Prognostic and predictive biomarker developments in multiple myeloma

Prognostic signature based on mitochondria- and angiogenesis-related genes associated with immune microenvironment of multiple myeloma

INTRODUCTION

Mitochondria and angiogenesis play key roles in multiple myeloma (MM) development, but their interrelated genes affecting MM prognosis are under-studied.

METHODS

We analyzed TCGA_MMRF and GSE4581 datasets to identify four genes - CCNB1, CDC25C, HSP90AA1, and PARP1 - that significantly correlate with MM prognosis, with high expression indicating poor outcomes.

RESULTS

A prognostic signature based on these genes stratified patients into high- and low-risk groups, with the latter showing better survival. The signature was validated as an independent prognostic factor. Biological function analysis revealed differences in cell cycle processes between risk groups, and immune microenvironment analysis showed distinct immune cell infiltration patterns.

CONCLUSION

This mitochondria- and angiogenesis-related prognostic signature could enhance MM prognosis assessment and offer new therapeutic insights.

Individualized dynamic risk assessment and treatment selection for multiple myeloma

BACKGROUND

Individualized treatment decisions for multiple myeloma (MM) patients require accurate risk stratification that accounts for patient-specific consequences of cytogenetic abnormalities on disease progression.

METHODS

Previously, SYstems Genetic Network AnaLysis (SYGNAL) of multi-omics tumor profiles from 881 MM patients generated a mmSYGNAL network of transcriptional programs underlying disease progression across MM subtypes. Here, through machine learning on activity profiles of mmSYGNAL programs we have generated a unified framework of cytogenetic subtype-specific models for individualized risk classifications and prediction of treatment response.

RESULTS

Testing on 1,367 patients across five independent cohorts demonstrated that the framework of mmSYGNAL risk models significantly outperformed cytogenetics, International Staging System, and multi-gene biomarker panels in predicting PFS at primary diagnosis, pre- and post-transplant and even after multiple relapses, making it useful for individualized risk assessment throughout the disease trajectory. Further, treatment response predictions were significantly concordant with efficacy of 67 drugs in killing myeloma cells from eight relapsed refractory patients. The model also provided new insights into matching MM patients to drugs used in standard of care, at relapse, and in clinical trials.

CONCLUSION

Activities of transcriptional programs offer significantly better prognostic and predictive assessments of treatments across different stages of MM in an individual patient.

Establishing a cryopreserved biobank of living tumor tissues for drug sensitivity testing

The cryopreservation of cancer tissues to generate frozen libraries is a common practice used worldwide for storing patient samples for later applications. However, frozen samples stored by existing methods cannot be used for initiating living cell cultures, such as patient-derived tumor organoids (PDOs), which offer great potential for personalized treatment. To overcome this challenge, we developed a novel procedure for culturing PDOs using frozen live tumor tissues. We show that tumor specimens stored using this technique maintain their viability and can be successfully used to generate organoids even after long-term freezing, with an impressive success rate of 95.2 %. Importantly, we found that the structural features, tumor marker expression, and drug responses of organoids derived from frozen tissues are similar to those derived from fresh tissues. Moreover, organoids derived from frozen tissues can be routinely passaged and frozen, making them ideal for high-throughput drug screening at any time. Notably, cryopreserved tumor tissues can also be utilized in air-liquid interface (ALI) culture. This method allows for preserving the original tumor microenvironment, making it an invaluable resource for conducting tests on antitumor drug responses, including immune checkpoint inhibitors (ICIs). This innovation has the potential to enable the identification of potentially effective drugs for patients and facilitate the development of novel therapeutic drugs. Thus, we have established protocols for the long-term cryopreservation of cancer tissues to maintain their viability and microenvironment, which are useful for personalized therapy.

Prognostic and clinicopathological value of the controlling nutritional status score in patients with multiple myeloma: a meta-analysis

Background

The effect of the controlling nutritional status (CONUT) score on forecasting multiple myeloma (MM) prognosis is previously analyzed, whereas the results remained inconsistent. The present meta-analysis focused on identifying the exact function of CONUT in forecasting MM prognosis.

Methods

Web of Science, PubMed, Embase, CNKI, and Cochrane Library were comprehensively searched between inception and 1 February 2025. The effect of CONUT on forecasting MM overall survival (OS) and progression-free survival (PFS) was determined by computing pooled hazard ratios (HRs) together with 95% confidence intervals (CIs).

Results

There were nine studies with 1,176 patients being recruited into the present work. As indicated by our pooled data, elevated CONUT was related to the dismal OS (HR = 1.87, 95% CI = 1.37-2.54, p < 0.001) of patients with MM. Nonetheless, CONUT was not significantly related to PFS (HR = 1.33, 95% CI = 0.81-2.19, p = 0.254) of MM. Furthermore, higher CONUT score showed a significant relationship to bone marrow plasma cells >30% (OR = 2.30, 95% CI = 1.32-3.99, p = 0.003). On the other hand, CONUT was not markedly correlated with gender (OR = 2.68, 95% CI = 0.81-8.82, p = 0.105), ISS stage (OR = 1.28, 95% CI = 0.94-1.75, p = 0.119), or ECOG PS (OR = 1.30, 95% CI = 0.84-2.01, p = 0.234) of MM.

Conclusion

Collectively, according to our results in this meta-analysis, higher CONUT score is markedly related to dismal OS, but not PFS in patients with MM. CONUT score can be used as a candidate marker used to predict MM prognosis in the clinic in the future.

Raman spectroscopy of dried serum for the detection of rapid noninvasive multiple myeloma

Raman spectroscopy has recently emerged as an attractive focus of research interest in studies of hematological diseases. However, comprehensive Raman spectral analyses of serum samples from patients with multiple myeloma (MM) or other forms of lymphatic neoplastic disease are lacking. In this study, laser Raman spectroscopy and orthogonal partial least-squares discrimination analysis (OPLS-DA) were employed to develop a simple, noninvasive approach to MM diagnosis based on dried serum samples. To that end, systematic OPLS-DA analyses of dried serum from 35 patients with MM and 13 healthy controls were performed, revealing clear differences between these two groups in terms of the resultant serum spectral data. Specifically, significant reductions in the intensities of Raman peaks corresponding to nucleic acids (726, 781, 1579 cm-1), proteins (621, 1603, 1616 cm-1), lipids (1437, 1443, 1446 cm-1) and carotenoids (957, 1160, 1520 cm-1) were observed in MM, together with increases in the intensities of peaks corresponding to carbohydrates (920, 1123 cm-1) and collagen (1345 cm-1). Through combined analyses of serological indices associated with metabolic activity, MM patients were confirmed to exhibit elevated serum glucose levels and decreased levels of high-density lipoprotein cholesterol. These results offer a spectroscopic foundation for the relationships between MM classification and serological testing data, offering new evidence that can guide the early and efficient identification and characterization of this deadly cancer type. This exploratory study thus offers insight into the potential utility of Raman spectroscopy as a tool for the noninvasive detection of specific subtypes of MM.

Multiple Myeloma: Improved Outcomes Resulting from a Rapidly Expanding Number of Therapeutic Options

Multiple myeloma (MM) is a bone-marrow-based cancer of plasma cells. Over the last 2 decades, marked treatment advances have led to improvements in the overall survival (OS) of patients with this disease. Key developments include the use of chemotherapy, immunomodulatory drugs, proteasome inhibitors, and monoclonal antibodies. MM remains incurable, with outcomes influenced by many factors, including age, sex, genetics, and treatment response. This review summarizes recent studies regarding monitoring and treatment of MM, emphasizing the efficacy of new therapies, the impact of maintenance treatments, and approaches for managing relapsed or refractory MM. The role of specific drug classes used to treat MM, including immunomodulatory drugs, proteasome inhibitors, monoclonal antibodies, and newer treatments such as chimeric antigen receptor T-cell therapies and bispecific antibodies are discussed. Combination therapies have significantly improved outcomes. Maintenance therapies, particularly with lenalidomide, have been effective in extending OS but lead to an increased risk of secondary cancers. Venetoclax, selinexor, and ruxolitinib have shown potential as new therapeutic options for patients with relapsed or refractory MM. Immune-based treatments, such as chimeric antigen receptor T-cell therapy and bispecific antibodies, mark a major advancement for heavily pretreated patients, although challenges remain related to cost, availability, and side effects. The treatment landscape for patients with MM has seen significant progress, with current therapies providing a longer OS and better quality of life. Future research should focus on optimizing these strategies, personalizing therapies, and exploring new therapeutic targets.

GDF15-mediated enhancement of the Warburg effect sustains multiple myeloma growth via TGFβ signaling pathway

The Warburg effect, characterized by the shift toward aerobic glycolysis, is closely associated with the onset and advancement of tumors, including multiple myeloma (MM). Nevertheless, the specific regulatory mechanisms of glycolysis in MM and its functional role remain unclear. In this study, we identified that growth differentiation factor 15 (GDF15) is a glycolytic regulator, and GDF15 is highly expressed in MM cells and patient samples. Through gain-of-function and loss-of-function experiments, we demonstrated that GDF15 promotes MM cell proliferation and inhibits apoptosis. Moreover, GDF15 enhances Warburg-like metabolism in MM cells, as evidenced by increased glucose uptake, lactate production, and extracellular acidification rate, while reducing oxidative phosphorylation. Importantly, the tumor-promoting effects of GDF15 in MM cells are fermentation-dependent. Mechanistically, GDF15 was found to promote the expression of key glycolytic genes, particularly the glucose transporter GLUT1, through the activation of the TGFβ signaling pathway. Pharmacological inhibition of the TGFβ signaling pathway effectively abrogated the oncogenic activities of GDF15 in MM cells, including cell proliferation, apoptosis, and fermentation. In vivo experiments using a subcutaneous xenotransplanted tumor model confirmed that GDF15 knockdown led to a significant reduction in tumor growth, while GDF15 overexpression promoted tumor growth. Overall, our study provides insights into the molecular mechanisms underlying MM pathogenesis and highlights the potential of targeting GDF15-TGFβ signaling -glycolysis axis as a therapeutic approach for future therapeutic interventions in MM.

The Functional Transcriptomic Landscape Informs Therapeutic Strategies in Multiple Myeloma

Several therapeutic agents have been approved for treating multiple myeloma, a cancer of bone marrow-resident plasma cells. Predictive biomarkers for drug response could help guide clinical strategies to optimize outcomes. In this study, we present an integrated functional genomic analysis of tumor samples from patients multiple myeloma that were assessed for their ex vivo drug sensitivity to 37 drugs, clinical variables, cytogenetics, mutational profiles, and transcriptomes. This analysis revealed a multiple myeloma transcriptomic topology that generates "footprints" in association with ex vivo drug sensitivity that have both predictive and mechanistic applications. Validation of the transcriptomic footprints for the anti-CD38 mAb daratumumab (DARA) and the nuclear export inhibitor selinexor (SELI) demonstrated that these footprints can accurately classify clinical responses. The analysis further revealed that DARA and SELI have anticorrelated mechanisms of resistance, and treatment with a SELI-based regimen immediately after a DARA-containing regimen was associated with improved survival in three independent clinical trials, supporting an evolutionary-based strategy involving sequential therapy. These findings suggest that this unique repository and computational framework can be leveraged to inform underlying biology and to identify therapeutic strategies to improve treatment of multiple myeloma. Significance: Functional genomic analysis of primary multiple myeloma samples elucidated predictive biomarkers for drugs and molecular pathways mediating therapeutic response, which revealed a rationale for sequential therapy to maximize patient outcomes.

Association between BCL2 interacting protein 3 like (BNIP3L) genetic polymorphisms and the risk of multiple myeloma in China

BACKGROUND

The BCL2 interacting protein 3-like (BNIP3L) protein is involved in multiple myeloma (MM) development and progression. This study aims to explore the connection between BNIP3L single-nucleotide polymorphisms (SNPs) and MM.

METHODS

SNaPshot was used to examine six SNP loci of the BNIP3L gene in enrolled subjects. The relationship between these loci and MM susceptibility and prognosis was explored. Survival analysis was used to evaluate the impact of different factors on patient survival.

RESULTS

The rs2874670 AA genotype and A allele were associated with increased MM risk (P < 0.05). The CCACAC haplotype had a higher frequency in MM, while CCGCAC had a higher frequency in normal patients (all P < 0.05). Patients with R-ISS stage I and II had higher survival rates than those with stage III (P < 0.05). Patients, who received chemotherapy followed by autologous stem cell transplantation, had longer survival time than those who only received chemotherapy (P < 0.05). Low levels of LDH and β2-MG were associated with better survival rates (P < 0.05). Cox regression identified that LDH levels, β2-MG levels, and R-ISS staging were the risk factors for the death of MM. Mann-Whitney U test found a significant difference in survival time between MM patients with different BNIP3L rs2874670 genotypes after BD chemotherapy (P < 0.05).

CONCLUSION

To our knowledge, this is the first study to find that BNIP3L rs2874670 could increase MM susceptibility in China. Different BNIP3L rs2874670 genotypes may affect the prognosis of MM patients receiving BD chemotherapy.

5-Hydroxymethylcytosine Profiles of cfDNA in Urine as Diagnostic, Differential Diagnosis and Prognostic Markers for Multiple Myeloma

BACKGROUND

An effective urine-based method for the diagnosis, differential diagnosis and prognosis of multiple myeloma (MM) has not yet been developed. Urine cell-free DNA (cfDNA) carrying cancer-specific genetic and epigenetic aberrations may enable a noninvasive "liquid biopsy" for diagnosis and monitoring of cancer.

METHODS

We first identified MM-specific hydroxymethylcytosine signatures by comparing 64 MM patients, 23 amyloidosis (AM) patients and 59 healthy cohort. Then, we applied a machine learning algorithm to develop diagnostic and differential diagnosis model. Finally, the prognosis of MM patients was predicted based on their survival time at the last follow-up.

RESULTS

We identified 11 5hmC markers using logistic regression algorithm could effectively diagnosis MM (AUC = 0.902), and achieved 85.00% specificity and 85.71% sensitivity. These 11 markers could also effectively differential diagnosis MM (AUC = 0.805) with 88.89% specificity and 73.08% sensitivity. In addition, the prognostic prediction model also effectively predicted the prognosis of patients with MM (p < 0.01), of which 4 differential markers (RAPGEF2, BRD1, TET2, TRAF3IP2) could independently predict the prognosis of MM.

CONCLUSIONS

Together, our findings showed the value of urine cfDNA hydroxymethylcytosine markers in the diagnosis, differential diagnosis and prognosis of MM. Meantime, our study provides a promising and completely non-invasive method for the diagnosis, differential diagnosis and prognosis prediction of MM.

Role of BACH1 in multiple myeloma

OBJECTIVE

Examine Bach1 protein expression in bone marrow biopsy specimens obtained from newly diagnosed multiple myeloma (NDMM) and iron deficiency anemia (IDA) patients. Conduct a thorough analysis to explore the potential connection between Bach1 and the onset as well as treatment response of NDMM.

METHODS

This study investigated Bach1 expression in bone marrow biopsy tissues from NDMM and IDA patients. Immunohistochemical staining and Image-pro Plus software were utilized for quantitatively obtaining the expression level of Bach1 protein. Arrange Bach1 expression levels from high to low, and use its median expression level as the threshold. Samples with Bach1 expression level above the median are categorized as the high-expression group, while those below the median are categorized as the low-expression group. Under this grouping, a detailed discussion was conducted to explore relationship of the Bach1 expression level with the patients' gender, ISS stage, and survival rate based on the Bortezomib (Btz) therapy.

RESULTS

Our experiment indicates that the expression level of Bach1 in NDMM patients is significantly higher than in IDA patients. Furthermore, we discovered that patients in the high-expression group exhibit better prognosis compared to those in the low-expression group after Btz-treatment. Bioinformatics analysis further confirms this conclusion.

CONCLUSION

By categorizing Bach1 expression level as high and low, our study offers a unique perspective on understanding the relationship between Bach1 and NDMM.

Epigallocatechin gallate induces apoptosis in multiple myeloma cells through endoplasmic reticulum stress induction and cytoskeletal disruption

Multiple myeloma (MM) is an incurable plasma cell malignancy that has prompted investigations into new potential therapeutic avenues. Epigallocatechin-3-gallate (EGCG), a major component of green tea, confers antioxidant, anti-inflammatory, and anti-tumor properties. Previous studies have shown that EGCG inhibits proliferation and induces apoptosis of multiple myeloma cells, however its underlying molecular mechanisms are largely unknown. In this study, we accordingly sought to examine the therapeutic effects and underlying mechanisms of EGCG on MM. Initially, using CCK8 (Cell Counting Kit-8) assays and Annexin V-FITC/PI staining, we demonstrated that EGCG dose-dependently reduced cell viability and induced apoptosis in the MM cell lines MM.1S and RPMI 8226. Subsequently, mRNA sequencing of EGCG-treated MM.1S cells revealed a significant upregulation of genes associated with endoplasmic reticulum stress (ERS), including P-eIF2α (phosphorylation-eukaryotic translation initiation factor 2 alpha), ATF4 (activating transcription factor 4), CHOP (C/EBP homologous protein, DDIT3), and PUMA (p53 upregulated modulator of apoptosis, BBC3), which were confirmed at the protein level by western blotting. Furthermore, treatment with the eIF2α inhibitor ISRIB reduced the rates of EGCG-induced apoptosis and promoted increases in the protein expression of all four ER stress-related molecules in MM cells. Additionally, mRNA-seq data revealed a downregulation of α-Tubulin 1b (TUBA1B) expression in EGCG-treated MM cells, which was confirmed by western blotting and immunofluorescence analyses. Moreover, we utilized a mouse model to show that EGCG inhibited myeloma tumor growth, which was inhibited by ISRIB. In summary, the findings of this novel study indicated that EGCG promotes apoptosis of MM cells, both via activation of the ER stress pathway and disruption of cytoskeletal integrity. These findings highlight the multi-faceted anti-tumor effects of EGCG and its potential clinical application in MM treatment.

Characterization of driver mutations identifies gene signatures predictive of prognosis and treatment sensitivity in multiple myeloma

In multiple myeloma (MM), while frequent mutations in driver genes are crucial for disease progression, they traditionally offer limited insights into patient prognosis. This study aims to enhance prognostic understanding in MM by analyzing pathway dysregulations in key cancer driver genes, thereby identifying actionable gene signatures. We conducted a detailed quantification of mutations and pathway dysregulations in 10 frequently mutated cancer driver genes in MM to characterize their comprehensive mutational impacts on the whole transcriptome. This was followed by a systematic survival analysis to identify significant gene signatures with enhanced prognostic value. Our systematic analysis highlighted 2 significant signatures, TP53 and LRP1B, which notably outperformed mere mutation status in prognostic predictions. These gene signatures remained prognostically valuable even when accounting for clinical factors, including cytogenetic abnormalities, the International Staging System (ISS), and its revised version (R-ISS). The LRP1B signature effectively distinguished high-risk patients within low/intermediate-risk categories and correlated with significant changes in the tumor immune microenvironment. Additionally, the LRP1B signature showed a strong association with proteasome inhibitor pathways, notably predicting patient responses to bortezomib and the progression from monoclonal gammopathy of unknown significance to MM. Through a rigorous analysis, this study underscores the potential of specific gene signatures in revolutionizing the prognostic landscape of MM, providing novel clinical insights that could influence future translational oncology research.

Automated segmentation and source prediction of bone tumors using ConvNeXtv2 Fusion based Mask R-CNN to identify lung cancer metastasis

Lung cancer, which is a leading cause of cancer-related deaths worldwide, frequently metastasizes to the bones, significantly diminishing patients' quality of life and complicating treatment strategies. This study aims to develop an advanced 3D Mask R-CNN model, enhanced with the ConvNeXt-V2 backbone, for the automatic segmentation of bone tumors and identification of lung cancer metastasis to support personalized treatment planning. Data were collected from two hospitals: Center A (106 patients) and Center B (265 patients). The data from Center B were used for training, while Center A's dataset served as an independent external validation set. High-resolution CT scans with 1 mm slice thickness and no inter-slice gaps were utilized, and the regions of interest (ROIs) were manually segmented and validated by two experienced radiologists. The 3D Mask R-CNN model achieved a Dice Similarity Coefficient (DSC) of 0.856, a sensitivity of 0.921, and a specificity of 0.961 on the training set. On the test set, it achieved a DSC of 0.849, a sensitivity of 0.911, and a specificity of 0.931. For the classification task, the model attained an AUC of 0.865, an accuracy of 0.866, a sensitivity of 0.875, and a specificity of 0.835 on the training set, while achieving an AUC of 0.842, an accuracy of 0.836, a sensitivity of 0.847, and a specificity of 0.819 on the test set. These results highlight the model's potential in improving the accuracy of bone tumor segmentation and lung cancer metastasis detection, paving the way for enhanced diagnostic workflows and personalized treatment strategies in clinical oncology.

Single-cell sequencing analysis of multiple myeloma heterogeneity and identification of new theranostic targets

Multiple myeloma (MM) is a heterogeneous and incurable tumor characterized by the malignant proliferation of plasma cells. It is necessary to clarify the heterogeneity of MM and identify new theranostic targets. We constructed a single-cell transcriptome profile of 48,293 bone marrow cells from MM patients and health donors (HDs) annotated with 7 continuous B lymphocyte lineages. Through CellChat, we discovered that the communication among B lymphocyte lineages between MM and HDs was disrupted, and unique signaling molecules were observed. Through pseudotime analysis, it was found that the differences between MM and HDs were mainly reflected in plasma cells. These differences are primarily related to various biological processes involving mitochondria. Then, we identified the key subpopulation associated with the malignant proliferation of plasma cells. This group of cells exhibited strong proliferation ability, high CNV scores, high expression of frequently mutated genes, and strong glucose metabolic activity. Furthermore, we demonstrated the therapeutic potential of WNK1 as a target. Our study provides new insights into the development of B cells and the heterogeneity of plasma cells in MM and suggests that WNK1 is a potential therapeutic target for MM.

Leveraging diverse cellular stress patterns for predicting clinical outcomes and therapeutic responses in patients with multiple myeloma

Tumour microenvironment harbours diverse stress factors that affect the progression of multiple myeloma (MM), and the survival of MM cells heavily relies on crucial stress pathways. However, the impact of cellular stress on clinical prognosis of MM patients remains largely unknown. This study aimed to provide a cell stress-related model for survival and treatment prediction in MM. We incorporated five cell stress patterns including heat, oxidative, hypoxic, genotoxic, and endoplasmic reticulum stresses, to develop a comprehensive cellular stress index (CSI). Then we systematically analysed the effects of CSI on survival outcomes, clinical characteristics, immune microenvironment, and treatment sensitivity in MM. Molecular subtypes were identified using consensus clustering analysis based on CSI gene profiles. Moreover, a prognostic nomogram incorporating CSI was constructed and validated to aid in personalised risk stratification. After screening from five stress models, a CSI signature containing nine genes was established by Cox regression analyses and validated in three independent datasets. High CSI was significantly correlated with cell division pathways and poor clinical prognosis. Two distinct MM subtypes were identified through unsupervised clustering, showing significant differences in prognostic outcomes. The nomogram that combined CSI with clinical features exhibited good predictive performances in both training and validation cohorts. Meanwhile, CSI was closely associated with immune cell infiltration level and immune checkpoint gene expression. Therapeutically, patients with high CSI were more sensitive to bortezomib and antimitotic agents, while their response to immunotherapy was less favourable. Furthermore, in vitro experiments using cell lines and clinical samples verified the expression and function of key genes from CSI. The CSI signature could be a clinically applicable indicator of disease evaluation, demonstrating potential in predicting prognosis and guiding therapy for patients with MM.

Construct prognostic models of multiple myeloma with pathway information incorporated

Multiple myeloma (MM) is a hematological disease exhibiting aberrant clonal expansion of cancerous plasma cells in the bone marrow. The effects of treatments for MM vary between patients, highlighting the importance of developing prognostic models for informed therapeutic decision-making. Most previous models were constructed at the gene level, ignoring the fact that the dysfunction of the pathway is closely associated with disease development and progression. The present study considered two strategies that construct predictive models by taking pathway information into consideration: pathway score method and group lasso using pathway information. The former simply converted gene expression to sample-wise pathway scores for model fitting. We considered three methods for pathway score calculation (ssGSEA, GSVA, and z-scores) and 14 data sources providing pathway information. We implemented these methods in microarray data for MM (GSE136324) and obtained a candidate model with the best prediction performance in interval validation. The candidate model is further compared with the gene-based model and previously published models in two external data. We also investigated the effects of missing values on prediction. The results showed that group lasso incorporating Vax pathway information (Vax(grp)) was more competitive in prediction than the gene model in both internal and external validation. Immune information, including VAX pathways, seemed to be more predictive for MM. Vax(grp) also outperformed the previously published models. Moreover, the new model was more resistant to missing values, and the presence of missing values (<5%) would not evidently deteriorate its prediction accuracy using our missing data imputation method. In a nutshell, pathway-based models (using group lasso) were competitive alternatives to gene-based models for MM. These models were documented in an R package (https://github.com/ShuoStat/MMMs), where a missing data imputation method was also integrated to facilitate future validation.

Focal adhesion kinase as a new player in the biology of onco-hematological diseases: the starting evidence

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase mainly found in the focal adhesion regions of the plasma membrane and it has a crucial role in migration and the remodeling of cellular morphology. FAK is also linked to several aspects of cancer biology, from cytokine production to angiogenesis, drug resistance, invasion, and metastasis, as well as epithelial-to-mesenchymal transition. The gene locus of FAK is frequently amplified in several human tumors, thus causing FAK overexpression in several cancers. Furthermore, FAK can influence extracellular matrix production and exosome secretion through cancer-associated fibroblasts, thus it has an important role in tumor microenvironment regulation. Although the role of FAK in solid tumors is well known, its importance in onco-hematological diseases remains poorly explored. This review collects studies related to FAK significance in onco-hematological diseases and their microenvironments. Overall, the importance of FAK in blood tumors is increasingly evident, but further research is required to confirm it as a new therapeutic target in hematological contexts.

PDCD4 interacting with PIK3CB and CTSZ promotes the apoptosis of multiple myeloma cells

The role of programmed cell death 4 (PDCD4) in multiple myeloma (MM) development remains unknown. Here, we investigated its role and action mechanism in MM. Bioinformatic analysis indicated that patients with MM and high PDCD4 expression had higher overall survival than those with low PDCD4 expression. PDCD4 expression promoted MM cell apoptosis and inhibited their viability in vitro and tumor growth in vivo. RNA-binding protein immunoprecipitation sequencing analysis showed that PDCD4 is bound to the 5' UTR of the apoptosis-related genes PIK3CB, Cathepsin Z (CTSZ), and X-chromosome-linked apoptosis inhibitor (XIAP). PDCD4 knockdown reduced the cell apoptosis rate, which was rescued by adding PIK3CB, CTSZ, or XIAP inhibitors. Dual luciferase reporter assays confirmed the internal ribosome entry site (IRES) activity of the 5' UTRs of PIK3CB and CTSZ. An RNA pull-down assay confirmed binding of the 5' UTR of PIK3CB and CTSZ to PDCD4, identifying the specific binding fragments. PDCD4 is expected to promote MM cell apoptosis by binding to the IRES domain in the 5' UTR of PIK3CB and CTSZ and inhibiting their translation. Our findings suggest that PDCD4 plays an important role in MM development by regulating the expression of PIK3CB, CTSZ, and XIAP, and highlight new potential molecular targets for MM treatment.

Analysis of the role of dihydromyricetin derived from vine tea (Ampelopsis grossedentata) on multiple myeloma by activating STAT1/RIG-I axis

Multiple myeloma (MM) is a plasma cell malignancy and remains incurable as it lacks effective curative approaches; thus, novel therapeutic strategies are desperately needed. The study aimed to explore the therapeutic role of dihydromyricetin (DHM) in MM and explore its mechanisms. Human MM and normal plasma samples, human MM cell lines, and normal plasma cells were used for in vitro experiments. Cell counting kit-8 (CCK-8), flow cytometry, and trans-well assays were performed for the assessment of cell viability, apoptosis, migration, and invasion, respectively. Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to assess the mRNA expression of signal transducer and activator of transcription 1 (STAT1) and retinoic acid-inducible gene I (RIG-I). Western blotting was employed to assess E-cadherin, N-cadherin, signal transducer, STAT1, p-STAT1, and RIG-I protein expression. A tumor xenograft model was used for in vivo experiments. Here, dihydromyricetin (DHM) dose-dependently restrained viability, apoptosis, migration, and invasion, and facilitated apoptosis of U266 cells. After DHM treatment, the E-cadherin level was increased and the N-cadherin level was decreased in U266 and RPMI-8226 cells, suggesting the inhibitory effects of DHM on epithelial-mesenchymal transition (EMT) in MM. Besides, the levels of p-STAT1/STAT1 and RIG-I were down-regulated in MM. However, the STAT1 inhibitor fludarabine undid the suppressive effect of DMH on the malignant characteristics of U266 cells. Also, DHM inhibited MM tumor growth and EMT, and activated STAT1/RIG-I pathway in vivo. Collectively, this study first revealed that DHM can restrain EMT and tumor growth in MM by activating STAT1/RIG-I signaling, which provides a novel drug for the treatment of MM.

Label-free and label-based electrochemical detection of disease biomarker proteins

Introduction

Biosensors, analytical devices integrating biological sensing elements with physicochemical transducers, have gained prominence as rapid and convenient tools for monitoring human health status using biochemical analytes. Due to its cost-effectiveness, simplicity, portability, and user-friendliness, electrochemical detection has emerged as a widely adopted method in biosensor applications. Crucially, biosensors enable early disease diagnosis by detecting protein biomarkers associated with various conditions. These biomarkers offer an objective indication of medical conditions that can be accurately observed from outside the patient.

Method

This review comprehensively documents both label-free and labelled detection methods in electrochemical biosensor techniques. Label-free detection mechanisms elicit response signals upon analyte molecule binding to the sensor surface, while labelled detection employs molecular labels such as enzymes, nanoparticles, and fluorescent tags.

Conclusion

The selection between label-free and labelled detection methods depends on various factors, including the biomolecular compound used, analyte type and biological binding site, biosensor design, sample volume, operational costs, analysis time, and desired detection limit. Focusing on the past six years, this review highlights the application of label-free and labelled electrochemical biosensors for detecting protein biomarkers of diseases.

Enhancing multiple myeloma staging: a novel cell death risk model approach

The prognostication of survival trajectories in multiple myeloma (MM) patients presents a substantial clinical challenge. Leveraging transcriptomic and clinical profiles from an expansive cohort of 2,088 MM patients, sourced from the Gene Expression Omnibus and The Cancer Genome Atlas repositories, we applied a sophisticated nested lasso regression technique to construct a prognostic model predicated on 28 gene pairings intrinsic to cell death pathways, thereby deriving a quantifiable risk stratification metric. Employing a threshold of 0.15, we dichotomized the MM samples into discrete high-risk and low-risk categories. Notably, the delineated high-risk cohort exhibited a statistically significant diminution in survival duration, a finding which consistently replicated across both training and external validation datasets. The prognostic acumen of our cell death signature was further corroborated by TIME ROC analyses, with the model demonstrating robust performance, evidenced by AUC metrics consistently surpassing the 0.6 benchmark across the evaluated arrays. Further analytical rigor was applied through multivariate COX regression analyses, which ratified the cell death risk model as an independent prognostic determinant. In an innovative stratagem, we amalgamated this risk stratification with the established International Staging System (ISS), culminating in the genesis of a novel, refined ISS categorization. This tripartite classification system was subjected to comparative analysis against extant prognostic models, whereupon it manifested superior predictive precision, as reflected by an elevated C-index. In summation, our endeavors have yielded a clinically viable gene pairing model predicated on cellular mortality, which, when synthesized with the ISS, engenders an augmented prognostic tool that exhibits pronounced predictive prowess in the context of multiple myeloma.

Construction of immune-related gene pairs signature to predict the overall survival of multiple myeloma patients based on whole bone marrow gene expression profiling

Multiple myeloma (MM) is a plasma cell dyscrasia that is characterized by the uncontrolled proliferation of malignant PCs in the bone marrow. Due to immunotherapy, attention has returned to the immune system in MM, and it appears necessary to identify biomarkers in this area. In this study, we created a prognostic model for MM using immune-related gene pairs (IRGPs), with the advantage that it is not affected by technical bias. After retrieving microarray data of MM patients, bioinformatics analyses like COX regression and least absolute shrinkage and selection operator (LASSO) were used to construct the signature. Then its prognostic value is assessed via time-dependent receiver operating characteristic (ROC) and the Kaplan-Meier (KM) analysis. We also used XCELL to examine the status of immune cell infiltration among MM patients. 6-IRGP signatures were developed and proved to predict MM prognosis with a P-value of 0.001 in the KM analysis. Moreover, the risk score was significantly associated with clinicopathological characteristics and was an independent prognostic factor. Of note, the combination of age and β2-microglobulin with risk score could improve the accuracy of determining patients' prognosis with the values of the area under the curve (AUC) of 0.73 in 5 years ROC curves. Our model was also associated with the distribution of immune cells. This novel signature, either alone or in combination with age and β2-microglobulin, showed a good prognostic predictive value and might be used to guide the management of MM patients in clinical practice.

Identification of COQ2 as a regulator of proliferation and lipid peroxidation through genome-scale CRISPR-Cas9 screening in myeloma cells

Multiple myeloma (MM) is the second most common malignant haematological disease with a poor prognosis. The limit therapeutic progress has been made in MM patients with cancer relapse, necessitating deeper research into the molecular mechanisms underlying its occurrence and development. A genome-wide CRISPR-Cas9 loss-of-function screening was utilized to identify potential therapeutic targets in our research. We revealed that COQ2 plays a crucial role in regulating MM cell proliferation and lipid peroxidation (LPO). Knockout of COQ2 inhibited cell proliferation, induced cell cycle arrest and reduced tumour growth in vivo. Mechanistically, COQ2 promoted the activation of the MEK/ERK cascade, which in turn stabilized and activated MYC protein. Moreover, we found that COQ2-deficient MM cells increased sensitivity to the LPO activator, RSL3. Using an inhibitor targeting COQ2 by 4-CBA enhanced the sensitivity to RSL3 in primary CD138+ myeloma cells and in a xenograft mouse model. Nevertheless, co-treatment of 4-CBA and RSL3 induced cell death in bortezomib-resistant MM cells. Together, our findings suggest that COQ2 promotes cell proliferation and tumour growth through the activation of the MEK/ERK/MYC axis and targeting COQ2 could enhance the sensitivity to ferroptosis in MM cells, which may be a promising therapeutic strategy for the treatment of MM patients.

[Detection of cytogenetic abnormalities in multiple myeloma by using optical genome mapping]

Multiple myeloma (MM) is a plasma cell neoplasm characterized by numerous chromosomal number and structural abnormalities, which are of great significance for risk stratification and response evaluation of MM patients. Optical genome mapping (OGM) is a novel technology that has the potential to resolve many of the issues and limitations associated with traditional cytogenetic methods. To date, the clinical utility of OGM has been validated in the fields of cancer, reproduction, and embryonic dysplasia, et al. In this study, we compared OGM to traditional techniques for the first time in five newly diagnosed MM patients, and evaluated the potential of OGM for detecting cytogenetic aberrations and its clinical application value in MM.

Predictors and Impact of Timing of Disease Progression Following Primary Therapy in Multiple Myeloma

In multiple myeloma (MM) significant variation in progression-free survival (PFS) and overall survival (OS) is observed. We examined the outcomes of 1557 MM patients stratified into short (<2 years), medium (between 2 and 5 years) and long (>5 years) PFS. Short PFS occurred in 758 patients (48.7%), medium in 561 patients (36.2%), and long in 238 patients (15.3%). Median post-progression PFS was 9.2 months (95% CI: 8.1-11.0) in the short PFS and 33.1 months (95% CI: 29.0-42.1; P < .001) in the long PFS group. Median post-progression OS was 26.6 months (95% CI: 23.9-29.8) in the short PFS and 87.8 months (95% CI: 71.3- NR; P < .001) in the long PFS. Worse survival in the short PFS was irrespective of high risk (HR) fluorescence in situ hybridization (FISH) features, defined as deletion 17p and/or translocation t(4;14), t(14;16), t(14;20). In a multivariable analysis short PFS was associated with HR FISH, extramedullary plasmacytoma, plasma cell labeling index ≥2% at diagnosis, nonimmunoglobulin G isotype, treatment without autologous stem cell transplantation and achieving less than very good partial remission. In conclusion, the duration of the PFS significantly influences survival, regardless of HR cytogenetic features. Therefore, it should be considered an important parameter for risk stratification in patients experiencing a relapse.

Curcumin in treatment of hematological cancers: Promises and challenges

Hematological cancers include leukemia, myeloma and lymphoma and up to 178.000 new cases are diagnosed with these tumors each year. Different kinds of treatment including radiotherapy, chemotherapy, immunotherapy and stem cell transplantation have been employed in the therapy of hematological cancers. However, they are still causing death among patients. On the other hand, curcumin as an anti-cancer agent for the suppression of human cancers has been introduced. The treatment of hematological cancers using curcumin has been followed. Curcumin diminishes viability and survival rate of leukemia, myeloma and lymphoma cells. Curcumin stimulates apoptosis and G2/M arrest to impair progression of tumor. Curcumin decreases levels of matrix metalloproteinases in suppressing cancer metastasis. A number of downstream targets including VEGF, Akt and STAT3 undergo suppression by curcumin in suppressing progression of hematological cancers. Curcumin stimulates DNA damage and reduces resistance of cancer cells to irradiation. Furthermore, curcumin causes drug sensitivity of hematological tumors, especially myeloma. For targeted delivery of curcumin and improving its pharmacokinetic and anti-cancer features, nanostructures containing curcumin and other anti-cancer agents have been developed.

Cuproptosis-Related Gene Signature Contributes to Prognostic Prediction and Immunosuppression in Multiple Myeloma

Cuproptosis is a type of programmed cell death triggered by accumulation of intracellular copper which was considered closely related to tumor progression. The study of cuproptosis in multiple myeloma (MM) is however limited. To determine the prognostic significance of cuproptosis-related gene signature in MM, we interrogated gene expression and overall survival with other available clinical variables from public datasets. Four cuproptosis-related genes were included to establish a prognostic survival model by least absolute shrinkage and selection operator (LASSO) Cox regression analysis, which showed a good performance on prognosis prediction in both training and validation cohorts. Patients with higher cuproptosis-related risk score (CRRS) exhibited worse prognosis compared with lower risk score. Survival prediction capacity and clinical benefit were elevated after integrating CRRS to existing prognostic stratification system (International Staging System, ISS or Revised International Staging System, RISS) both on 3-year and 5-year survival. Based on CRRS groups, functional enrichment analysis and immune infiltration in bone marrow microenvironment revealed correlation between CRRS and immunosuppression. In conclusion, our study found that cuproptosis-related gene signature is an independent poor prognostic factor and functions negatively on immune microenvironment, which provides another perspective on prognosis assessment and immunotherapy strategy in MM.

DEPDC1B enhances malignant phenotypes of multiple myeloma through upregulating CCNB1 and inhibiting p53 signaling pathway

The identification and investigation of key molecules involved in the pathogenesis of multiple myeloma (MM) hold paramount clinical significance. This study primarily focuses on elucidating the role of DEPDC1B within the context of MM. Our findings robustly affirm the abundant expression of DEPDC1B in MM tissues and cell lines. Notably, DEPDC1B depletion exerted inhibitory effects on MM cell proliferation and migration while concurrently facilitating apoptosis and G2 cell cycle arrest. These outcomes stand in stark contrast to the consequences of DEPDC1B overexpression. Furthermore, we identified CCNB1 as a putative downstream target, characterized by a co-expression pattern with DEPDC1B, mediating DEPDC1B's regulatory influence on MM. Additionally, our results suggest that DEPDC1B knockdown may activate the p53 pathway, thereby impeding MM progression. To corroborate these in vitro findings, we conducted in vivo experiments that further validate the regulatory role of DEPDC1B in MM and its interaction with CCNB1 and the p53 pathway. Collectively, our research underscores DEPDC1B as a potent promoter in the development of MM, representing a promising therapeutic target for MM treatment. This discovery bears significant implications for future investigations in this field.

An overview of multiple myeloma: A monoclonal plasma cell malignancy's diagnosis, management, and treatment modalities

Multiple Myeloma (MM) is a plasma cell cancer with high mortality and morbidity rates. Its incidence rate has increased by 143% since 1975. Adipokines, cytokines, chemokines, and genetic variations influence the development and progression of MM. Chromosomal translocations cause mutations associated with MM. The pathogenesis of MM is complicated by novel issues like miRNAs, RANKL, Wnt/DKK1, Wnt, and OPG. Conventional diagnosis methods include bone marrow biopsy, sPEP or uPEP, sIFE and uIFE, and sFLC assay, along with advanced techniques such as FISH, SNPA, and gene expression technologies. A novel therapeutic strategy has been developed recently. Chemotherapy, hematopoietic stem cell transplantation, and a variety of drug classes in combination are used to treat patients with high-risk diseases. Alkylating agents, PIs, and IMiDs have all been developed as effective treatment options for MM in recent years. This review overviews the current recommendations for managing MGUS, SMM, MM, SP and NSMM and discusses practices in diagnosing and treating MM.

The quantification of circular RNA 0007841 during induction therapy helps estimate the response and survival benefits to bortezomib-based regimen in multiple myeloma

OBJECTIVE

Circular RNA_0007841 (Circ_0007841) facilitates multiple myeloma (MM) progression and resistance of the bortezomib by experimental studies, while its clinical implication in MM patients is still unclear. This study intended to evaluate the longitudinal change and prognostic role of circ_0007841 expression in MM patients receiving bortezomib-based induction therapy.

METHODS

In this prospective study, bone marrow plasma cell (BMPC) samples were gained from 97 MM patients at diagnosis and after bortezomib-based induction therapy, and from 30 healthy controls (HCs) proposing BM donation. Then, circ_0007841 expression in BMPC samples was measured by reverse transcription-quantitative polymerase chain reaction. Additionally, MM patients were followed up for a median of 29.4 months.

RESULTS

Circ_0007841 expression was increased in MM patients compared to HCs (P < 0.001), but it was decreased after bortezomib-based induction therapy in MM patients (P < 0.001). Moreover, circ_0007841 expression at diagnosis was associated with the presence of t (4; 14) (P = 0.034), while its expression after bortezomib-based induction therapy was linked with higher revised international staging system stage (P = 0.025) in MM patients. Interestingly, circ_0007841 expression after bortezomib-based induction therapy was lower in MM patients who achieved complete remissions (P = 0.001) and overall responses (P = 0.002) compared to those who did not. Prognostically, circ_0007841 expression after bortezomib-based induction therapy (over the median vs. below the median) independently predicted shorter progression-free survival (hazard ratio (HR): 2.497, P = 0.002) and overall survival (HR: 3.107, P = 0.008) in MM patients.

CONCLUSION

Circ_0007841 quantification during induction therapy may reflect the response and survival benefits to bortezomib-based regimen in MM patients.

TheMarker: a comprehensive database of therapeutic biomarkers

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

New diagnostic strategy for multiple myeloma: A review

Multiple myeloma (MM) is the second most prevalent hematological malignancy and is distinguished by the aberrant proliferation of monoclonal plasma cells inside the bone marrow and production of M-protein. This condition frequently results in bone deterioration, acute kidney damage, anemia, and hypercalcemia. However, the clinical manifestations and accompanying symptoms of MM vary and may change as the condition evolves. Therefore, diagnosis of MM is difficult. At present, the confirmation of MM diagnosis necessitates the use of bone marrow biopsy, a procedure that is both invasive and challenging for assessing dynamic alterations in the disease. The integration of laboratory testing technologies with imaging technology has the potential to enhance the diagnostic effectiveness and provide a thorough evaluation of disease progression and prognosis in patients with MM. All the examination methods have advantages and disadvantages. Therefore, diagnosis is determined by the application of clinical characteristics, serological tests, and imaging investigations.

HAPLN1 matrikine: a bone marrow homing factor linked to poor outcomes in patients with MM

The bone marrow (BM) microenvironment is critical for dissemination, growth, and survival of multiple myeloma (MM) cells. Homing of myeloma cells to the BM niche is a crucial step in MM dissemination, but the mechanisms involved are incompletely understood. In particular, any role of matrikines, neofunctional peptides derived from extracellular matrix proteins, remains unknown. Here, we report that a matrikine derived from hyaluronan and proteoglycan link protein 1 (HAPLN1) induces MM cell adhesion to the BM stromal components, such as fibronectin, endothelial cells, and stromal cells and, furthermore, induces their chemotactic and chemokinetic migration. In a mouse xenograft model, we show that MM cells preferentially home to HAPLN1 matrikine-conditioned BM. The transcription factor STAT1 is activated by HAPLN1 matrikine and is necessary to induce MM cell adhesion, migration, migration-related genes, and BM homing. STAT1 activation is mediated by interferon beta (IFN-β), which is induced by NF-κB after stimulation by HAPLN1 matrikine. Finally, we also provide evidence that higher levels of HAPLN1 in BM samples correlate with poorer progression-free survival of patients with newly diagnosed MM. These data reveal that a matrikine present in the BM microenvironment acts as a chemoattractant, plays an important role in BM homing of MM cells via NF-κB-IFN-β-STAT1 signaling, and may help identify patients with poor outcomes. This study also provides a mechanistic rationale for targeting HAPLN1 matrikine in MM therapy.

Bleeding and Thrombosis in Multiple Myeloma: Platelets as Key Players during Cell Interactions and Potential Use as Drug Delivery Systems

Multiple myeloma (MM) is a hematological malignancy originated in the bone marrow and characterized by unhindered plasma cell proliferation that results in several clinical manifestations. Although the main role of blood platelets lies in hemostasis and thrombosis, platelets also play a pivotal role in a number of other pathological conditions. Platelets are the less-explored components from the tumor microenvironment in MM. Although some studies have recently revealed that MM cells have the ability to activate platelets even in the premalignant stage, this phenomenon has not been widely investigated in MM. Moreover, thrombocytopenia, along with bleeding, is commonly observed in those patients. In this review, we discuss the hemostatic disturbances observed in MM patients and the dynamic interaction between platelets and myeloma cells, along with present and future potential avenues for the use of platelets for diagnostic and therapeutic purposes.

Artesunate induces ferroptosis by inhibiting the nuclear localization of SREBP2 in myeloma cells

Objective: Multiple myeloma (MM) is an incurable haematological cancer characterized by abnormal proliferation of plasma cells. The promising therapeutic effect of selective inhibitors of nuclear export in MM reveals the broad therapeutic prospects of nuclear localization intervention. Sterol regulatory element binding protein 2 (SREBP2) is a lipid regulatory molecule that has been implicated in the effect of drug therapy for MM. SREBP2 has been reported to be regulated by the antimalarial drug artesunate (ART) through alteration of its nuclear localization and has been shown to inhibit ferroptosis in other tumours. However, the mechanism through which this might occur has not been clarified in MM. Our study aimed to explore whether ART can induce ferroptosis in MM through nuclear localization of SREBP2. Methods: To evaluate whether ferroptosis is induced by treatment with ART in myeloma, we used two types of myeloma cell lines. We first used a series of molecular approaches and other techniques to investigate the impact of ART on cell growth, production of reactive oxygen species (ROS), Fe2+ levels, lipid peroxidation and expression of genes related to ferroptosis. Then, we further explored the mechanism through which ferroptosis may occur in these cells and the relationship between ferroptosis and the nuclear localization of SREBP2. Results: Upregulation of ROS, Fe2+, and lipid peroxidation as well as inhibition of cell growth were observed in myeloma cells after treatment with ART. Expression of acyl CoA synthase long chain family member 4 (ACSL4) was increased, while glutathione peroxidase 4 (GPX4) expression was reduced in cells treated with ART. ART-induced cell death could be reversed by ferropstatin-1 (Fer-1) and deferoxamine mesylate (DFO). Nuclear localization of SREBP2 in myeloma cells was inhibited, accompanied by downregulation of isopentenyl pyrophosphate (IPP) and GPX4, after treatment with ART. Conclusion: In conclusion, our study demonstrated that the antimalarial drug ART can inhibit nuclear localization of SREBP2, downregulate IPP and GPX4, and eventually trigger ferroptosis in myeloma cells. Through this study, we hope to establish a correlation between nuclear localization pathways and mediation of ferroptosis in myeloma cells and provide an innovative direction for exploration-related therapy.

Clinical Outcomes and Serum B-Cell Maturation Antigen Levels in a Real-World Unselected Population of Newly Diagnosed Multiple Myeloma Patients

BACKGROUND

Progression-free survival (PFS) and overall survival (OS) of newly diagnosed multiple myeloma (MM) patients have been widely published in the clinical trials setting, but data published from real-world settings are limited.

OBJECTIVE

We determined the survival and factors that predict outcomes among 161 unselected, newly diagnosed MM patients whose frontline therapy was started at a single clinic specializing in the treatment of this B-cell malignancy.

PATIENTS AND METHODS

None of these patients underwent an autologous stem cell transplantation as part of their initial therapy and the population had a high proportion (35%) of cytogenetic high-risk patients.

RESULTS

With a median follow-up of 42.7 months, the cohort had a median PFS of 22.8 months and a median OS of 136.2 months. The 1-, 3-, and 5-year survival rates were 97.5%, 85.3%, and 76.2%, respectively. These results are considerably better than those reported from patients enrolled in clinical trials and those from countries with national registries. Age <65 years predicted for a longer OS (p = 0.0004). Baseline serum B-cell maturation antigen (sBCMA) levels were also assessed and showed median and mean levels of 320.3 ng/mL and 551.1 ng/mL, respectively. Furthermore, patients with baseline sBCMA levels in the lowest quartile (≤136.2 ng/mL) showed a longer PFS (p = 0.0262).

CONCLUSION

These results provide clinicians with a real-world understanding of the survival of unselected, newly diagnosed patients initiating therapy in a clinic specializing in the care of MM patients.

Proteasome inhibition in combination with immunotherapies: State-of-the-Art in multiple myeloma

Multiple myeloma (MM) is a malignant plasma cell disorder accounting for around 1.8% of all neoplastic diseases. Nowadays, clinicians have a broad arsenal of drugs at their disposal for the treatment of MM, such as proteasome inhibitors, immunomodulatory drugs, monoclonal antibodies, bispecific antibodies, CAR T-cell therapies and antibody-drug conjugates. In this paper we briefly highlight essential clinical elements relating to proteasome inhibitors, such as bortezomib, carfilzomib and ixazomib. Studies suggest that the early use of immunotherapy may improve outcomes significantly. Therefore, in our review we specifically focus on the combination therapy of proteasome inhibitors with novel immunotherapies and/or transplant. A high number of patients develop PI resistance. Thus, we also review new generation PIs, such as marizomib, oprozomib (ONX0912) and delanzomib (CEP-18770) and their combinations with immunotherapies.

Biomarkers of obesity-mediated insulin resistance: focus on microRNAs

Obesity and metabolic syndromes are becoming increasingly prevalent worldwide. Insulin resistance (IR) is a common complication of obesity. However, IR occurrence varies across individuals with obesity and may involve epigenetic factors. To rationalize the allocation of healthcare resources, biomarkers for the early risk stratification of individuals with obesity should be identified. MicroRNAs (miRNAs) are closely associated with metabolic diseases and involved in epigenetic regulation. In this review, we have summarized the changes in miRNA expression in the peripheral circulation and tissues of patients and animals with obesity-associated IR over the last 5 years and identified several candidate biomarkers that predict obesity-related IR. There are areas for improvement in existing studies. First, more than the predictive validity of a single biomarker is required, and a biomarker panel needs to be formed. Second, miRNAs are often studied in isolation and do not form a network of signaling pathways. We believe that early biomarkers can help clinicians accurately predict individuals prone to obesity-related IR at an early stage. Epigenetic regulation may be one of the underlying causes of different clinical outcomes in individuals with obesity. Future studies should focus on objectively reflecting the differences in miRNA profile expression in individuals with obesity-related IR, which may help identify more reliable biomarkers. Understanding the metabolic pathways of these miRNAs can help design new metabolic risk prevention and management strategies, and support the development of drugs to treat obesity and metabolic disorders.

Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.

A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM's great heterogeneity and incurability, risk assessment and treatment choices are still difficult. Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes. Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study. Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients.

Liquid biopsy-based monitoring of residual disease in multiple myeloma by analysis of the rearranged immunoglobulin genes-A feasibility study

The need for sensitive monitoring of minimal/measurable residual disease (MRD) in multiple myeloma emerged as novel therapies led to deeper responses. Moreover, the potential benefits of blood-based analyses, the so-called liquid biopsy is prompting more and more studies to assess its feasibility. Considering these recent demands, we aimed to optimize a highly sensitive molecular system based on the rearranged immunoglobulin (Ig) genes to monitor MRD from peripheral blood. We analyzed a small group of myeloma patients with the high-risk t(4;14) translocation, using next-generation sequencing of Ig genes and droplet digital PCR of patient-specific Ig heavy chain (IgH) sequences. Moreover, well established monitoring methods such as multiparametric flow cytometry and RT-qPCR of the fusion transcript IgH::MMSET (IgH and multiple myeloma SET domain-containing protein) were utilized to evaluate the feasibility of these novel molecular tools. Serum measurements of M-protein and free light chains together with the clinical assessment by the treating physician served as routine clinical data. We found significant correlation between our molecular data and clinical parameters, using Spearman correlations. While the comparisons of the Ig-based methods and the other monitoring methods (flow cytometry, qPCR) were not statistically evaluable, we found common trends in their target detection. Regarding longitudinal disease monitoring, the applied methods yielded complementary information thus increasing the reliability of MRD evaluation. We also detected indications of early relapse before clinical signs, although this implication needs further verification in a larger patient cohort.

Ferroptosis in Haematological Malignancies and Associated Therapeutic Nanotechnologies

Haematological malignancies are heterogeneous groups of cancers of the bone marrow, blood or lymph nodes, and while therapeutic advances have greatly improved the lifespan and quality of life of those afflicted, many of these cancers remain incurable. The iron-dependent, lipid oxidation-mediated form of cell death, ferroptosis, has emerged as a promising pathway to induce cancer cell death, particularly in those malignancies that are resistant to traditional apoptosis-inducing therapies. Although promising findings have been published in several solid and haematological malignancies, the major drawbacks of ferroptosis-inducing therapies are efficient drug delivery and toxicities to healthy tissue. The development of tumour-targeting and precision medicines, particularly when combined with nanotechnologies, holds potential as a way in which to overcome these obstacles and progress ferroptosis-inducing therapies into the clinic. Here, we review the current state-of-play of ferroptosis in haematological malignancies as well as encouraging discoveries in the field of ferroptosis nanotechnologies. While the research into ferroptosis nanotechnologies in haematological malignancies is limited, its pre-clinical success in solid tumours suggests this is a very feasible therapeutic approach to treat blood cancers such as multiple myeloma, lymphoma and leukaemia.

Olfactory Neuroblastoma-A Challenging Fine Line between Metastasis and Hematology

Developing in a limited space, rare tumors located at the nose and paranasal sinuses are sometimes difficult to diagnose due to their modest clinical presentation, which is uncorrelated with anatomopathological diversity. This limits the preoperative diagnosis without added immune histochemical study; for that reason, we present our experience with these tumors with the intention of raising awareness. The patient included in our study was investigated by our department through clinical and endoscopic examination, imaging investigations, and an anatomic-pathological study. The selected patient gave consent for participation and inclusion in this research study in compliance with the 1964 Declaration of Helsinki.

A novel prognostic model based on pyroptosis-related genes for multiple myeloma

BACKGROUND

Multiple myeloma (MM) is an incurable and relapse-prone disease with apparently prognostic heterogeneity. At present, the risk stratification of myeloma is still incomplete. Pyroptosis, a type of programmed cell death, has been shown to regulate tumor growth and may have potential prognostic value. However, the role of pyroptosis-related genes (PRGs) in MM remains undetermined. The aims of this study were to identify potential prognostic biomarkers and to construct a predictive model related to PRGs.

METHODS

Sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Non-negative matrix factorization (NMF) was performed to identify molecular subtype screening. LASSO regression was used to screen for prognostic markers, and then a risk score model was constructed. The Maxstat package was utilized to calculate the optimal cutoff value, according to which patients were divided into a high-risk group and a low-risk group, and the survival curves were plotted using the Kaplan-Meier (K-M) method. Nomograms and calibration curves were established using the rms package.

RESULTS

A total of 33 PRGs were extracted from the TCGA database underlying which 4 MM molecular subtypes were defined. Patients in cluster 1 had poorer survival than those in cluster 2 (p = 0.035). A total of 9 PRGs were screened out as prognostic markers, and the predictive ability of the 9-gene risk score for 3-year survival was best (AUC = 0.658). Patients in the high-risk group had worse survival than those in the low-risk group (p < 0.001), which was consistent with the results verified by the GSE2658 dataset. The nomogram constructed by gender, age, International Staging System (ISS) stage, and risk score had the best prognostic predictive performance with a c-index of 0.721.

CONCLUSION

Our model could enhance the predictive ability of ISS staging and give a reference for clinical decision-making. The new, prognostic, and pyroptosis-related markers screened out by us may facilitate the development of novel risk stratification for MM.

CLINICAL TRIAL REGISTRATION

Not applicable.

A pooled analysis of outcomes according to cytogenetic abnormalities in patients receiving ixazomib- vs placebo-based therapy for multiple myeloma

Some cytogenetic abnormalities (CAs) are associated with poorer prognosis in multiple myeloma (MM); proteasome inhibitors appear to benefit patients with high-risk CAs. We evaluated 2247 MM patients from the TOURMALINE-MM1/-MM2/-MM3/-MM4 trials to assess the PFS benefit of ixazomib plus lenalidomide-dexamethasone (Rd) vs placebo-Rd (TOURMALINE-MM1/-MM2) or ixazomib vs placebo (TOURMALINE-MM3/-MM4) in specific high-risk CAs. After a pooled median follow-up of 25.6 months, the hazard ratio (HR) for PFS with ixazomib- vs placebo-based therapy for high-risk patients was 0.74 (95% confidence interval [CI]: 0.59-0.93; median PFS [mPFS] 17.8 vs 13.2 months), and 0.70 (95% CI: 0.62-0.80; mPFS 26.3 vs 17.6 months) for complementary standard-risk patients. The HR for expanded high-risk patients was 0.75 (95% CI: 0.64-0.87; mPFS 18.1 vs 14.1 months), and 0.71 (95% CI: 0.59-0.85; mPFS 36.1 vs 21.4 months) for complementary standard-risk patients. The HR for PFS with ixazomib- vs placebo-based therapy was 0.68 in patients with t(4;14) (95% CI: 0.48-0.96; mPFS 22.4 vs 13.2 months), and 0.77 for patients with amp1q21 (95% CI: 0.63-0.93; mPFS 18.8 vs 14.5 months). A PFS benefit was demonstrated with ixazomib- vs placebo-based therapy regardless of cytogenetic status, with greatest benefit observed in patients with t(4;14) and amp1q21.

Comprehensive analysis of thirteen-gene panel with prognosis value in Multiple Myeloma

BACKGROUND

Although there are many treatments for Multiple myeloma (MM), patients with MM still unable to escape the recurrence and aggravation of the disease.

OBJECTIVE

We constructed a risk model based on genes closely associated with MM prognosis to predict its prognostic value.

METHODS

Gene function enrichment and signal pathway enrichment analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, univariate and multivariate Cox regression analysis, Kaplan-Meier (KM) survival analysis and Receiver Operating Characteristic (ROC) analysis were used to identify the prognostic gene signature for MM. Finally, the prognostic gene signature was validated using the Gene Expression Omnibus (GEO) database.

RESULTS

Thirteen prognostic genes were screened by univariate Cox analysis and LASSO regression analysis. Multivariate Cox analysis revealed risk score to be an independent prognostic factor for patients with MM [Hazard Ratio (HR) = 2.564, 95% Confidence Interval (CI) = 2.223-2.958, P< 0.001]. The risk score had a high level of predictive value according to ROC analysis, with an area under the curve (AUC) of 0.744.

CONCLUSIONS

The potential prognostic signature of thirteen genes were assessed and a risk model was constructed that significantly correlated with prognosis in MM patients.

Identification and validation of a novel cuproptosis-related gene signature in multiple myeloma

Background: Cuproptosis is a newly identified unique copper-triggered modality of mitochondrial cell death, distinct from known death mechanisms such as necroptosis, pyroptosis, and ferroptosis. Multiple myeloma (MM) is a hematologic neoplasm characterized by the malignant proliferation of plasma cells. In the development of MM, almost all patients undergo a relatively benign course from monoclonal gammopathy of undetermined significance (MGUS) to smoldering myeloma (SMM), which further progresses to active myeloma. However, the prognostic value of cuproptosis in MM remains unknown. Method: In this study, we systematically investigated the genetic variants, expression patterns, and prognostic value of cuproptosis-related genes (CRGs) in MM. CRG scores derived from the prognostic model were used to perform the risk stratification of MM patients. We then explored their differences in clinical characteristics and immune patterns and assessed their value in prognosis prediction and treatment response. Nomograms were also developed to improve predictive accuracy and clinical applicability. Finally, we collected MM cell lines and patient samples to validate marker gene expression by quantitative real-time PCR (qRT-PCR). Results: The evolution from MGUS and SMM to MM was also accompanied by differences in the CRG expression profile. Then, a well-performing cuproptosis-related risk model was developed to predict prognosis in MM and was validated in two external cohorts. The high-risk group exhibited higher clinical risk indicators. Cox regression analyses showed that the model was an independent prognostic predictor in MM. Patients in the high-risk group had significantly lower survival rates than those in the low-risk group (p < 0.001). Meanwhile, CRG scores were significantly correlated with immune infiltration, stemness index and immunotherapy sensitivity. We further revealed the close association between CRG scores and mitochondrial metabolism. Subsequently, the prediction nomogram showed good predictive power and calibration. Finally, the prognostic CRGs were further validated by qRT-PCR in vitro. Conclusion: CRGs were closely related to the immune pattern and self-renewal biology of cancer cells in MM. This prognostic model provided a new perspective for the risk stratification and treatment response prediction of MM patients.

Serum proteomic profiling reveals MTA2 and AGO2 as potential prognostic biomarkers associated with disease activity and adverse outcomes in multiple myeloma

Multiple myeloma (MM) is an incurable plasma cell malignancy accounting for approximately 10% of hematological malignancies. Identification of reliable biomarkers for better diagnosis and prognosis remains a major challenge. This study aimed to identify potential serum prognostic biomarkers corresponding to MM disease activity and evaluate their impact on patient outcomes. Serum proteomic profiles of patients with MM and age-matched controls were performed using LC-MS/MS. In the verification and validation phases, the concentration of the candidate biomarkers was measured using an ELISA technique. In addition, the association of the proposed biomarkers with clinical outcomes was assessed. We identified 23 upregulated and 15 downregulated proteins differentially expressed in newly diagnosed and relapsed/refractory MM patients compared with MM patients who achieved at least a very good partial response to treatment (≥VGPR). The top two candidate proteins, metastasis-associated protein-2 (MTA2) and argonaute-2 (AGO2), were selected for further verification and validation studies. Both MTA2 and AGO2 showed significantly higher levels in the disease-active states than in the remission states (p < 0.001). Regardless of the patient treatment profile, high MTA2 levels were associated with shorter progression-free survival (p = 0.044; HR = 2.48; 95% CI, 1.02 to 6.02). Conversely, high AGO2 levels were associated with IgG and kappa light-chains isotypes and an occurrence of bone involvement features (p < 0.05) and were associated with prolonged time to response (p = 0.045; HR = 3.00; 95% CI, 1.03 to 8.76). Moreover, the analytic results using a publicly available NCBI GEO dataset revealed that AGO2 overexpression was associated with shorter overall survival among patients with MM (p = 0.032, HR = 1.60, 95% CI, 1.04 to 2.46). In conclusion, MTA2 and AGO2 proteins were first identified as potential biomarkers that reflect disease activity, provide prognostic values and could serve as non-invasive indicators for disease monitoring and outcome predicting among patients with MM.