The mRNA expression of GPSM1 in B-ALL was analyzed within the Oncomine (www.oncomine.org) database (19). Survival prediction was performed using the Gene Expression Profiling Interactive Analysis (GEPIA) (20) (http://gepia.cancer-pku.cn/). A total of 173 LAML and 70 normal samples with gene expression data (TPM) and clinical information were collected from TCGA (https://portal.gdc.cancer.gov/) and GTEx (https://gtexportal.org/). ROC (receiver operating characteristic) analysis was then performed by pROC package (21) to assess the effectiveness of the transcriptional expression of GPSM1 to discriminate acute myeloid leukemia from healthy samples. The computed area under the curve (AUC) value ranging from 0.5 to 1.0 indicates the discrimination ability from 50 to 100%. Gene set enrichment analysis (GSEA) was performed using GSEA 4.1.0 software (http://www.broadinstitute.org/gsea/). The GSE87070 dataset was analyzed to determine the signaling pathways related to GPSM1 expression in B-cell precursor ALL (BCP-ALL). GSEA of GPSM1 was performed using the Pearson correlation coefficient with 1,000 permutations against collection C2 (curated gene set), which is publicly available at MsigDB (http://www.broad.mit.edu/gsea/msigdb/index.jsp). All other parameters were set to the default values. p < 0.05 was chosen as the significance cutoff criterion. The leading-edge genes were mapped by means of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database using KEGG Mapper (https://www.genome.jp/kegg/mapper.html). UALCAN (http://ualcan.path.uab.edu/) was used for gene expression correlation analysis.

Anti‐GPSM1 (sc-271721, 1:1,000 dilution) was purchased from Santa Cruz (Santa Cruz Biotechnology, Santa Cruz, CA). Anti‐GAPDH (60004-1-Ig, 1:10,000 dilution) and anti‐ADCY6 (14616-1-AP, 1:1,000 dilution) were purchased from Proteintech (Wuhan, China). Anti‐RAPGEF3 (4,155, 1:1,000 dilution) and anti-JNK (9,252, 1:1,500 dilution) were purchased from Cell Signaling Technology (Cell Signaling Technology Inc., MA, United States). The RAPGEF3 inhibitor ESI-09 was purchased from Selleck Chemicals (Selleck, United States). ESI-09 concentrations used to test dose-dependent inhibition were at 0, 10, 20, 40 μM for 24 h. To examine the time-dependency, cells were incubated with 20 μM ESI-09 for 0, 12, 24 36 h.

A human B lymphoblast cell line (HMy2.CIR) was purchased from Procell Life Science and Technology Co., Ltd (Wuhan, China). A human B-ALL cell line (BALL-1) was obtained from the Chinese Academy of Sciences (Kunming, China). A human T-cell lymphoblastic leukemia cell line (Jurkat) was obtained from Procell Life Science and Technology Co., Ltd. A human lymphoblastic leukemia cell line (Reh) was obtained from Shanghai Genechem Co., Ltd (Shanghai, China). HMy2.CIR cells were cultured in Iscove’s modified Dulbecco’s medium (IMDM; Gibco, Grand Island, NY, United States) containing 10% fetal bovine serum (FBS; Gibco), 100 U/ml penicillin, and 100 μg/ml streptomycin (Gibco). BALL-1, Jurkat and Reh cells were cultured in RPMI medium 1,640 (Gibco) supplemented with 10% FBS and antibiotics (100 U/ml penicillin and 100 μg/ml streptomycin). All cells were cultured at 37°C in an incubator with a 5% CO₂ atmosphere. Logarithmically growing cells were used in all experiments.

The knockdown study was performed using adenovirus-mediated RNA interference. Adenoviral transduction particles were purchased from HanHeng Trading Co. Ltd. (Shanghai, China). GPSM1 shRNA (sh-GPSM1) and a scrambled shRNA (sh-Con) sequences are described in Table1. Infection with the adenovirus was executed in accordance with manufacturer’s product protocol. Briefly, cells were plated in 6-well plates at 1 × 10⁶ cells/well. BALL-1 and Reh cells were then infected with 100 MOI (multiplicity of infection)/ml prepared virus in 1 ml of media for 48 h. The adenovirus was subsequently washed off with PBS, and the cells underwent the indicated treatments. The knockdown effect was evaluated at the RNA level by real-time PCR and at the protein level by Western blot analyses as described in the following sections.

Total RNA was obtained from cells using TRIzol reagent (Life Technologies, Waltham, MA, United States). A total of 1 μg RNA from each sample was reverse-transcribed using the PrimeScript™RT reagent kit with gDNA Eraser (TakaRa, Japan) and amplified using SYBR Premix Ex Taq ^TM Ⅱ (TakaRa) on a StepOne™ Real-Time PCR System (Life Technologies). The data were calculated using the following equation: relative mRNA expression = 2^−ΔΔCt, where ΔΔCt = (Ct sample − Ct control) treatment − (Ct sample − Ct control) normal. GAPDH was used as internal control. All primers were designed and purchased from Sangon Biotech (Shanghai, China) (Table 2).

Total protein was extracted by lysing samples in RIPA buffer with 1% PMSF, and the protein levels were quantified by a protein quantitative analysis kit (KeyGEN, China). Cell lysates were diluted at a ratio of 1:5 with protein loading buffer (6×) and heated at 100°C for 5 min. Thirty micrograms of each protein sample was separated on 10% SDS-PAGE gels at 100 V for 2 h and then transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, United States) for 120 min at 100 mA. After blocking in 5% nonfat dried milk in TBST for at least 1 h, the membranes were incubated with the primary antibodies listed under Reagents in TBST overnight at 4°C. After washing three times with TBST, the bands were then incubated with HRP-linked secondary antibody anti-rabbit IgG or anti-mouse IgG (Cell Signaling Technology). The results were visualized via the SuperSignal West Pico Chemiluminescent Substrate kit (Thermo, United States). Densitometry of the immunoreactive bands was performed with Tanon Image software. GAPDH was regarded as an internal control.

The Cell Counting Kit-8 (CCK-8, KeyGEN, China) assay was used to assess cell viability. In each group, BALL-1 and Reh cells (100 μl) were seeded in a 96-well plate at a density of 1 × 10⁵ cells/ml. Cell viability was determined at 0 h and every 24 h over the following four days. Ten microliters of CCK-8 were added to each well and cultured for 2 h. The optical density (OD) value at a 450 nm wavelength was determined by a microplate reader (BioTek Instruments Inc., Winooski, VT, United States). All experiments were performed three times, and each experiment contained three replicates.

Cells were fixed in 70% ice-cold ethanol overnight. Then, the cells were washed with PBS and stained with 0.5 ml PI/RNase Staining Buffer (BD Biosciences, CA, United States) at room temperature for 15 min. Cell-cycle phase distributions were determined on a flow cytometry (Calibur, BD Biosciences) and analyzed by the ModFitLT V3.0 program (BD Biosciences). The percentage of cells in G1, S, and G2 phase were counted and compared. Tests were performed three times for each sample.

Apoptosis was measured by annexin V-PE/7-AAD double-staining (Annexin V-PE/7-AAD Apoptosis Detection Kit, BD Biosciences). In each group, BALL-1 and Reh cells (1 × 10⁶ cells/tube) were collected and washed with PBS, and then resuspended in 100 μl 1× binding buffer. Then, the cell suspension was incubated with 5 μl annexin V-PE and 5 μl 7-AAD at room temperature for 15 min. After this incubation, 400 μl 1× binding buffer was added to the cells before analysis with a LSRFortessa flow cytometer (BD Biosciences). FACSDiva software (version 6.2, BD Biosciences) was used for data analysis. The early apoptosis and late apoptosis rates are indicated as the percentage of annexin V-PE⁺/7-AAD⁻ or annexin V-PE⁺/7-AAD⁺ cells, respectively.

All data were analyzed using GraphPad Prism 7.0 software (GraphPad Software, San Diego, CA, United States) and expressed as the mean ± SD of at least three experiments. Comparisons between two groups were performed using Student’s t test, and comparisons among multiple groups were performed using one-way ANOVA with post-hoc intergroup comparisons using Tukey’s test. Differences with p < 0.05 were considered statistically significant.

To determine the function of GPSM1 in tumors, we explored GPSM1 through bioinformatics analysis (Figure 1A). As shown in Figure 1B, the mRNA expression of GPSM1 in 20 types of cancers was compared to that in normal tissues by means of the Oncomine database. Significantly higher mRNA expression of GPSM1 was found in B-ALL in multiple datasets. In the Haferlach Leukemia dataset, the expression levels of GPSM1 in B-ALL, childhood B-ALL and Pro-B ALL were 5.09 (p = 7.08E-67), 5.051 (p = 6.33E-92) and 6.671 (p = 1.31E-40) fold higher than those in PBMCs (Figure 1C). Additionally, in the Haferlach Leukemia 2 dataset, the expression levels of GPSM1 in B-ALL, childhood B-ALL and Pro-B ALL samples were 2.917 (p = 3.50E-49), 2.997 (p = 2.34E-68) and 4.234 (p = 8.24E-13) fold higher than those in PBMCs (Figure 1D). Taken together, our results showed that GPSM1 was highly expressed in patients with B-ALL.

To clarify the prognostic role of GPSM1 in leukemia, we used GEPIA to assess survival. The GEPIA results showed that high mRNA expression of GPSM1 was associated with poor overall survival (OS) in acute myeloid leukemia patients (HR = 1.9, p = 0.021) (Figure 1E). We performed ROC analysis of data from acute myeloid leukemia patients and healthy people to measure the discrimination value of the GPSM1. The AUC was 0.769, representing a moderate discrimination value for LAML (Figure 1F). These results implied that high mRNA expression of GPSM1 could significantly affect leukemia patient prognosis.

GSEA is a conventional approach for identifying pathways related to gene expression. To elucidate whether GPSM1 was involved in BCP-ALL, we performed GSEA of the GSE87070 dataset. As shown in Figures 2A–D, cell proliferation, GPCR ligand binding, Gαs signaling and calcium signaling pathways were significantly associated with GPSM1 expression, which suggested that GPSM1 may play a key role in BCP-ALL. The leading-edge subset from the GSEA results can be interpreted as the group of core enriched genes (22). Interestingly, ADCY was one of the leading-edge genes in this subset and significantly contributed to the core enrichment score (Figure 2E).

The level of GPSM1 in ALL was assessed by RT-qPCR and western blotting. As demonstrated, the mRNA and protein levels of GPSM1 in three ALL cell lines, BALL-1, Jurkat, and Reh, were higher than those in the HMy2.CIR cell line (Figures 3A–C). BALL-1 and Reh cells, derived from B-ALL, were used for the following loss-of-function investigation. We employed adenovirus-mediated short hairpin RNA (shRNA) delivery to knockdown GPSM1 in BALL-1 and Reh cells. The mRNA interference efficiency of sh-GPSM1 in BALL-1 and Reh cells was 48 and 42%, respectively (Figure 3D). The protein interference efficiency of sh-GPSM1 in BALL-1 and Reh cells was 45 and 60%, respectively (Figures 3E,F). Compared with sh-Con-infected cells, cells infected with sh-GPSM1 exhibited significantly decreased GPSM1 mRNA and protein levels.

To dissect the function of GPSM1 in B-ALL, we assessed cell proliferation in BALL-1 and Reh cells after GPSM1 shRNA transfection. CCK-8 assays were carried out to detect cell proliferation. The results showed that knockdown of GPSM1 significantly suppressed cell proliferation in BALL-1 and Reh cells (Figure 3G). Cell cycle regulation is important for cell proliferation; therefore, the cell cycle populations of BALL-1 and Reh cells were determined by propidium iodide (PI) staining and flow cytometry. Figures 3H,I, implies that GPSM1 knockdown was able to increase the proportion of cells in S phase and reduce the proportion of cells in G1 phase. This indicated that GPSM1 knockdown blocks cell cycle progression. We further stained cells with annexin V-PE and 7-AAD to examine the apoptosis rate of BALL-1 and Reh cells. FACS was used to detect and quantify the percentage of apoptotic cells after GPSM1 viral infection. The sum of early apoptotic and late apoptotic cell percentages in the sh-GPSM1 group was remarkably elevated compared with that in the sh-Con group of BALL-1 and Reh cells (Figures 3J,K). Collectively, GPSM1 knockdown inhibited proliferation, enhanced cell apoptosis, and induced cell cycle arrest in BALL-1 and Reh cells.

ADCY was one of the leading-edge genes in the calcium signaling pathway regulated by GPSM1 in BCP-ALL, as shown above by GSEA (Figure 2E). We used UALCAN to analyze GPSM1-related genes in the LAML dataset for acute myeloid leukemia, and the correlation analysis results showed that GPSM1 was positively correlated with ADCY6 (Figure 4A). ADCY6 was downregulated after GPSM1 knockdown in BALL-1 and Reh cells (Figures 4B–D), which corresponded to the findings in the LAML dataset analysis.

In mammals, cAMP is produced by ADCY isoforms (23). Epac1 (RAPGEF3) and Epac2 (RAPGEF4) are known to be important mediators of cAMP signaling (24). After GPSM1 knockdown, RAPGEF3 was downregulated in BALL-1 and Reh cells (Figures 4C,E,F).

GPSM1 binds to Gαi and leads to dissociation of Gβγ from Gαi subunits; although the precise mechanisms through which Gβγ modulates signaling have not been elucidated, Gβγ has been implicated in activation of MAPK (25). The JNK signaling pathway plays an important role in tumor cell proliferation, differentiation and survival (26). To study whether GPSM1 expression has an effect on the JNK signaling pathway, we assessed the expression of JNK in BALL-1 and Reh cells after GPSM1 knockdown. The results showed that JNK was downregulated after GPSM1 knockdown in BALL-1 and Reh cells (Figures 5A–C). To verify whether GPSM1 regulates JNK by modulating RAPGEF3, the RAPGEF3 inhibitor ESI-09 was used in further studies. The results depicted in Figures 5D–G clearly indicate ESI-09 downregulated the expression of JNK in BALL-1 and Reh cells in the dose- and time-dependent manner. This further confirmed that RAPGEF3 mediates JNK upregulation.