropic regulators of hematologic malignancies biology, first describing the physiological role of their signaling in T- and B-cell development and homeostasis, in order to fully understand the pathological alterations reported.

Digital PCR: A Reliable Tool for Analyzing and Monitoring Hematologic Malignancies

The digital polymerase chain reaction (dPCR) is considered to be the third-generation polymerase chain reaction (PCR), as it yields direct, absolute and precise measures of target sequences. dPCR has proven particularly useful for the accurate detection and quantification of low-abundance nucleic acids, highlighting its advantages in cancer diagnosis and in predicting recurrence and monitoring minimal residual disease, mostly coupled with next generation sequencing. In the last few years, a series of studies have employed dPCR for the analysis of hematologic malignancies. In this review, we will summarize these findings, attempting to focus on the potential future perspectives of the application of this promising technology.

OSdlbcl: An online consensus survival analysis web server based on gene expression profiles of diffuse large B-cell lymphoma

Diffuse large B‐cell lymphoma (DLBCL) is the most common subtype of non‐Hodgkin lymphoma (NHL) and is a clinical, pathological, and molecular heterogeneous disease with highly variable clinical outcomes. Currently, valid prognostic biomarkers in DLBCL are still lacking. To optimize targeted therapy and improve the prognosis of DLBCL, the performance of proposed biomarkers needs to be evaluated in multiple cohorts, and new biomarkers need to be investigated in large datasets. Here, we developed a consensus Online Survival analysis web server for Diffuse Large B‐Cell Lymphoma, abbreviated OSdlbcl, to assess the prognostic value of individual gene. To build OSdlbcl, we collected 1100 samples with gene expression profiles and clinical follow‐up information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. In addition, DNA mutation data were also collected from the TCGA database. Overall survival (OS), progression‐free survival (PFS), disease‐specific survival (DSS), disease‐free interval (DFI), and progression‐free interval (PFI) are important endpoints to reflect the survival rate in OSdlbcl. Moreover, clinical features were integrated into OSdlbcl to allow data stratifications according to the user's special needs. By inputting an official gene symbol and selecting desired criteria, the survival analysis results can be graphically presented by the Kaplan‐Meier (KM) plot with hazard ratio (HR) and log‐rank p value. As a proof‐of‐concept demonstration, the prognostic value of 23 previously reported survival associated biomarkers, such as transcription factors FOXP1 and BCL2, was evaluated in OSdlbcl and found to be significantly associated with survival as reported (HR = 1.73, P < .01; HR = 1.47, P = .03, respectively). In conclusion, OSdlbcl is a new web server that integrates public gene expression, gene mutation data, and clinical follow‐up information to provide prognosis evaluations for biomarker development for DLBCL. The OSdlbcl web server is available at https://bioinfo.henu.edu.cn/DLBCL/DLBCLList.jsp.