Microchromosome maintenance protein 2 is essential in immune infiltration-correlated prognosis of multiple myeloma patients by regulating cell cycle

Aziguli Maihemaiti; Lu Tang; Hui Zhou; Zhao Xu; Jifeng Jiang; Jianguo Fang; Yifeng Sun

doi:10.1007/s13402-026-01241-2

. 2026 Jun 12;49(5):112. doi: 10.1007/s13402-026-01241-2

Microchromosome maintenance protein 2 is essential in immune infiltration-correlated prognosis of multiple myeloma patients by regulating cell cycle

Aziguli Maihemaiti ^1,^#, Lu Tang ^3,^#, Hui Zhou ^4,^#, Zhao Xu ⁵, Jifeng Jiang ⁶, Jianguo Fang ², Yifeng Sun ^2,^✉

PMCID: PMC13486458 PMID: 42286309

Abstract

Purpose

Multiple myeloma (MM) is a common hematological malignancy, while the prognostic value of tumor-infiltrating immune cells in MM remains elusive. This study aimed to construct an immune-related prognostic model and identify potential therapeutic targets for MM.

Methods

RNA-seq and clinical data of 751 newly diagnosed MM patients were analyzed. LASSO regression was applied to establish an immune pathway-based prognostic model for patient risk stratification. WGCNA was used to screen hub genes, and in vitro and in vivo experiments validated gene functions and therapeutic effects.

Results

Five immune cell pathways were significantly correlated with MM prognosis. MCM2 was identified as the key hub gene associated with risk scores. MCM2 knockdown induced G2-phase cell cycle arrest and suppressed MM proliferation both in vitro and in vivo. Moreover, MCM2 inhibition enhanced the antitumor efficacy of PD1/PDL1 inhibitor BMS1, and CDK inhibitor PHA767491 sensitized MM to immunotherapy.

Conclusion

The immune-based model reliably predicts MM prognosis. MCM2 serves as a vital prognostic biomarker. Targeting MCM2 combined with PD1/PDL1 and CDK inhibitors represents a promising therapeutic strategy for MM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13402-026-01241-2.

Keywords: Multiple myeloma, Immune infiltration, Microchromosome maintenance protein 2, Cell cycle

Introduction

Multiple myeloma (MM) is the second most common hematological malignancy and its morbidity is increasing globally, particularly in individuals aged > 50 [1]. Traditionally, MM has been characterized by hypercalcemia, renal insufficiency, anemia, and bone lesions [2]. Despite prolonged overall survival owing to the emergence of proteasome inhibitors, immunomodulatory drugs, and CD38 monoclonal antibodies, patients with MM are still considered incurable. MM relapses and becomes refractory to several lines of therapy [3]. Currently, there are few treatment options for patients with relapsed/refractory disease. Therefore, hematologists need new treatment strategies for MM patients. In recent years, the therapeutic landscape of MM has undergone a paradigm shift, with research focus moving away from immune checkpoint inhibitors (ICIs) toward cellular immunotherapies, particularly bispecific antibodies and CAR-T cell therapies. Bispecific agents targeting BCMA and GPRC5D have achieved deep and durable responses in relapsed/refractory MM, while several CAR-T products have delivered high rates of minimal residual disease (MRD) negativity. These innovative modalities are increasingly being investigated in earlier lines of therapy, including first relapse and frontline settings, and their combinations with conventional regimens have further improved treatment efficacy. In parallel, novel strategies such as antibody–drug conjugates (ADCs) and off-the-shelf CAR-T cells continue to emerge, expanding the precision therapeutic armamentarium for MM. Unfortunately, MM progression involves fewer lymphocytes and exhibits moderate clonal mutations, indicating that immune checkpoint inhibitor (ICI) PD-1/PD-L1 inhibitors do not play a dominant role in the overall progression of MM [4]. An analysis of the efficacy of single drugs and hematological malignancies such as Hodgkin’s lymphoma, follicular lymphoma, and diffuse large B-cell lymphoma showed that patients responded better [5–7]; whereas MM exhibited poor efficacy with PD-1/PD-L1 inhibitors [8, 9]. The effectiveness of PD-1/PD-L1 inhibitors against tumors depends on two factors: tumorigenesis mechanism and lymphocytes infiltration [10]. To explore whether PD-1/PD-L1 inhibitors or other immunomodulators can function in MM, we focused on tumor-infiltrating immune cells in the tumor microenvironments (TMEs).

TMEs are critical for cancer progression and metastasis [11, 12]. There is evidence that tumor-related immune cells are closely associated with the outcome of patients with different cancers [13–15]. However, few studies have clarified the prognostic value of tumor-infiltrating immune cells in MM patients. Furthermore, a deep understanding of immune infiltration has led to checkpoint inhibitor-based immunotherapy. ICIs block the interaction between immune and neoplastic cells and enhance antitumor immunity [16]. Although the emergence of ICIs has tremendously improved the treatment of various hematological malignancies, patients with MM seem to be unresponsive to ICIs [17]. It is hypothesized that some unknown factors in the tumor microenvironment might have inhibited the effect of ICIs, and the critical challenge is to identify the factors that play an important role.

Herein, we managed to indicate the prognostic value of tumor-infiltrating lymphocytes (TILs) and clarify the hub gene influencing immune infiltration-correlated prognosis in MM patients.

Materials and methods

Estimation of tumor-related immune cell infiltration

Gene sets for 28 specific subpopulations of tumor-infiltrating immune cells were constructed by Charoentong et al. [10]. The infiltration levels of these subpopulations were estimated using Single-Sample Gene Set Enrichment Analysis (ssGSEA) [18] in the R Bioconductor package. Gene Set Variation Analysis (GSVA, v.3.5) and standardized enrichment scores (NES) were calculated. ESTIMATE (https://bioinformatics.mdanderson.org/estimate/) was used to calculate the tumor purity of each CD138 + MM sample. RNA-seq data and corresponding clinical profiles were downloaded from the MMRF CoMMpass database using the UCSC Xena browser (https://xenabrowser.net). The expression data of CD138 + multiple myeloma cells in the bone marrow were selected for further analysis.

Construction of prognosis risk model

Kaplan–Meier plots were plotted to analyze overall survival (OS) and compared using the log-rank test. Statistical significance was set at P < 0.05. Least absolute shrinkage and selection operator (Lasso) regression was performed to obtain variables correlated with survival outcomes, and the risk score was calculated as follows:

where n is the number of tumor-infiltrating immune cells that are univariant-correlated with OS, NES_i is the NES of immune cell subpopulation i, and β_i is the coefficient of i calculated by LASSO regression. We considered the median risk score as the cut-off, and patients with MM were divided into low- and high-risk groups. We then constructed a nomogram model based on stepwise Cox regression. Decision curve analysis (DCA) and concordance index (C-index) were used to evaluate the discrimination ability of the nomogram model, and a calibration curve was used to evaluate the agreement of our model.

Weighted gene co-expression network analysis (WGCNA)

We performed WGCNA using the R package [19], and the gene count matrix was filtered to remove zero-variance genes. In this study, we chose R² = 0.88 and a soft-threshold β = 12. Subsequently, the adjacency matrix was transformed into a topological overlap matrix (TOM) to obtain the corresponding module eigengenes (MEs). We evaluated the association between MEs and clinical traits (including risk scores, status, sex, and ISS stage) and identified a significant module. The correlation of the genes with the specific ME was assessed using Pearson’s correlation, and the gene with the highest correlation coefficient was considered the hub gene. The correlation coefficients were calculated based on module membership. Gene Ontology (GO) enrichment analysis was conducted using the R package cluster profiler [20] to reveal the unique biological significance associated with genes in a specific ME.

Patients

Histological diagnosis of MM was established based on the IMWG 2014 criteria [21]. This study was approved by the Shanghai Ninth People’s Hospital Review Board. Informed consent was obtained from all the patients in accordance with the Declaration of Helsinki. Bone marrow-derived mononuclear cells (BMMCs) were isolated from bone marrow samples of patients with MM by ficoll-isopaque centrifugation. 88 MM patients and 3 healthy individuals were included in the study.

Cells and reagents

The MM cell line H929 was cultured in RPMI-1640 medium containing 10% heat-inactivated fetal bovine serum (FBS) in a humidified atmosphere of 95% air and 5% CO2 at 37 °C. The MM cell line, NCI-H929, was purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). BMS1 and PHA-767,491 were purchased from Selleck (Shanghai, China). We quantified nine soluble biomarkers (PD‑L1, IDO1, CTLA‑4, VISTA, CXCL10, CCL5, IFN‑β, CXCL9, IL‑6) using custom‑configured MILLIPLEX Luminex xMAP multiplex immunoassays from Merck Millipore (Massachusetts, USA). SigmaAll experiments were performed using mycoplasma-free cells.

Detection of gene expression

Total RNA was extracted from the BMMCs and cell lines. Gene expression was analyzed by real-time RT-PCR using a 7500HT Fast Real-time PCR system (Applied Biosystems, Foster City, CA, USA). The primers used are listed below.

Gene	Forward(5ʹ-3ʹ)	Reverse(5ʹ-3ʹ)
GAPDH	GAAGGTGAAGGTCGGAGTC	GAAGATGGTGATGGGATTTC
MCM2	TCGGAGAGAAGGTAGGTGGA	TCCCCAATGCGAGTTTACCT
PD1(PDCD1)	GACTTGGCCCCTTACTGACT	GAGGCAGAGGTGGGAGAATT
PDL1(CD274)	TTCCTTTCCCTCTTGGCCAT	CAGCCACAATTCTTGCCTGT

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Relative expressions were calculated by the method of 2^−ΔΔCT

Western blot

Western blot was performed as described previously [22]. Antibodies against MCM2 (ab108935) and GAPDH (ab9485) were purchased from Abcam (Cambridge, USA). GAPDH was used to ensure equivalent protein loading for all samples.

Cell transfection

The vector plasmid was linearized using restriction endonucleases, and the target gRNA sequence was used to construct a CRISPR/Cas 9 vector with the target sequence. Lentiviral vector was extracted from the vector plasmid. Lenti-virus with MCM2-KD or control vector was synthesized by Genomeditech (Shanghai, China). H929 cells were infected with the lenti-virus at a cell-virus ratio of 1:100. The transfected clones were detected 72 h after transfection.

Cell proliferation assay

Cells were seeded at a density of 1 × 10⁵ cells/well in 6 well plates and incubated at 37 °C. Cell proliferation was measured after 24 and 48 h respectively and 0.1 mg CCK8 (CK04, DOJINDO Laboratories, JAPAN) was added to each well. The absorbance was measured at 490 nm using a spectrophotometer.

Establishment of a mice subcutaneous tumor model

B-NDG mice (strain name: NOD.Cg-Prkdc^scid/NifdcSmoc, catalog number: SM-019) with severe immunodeficiency were purchased from Shanghai Model Organism. Mice at four weeks of age were selected and fed in a room with no specific pathogen-free (SPF) level (stable temperature between 22–29℃, relative humidity between 40% and 70%, and good ventilation and air exchange, with a high-efficiency air filtration system that filters out particles with a diameter greater than 0.3 microns). 2.5 × 10⁶ H929 lentivirus stable transgenic cells were injected subcutaneously into the right shoulder of the mice. Mice were sacrificed by cervical dislocation, which resulted in instant death 21 days after tumor formation or when the length of the tumor was > 20 mm. Four mice each were used in the test group and 4 in control groups.

Immunohistochemistry

Immunohistochemical analyses were performed with an indirect immunoperoxidase method by antibodies against MCM2 (ab95361, Abcam, Cambridge, MA, USA) and CD3 (ab11089, Abcam, Cambridge, MA, USA). Anti-rabbit-IgG was purchased from Invitrogen (Carlsbad, CA). The nuclei were stained with DAPI.

Cell cycle analysis

The cell cycle was assessed based on the distribution of the nuclear DNA content. Cells were collected, washed in phosphate-buffered saline (PBS), fixed overnight in 75% ethanol at − 20 °C, treated with 1% RNase A for at least 15 min at 37 °C, and stained with 50 µg/ml propidium iodide included in the Cell Cycle and Apoptosis Analysis Kit (HY-K1071, MedChemExpress LLC, Shanghai, China). After staining, the cells were detected using flow cytometry, and the data were analyzed using FlowJo 7.6.

Collection of mononuclear cells and cell co-culture

First, the normal peripheral blood specimen was centrifuged at 3,000 rpm for 10 min and the supernatant was collected as plasma. The sediment was added to an equal volume of human lymphocyte separation solution (Hao Yang Biological, Tianjin, China; LTS1077). After centrifugation at 3,000 rpm for 15 min, the middle white suspension layer and mononuclear cell layer were carefully pipetted into a new centrifuge tube and washed three times with PBS. Mononuclear cells were cultured in the upper compartment, while MM cells were cultured in the lower compartment. The upper and lower compartments were separated by polycarbonate membranes (0.4 μm pore size).

Statistical analysis

Each experiment was repeated three times, and the experimental results obtained from three separate experiments were analyzed using a t-test to compare variances. Statistical significance was set at P < 0.05.

Results

Five immune cell pathways univariately correlated with outcomes of MM patients

To determine the prognostic value of tumor-infiltrating lymphocytes (TILs) and clarify the hub genes influencing immune infiltration-correlated prognosis in patients with MM, 751 newly diagnosed patients with MM were enrolled after combining RNA-seq data and clinical profiles.

Although CD138 + myeloma cells were isolated from the bone marrow mononuclear cells before further calculations, it was impossible to eliminate all other cells during purification. Therefore, we performed ESTIMATE to determine the tumor purity in all samples. The average tumor purity of the myeloma samples was 0.874 and the median tumor purity was 0.888. The standard deviation was 0.062 and the coefficient of variation was 0.071, demonstrating excellent agreement between the samples (Fig. 1A). The enrichment scores of 28 subpopulations of tumor-infiltrating immune cells were obtained using ssGSEA. We then described the landscape of these subpopulations in myeloma samples (Fig. 1B).

Fig. 1 — Five immune cell pathways univariately correlated with outcomes of MM patients. (A) The tumor purity of multiple myeloma samples. (B) The landscape of tumor-infiltrating immune cells in MM samples. (C-G) The effects of tumor-infiltrating immune cell subpopulations on the prognosis of MM patients. The activation of CD4 + T cells (C, hazard ratio 1.486, 95% CI of ratio 0.9974 to 2.213), CD8 + T cells (D, hazard ratio 2.121, 95% CI of ratio 1.350 to 3.334), Type2 T helper cells (E, hazard ratio 1.666, 95% CI of ratio 1.092 to 2.543), and memory B cells (F, hazard ratio 1.924, 95% CI of ratio 1.257 to 2.945) was correlated to poor prognosis; the activation of immature B cells (G, hazard ratio 0.7334, 95% CI of ratio 0.5037 to 1.068) was correlated to a better prognosis

The log-rank test and Kaplan–Meier plots were used to determine the prognostic value of the immune cell pathways. Among the 28 immune cell pathways, the activation of CD4 + T cells (Fig. 1C), CD8 + T cells (Fig. 1D), Type 2 T helper cells (Fig. 1E), and memory B cells (Fig. 1F) was correlated with poor prognosis in MM patients. In contrast, the activation of immature B cells (Fig. 1G) was associated with good prognosis in patients with MM.

Patients in the high-risk of immune infiltration group have poor prognosis independently of the ISS stage

LASSO regression was used to calculate the risk score based on the five aforementioned prognosis-related immune pathways, and the minimum value of lambda was 0.0045 (Fig. 2A). After calculation, the risk score = 6.595×NES of activated CD4 + T cell pathway + 3.34×NES of Type II T helper cell pathway + 1.397×NES of memory B cell pathway + 0.164×NES of activated CD8 + T cell pathway − 7.909×NES of immature B cell pathway was calculated (Fig. 2B). Subsequently, all 751 patients with MM were classified into high- and low-risk groups based on the median value of the risk score (Fig. 2C).

As shown in Fig. 2D, the high-risk group had poorer prognosis than the low-risk group. We constructed a nomogram model based on stepwise Cox regression, including ISS stage, risk groups, and sex (Fig. 2E). Patients in the high-risk group had a poor prognosis after excluding the influence of the ISS stage. The concordance index was 0.6998 after the calculations.

MCM2 was screened as the hub gene associated with the risk score

The expression data and clinical profiles of 751 patients with MM were included in the WGCNA database. Prior to further analysis, genes without any variance were removed. A sample clustering tree was plotted and one sample (MMRF_2015) was deleted based on the sample dendrogram (Fig. 3A). The soft threshold was set at 12 (R² = 0.88) to construct a scale-free network based on the scale-free index and the mean connectivity analysis (Fig. 3B). Forty-four module eigengenes were identified after clustering, and the black module which contained 1046 genes was highly correlated with the risk score (Fig. 3C). Gene significance for the risk score and module membership in the black module showed a strong correlation, demonstrating that genes in the black module were highly correlated with risk scores (Fig. 3D).

Fig. 3 — Hub gene associated with the risk score was screened by WGCNA and GO analysis. (A) Clustering dendrogram of 751 samples. (B) Determination of soft-threshold power in the WGCNA. (C) Heatmap of the correlation between the module eigengenes and clinical traits of MM patients. (D) The correlation between black modules and genes in black modules. (E) GO-biological process (GO-BP) enrichment analysis. (F) GO-cellular component (GO-CC) enrichment analysis. (G) GO-molecular function (GO-MF) enrichment analysis

Then, Gene Ontology (GO) enrichment analysis was performed to figure out the functions of these genes in the black module. The genes were significantly enriched in organelle fission, nuclear division, and chromosome segregation based on GO-biological process (GO-BP) enrichment analysis (Fig. 3E). GO cellular component enrichment analysis revealed that the genes were significantly enriched in the chromosomal region, spindle, and centromeric regions of chromosomes by GO-cellular component (GO-CC) enrichment analysis (Fig. 3F). Furthermore, the genes were significantly enriched in ATPase activity, catalytic activity acting on DNA, and tubulin binding based on GO-molecular function (GO-MF) enrichment analysis (Fig. 3G). In our WGCNA analysis, microchromosome maintenance protein 2 (MCM2) was identified as the hub gene in the black module based on the highest module membership. We then performed functional enrichment analysis for genes within the black module, which showed that this module was mainly enriched in proliferation-related biological processes, including cell cycle, nuclear division, and chromosome segregation. Based on these findings, MCM2 was selected as the representative hub gene most closely associated with proliferative activity.

MCM2 expression was related to the prognosis of MM patients and involved in the cell cycle and m-TOR pathways

A total of 751 MM patients were divided into MCM2 high expression group and MCM2 low expression group using the median MCM2 expression value as the cut-off value. As shown in Fig. 4A, the OS of patients with MM with low MCM2 expression was significantly longer than that of patients with high MCM2 expression. We constructed a nomogram model based on stepwise Cox regression, including ISS stage, risk groups, and sex. A nomogram graph (Fig. 4B) was constructed to quantify the specific survival probability of MM patients. Based on the statistically significant variables in the multivariate stepwise Cox regression model, the nomogram graphs could predict 1-, 2-, and 4-year OS and PFS.

Fig. 4 — MCM2 expression was related to the prognosis of MM patients and involved in cell cycle and m-TOR pathways. (A) The Kaplan-Meier curve was plotted to evaluate the overall survival of different MCM2 expression groups of MM patients (P<0.0001, hazard ratio 2.416, 95% CI of ratio 1.635 to 3.568). (B) The nomogram showed a quantifiable predictive model including the expression of MCM2. (C) The upper panel presented a linear relationship between the expression of MCM2 and the G2M checkpoint, as illustrated by a correlation scatter plot. The nether panel revealed that high expression of MCM2 activated the G2M checkpoint, as determined by GSEA analysis. (D) The upper panel demonstrated a linear relationship between the expression of MCM2 and E2F1, as illustrated by a correlation scatter plot. The nether panel showed that high expression of MCM2 activated the E2F1 targets pathway, as determined by GSEA analysis. (E) The upper panel revealed a linear relationship between the expression of MCM2 and CDK1, as shown by a correlation scatter plot. The nether panel indicated that expression of CDK1 in the high MCM2 expression group was significantly higher than that in the low MCM2 expression group

We used data from 85 patients with MM to confirm the relationship between MCM2 expression and OS and PFS. The baseline characteristics of patients are shown in Supplementary Table 1. As shown in Supplementary Fig. 1, the OS and PFS of MM patients with low MCM2 expression were significantly longer than those of patients with high MCM2 expression (P = 0.04 and 0.02, respectively).

To further analyze the correlation between MCM2 expression and pathways, we divided the patients into MCM2 high and low expression groups. Differentially expressed genes were identified using volcano plots and GSEA, and we found that E2F and G2M had the highest correlation (Fig. 4C and D), which was consistent with the results of the correlation analysis. We also found that MCM2 expression was highly correlated with CDK1 expression (Fig. 4E).

Knockdown of MCM2 inhibited proliferation by regulating the cell cycle and activating the effect of the PD1 inhibitor in MM

To investigate the function of MCM2 in MM cells, we first compared the expression of MCM2 between initial MM bone marrow samples and control samples. As shown in Fig. 5A, the expression level of MCM2 in patients with MM was significantly higher than that in normal controls, at both the transcriptional and protein levels. We then chose the H929 cell line to construct a stable transgenic MM cell line with knockdown of MCM2 (MCM2-KD, Fig. 5B). To explore the effects of MCM2 knockdown on MM tumor proliferation in vivo, we established a mouse MM subcutaneous tumor model by subcutaneously injecting H929 cells. As shown in Fig. 5C, three out of four mice injected with MCM2-NC developed tumors, whereas only one out of four mice injected with MCM2-KD developed tumors.

Fig. 5 — Knockdown of MCM2 inhibited proliferation by regulating cell cycle and activating the effect of PD1 inhibitor in MM. (A) Left panel showed that MCM2 was highly expressed in MM patients at the transcriptional level (P = 0.0373, N = 3). Right panel showed MCM2 was also highly expressed in MM patients at the protein level (N = 2). (B) The H929 cell line was selected to construct stable transgenic MM cell lines with MCM2 knockdown (MCM2-KD, P = 0.005). (C) Mice MM subcutaneous tumor model was established by subcutaneous injection of H929 cells. Three out of four mice with MCM2-NC cells injection developed tumors whereas only one out of four mice with MCM2-KD cells injection developed a tumor. (D) The expression of MCM2 and CD3 in Mice MM subcutaneous tumors was measured by immunohistochemistry. The expression of CD3 was higher in MCM2-KD tumors (P = 0.0018). (E) The cell cycle results for MCM2-NC, MCM2-KD, MCM2-NC+BMS1 and MCM2-NC+BMS1 cells were analyzed by flow cytometry. The proportion of cells in the G2/G1 phase in MCM2-KD cells was higher than that in MCM2-NC cells (P = 0.0015). The proportion of cells in the G2/G1 phase in MCM2-KD+BMS1 cells was higher than that in MCM2-NC+BMS1 cells (P = 0.0029). The proportion of G2/G1 in MCM2-KD+BMS1 cells was higher than that in MCM2-KD cells (P = 0.0236). (F) Left panel showed the growth curves of MCM2-NC and MCM2-KD cells. Middle panel showed the growth curves of MCM2-NC+BMS1 and MCM2-KD+BMS1 cells. After 48 h of culture, the optical density (OD) value of the MCM2-KD cells was lower than that of the MCM2-NC cells (P < 0.0001). The OD value of the MCM2-KD+BMS1 cells was lower than that of the MCM2-NC+BMS1 cells (P = 0.0047). OD value of the MCM2-KD+BMS1 cells was lower than that of the MCM2-KD cells (P = 0.0288). (G) The expression of MCM2 was lower in PHA-767,491 than control (P = 0.0194). (H) The results of the cell cycle analysis for Control, PHA-767,491, BMS1 and BMS1 + PHA-767,491 cells were shown by flow cytometry. The proportion of G2/G1 phase cells in PHA-767,491-treated cells was higher than that in Control cells (P = 0.0005). The proportion of G2/G1 phase cells in BMS1 + PHA-767,491-treated cells was higher than that in BMS1-treated cells (P = 0.0004). The proportion of G2/G1 phase cells in BMS1 + PHA-767,491-treated cells was higher than that in BMS1-treated cells (P < 0.0001). (I) MM cells were co-cultured with normal mononuclear cells. Left panel showed growth curve of Control and PHA-767,491 cells. Middle panel showed growth curve of BMS1 + PHA-767,491 and BMS1 cells. When cells were cultured for 48 h, OD value of PHA-767,491 was lower than that of Control (P < 0.0001). OD value of BMS1 + PHA-767,491 was lower than that of PHA-767,491 (P = 0.0377). OD value of BMS1 + PHA-767,491 was lower than that of BMS1 (P = 0.0111)

We used the T-cell surface marker CD3 to detect the effect of MCM2 on tumor immune infiltration. Interestingly, CD3 expression significantly increased in MCM2-KD cells (Fig. 5D). These results inferred that the expression of MCM2 inhibited T cell immune infiltration in MM cells.

Because of the close correlation between the expression of MCM2 and the cell cycle pathway by WGCNA analysis, we used flow cytometry to investigate the different conditions of the cell cycle in MCM2-NC and MCM2-KD cells (Supplementary Fig. 2). Compared with MCM2-NC cells, the proportion of G2 phase cells in the MCM2-KD cells was significantly higher. When we added BMS1, a small molecule inhibitor of PD1, at a concentration of 6nM to both cells, we found that BMS1 had no significant effect on the cell cycle of MCM2-NC and MCM-KD cells (Fig. 5E). The CCK8 assay was used to detect cell proliferation. We found that the proliferative ability of MCM2-KD cells was significantly lower than that of MCM2-NC cells, whereas the addition of BMS1 had no significant effect on cell proliferation (Fig. 5F). However, when MCM2 was knocked down, BMS1 significantly increased the proportion of G2 phase cells and reduced cell proliferation (Fig. 5E and F).

Combining CDK inhibitor with PD1 inhibitor reduced the ability of cell proliferation of MM cells by increased the proportion of G2 phase cells

Because of the significant correlation between the expression of MCM2 and cell cycle-associated pathways in the previous WGCNA analysis, we chose PHA-767,491, an effective CDK inhibitor, to simulate the effects of MCM2-KD in H929 cells. Similar to the knockdown of MCM2, adding 5µM PHA-767,491 significantly inhibited the expression of MCM2 (Fig. 5G) and increased the proportion of G2 phase cells in H929 cells (Fig. 5H). The addition of BMS1 monotherapy did not increase the number of cells in G2 phase. However, compared to PHA-767,491 monotherapy, the combination of PHA-767,491 and BMS1 significantly increased the proportion of G2 phase cells (Fig. 5H). When the cell cycle was inhibited by PHA-767,491, BMS1 significantly reduced the proliferation of MM cells co-cultured with normal mononuclear cells (Fig. 5I). We utilized the CCK-8 assays to determine the cell inhibition rates of PHA-767,491 and BMS1 alone and in combination. As shown in Supplementary Table 2, the synergy score was + 0.105, indicating a strong synergistic effect between PHA-767,491 and BMS1.

Discussion

The infiltration of activated CD4 + T cells, especially regulatory CD4 + T cells, is associated with poor prognosis in patients with several solid cancers [23, 24]. In our study, the abnormal activation of CD4 + T cells contributed the most to the poor prognosis of patients with MM. CD4 + T cells include T-helper 1, T-helper 2, T-helper 17, and Tregs. Only T-helper 2 cells were independently associated with a poor prognosis. T-helper 2 cells may promote cancer progression by secreting IL-4, IL-6, IL-10, and IL-13. These cytokines can activate M2 macrophages and enhance tumor angiogenesis [25]. In contrast, immature B cells were the only tumor-infiltrating lymphocytes that correlated with good prognosis in patients with MM. Single nucleotide polymorphisms (SNPs) influence the risk of developing MM from immature B cells during class-switch recombination [26].

Risk classification based on tumor-infiltrating lymphocytes can predict the prognosis of patients with MM, independent of the ISS staging system. In this study, we divided patients with MM into high- and low-risk groups based on the median value of risk scores with a cut-off of -0.458. We found that MM patients in the high-risk group experienced poor outcomes, independent of the ISS staging system. Several cytogenetic abnormalities have been confirmed to influence the prognosis of MM patients. Patients with MM with t(11;14), t(14;16), t(14;20), del(17p), and gain(1q) were considered at high group [27]. However, few studies have focused on the prognostic value of tumor-infiltrating lymphocytes. Therefore, we constructed a formula to calculate the contribution of tumor-infiltrating lymphocytes to the prognosis of patients with MM, which provides a new perspective on risk stratification.

We performed WGCNA and found that the black module was most correlated with the risk scores. Genes in the black module were enriched in cell division and cell cycle processes. Therefore, tumor-infiltrating lymphocytes may influence the cell cycle or cell division functions of myeloma cells, affecting the prognosis of patients with MM. Among all genes in the black module, MCM2 was found to be the most correlated with module membership. Therefore, we considered MCM2 to be a hub gene. MCM2 is a part of the MCM complex and often functions with other MCM family members as a hetero-hexamer [28, 29]. MCM2 is responsible for DNA synthesis and replication [30]. In the cell cycle, MCM2 binds to chromatin during the late M to G1 phase to initiate DNA replication, and is separated from the chromatin in the S phase [31]. Overexpression of MCM2 has been detected in multiple types of cancers, and dysfunction of MCM2 has been correlated with the progression and poor prognosis of malignant tumors. In addition, MCM2 is expressed at high levels in proliferating cells and is an effective marker for malignant cells [32, 33]. However, MCM2 is downregulated in differentiated somatic cells [34]. Furthermore, MCM2 is also a prognostic marker for patients with cancers such as prostate cancer [35], lung cancer [32], and MM [36]. However, no evidence supports the conclusion that MCM2 up-regulation is associated with tumor-infiltrating immune cells.

Based on the altered expression of MCM2 and its correlation with the clinicopathological features of cancer patients, MCM2 is regarded as a sensitive biomarker for the diagnosis, prognosis, and chemotherapy response in cancer. The antitumor effect induced by MCM2 inhibition suggests the potential of MCM2 as a novel therapeutic target for cancer treatment. Since DNA replication stress, which may stimulate antitumor immunity, frequently occurs in MCM2-deficient cells, this also indicates that treatment targeting MCM2 improves the effect of tumor immunotherapy [37]. DNA replication stress activates the tumor cell-intrinsic immunity [38, 39]. Since DNA replication stress frequently occurs in MCM2-deficient cells, it is important to explore whether targeting MCM2 can simultaneously inhibit proliferation and stimulate anti-tumor immunity. targeting MCM2 makes cold tumors may enhance the sensitivity of tumor immunotherapy [37]. MCM2 knockdown resulted in a decrease in CD3 expression in mouse tumors, indicating a significant reduction in T cell infiltration in tumors. The above results indicate that targeting MCM2 could increase T cell infiltration in MM, thereby enhancing the efficacy of PD1 inhibitors in MM.

Our results showed that MCM2 knockdown inhibited MM cell proliferation in vitro and in vivo by maintaining the cell cycle in the G2 phase. Moreover, MCM2 knockdown activated the antitumor effects of the PD1/PDL1 inhibitor, BMS1. Therefore, MCM2 may be a potential target for MM treatment. MCM2 knockdown significantly reduced PDL1 and IDO1 protein expression (verified by both in vitro and in vivo experiments, Supplementary Fig. 4A). MCM2-KD had no significant effect on CTLA4 and VISTA expression. MCM2 knockdown upregulated CXCL10, CCL5 and IFN-β, but had no effect on CXCL9 and IL-6 (Supplementary Fig. 4B). This maybe because MCM2 depletion induces DNA replication stress and activates the cGAS-STING-IRF3 axis, which mainly drives type I IFN and related chemokine expression.CXCL9 is more dependent on IFN-γ-STAT1 signaling, while IL-6 is primarily regulated by NF-κB, which was not significantly activated in our model.

The application of cyclin-dependent kinase (CDK) inhibitors may not represent a targeted treatment for MCM2 but rather represent the idea of a sensitized PD1/PDL1 inhibitor acting on MM. Therefore, there is insufficient evidence to confirm the relationship between MCM2 inhibition and PD1/PDL1 inhibitor application. This study demonstrated from beginning to end the screening of immune infiltration-related pathways and genes from a large database and the screening of hub gene signaling pathways through bioinformatics. This information has been used to speculate and validate the sensitizers of ICIs, and new strategies for treating MM have been proposed. In addition, upon MCM2 knockdown, PD-L1 expression was moderately downregulated in MM cells and tissues (Supplementary Fig. 6). We hypothesize that this phenomenon is driven by sustained STING-triggered PD-L1 protein degradation. Activated cGAS-STING signaling drastically boosts chemokine release in MM, facilitating massive infiltration of CD8⁺ effector T cells into tumor lesions and driving prominent PD-1 upregulation on these recruited T lymphocytes. Although PD-1 was rarely expressed in MM cells, the markedly increased PD-1-positive T cells provide abundant binding targets for PD-1 inhibitor BMS1. In addition, elevated tumor neoantigen burden and reduced immunosuppressive TME further sensitized MM to anti-PD1 treatment, resulting in enhanced antitumor synergy despite decreased tumor-intrinsic PD-L1.

When discussing new treatment strategies, immune modulation, cell cycle regulation, and targeting angiogenesis have emerged as the three most important strategies for MM treatment [40]. In addition to the immune modulation and cell cycle regulation mentioned in our study, targeting angiogenesis is extremely significant. For instance, the investigational agent MP0250 simultaneously neutralized VEGF and HGF, thereby inhibiting angiogenesis and potentiating the effects of bortezomib [41]. MP0250 reduces endothelial cell migration and vessel formation both in vitro and in vivo, underscoring the role of angiogenic pathways in sustaining the tumor microenvironment [42]. These findings suggest that the combined targeting of angiogenesis, cell cycle progression, and immune checkpoints could represent a synergistic approach to improving therapeutic outcomes in MM. Future investigations should explore how such multimodal strategies could be integrated with the modulation of hub genes, such as MCM2, to optimize patient responses.

Currently, the classic treatment for MM is based on targeted treatments, such as proteasome inhibitors and iMids. Monoclonal antibodies and bispecific antibodies are also widely used as new targeted therapies. However, immune therapies such as PD1 inhibitors are difficult to be effective, which poses a challenge for the treatment of MM. Early clinical trials have shown that the efficacy of PD-1 inhibitors as monotherapy for MM is low, and some trials have been terminated owing to serious adverse events, such as an increased risk of death. This may be related to the insufficient lymphocyte infiltration [43–45]. Through bioinformatics analysis of immune infiltration and in vitro and in vivo exploration, we found that the absence of immune T-cell infiltration in MM tumors was related to MCM2. New solutions may be provided in this field by inhibiting MCM2 or the cell cycle regulatory pathway in which MCM2 is located. In conclusion, MCM2 may be a potential target for MM treatment. However, it still needs further researches.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1^{(25KB, pdf)}

13402_2026_1241_MOESM2_ESM.tif^{(8.7MB, tif)}

Supplementary Material 2: Supplementary Figure 1. MCM2 expression was related to the prognosis of MM patients. (A) The Kaplan-Meier curve was plotted to evaluate the overall survival of different MCM2 expression groups of MM patients. (B) The Kaplan-Meier curve was plotted to evaluate the progression free survival of different MCM2 expression groups of MM patients

13402_2026_1241_MOESM3_ESM.tif^{(8.6MB, tif)}

Supplementary Material 3: Supplementary Figure 2. The flow-cytometry gating strategy of cell cycle

13402_2026_1241_MOESM4_ESM.jpg^{(1.2MB, jpg)}

Supplementary Material 4: Supplementary Figure 3. The flowchart of the study design

13402_2026_1241_MOESM5_ESM.tif^{(20.1MB, tif)}

Supplementary Material 5: Supplementary Figure 4. Expression of immune escape related proteins and pro-inflammatory chemokines changes after MCM2-KD. (A) MCM2-KD significantly reduced PDL1 and IDO1 protein expression (verified by both in vitro and in vivo experiments). MCM2-KD had no significant effect on CTLA4 and VISTA expression. (B) MCM2 knockdown upregulated CXCL10, CCL5 and IFN-β, but had no effect on CXCL9 and IL-6

13402_2026_1241_MOESM6_ESM.tif^{(2.8MB, tif)}

Supplementary Material 6: Supplementary Figure 5. Internal validation by splitting cohort into a training cohort and a testing cohort

13402_2026_1241_MOESM7_ESM.tif^{(3.6MB, tif)}

Supplementary Material 7: Supplementary Figure 6. Expression of PD1 and PDL1 changes after MCM2-KD. (A) MCM2-KD significantly reduced PDL1 gene expression in H929 cells (P = 0.0065). MCM2-KD had no significant effect on PD1 expression in H929 cells. (B) MCM2-KD significantly reduced PDL1 gene expression in tumor tissues (P = 0.0417). MCM2-KD had no significant effect on PD1 expression in tumor tissues.

Supplementary Material 8^{(18.9KB, docx)}

Supplementary Material 9^{(14KB, docx)}

Acknowledgements

We appreciate the support of all participating hospitals and the hard work of all members. Special thanks to Ms. Hongyan Qiu and Ms. Jingqi Sun for their assistance.

Author contributions

YS, AM and LT designed the experiments. AM, LT, ZX, JJ and YS performed experiments. AM, LT, HZ, ZX and JF analyzed the data. YS, AM, HZ and ZX provided professional writing services and materials, and wrote the manuscript. All the authors met the criteria for authorship and approved the final manuscript for publication.

Funding

This work was supported by the National Natural Science Foundation of China (82100216) and Shanghai Science and Technology Commission Yangfan Program Project (21YF1406200).

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

All animal experiments were approved by the Biomedical Research Ethics Committee of the Shanghai Ninth People’s Hospital. The study involving patients was approved by Shanghai Ninth People’s Hospital. Informed consent was obtained from all the participants in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Aziguli Maihemaiti, Lu Tang and Hui Zhou contributed equally to this work.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(25KB, pdf)}

13402_2026_1241_MOESM2_ESM.tif^{(8.7MB, tif)}

13402_2026_1241_MOESM3_ESM.tif^{(8.6MB, tif)}

Supplementary Material 3: Supplementary Figure 2. The flow-cytometry gating strategy of cell cycle

13402_2026_1241_MOESM4_ESM.jpg^{(1.2MB, jpg)}

Supplementary Material 4: Supplementary Figure 3. The flowchart of the study design

13402_2026_1241_MOESM5_ESM.tif^{(20.1MB, tif)}

13402_2026_1241_MOESM6_ESM.tif^{(2.8MB, tif)}

Supplementary Material 6: Supplementary Figure 5. Internal validation by splitting cohort into a training cohort and a testing cohort

13402_2026_1241_MOESM7_ESM.tif^{(3.6MB, tif)}

Supplementary Material 8^{(18.9KB, docx)}

Supplementary Material 9^{(14KB, docx)}

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

[CR1] 1.J. Huang et al., The epidemiological landscape of multiple myeloma: a global cancer registry estimate of disease burden, risk factors, and temporal trends. Lancet Haematol. 9(9), e670–e677 (2022) [DOI] [PubMed] [Google Scholar]

[CR2] 2.S.V. Rajkumar et al., International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 15(12), e538–e548 (2014) [DOI] [PubMed] [Google Scholar]

[CR3] 3.A.J. Cowan et al., Diagnosis and Management of Multiple Myeloma: A Review. Jama. 327(5), 464–477 (2022) [DOI] [PubMed] [Google Scholar]

[CR4] 4.S.K. Kumar, K.C. Anderson, Immune Therapies in Multiple Myeloma. Clin. Cancer Res. 22(22), 5453–5460 (2016) [DOI] [PubMed] [Google Scholar]

[CR5] 5.S.L. Topalian, C.G. Drake, D.M. Pardoll, Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell. 27(4), 450–461 (2015) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR6] 6.S.M. Ansell et al., PD-1 blockade with nivolumab in relapsed or refractory Hodgkin’s lymphoma. N Engl. J. Med. 372(4), 311–319 (2015) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR7] 7.D.M. Pardoll, The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer. 12(4), 252–264 (2012) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR8] 8.A.M. Lesokhin et al., Nivolumab in Patients With Relapsed or Refractory Hematologic Malignancy: Preliminary Results of a Phase Ib Study. J. Clin. Oncol. 34(23), 2698–2704 (2016) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR9] 9.R. Berger et al., Phase I safety and pharmacokinetic study of CT-011, a humanized antibody interacting with PD-1, in patients with advanced hematologic malignancies. Clin. Cancer Res. 14(10), 3044–3051 (2008) [DOI] [PubMed] [Google Scholar]

[CR10] 10.P. Charoentong et al., Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell. Rep. 18(1), 248–262 (2017) [DOI] [PubMed] [Google Scholar]

[CR11] 11.T. Wu, Y. Dai, Tumor microenvironment and therapeutic response. Cancer Lett. 387, 61–68 (2017) [DOI] [PubMed] [Google Scholar]

[CR12] 12.C. Giannotta, F. Autino, M. Massaia, The immune suppressive tumor microenvironment in multiple myeloma: The contribution of myeloid-derived suppressor cells. Front. Immunol. 13, 1102471 (2022) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR13] 13.T. Sasada et al., CD4 + CD25+ regulatory T cells in patients with gastrointestinal malignancies: possible involvement of regulatory T cells in disease progression. Cancer. 98(5), 1089–1099 (2003) [DOI] [PubMed] [Google Scholar]

[CR14] 14.C. Chen et al., Tumor microenvironment-mediated immune evasion in hepatocellular carcinoma. Front. Immunol. 14, 1133308 (2023) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR15] 15.T.F. Gajewski, H. Schreiber, Y.X. Fu, Innate and adaptive immune cells in the tumor microenvironment. Nat. Immunol. 14(10), 1014–1022 (2013) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR16] 16.Q. Tang et al., The role of PD-1/PD-L1 and application of immune-checkpoint inhibitors in human cancers. Front. Immunol. 13, 964442 (2022) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR17] 17.Z.Y. Xu-Monette, J. Zhou, K.H. Young, PD-1 expression and clinical PD-1 blockade in B-cell lymphomas. Blood. 131(1), 68–83 (2018) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR18] 18.S. Hänzelmann, R. Castelo, J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 14, 7 (2013) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR19] 19.P. Langfelder, S. Horvath, WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR20] 20.G. Yu et al., clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 16(5), 284–287 (2012) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR21] 21.W.J. Chng et al., IMWG consensus on risk stratification in multiple myeloma. Leukemia. 28(2), 269–277 (2014) [DOI] [PubMed] [Google Scholar]

[CR22] 22.Y. Sun et al., High Expression of Succinate Dehydrogenase Subunit A Which Is Regulated by Histone Acetylation, Acts as a Good Prognostic Factor of Multiple Myeloma Patients. Front. Oncol. 10, 563666 (2020) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR23] 23.G.J. Bates et al., Quantification of regulatory T cells enables the identification of high-risk breast cancer patients and those at risk of late relapse. J. Clin. Oncol. 24(34), 5373–5380 (2006) [DOI] [PubMed] [Google Scholar]

[CR24] 24.M.J. Gooden et al., The prognostic influence of tumour-infiltrating lymphocytes in cancer: a systematic review with meta-analysis. Br. J. Cancer. 105(1), 93–103 (2011) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR25] 25.D.G. DeNardo, L.M. Coussens, Inflammation and breast cancer. Balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression. Breast Cancer Res. 9(4), 212 (2007) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR26] 26.J. Stavnezer, J.E. Guikema, C.E. Schrader, Mechanism and regulation of class switch recombination. Annu. Rev. Immunol. 26, 261–292 (2008) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR27] 27.S.V. Rajkumar, Multiple, 2020 update on diagnosis, risk-stratification and management. Am. J. Hematol. 95(5), 548–567 (2020) [DOI] [PubMed] [Google Scholar]

[CR28] 28.S.E. Kearsey, K. Labib, MCM proteins: evolution, properties, and role in DNA replication. Biochim. Biophys. Acta. 1398(2), 113–136 (1998) [DOI] [PubMed] [Google Scholar]

[CR29] 29.S.L. Forsburg, M.C.M. Eukaryotic, proteins: beyond replication initiation. Microbiol. Mol. Biol. Rev. 68(1), 109–131 (2004) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR30] 30.M. Fujita et al., Nuclear organization of DNA replication initiation proteins in mammalian cells. J. Biol. Chem. 277(12), 10354–10361 (2002) [DOI] [PubMed] [Google Scholar]

[CR31] 31.S.G. Pasion, S.L. Forsburg, Nuclear localization of Schizosaccharomyces pombe Mcm2/Cdc19p requires MCM complex assembly. Mol. Biol. Cell. 10(12), 4043–4057 (1999) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR32] 32.N. Ramnath et al., MCM2 is an independent predictor of survival in patients with non-small-cell lung cancer. J. Clin. Oncol. 19(22), 4259–4266 (2001) [DOI] [PubMed] [Google Scholar]

[CR33] 33.K. Rodins et al., Minichromosome maintenance protein 2 expression in normal kidney and renal cell carcinomas: relationship to tumor dormancy and potential clinical utility. Clin. Cancer Res. 8(4), 1075–1081 (2002) [PubMed] [Google Scholar]

[CR34] 34.J.J. Blow, B. Hodgson, Replication licensing–defining the proliferative state? Trends Cell. Biol. 12(2), 72–78 (2002) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR35] 35.M.V. Meng et al., Minichromosome maintenance protein 2 expression in prostate: characterization and association with outcome after therapy for cancer. Clin. Cancer Res. 7(9), 2712–2718 (2001) [PubMed] [Google Scholar]

[CR36] 36.L. Quan et al., Prognostic role of minichromosome maintenance family in multiple myeloma. 27(10–11), 819–829 (2020) [DOI] [PubMed]

[CR37] 37.Y. Sun, Z. Cheng, S. Liu, MCM2 in human cancer: functions, mechanisms, and clinical significance. 28(1), 128 (2022) [DOI] [PMC free article] [PubMed]

[CR38] 38.W. Zhang et al., Targeting KDM4A epigenetically activates tumor-cell-intrinsic immunity by inducing DNA replication stress. Mol. Cell. 81(10), 2148–2165e9 (2021) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR39] 39.Y. Peng et al., SKP2 inhibition activates tumor cell-intrinsic immunity by inducing DNA replication stress and genomic instability. 132(1), 81–92 (2025) [DOI] [PMC free article] [PubMed]

[CR40] 40.C. Gleason, J. Kaufman, New Treatment Strategies Making an Impact in Multiple Myeloma. J. Adv. Pract. Oncol. 8(3), 285–290 (2017) [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR41] 41.S. Knop, M. Szarejko, N. Grząśko, A phase 1b/2 study evaluating efficacy and safety of MP0250, a designed ankyrin repeat protein (DARPin) simultaneously targeting vascular endothelial growth factor (VEGF) and hepatocyte growth factor (HGF), in combination with bortezomib and dexamethasone, in patients with relapsed or refractory multiple myeloma. 5(5), 940–950 (2024) [DOI] [PMC free article] [PubMed]

[CR42] 42.L. Rao et al., Targeting angiogenesis in multiple myeloma by the VEGF and HGF blocking DARPin(^®) protein MP0250: a preclinical study. Oncotarget. 9(17), 13366–13381 (2018) [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Microchromosome maintenance protein 2 is essential in immune infiltration-correlated prognosis of multiple myeloma patients by regulating cell cycle

Aziguli Maihemaiti

Lu Tang

Hui Zhou

Zhao Xu

Jifeng Jiang

Jianguo Fang

Yifeng Sun

Abstract

Purpose

Methods

Results

Conclusion

Supplementary Information

Introduction

Materials and methods

Estimation of tumor-related immune cell infiltration

Construction of prognosis risk model

Weighted gene co-expression network analysis (WGCNA)

Patients

Cells and reagents

Detection of gene expression

Western blot

Cell transfection

Cell proliferation assay

Establishment of a mice subcutaneous tumor model

Immunohistochemistry

Cell cycle analysis

Collection of mononuclear cells and cell co-culture

Statistical analysis

Results

Five immune cell pathways univariately correlated with outcomes of MM patients

Fig. 1.

Patients in the high-risk of immune infiltration group have poor prognosis independently of the ISS stage

Fig. 2.

MCM2 was screened as the hub gene associated with the risk score

Fig. 3.

MCM2 expression was related to the prognosis of MM patients and involved in the cell cycle and m-TOR pathways

Fig. 4.

Knockdown of MCM2 inhibited proliferation by regulating the cell cycle and activating the effect of the PD1 inhibitor in MM

Fig. 5.

Combining CDK inhibitor with PD1 inhibitor reduced the ability of cell proliferation of MM cells by increased the proportion of G2 phase cells

Discussion

Supplementary Information

Acknowledgements

Author contributions

Funding

Data availability

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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