OBJECTIVES: Multiple myeloma (MM) is a highly heterogeneous hematologic malignancy, with disease progression driven by cytogenetic abnormalities and a complex bone marrow microenvironment. This study aims to construct a prognostic model for MM based on transcriptomic data and lipid metabolism related genes (LRGs), and to identify potential drug targets for high-risk patients to support clinical decision-making. METHODS: In this study, 2 transcriptomic datasets covering 985 newly diagnosed MM patients were retrieved from the Gene Expression Omnibus (GEO) database. Univariate Cox regression and 101 machine learning algorithms were used for gene selection. An LRG-based prognostic model was constructed using Stepwise Cox (both directions) and random survival forest (RSF) algorithms. The association between the prognostic score and clinical events was evaluated, and model performance was assessed using time-dependent receiver operating characteristic (ROC) curves and the C-index. The added predictive value of combining prognostic scores with clinical variables and staging systems was also analyzed. Differentially expressed genes between high- and low-risk groups were identified using limma and clusterProfiler and subjected to pathway enrichment analysis. Drug sensitivity analysis was conducted using the Genomics of Drug Sensitivity in Cancer (GDSC) database and oncoPredict to identify potential therapeutic targets for high-risk patients. The functional role of key LRGs in the model was validated via in vitro cell experiments. RESULTS: An LRG-based prognostic model (LRG17) was successfully developed using transcriptomic data and machine learning. The model demonstrated robust predictive performance, with area under the curve (AUC) values of 0.962, 0.912, and 0.842 for 3-, 5-, and 7-year survival, respectively. Patients were stratified into high- and low-risk groups, with high-risk patients showing significantly shorter overall survival (OS) and event-free survival (EFS) (both P<0.001) and worse clinical profiles (e.g., lower albumin, higher β2-microglobulin and lactate dehydrogenase levels). Enrichment analysis revealed that high-risk patients were significantly enriched for pathways related to chromosome segregation and mitosis, whereas low-risk patients were enriched for immune response and immune cell activation pathways. Drug screening suggested that AURKA inhibitor BMS-754807 and FGFR3 inhibitor I-BET-762 may be more effective in high-risk patients. Functional assays demonstrated that silencing of key LRG PLA2G4A significantly inhibited cell viability and induced apoptosis. CONCLUSIONS LRGs serve as promising biomarkers for prognosis prediction and risk stratification in MM. The overexpression of chromosomal instability-related and high-risk genetic event-associated genes in high-risk patients may explain their poorer outcomes. Given the observed resistance to bortezomib and lenalidomide in high-risk patients, combination therapies involving BMS-754807 or I-BET-762 may represent effective alternatives.
Humans ; Multiple Myeloma/mortality* ; Prognosis ; Lipid Metabolism/genetics* ; Transcriptome ; Machine Learning ; Male ; Female ; Gene Expression Profiling ; Algorithms

OBJECTIVES: Multiple myeloma (MM) is a hematologic malignancy characterized by clonal proliferation of plasma cells and remains incurable. Patients with primary refractory multiple myeloma (PRMM) show poor response to initial induction therapy. This study aims to develop a machine learning-based model to predict treatment response in newly diagnosed multiple myeloma (NDMM) patients, in order to optimize therapeutic strategies. METHODS: NDMM and post-treatment MM patients hospitalized in the Department of Hematology, Third Xiangya Hospital, Central South University, between August 2022 and July 2023 were enrolled. Post-treatment MM patients were categorized into PRMM patients and treatment-responsive MM (TRMM) patients based on therapeutic efficacy. Serum metabolites were detected and analyzed via metabolomics. Based on the metabolomics analysis results and combined with transcriptomic sequencing data of NDMM patients from databases, differentially expressed amino acid metabolism-related genes (AAMGs) among post-treatment NDMM patients with varying therapeutic outcomes were screened. Using bioinformatics analyses and machine learning algorithms, a predictive model for treatment response in NDMM was constructed and used to identify patients at risk for PRMM. RESULTS: A total of 61 patients were included: 22 NDMM, 23 TRMM, and 16 PRMM patients. Significant differences in metabolite levels were observed among the 3 groups, with differential metabolites mainly enriched in amino acid metabolism pathways. Follow-up data were available for 16 of the 22 NDMM patients, including 12 treatment responders (ND_TR group) and 4 with PRMM (ND_PR group). A total of 23 differential metabolites were identified between these 2 groups: 6 metabolites (e.g., tryptophan) were upregulated and 17 (e.g., citric acid) were downregulated in the ND_TR group. Transcriptomic data from 108 TRMM and 77 PRMM patients were analyzed to identify differentially expressed AAMGs, which were then used to construct a prediction model. The area under the receiver operating characteristic curve (AUC) for the model exceeded 0.8, and AUC values in 3 external validation cohorts were all above 0.7. CONCLUSIONS This study delineated the metabolic alterations in MM patients with different treatment response, suggesting that dysregulated amino acid metabolism may be associated with poor treatment response in PRMM. By integrating metabolomics and transcriptomics, a machine learning-based predictive model was successfully established to forecast treatment response in NDMM patients.
Humans ; Multiple Myeloma/drug therapy* ; Machine Learning ; Male ; Female ; Metabolomics/methods* ; Middle Aged ; Aged ; Treatment Outcome ; Transcriptome ; Computational Biology ; Adult ; Multiomics

OBJECTIVES: Multiple myeloma (MM) is a hematologically malignant clonal plasma cell disease. This study aims to explore the association between immunophenotypes and prognosis in patients with MM, to determine whether the expression of CD45 and CD200 is related to the prognosis of newly diagnosed MM (NDMM) patients, and to evaluate the significance of the combined expression of CD45 and CD200 in NDMM. METHODS: A total of 123 NDMM patients admitted to Shengjing Hospital of China Medical University from July 2015 to August 2019 were enrolled. Five key immunophenotypic markers (including CD38, CD138, CD45, CD56, and CD200) were screened through flow cytometry and identified using random forest analysis and univariate Cox regression analysis. Patients were divided into 3 groups: Group A, CD45 and CD200 double-positive; Group B, CD45 or CD200 single-positive; Group C, CD45 and CD200 double-negative. Kaplan-Meier curves were used to analyze overall survival (OS) and progression-free survival (PFS) across groups. Multivariate Cox regression was performed to evaluate prognostic factors, and a nomogram was constructed based on these results. RESULTS: The OS and PFS of single-positive groups for CD38, CD138, CD45, CD56, and CD200 were all shorter than those of their respective single-negative groups (all P<0.05). Significant differences were observed in OS (P<0.001) and PFS (P=0.001) among Groups A, B, and C. Group A had shorter OS and PFS (all P=0.001) compared to the Group B+C (cases from Group B and Group C were combined). CD45 and CD200 double-positive was an independent prognostic factor for NDMM [hazard ratio (HR)=2.178, 95% confidence interval (CI) 1.048 to 4.529; P=0.037]. The nomogram and calibration curves constructed from multivariate Cox regression analysis demonstrated good concordance (concordance index=0.706; 95% CI 0.661 to 0.751). CONCLUSIONS NDMM patients with double-positive expression of CD45 and CD200 have significantly shorter OS and PFS. Compared with the use of either marker alone, the combined assessment of CD45 and CD200 may provide better prognostic stratification for MM patients.
Humans ; Multiple Myeloma/metabolism* ; Male ; Female ; Middle Aged ; Antigens, CD/metabolism* ; Prognosis ; Leukocyte Common Antigens/metabolism* ; Aged ; Adult ; Immunophenotyping ; Nomograms ; Biomarkers, Tumor ; Clinical Relevance

Multiple myeloma (MM) is a common hematologic malignancy that originates from precursor conditions such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). Identifying its risk factors is crucial for early intervention. The etiology of MM is multifactorial, involving race, familial clustering, gender, age, obesity, cytogenetic abnormalities, and environmental exposures. Among these, cytogenetic abnormalities and modifiable factors play pivotal roles in MM pathogenesis and progression. 1) cytogenetic abnormalities. Primary abnormalities [e.g., hyperdiploidy, t(11;14), t(14;16)] emerge at the MGUS stage, while secondary abnormalities [e.g., 1q+, del(17p)] drive disease progression. The accumulation of 1q+ promotes clonal evolution, and del(17p) is associated with significantly reduced survival. 2) modifiable risk factors. Obesity promotes MM via the acetyl-CoA synthetase 2 (ACSS2)-interferon regulatory factor 4 (IRF4) pathway. Vitamin D deficiency weakens immune surveillance. Exposure to herbicides such as Agent Orange and glyphosate increases MGUS incidence. Insufficient UV exposure, by reducing vitamin D synthesis, elevates MM risk. Gut microbiota dysbiosis (enrichment of nitrogen-cycle bacteria and depletion of short-chain fatty acids producers) induces chromosomal instability through the ammonium ion-solute carrier family 12 member 22 (SLC12A2)-NEK2 axis. Therefore, risk-based screening among high-risk populations (e.g., those who are obese, elderly, or chemically exposed), along with early interventions targeting cytogenetic abnormalities [e.g., B cell lymphoma 2 (Bcl-2) inhibitors for t(11;14), ferroptosis inducers for t(4;14)] and modifiable factors (e.g., vitamin D supplementation, gut microbiota modulation), may effectively delay disease progression and improve prognosis.
Humans ; Multiple Myeloma/epidemiology* ; Risk Factors ; Obesity/complications* ; Chromosome Aberrations ; Monoclonal Gammopathy of Undetermined Significance/etiology* ; Gastrointestinal Microbiome ; Vitamin D Deficiency/complications* ; Precancerous Conditions/genetics*

OBJECTIVES: To investigate the effects of live combined Bacillus subtilis and Enterococcus faecium (LCBE) on glucose and lipid metabolism in mice with type 2 diabetes mellitus (T2DM) and circadian rhythm disorder (CRD) and explore the possible mechanisms. METHODS: KM mice were randomized into normal diet (ND) group (n=8), high-fat diet (HFD) group (n=8), and rhythm-intervention with HFD group (n=16). After 8 weeks of feeding, the mice were given an intraperitoneal injection of streptozotocin (100 mg/kg) to induce T2DM. The mice in CRD-T2DM group were further randomized into two equal groups for treatment with LCBE (225 mg/kg) or saline by gavage; the mice in ND and HFD groups also received saline gavage for 8 weeks. Blood glucose level of the mice was measured using a glucometer, and serum levels of Bmal1, PER2, insulin, C-peptide and lipids were determined with ELISA. Colon morphology and hepatic lipid metabolism of the mice were examined using HE staining and Oil Red O staining, respectively, and fecal short-chain fatty acids (SCFAs) was detected using LC-MS; GPR43 and GLP-1 expression levels were analyzed using RT-qPCR and Western blotting. RESULTS: Compared with those in CRD-T2DM group, the LCBE-treated mice exhibited significant body weight loss, lowered levels of PER2, insulin, C-peptide, total cholesterol (TC) and LDL-C, and increased levels of Bmal1 and HDL-C levels. LCBE treatment significantly increased SCFAs, upregulated GPR43 and GLP-1 expressions at both the mRNA and protein levels, and improved hepatic steatosis and colon histology. CONCLUSIONS LCBE ameliorates lipid metabolism disorder in CRD-T2DM mice by reducing body weight and improving lipid profiles and circadian regulators possibly via the SCFAs/GPR43/GLP-1 pathway.
Animals ; Mice ; Lipid Metabolism ; Diabetes Mellitus, Type 2/metabolism* ; Enterococcus faecium ; Glucagon-Like Peptide 1/metabolism* ; Bacillus subtilis ; Diabetes Mellitus, Experimental/metabolism* ; Circadian Rhythm ; Blood Glucose/metabolism* ; Receptors, G-Protein-Coupled/metabolism* ; Fatty Acids, Volatile/metabolism* ; Male ; Chronobiology Disorders/metabolism*

Objective To evaluate the clinical efficacy of a novel sleep-aid decoction in treating elderly insomnia patients with different traditional Chinese medicine (TCM) constitutional types, and its effects on neurotransmitter and inflammatory factor levels. Methods A total of 200 patients with four different TCM constitutions-peaceful, Qi-deficient, Yin-deficient, and Yang-deficient-were recruited. Peripheral blood neurotransmitter and inflammatory factor levels were measured for variations among insomnia patients across different constitutions. These patients were treated using the novel sleep-aid decoction, the effects of which were evaluated based on changes in neurotransmitters and inflammatory factors. Results Compared to the peaceful constitution group, insomnia patients with Qi-deficient, Yin-deficient, and Yang-deficient constitutions exhibited significantly elevated baseline levels of neurotransmitters (5-HT, GABA) and inflammatory factors (IL-6, TNF-α, IL-1β, CRP). Following the treatment, the Qi-deficient and Yin-deficient groups showed a marked increase in 5-HT levels, restored balance of Glu, GABA, and melatonin, and significant reductions in IL-6 and TNF-α levels. The overall effective rate was 83.5%, with optimal efficacy observed in the Qi-deficient (97.72%) and Yin-deficient (95.34%) groups. Conclusion The novel sleep-aid decoction is effective in treating insomnia in elderly patients, with the best results observed in the Qi-deficient and Yin-deficient constitution groups.
Humans ; Sleep Initiation and Maintenance Disorders/blood* ; Aged ; Male ; Female ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Middle Aged ; Tumor Necrosis Factor-alpha/blood* ; Sleep Aids, Pharmaceutical/therapeutic use* ; Anti-Inflammatory Agents/therapeutic use* ; Interleukin-6/blood* ; Interleukin-1beta/blood* ; Neurotransmitter Agents/blood* ; Aged, 80 and over ; C-Reactive Protein/metabolism*

OBJECTIVE: To screen novel diagnostic marker or therapeutic target for multiple myeloma (MM). METHODS: Sel1L, SPAG4, KCNN3 and PARM1 were identified by bioinformatics method based on GEO database as high expression genes in MM. Their RNA and protein expression levels in bone marrow mononuclear cells from myeloma cell lines U266, NCI-H929, MM.1s, RPMI8226 and leukemia cell line THP1, as well as 31 MM patients were evaluated by RT-PCR and Western blot, respectively. Meanwhile, 5 samples of bone marrow from healthy donors for allogeneic hematopoietic stem cell transplantation were employed as controls. RESULTS: Compared with leukemia cell line THP1, the expression levels of KCNN3, PARM1 and Sel1L mRNA were significantly increased in myeloma cell lines U266, NCI-H929 and MM.1s, while PARM1 was further increased in myeloma cell lines 8226. Western blot showed that the 4 genes were all expressed in the 4 myeloma cell lines. Compared with healthy controls, the expression levels of Sel1L, SPAG4, KCNN3 and PARM1 mRNA were significantly higher in MM patients (all P < 0.05). Western blot showed that the 4 genes were all expressed in MM patients, and the protein expression level of Sel1L and KCNN3 were significantly different compared with healthy donors (all P < 0.01). CONCLUSION Sel1L, SPAG4, KCNN3 and PARM1 may be potential diagnostic markers and therapeutic targets for MM.
Humans ; Multiple Myeloma/genetics* ; Cell Line, Tumor ; Proteins/metabolism* ; Computational Biology ; RNA, Messenger/genetics*

OBJECTIVE: To explore the construction method of a resistant multiple myeloma (MM) patient-derived xenotransplantation (PDX) model. METHODS: 1.0×10⁷ MM patient-derived mononuclear cells (MNCs), 2.0×10⁶ MM.1S cells and 2.0×10⁶ NCI-H929 cells were respectively subcutaneously inoculated into NOD.CB17-Prkdc^scid Il2rg^tm1/Bcgen (B-NDG) mice with a volume of 100 μl per mouse to establish mouse model. The morphologic, phenotypic, proliferative and genetic characteristics of PDX tumor were studied by hematoxylin-eosin staining, immunohistochemical staining (IHC), cell cycle analysis, flow cytometry and fluorescence in situ hybridization (FISH). The sensitivity of PDX tumor to bortezomib and anlotinib monotherapy or in combination was investigated through cell proliferation, apoptosis and in vitro and in vivo experiments. The effects of anlotinib therapy on tumor blood vessel and cell apoptosis were analyzed by IHC, TUNEL staining and confocal fluorescence microscope. RESULTS: MM PDX model was successfully established by subcutaneously inoculating primary MNCs. The morphologic features of tumor cells from MM PDX model were similar to those of mature plasma cells. MM PDX tumor cells positively expressed CD138 and CD38, which presented 1q21 amplification, deletion of Rb1 and IgH rearrangement, and had a lower proliferative activity than MM cell lines. in vitro, PDX, MM.1S and NCI-H929 cells were treated by bortezomib and anlotinib for 24 hours, respectively. Cell viability assay showed that the IC₅₀ value of bortezomib were 5 716.486, 1.025 and 2.775 nmol/L, and IC₅₀ value of anlotinib were 5 5107.337, 0.706 and 5.13 μmol/L, respectively. Anlotinib treatment increased the apoptosis of MM.1S cells (P < 0.01), but did not affect PDX tumor cells (P >0.05). in vivo, there was no significant difference in PDX tumor growth between bortezomib monotherapy group and control group (P >0.05), while both anlotinib monotherapy and anlotinib combined with bortezomib effectively inhibited PDX tumor growth (both P < 0.05). The vascular perfusion and vascular density of PDX tumor were decreased in anlotinib treatment group (both P < 0.01). The apoptotic cells in anlotinib treatment group were increased compared with those in control group (P < 0.05). CONCLUSION Bortezomib-resistant MM PDX model can be successfully established by subcutaneous inoculation of MNCs from MM patients in B-NDG mice. This PDX model, which retains the basic biological characteristics of MM cells, can be used to study the novel therapies.
Animals ; Bortezomib ; Humans ; Multiple Myeloma/pathology* ; Mice ; Apoptosis ; Drug Resistance, Neoplasm ; Cell Line, Tumor ; Xenograft Model Antitumor Assays ; Mice, Inbred NOD ; Disease Models, Animal ; Cell Proliferation ; Transplantation, Heterologous

OBJECTIVE: To explore the clinical significance of 26 circulating tumor DNA (ctDNA) associated with multiple myeloma (MM) in peripheral blood of new diagnosed patients. METHODS: We conducted a study to detect 26 ctDNA mutations in the peripheral blood of 31 newly diagnosed multiple myeloma (NDMM) patients. RESULTS: Among the 31 NDMM patients, the ctDNA detection rate was 93.55%, significantly higher than that of FISH and chromosome screening methods. The most frequently mutated genes in NDMM were ACTG1 and GNAS. Notably, ACTG1 mutations were exclusive to NDMM patients, furthermore, resulted from the missense mutation of the exon 4. ACTG1 was the gene most frequently co-mutated with others. All patients with ACTG1 mutations were surviving, and there was a positive correlation between ACTG1 mutation and the survival of patients. GNAS mutations were confined to exon 1. CONCLUSION The detection rate of ctDNA sequencing in peripheral blood of NDMM patients was higher than that in bone marrow. ACTG1 and GNAS genes have a guiding role in the prognosis of newly diagnosed patients.
Humans ; Multiple Myeloma/blood* ; Circulating Tumor DNA/genetics* ; Mutation ; Prognosis ; GTP-Binding Protein alpha Subunits, Gs/genetics* ; Chromogranins ; Male ; Female ; Middle Aged

OBJECTIVE: To investigate the predictive value of serum soluble interleukin-2 receptor(sIL-2R), tumor necrosis factor alpha(TNF-α), IgG and IgA for the recurrence in patients with multiple myeloma(MM). METHODS: A total of 108 MM patients who were initially diagnosed and treated in our hospital from January 2017 to March 2019, and 72 patients who met the diagnostic criteria and had complete follow-up data were selected as the study subjects. MM recurrence was the endpoint event, and follow-up was conducted until the occurrence of the endpoint event or the deadline of this study. MM patients were divided into recurrent group(RG) and non-recurrent group(NRG) based on whether they have relapsed or not. Venous blood was collected from patients at the first diagnosis and follow-up (at the occurrence of endpoint events or termination of the study), and enzyme-linked immunosorbent assay(ELISA) was used to detect sIL-2R and TNF-α levels in the patient's serum. An automatic immune analyzer was used to detect the levels of IgG and IgA in the patient's serum. The differences in expression levels of the factors between two groups were compared and the correlations between sIL-2R and TNF-α, IgG and IgA at the first diagnosis and follow-up were analyzed. At the same time, venous blood was collected from patients during complete remission, and their serum sIL- 2R levels were measured to compare the differences in sIL-2R expression levels at the first diagnosis, complete remission and recurrence. Receiver operating characteristic(ROC) curves was used to determine the optimal cutoff values for serum sIL-2R, TNF-α, IgG and IgA, and the predictive value of sIL-2R, TNF-α, IgG and IgA in the recurrence of MM patients were analyzed based on the area under the curve(AUC). RESULTS: The serum sIL-2R levels of MM patients at the first diagnosis and recurrence were significantly higher than at complete remission (P < 0.05). At the first diagnosis, the hemoglobin content of RG was lower than that of NRG, while the β₂-microglobulin content was higher than that of NRG (P < 0.001). There was no significant difference in other clinical parameters between the two groups (P >0.05). The levels of sIL-2R, TNF-α, IgG and IgA at the first diagnosis and follow-up of RG were higher than those of NRG (P < 0.05). There was a significant correlation between sIL-2R and TNF-α, IgG and IgA at the first diagnosis and follow-up (P < 0.001). The ROC curve showed that, at the first diagnosis, sIL-2R, TNF-α, IgG and IgA predicted the AUC of MM patients were 0.919, 0.850, 0.766 and 0.795, respectively, after follow-up, they predicted AUC of MM were 0.890, 0.815, 0.760 and 0.794, respectively (P < 0.001). CONCLUSION The serum sIL-2R has the highest predictive value for MM patient's recurrence, and it is possible to detect the TNF-α, IgG and IgA levels at specific times to infer changes in sIL-2R levels and evaluate the patient's prognosis.
Humans ; Multiple Myeloma/blood* ; Immunoglobulin A/blood* ; Immunoglobulin G/blood* ; Tumor Necrosis Factor-alpha/blood* ; Receptors, Interleukin-2/blood* ; Recurrence ; Male ; Female ; Neoplasm Recurrence, Local ; Middle Aged ; Prognosis