A prognostic survival model based on metabolism-related gene expression in plasma cell myeloma

Enhancing staging in multiple myeloma using an m6A regulatory gene-pairing model

Multiple myeloma (MM) is characterized by clonal plasma cell proliferation in the bone marrow, challenging prognosis prediction. We developed a gene-pairing prognostic risk model using m6A regulatory genes and a nested LASSO method. A cutoff of - 0.133 categorized MM samples into high-risk and low-risk groups. The model showed strong prognostic performance in 2088 newly diagnosed MM samples and predicted response to combination therapy (daratumumab, carfilzomib, lenalidomide, and dexamethasone) in patients who failed or relapsed from bortezomib-containing regimens, with an AUC of 0.9. It distinguished between smoldering MM and MM (cutoff: - 0.45) and between MM and plasma cell leukemia (cutoff: 0.0857). Single-cell analysis revealed higher risk scores at relapse. Combining MM cell lines and sample data, we identified potential drugs and targets (ADAT2 and NUP153) effective against high-risk MM. Integrating the m6A risk model with the International Staging System (ISS) enhanced stratification accuracy. These insights support precision treatment of MM.

Dysregulated arginine metabolism in precursor B-cell acute lymphoblastic leukemia in children: a metabolomic study

BACKGROUND

Precursor B-cell acute lymphoblastic leukemia (B-ALL) is the most common cancers in children. Failure of induction chemotherapy is a major factor leading to relapse and death in children with B-ALL. Given the importance of altered metabolites in the carcinogenesis of pediatric B-ALL, studying the metabolic profile of children with B-ALL during induction chemotherapy and in different minimal residual disease (MRD) status may contribute to the management of pediatric B-ALL.

METHODS

We collected paired peripheral blood plasma samples from children with B-ALL at pre- and post-induction chemotherapy and analyzed the metabolomic profiling of these samples by ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). Healthy children were included as controls. We selected metabolites that were depleted in pediatric B-ALL and analyzed the concentrations in pediatric B-ALL samples. In vitro, we study the effects of the selected metabolites on the viability of ALL cell lines and the sensitivity to conventional chemotherapeutic agents in ALL cell lines.

RESULTS

Forty-four metabolites were identified with different levels between groups. KEGG pathway enrichment analyses revealed that dysregulated linoleic acid (LA) metabolism and arginine (Arg) biosynthesis were closely associated with pediatric B-ALL. We confirmed that LA and Arg were decreased in pediatric B-ALL samples. The treatment of LA and Arg inhibited the viability of NALM-6 and RS4;11 cells in a dose-dependent manner, respectively. Moreover, Arg increased the sensitivity of B-ALL cells to L-asparaginase and daunorubicin.

CONCLUSION

Arginine increases the sensitivity of B-ALL cells to the conventional chemotherapeutic drugs L-asparaginase and daunorubicin. This may represent a promising therapeutic approach.

Cytidine triphosphate synthase 1-mediated metabolic reprogramming promotes proliferation and drug resistance in multiple myeloma

Upregulation of metabolism-related gene cytidine triphosphate synthase 1 (CTPS1) is associated with poor prognosis in multiple myeloma (MM). However, its role in MM remains unclear. In this study, bioinformatics analysis revealed significant differences in CTPS1 expression levels among various plasma cell malignancies. The patients with high CTPS1 expression had poor overall survival, progression-free survival, and event-free survival. CTPS1 was significantly correlated with sex, albumin, β2 microglobulin, lactate dehydrogenase, and advanced disease. In vitro experiments demonstrated that CTPS1-overexpressing (CTPS1-OE) cells proliferated faster than CTPS1-short hairpin RNA (CTPS1-sh) cells. NRG-SGM3 mice showed significantly accelerated tumor growth in the CTPS1-OE group. CTPS1-OE decreased sensitivity to bortezomib, whereas CTPS1-sh increased sensitivity to bortezomib in MM cell lines. Mechanistically, CTPS1 was primarily involved in metabolism processes. Additionally, CTPS1 was closely related to several co-expressed genes such as MYC and the bone marrow immune microenvironment. In conclusion, CTPS1 is a significant prognostic biomarker for patients with MM, suggesting a potential therapeutic target.

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.

Bone marrow stromal cells dictate lanosterol biosynthesis and ferroptosis of multiple myeloma

Ferroptosis has been demonstrated a promising way to counteract chemoresistance of multiple myeloma (MM), however, roles and mechanism of bone marrow stromal cells (BMSCs) in regulating ferroptosis of MM cells remain elusive. Here, we uncovered that MM cells were more susceptible to ferroptotic induction under the interaction of BMSCs using in vitro and in vivo models. Mechanistically, BMSCs elevated the iron level in MM cells, thereby activating the steroid biosynthesis pathway, especially the production of lanosterol, a major source of reactive oxygen species (ROS) in MM cells. We discovered that direct coupling of CD40 ligand and CD40 receptor constituted the key signaling pathway governing lanosterol biosynthesis, and disruption of CD40/CD40L interaction using an anti-CD40 neutralizing antibody or conditional depletion of Cd40l in BMSCs successfully eliminated the iron level and lanosterol production of MM cells localized in the Vk*MYC Vk12653 or NSG mouse models. Our study deciphers the mechanism of BMSCs dictating ferroptosis of MM cells and highlights the therapeutic potential of non-apoptosis strategies for managing refractory or relapsed MM patients.

The construction of a nomogram to predict the prognosis and recurrence risks of UPJO

Objective

This study was conducted to explore the risk factors for the prognosis and recurrence of ureteropelvic junction obstruction (UPJO).

Methods

The correlation of these variables with the prognosis and recurrence risks was analyzed by binary and multivariate logistic regression. Besides, a nomogram was constructed based on the multivariate logistic regression calculation. After the model was verified by the C-statistic, the ROC curve was plotted to evaluate the sensitivity of the model. Finally, the decision curve analysis (DCA) was conducted to estimate the clinical benefits and losses of intervention measures under a series of risk thresholds.

Results

Preoperative automated peritoneal dialysis (APD), preoperative urinary tract infection (UTI), preoperative renal parenchymal thickness (RPT), Mayo adhesive probability (MAP) score, and surgeon proficiency were the high-risk factors for the prognosis and recurrence of UPJO. In addition, a nomogram was constructed based on the above 5 variables. The area under the curve (AUC) was 0.8831 after self cross-validation, which validated that the specificity of the model was favorable.

Conclusion

The column chart constructed by five factors has good predictive ability for the prognosis and recurrence of UPJO, which may provide more reasonable guidance for the clinical diagnosis and treatment of this disease.

CTPS1 is a novel therapeutic target in multiple myeloma which synergizes with inhibition of CHEK1, ATR or WEE1

Targeting nucleotide biosynthesis is a proven strategy for the treatment of cancer but is limited by toxicity, reflecting the fundamental nucleotide requirement of dividing cells. The rate limiting step in de novo pyrimidine synthesis is of interest, being catalyzed by two homologous enzymes, CTP synthase 1 (CTPS1) and CTPS2, that could be differentially targeted. Herein, analyses of publicly available datasets identified an essential role for CTPS1 in multiple myeloma (MM), linking high expression of CTPS1 (but not CTPS2) with advanced disease and poor outcomes. In cellular experiments, CTPS1 knockout induced apoptosis of MM cell lines. Exposure of MM cells to STP-B, a novel and highly selective pharmacological inhibitor of CTPS1, inhibited proliferation, induced S phase arrest and led to cell death by apoptosis. Mechanistically, CTPS1 inhibition by STP-B activated DNA damage response (DDR) pathways and induced double-strand DNA breaks which accumulated in early S phase. Combination of STP-B with pharmacological inhibitors of key components of the DDR pathway (ATR, CHEK1 or WEE1) resulted in synergistic growth inhibition and early apoptosis. Taken together, these findings identify CTPS1 as a promising new target in MM, either alone or in combination with DDR pathway inhibition.

A novel NHEJ gene signature based model for risk stratification and prognosis prediction in hepatocellular carcinoma

BACKGROUND

Non-homologous DNA end joining (NHEJ) is the predominant DNA double-strand break (DSB) repair pathway in human. However, the relationship between NHEJ pathway and hepatocellular carcinoma (HCC) is unclear. We aimed to explore the potential prognostic role of NHEJ genes and to develop an NHEJ-based prognosis signature for HCC.

METHODS

Two cohorts from public database were incorporated into this study. The Kaplan-Meier curve, the Least absolute shrinkage and selection operator (LASSO) regression analysis, and Cox analyses were implemented to determine the prognostic genes. A NHEJ-related risk model was created and verified by independent cohorts. We derived enriched pathways between the high- and low-risk groups using Gene Set Enrichment Analysis (GSEA). CIBERSORT and microenvironment cell populations-counter algorithm were used to perform immune infiltration analysis. XRCC6 is a core NHEJ gene and immunohistochemistry (IHC) was further performed to elucidate the prognostic impact. In vitro proliferation assays were conducted to investigate the specific effect of XRCC6.

RESULTS

A novel NHEJ-related risk model was developed based on 6 NHEJ genes and patients were divided into distinct risk groups according to the risk score. The high-risk group had a poorer survival than those in the low-risk group (P < 0.001). Meanwhile, an obvious discrepancy in the landscape of the immune microenvironment also indicated that distinct immune status might be a potential determinant affecting prognosis as well as immunotherapy reactiveness. High XRCC6 expression level associates with poor outcome in HCC. Moreover, XRCC6 could promote HCC cell proliferation in vitro.

CONCLUSIONS

In brief, this work reveals a novel NHEJ-related risk signature for prognostic evaluation of HCC patients, which may be a potential biomarker of HCC immunotherapy.

Metabolic Alterations in Multiple Myeloma: From Oncogenesis to Proteasome Inhibitor Resistance

Despite significant improvements in treatment strategies over the past couple of decades, multiple myeloma (MM) remains an incurable disease due to the development of drug resistance. Metabolic reprogramming is a key feature of cancer cells, including MM, and acts to fuel increased proliferation, create a permissive tumour microenvironment, and promote drug resistance. This review presents an overview of the key metabolic adaptations that occur in MM pathogenesis and in the development of resistance to proteasome inhibitors, the backbone of current MM therapy, and considers the potential for therapeutic targeting of key metabolic pathways to improve outcomes.

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.

Recent Advancement in Anticancer Compounds from Marine Organisms: Approval, Use and Bioinformatic Approaches to Predict New Targets

In recent years, the study of anticancer bioactive compounds from marine sources has received wide interest. Contextually, world regulatory authorities have approved several marine molecules, and new synthetic derivatives have also been synthesized and structurally improved for the treatment of numerous forms of cancer. However, the administration of drugs in cancer patients requires careful evaluation since their interaction with individual biological macromolecules, such as proteins or nucleic acids, determines variable downstream effects. This is reflected in a constant search for personalized therapies that lay the foundations of modern medicine. The new knowledge acquired on cancer mechanisms has certainly allowed advancements in tumor prevention, but unfortunately, due to the huge complexity and heterogeneity of cancer, we are still looking for a definitive therapy and clinical approaches. In this review, we discuss the significance of recently approved molecules originating from the marine environment, starting from their organism of origin to their structure and mechanism of action. Subsequently, these bio-compounds are used as models to illustrate possible bioinformatics approaches for the search of new targets that are useful for improving the knowledge on anticancer therapies.

Integrated assessment of the clinical and biological value of ferroptosis-related genes in multiple myeloma

Background

Ferroptosis is an iron-dependent mode of cell death that could be induced by erastin and exert antitumor effects. However, the clinical and biological roles of ferroptosis-related gene (FRG) signature and the therapeutic value of erastin in multiple myeloma (MM) remained unknown.

Methods

Clinical and gene expression data of MM subjects were extracted from the Gene Expression Omnibus (GEO) public database. Univariable cox analysis was applied to determine FRGs related to survival and the least absolute shrinkage and selection operator (LASSO) regression analysis was used to develop a prognostic model. Prediction accuracy of the model was estimated by receiver operating characteristic (ROC) curves. Functional pathway enrichments and infiltrating immune status were also analyzed. We conducted in vitro experiments to investigate the combination therapy of erastin and doxorubicin.

Results

17 FRGs were strongly associated with patient survival and 11 genes were identified to construct the prognostic model. ROC curves indicated great predictive sensitivity and specificity of the model in all cohorts. Patients were divided into low- and high-risk groups by median risk score in each cohort and the survival of the low-risk group was significantly superior than that of the high-risk group. We also observed a close relevance between functional pathways and immune infiltration with risk scores. Moreover, we combined erastin and doxorubicin in our in vitro experiments and found synergetic antitumor effects of the two agents, and the underlying mechanism is the overgeneration of intracellular Reactive Oxygen Species (ROS).

Conclusions

We demonstrated the important value of ferroptosis in patient prognosis and as a potential antitumor target for MM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02742-4.

A novel immune-related radioresistant lncRNAs signature based model for risk stratification and prognosis prediction in esophageal squamous cell carcinoma

Background and purpose: Radioresistance remains a major reason of radiotherapeutic failure in esophageal squamous cell carcinoma (ESCC). Our study is to screen the immune-related long non-coding RNA (ir-lncRNAs) of radiation-resistant ESCC (rr-ESCC) via Gene Expression Omnibus (GEO) database and to construct a prognostic risk model.

Methods: Microarray data (GSE45670) related to radioresistance of ESCC was downloaded from GEO. Based on pathologic responses after chemoradiotherapy, patients were divided into a non-responder (17 samples) and responder group (11 samples), and the difference in expression profiles of ir-lncRNAs were compared therein. Ir-lncRNA pairs were constructed for the differentially expressed lncRNAs as prognostic variables, and the microarray dataset (GSE53625) was downloaded from GEO to verify the effect of ir-lncRNA pairs on the long-term survival of ESCC. After modelling, patients are divided into high- and low-risk groups according to prognostic risk scores, and the outcomes were compared within groups based on the COX proportional hazards model. The different expression of ir-lncRNAs were validated using ECA 109 and ECA 109R cell lines via RT-qPCR.

Results: 26 ir-lncRNA genes were screened in the GSE45670 dataset with differential expression, and 180 ir-lncRNA pairs were constructed. After matching with ir-lncRNA pairs constructed by GSE53625, six ir-lncRNA pairs had a significant impact on the prognosis of ESCC from univariate analysis model, of which three ir-lncRNA pairs were significantly associated with prognosis in multivariate COX analysis. These three lncRNA pairs were used as prognostic indicators to construct a prognostic risk model, and the predicted risk scores were calculated. With a median value of 2.371, the patients were divided into two groups. The overall survival (OS) in the high-risk group was significantly worse than that in the low-risk group (p < 0.001). The 1-, 2-, and 3-year prediction performance of this risk-model was 0.666, 0.702, and 0.686, respectively. In the validation setting, three ir-lncRNAs were significantly up-regulated, while two ir-lncRNAs were obviouly down-regulated in the responder group.

Conclusion: Ir-lncRNAs may be involved in the biological regulation of radioresistance in patients with ESCC; and the prognostic risk-model, established by three ir-lncRNAs pairs has important clinical value in predicting the prognosis of patients with rr-ESCC.

Metabolism-Related Bioinformatics Analysis Reveals That HPRT1 Facilitates the Progression of Oral Squamous Cell Carcinoma In Vitro

Objectives

Many studies have shown that dysregulation of metabolism contributes to oncogenesis. However, the exact roles of metabolism-related genes (MRGs) in oral squamous cell carcinoma (OSCC) remain unclear. Thus, we aimed to identify a prognostic signature related to MRGs in OSCC.

Methods

The gene sequencing data of OSCC samples and the MRG set were downloaded from The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB). The Wilcoxon rank-sum test was used to identify differentially expressed MRGs. Then, a prognostic signature was established by multivariate Cox regression analysis. Finally, prognosis-related MRGs were selected and further validated in OSCC tissues and cell lines.

Results

A prognostic signature that included 8 MRGs was constructed. Multiple survival analysis revealed that only HPRT1 might be an independent biomarker and indicator of poor overall survival in OSCC patients. The expression of HPRT1 was then found to be upregulated in OSCC tissues and cell lines, and suppression of HPRT1 gene expression by siRNA inhibited the proliferation, migration, and invasion of OSCC cells in vitro.

Conclusions

MRGs play an important role in the development of OSCC. Furthermore, HPRT1 might be an independent biomarker of OSCC and enhance OSCC proliferation, migration, and invasion in vitro; these results emphasize the potential utility of HPRT1 in OSCC therapy.

Metabolic Vulnerabilities in Multiple Myeloma

Multiple myeloma (MM) remains an incurable malignancy with eventual emergence of refractory disease. Metabolic shifts, which ensure the availability of sufficient energy to support hyperproliferation of malignant cells, are a hallmark of cancer. Deregulated metabolic pathways have implications for the tumor microenvironment, immune cell function, prognostic significance in MM and anti-myeloma drug resistance. Herein, we summarize recent findings on metabolic abnormalities in MM and clinical implications driven by metabolism that may consequently inspire novel therapeutic interventions. We highlight some future perspectives on metabolism in MM and propose potential targets that might revolutionize the field.

Development and Validation of a Novel Prognostic Model for Overall Survival in Newly Diagnosed Multiple Myeloma Integrating Tumor Burden and Comorbidities

Background

Multiple myeloma (MM) is a highly heterogeneous disease with enormously variable outcomes. It remains to be a major challenge to conduct a more precise estimation of the survival of MM patients. The existing stratifications attached less importance to the prognostic significance of comorbidities. In the present study, we aimed to develop and validate a novel and simple prognostic stratification integrating tumor burden and comorbidities measured by HCT-CI.

Method

We retrospectively enrolled 385 consecutive newly diagnosed multiple myeloma (NDMM) patients in Xijing Hospital from January 2013 to December 2020. The cohort between January 2016 and December 2020 was selected as development cohort (N = 233), and the cohort between January 2013 and December 2015 was determined as validation cohort (N = 152). By using LASSO analysis and univariate and multivariable Cox regression analyses, we developed the MM-BHAP model in the way of nomogram composed of β2-MG, HCT-CI, ALB, and PBPC. We internally and externally validated the MM-BHAP model and compared it with ISS stage and R-ISS stage.

Results

The MM-BHAP model was superior to the ISS stage and partially better than the R-ISS stage according to time-dependent AUC, time-dependent C-index, DCA, IDI, and continuous NRI analyses. In predicting OS, only the MM-BHAP stratification clearly divided patients into three groups while both the ISS stage and R-ISS stage had poor classifications in patients with stage I and stage II. Moreover, the MM-BHAP stratification and the R-ISS stage performed well in predicting PFS, but not for the ISS stage. Besides, the MM-BHAP model was also applied to the patients with age ≤65 or age >65 and with or without HRCA and could enhance R-ISS or ISS classifications.

Conclusions

Our study offered a novel simple MM-BHAP stratification containing tumor burden and comorbidities to predict outcomes in the real-world unselected NDMM population.

A Novel Inflammatory-Related Gene Signature Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

Inflammation is an important hallmark of cancer and plays a role in both neogenesis and tumor development. Despite this, inflammatory-related genes (IRGs) remain to be poorly studied in lung adenocarcinoma (LUAD). We aim to explore the prognostic value of IRGs for LUAD and construct an IRG-based prognosis signature. The transcriptomic profiles and clinicopathological information of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression were applied in the TCGA set to generate an IRG risk signature. LUAD cases with from the GSE31210 and GSE30219 datasets were used to validate the predictive ability of the signature. Analysis of the TCGA cohort revealed a five-IRG risk signature consisting of EREG, GPC3, IL7R, LAMP3, and NMUR1. This signature was used to divide patients into two risk groups with different survival rates. Multivariate Cox regression analysis verified that the risk score from the five-IRG signature negatively correlated with patient outcome. A nomogram was developed using the IRG risk signature and stage, with C-index values of 0.687 (95% CI: 0.644-0.730) in the TCGA training cohort, 0.678 (95% CI: 0.586-0.771) in GSE30219 cohort, and 0.656 (95% CI: 0.571-0.740) in GSE30219 cohort. Calibration curves were consistent between the actual and the predicted overall survival. The immune infiltration analysis in the TCGA training cohort and two GEO validation cohorts showed a distinctly differentiated immune cell infiltration landscape between the two risk groups. The IRG risk signature for LUAD can be used to predict patient prognosis and guide individual treatment. This risk signature is also a potential biomarker of immunotherapy.

Metabolic Disorders in Multiple Myeloma

Multiple myeloma (MM) is the second most common hematological malignancy and is attributed to monoclonal proliferation of plasma cells in the bone marrow. Cancer cells including myeloma cells deregulate metabolic pathways to ensure proliferation, growth, survival and avoid immune surveillance, with glycolysis and glutaminolysis being the most identified procedures involved. These disorders are considered a hallmark of cancer and the alterations performed ensure that enough energy is available for rapid cell proliferation. An association between metabolic syndrome, inflammatory cytokinesand incidence of MM has been also described, while the use of metformin and statins has been identified as a positive prognostic factor for the disease course. In this review, we aim to present the metabolic disorders that occur in multiple myeloma, the potential defects on the immune system and the potential advantage of targeting the dysregulated pathways in order to enhance antitumor therapeutics.