Increased Expression of SRSF1 Predicts Poor Prognosis in Multiple Myeloma

Background Multiple myeloma (MM) is a clonal plasma cell disorder which still lacks sufficient prognostic factors. The serine/arginine-rich splicing factor (SRSF) family serves as an important splicing regulator in organ development. Among all members, SRSF1 plays an important role in cell proliferation and renewal. However, the role of SRSF1 in MM is still unknown. Methods SRSF1 was selected from the primary bioinformatics analysis of SRSF family members, and then we integrated 11 independent datasets and analyzed the relationship between SRSF1 expression and MM clinical characteristics. Gene set enrichment analysis (GSEA) was conducted to explore the potential mechanism of SRSF1 in MM progression. ImmuCellAI was used to estimate the abundance of immune infiltrating cells between the SRSF1high and SRSF1low groups. The ESTIMATE algorithm was used to evaluate the tumor microenvironment in MM. The expression of immune-related genes was compared between the groups. Additionally, SRSF1 expression was validated in clinical samples. SRSF1 knockdown was conducted to explore the role of SRSF1 in MM development. Results SRSF1 expression showed an increasing trend with the progression of myeloma. Besides, SRSF1 expression increased as the age, ISS stage, 1q21 amplification level, and relapse times increased. MM patients with higher SRSF1 expression had worse clinical features and poorer outcomes. Univariate and multivariate analysis indicated that upregulated SRSF1 expression was an independent poor prognostic factor for MM. Enrichment pathway analysis confirmed that SRSF1 takes part in the myeloma progression via tumor-associated and immune-related pathways. Several checkpoints and immune-activating genes were significantly downregulated in the SRSF1high groups. Furthermore, we detected that SRSF1 expression was significantly higher in MM patients than that in control donors. SRSF1 knockdown resulted in proliferation arrest in MM cell lines. Conclusion The expression value of SRSF1 is positively associated with myeloma progression, and high SRSF1 expression might be a poor prognostic biomarker in MM patients.


Introduction
Multiple myeloma (MM) is characterized by abnormal proliferation of clonal plasma cells that produce monoclonal immunoglobulin or M protein in bone marrow, leading to organ dysfunctions, such as hypercalcemia, renal failure, anemia, and bone lesions [1,2]. Monoclonal gammopathy of aggressive MM variant defned by the presence of 5% or more circulating plasma cells in peripheral blood smears in patients with symptomatic MM [5][6][7].
Alternative splicing generates diferent RNA isoforms and increases protein diversity to ensure normal development. Tus, abnormal regulation of mRNA splicing may produce altered proteins with oncogenic potential that contribute to cancer development [8,9]. Previous studies have demonstrated that alternative splicing mediated by mutant splicing regulators could drive the initiation and progression of hematological malignancies [10][11][12][13]. Te serine/arginine-rich splicing factor (SRSF) family has 12 members that share the conserved serine/arginine (SR) domain and have been reported to be essential for development [14]. Loss of SRSF genes leads to embryonic lethality and organ failure [15][16][17]. Previous studies have shown that SRSF1 is a potent oncogene and upregulated in many solid tumors, including breast, lung, and liver cancer [18][19][20]. Additionally, studies about the role of SRSF1 in normal hemopoiesis and hematological malignancies have been emerging as well. For example, SRSF1 is a critical posttranscriptional regulator in the late stage of thymocyte development [21,22]. Upregulated SRSF1, with the cooperation of PRTM1, acts as an adverse factor in pediatric acute lymphoblastic leukemia [23]. In chronic myeloid leukemia, overexpression of SRSF1 resulted in impaired imatinib sensitivity via BCR-ABL1 and cytokine-mediated signaling pathways [24]. However, the role of SRSF1 in MM is still unclear.
In order to explore the role of SRSF1 in MM, we investigated the relationship between SRSF1 expression and MM progression, ISS stages, amplifcation of 1q21, relapse status, and prognosis. We also explore the possible underlying mechanisms of SRSF1 in MM. To make our results more credible, we detected SRSF1 expression in clinical samples and reduced SRSF1 expression in MM cell lines to investigate the role of SRSF1 in MM development. Using a combination of comprehensive bioinformatic and experimental analysis, we conclude that SRSF1 is an unfavorable prognostic indicator in MM and essential for MM development.

Data Sources.
In this study, we selected 11 datasets from the Gene Expression Omnibus (GEO) (http://www.ncbi. nlm.nih.gov/geo) to explore the role of SRSF1 in MM. A total of 3928 samples were included, among them were 32 normal controls, 62 MGUSs, 12 SMMs, 3792 MMs, and 30 PCLs. Te datasets of GSE39754 (n � 176), GSE5900 (n � 78), GSE116294 (n � 69), GSE13591 (n � 162), and GSE2113 (n � 52) were used for microarray expression analysis; GSE24080 (n � 559), GSE4204 (n � 538), GSE31161 (n � 1038), and GSE83503 (n � 602) were analyzed to determine the relationship between SRSF1 expression and age, ISS staging, 1q21 abnormalities, or disease relapse. For survival analysis, GSE24080 (n � 559), GSE4204 (n � 538), GSE2658 (n � 599), and GSE57317 (n � 55) were used. Among these datasets, GSE31161 and GSE83503 contain 255 and 403 relapsed patients, respectively; while the remaining datasets include only patients with newly diagnosed MM. Te cancer dependency score of SRSF1 was acquired from the DEPMAP portal. DEPMAP portal is a genome-wide CRISPR screening database that identifes essential genes for tumorigenesis (https://depmap.org/portal) [25]. A lower score means a gene is more likely to be dependent in a given tumor cell line. A median score of 0 means a gene is not essential for tumor cells, whereas a median score of −1 is equivalent to a gene that is essential for tumor cell lines.

Exploring the Role of SRSF1 in MM.
Te protein-protein interaction (PPI) network of the SRSF family was analyzed through the Search Tool for Interaction Genes (STRING) database [26]. Pearson's correlation test was applied to evaluate the relationship between members of the SRSF family. To defne the prognostic role of the SRSF family, univariate Cox regression was conducted in GSE24080. Te expression of the SRSF family between healthy donors and MM patients was analyzed in the GSE39754 dataset, and SRSF1 was chosen to do further analysis due to its diferential expression and prognostic value. Te relationship between SRSF1 expression and the clinical characteristics of MM patients was analyzed in GSE24080. Patients were divided into the SRSF1 low group and the SRSF1 high group based on the median expression values of SRSF1. Kaplan-Meier methods and log-rank test were used for survival analysis. Univariate Cox regression and multivariate Cox regression were constructed for event-free survival (EFS) and overall survival (OS), using the "Backward: LR" procedure. Te confdence interval (CI) was 95%.

Analysis of Immune Cell Characteristics and Immune-Specifc Gene Expression between SRSF1 high and SRSF1 low
Groups. Immune cell characteristics between the SRSF1 high and SRSF1 low groups were analyzed by ImmuCellAI (Immune Cell Abundance Identifer), an online tool to provide the quantitative infltration of immune cells by using gene expression matrix data (http://bioinfo.life.hust.edu.cn/ ImmuCellAI) [30]. ESTIMATE was used to score the tumor microenvironment (TME) of samples, including stromal score, immune score, ESTIMATE score, and tumor purity [31]. Diferences in the TME between the SRSF1 high and SRSF1 low groups were analyzed. Te correlation between SRSF1 expression and immune cell infltration was calculated by Pearson correlation analysis.

Cell Culture and Primary Cells from Normal Donors and MM
Patients. KM3, U266, 8226, and H929 cells were cultured in Roswell Park Memorial Institute (RPMI)-1640 medium (Gibco, USA) with 10% fetal bovine serum (FBS, Gibco, USA), 100 μ/mL penicillin, and 100 mg/mL streptomycin. All cells were acquired from the American Type Culture Collection (ATCC, USA). Eight patients with MM and three patients with benign diseases were included, and informed consents were obtained from all the participants. Te mononuclear cells were isolated from the bone marrow samples using Ficoll density centrifugation.

Knockdown of SRSF1 and Cell Proliferation Assay.
We used short hairpin RNA (shRNA) to reduce SRSF1 expression. Te shSRSF1 and shctrl plasmids were constructed with the PLKO.1 vector (Addgene, US). shSRSF1 target sequence: GCTGATGTTTACCGAGATGGC; control target sequence: TTCTCCGAACGTGTCACGT. Te target and control plasmids were separately cotransfected with the lentiviral packaging plasmids (pM2D.G and psPAX2) into HEK293 T cells with Liposomal Transfection Reagent (Yeasen, China) to produce lentiviruses. Target cells were infected with the virus and 10 μg/ml polybrene (Sigma, US) for 24 hours, and 2 μg/ml puromycin was added at 72 hours after infection. For the cell proliferation assay, a total of 5 × 10^3 H929 and 2 × 10^3 U266 were seeded in 96-well plates in triplicates and cultured at 37°C. Cell proliferation was determined by CCK-8 assays (Dojindo Lot.JE603).

Statistical
Analysis. SPSS statistical software (SPSS statistics 23.0), R software (version 3.6.3), GraphPad Prism 8.0, and GSEA software were used for statistical analyses. Gene expression datasets were obtained by using Afymetrix Human Genome 133 plus 2.0 Array. All experiment design, quality control, and data normalization follow the standard Afymetrix protocols. Tis study was conducted in accordance with the International Conference and the Declaration of Helsinki. Each dataset was frst evaluated for normality of distribution by the Kolmogorov-Smirnov test to decide whether a nonparametric rank-based analysis or a parametric analysis should be used. Te Fisher exact and Wilcoxon rank-sum tests were used to compare categorical and numerical data, respectively.

Te Role of SRSF Family in MM.
To explore the potential role of the SRSF family in MM, we performed gene expression profling in GSE24080. Te STRING database showed a close PPI network among SRSF family genes (Figure 1(a)). Te RNA expression levels were correlated with each other among SRSF members, and the correlation was the strongest between SRSF2 and SRSF10 (r � 0.67, Figure 1(b)). We next investigated whether SRSF members are prognostic for MM through univariate Cox regression analysis, and SRSF1, SRSF2, SRSF7, and SRSF10 were found to be associated with OS of MM patients (Figure 1(c), all p < 0.05, all hazard ratio (HR) > 1). Moreover, we utilized the GSE39754 dataset to screen diferentially expressed SRSF genes between healthy donors and MM patients. As shown in Figure 1(d), SRSF1, SRSF2, and SRSF7 were expressed signifcantly higher in MM than those in healthy control, and among these, SRSF1 was top-ranked in terms of HR value and was selected for further analysis.

Clinical and Molecular
Characteristics of Patients between SRSF1 high and SRSF1 low Groups. Using the GSE24080 dataset, we divided 559 patients into two groups, including the SRSF1 low (n � 280) and the SRSF1 high (n � 279) groups.
Ten, we analyzed the clinical and molecular characteristics between the two groups (Table 1). Compared with the SRSF1 low group, the SRSF1 high group was more likely related to race (p � 0.003), advanced ISS stage (p � 0.047), and increased beta-2 microglobulin (B2M) (p � 0.005). Te incidence of cytogenetic abnormality was higher in the SRSF1 high group than in the SRSF1 low group, although there was no signifcant statistical diference (p � 0.09). Additionally, the SRSF1 high group was associated with high expression of NOTCH2NL, MYBL2, and UBE2T and low expression of CXCR4 and IL18R1 (all p < 0.05). NOTCH2NL, MYBL2, and UBE2T were reported to be involved in tumorigenesis [36][37][38], while CXCR4 and IL18R1 were associated with immune response and pathway activation [39,40], indicating SRSF1 may take part in MM progression via tumor-related pathways. Between the two groups, there were no signifcant diferences in age, gender, isotype, and therapy options.

SRSF1 Predicted Survival Levels in MM Patients.
To validate the poor prognosis conferred by high SRFS1 expression in MM patients, we conducted survival analysis in GSE24080 and other independent cohorts. In GSE24080, we found that the SRSF1 high group had signifcantly shorter EFS and OS than the SRSF1 low group, both p < 0.    Figures 3B  and 3D). While in the 3.5 < B2M < 5.5 mg/l and B2M ≥ 5.5 mg/l groups, there were no signifcant diferences in OS and EFS between the SRSF1 high and the SRSF1 low expression groups (Supplementary Figures 2B-2F). Te lack of diference may suggest that the deleterious impact of B2M ≥ 5.5 mg/l on prognosis may override that of high SRSF1 expression.

Diferential Gene Expression and Pathway Enrichment
Analysis for SRSF1 high versus SRSF1 low . To gain insight into SRSF1 biological functions, we tried to identify the DEGs by comparing the SRSF1 high group with the SRSF1 low group in GSE24080. A total of 289 DEGs related to SRSF1 were identifed, of which 162 were upregulated and 124 were downregulated (p < 0.05, |log2 FC|≥0.378, Figure 5(a), Supplementary Table 1). Te heatmap showed the top 30 upregulated genes and 30 downregulated genes ( Figure 5(b)). Furthermore, we analyzed the top 20 GO terms and KEGG pathways to identify enriched categories and signaling pathways (Supplementary Tables 2 and 3). DEGs were mainly enriched in cell division, infammatory response, and positive regulation of immune response ( Figure 5(c)). In the KEGG pathway analysis, cytokinecytokine receptor interaction, systemic lupus erythematosus, transcriptional misregulation in cancer, and complement and coagulation cascades were the most enriched pathways (Figure 5(d)).

Te PPI Network and Correlation Analysis of DEGs.
Te PPI network in the STRING database showed the top 60 SRSF1-related DEGs' interaction ( Figure 6(a)). Ten, we discovered the subnetwork by using the MCODE in the Cytoscape (Figure 6(b)). In addition, we used the top of 60 DEGs to calculate the correlativity between those genes. Based on the expression heatmap, we found that SRSF1 was positively correlated with SLC20A1, MIR142, and EIF3C and negatively associated with IgK, GPHA2, and VCAM1 ( Figure 6(c), all p < 0.001). In addition, there were positive correlations between SRSF1 and many noncoding RNAs, such as EMC-AS1 and MIR142, and the function of most noncoding RNA in MM is still unknown.  Figure 4, all p < 0.01).

Analysis of Immune Cell Characteristics and Immune-Specifc Gene Expression between the SRSF1 high and SRSF1 low
Groups. ImmuCellAI is an online tool to estimate the infltration of immune cells based on the gene expression matrix data [30]. Te abundance of immune cells with signifcant diferences between the high-and low-risk groups is shown in Figure 8(a). Te levels of type 1 regulatory T (Tr1) cells, T helper 2 (T2) cells, and central memory T (TCM) cells in the SRSF1 high group were higher than those in the SRSF1 low group, while the levels of macrophage cells in the SRSF1 high group were lower than that in the SRSF1 low group (all p < 0.05). To determine the correlation between SRSF1 expression and tumorinfltrating immune cells, we found that the expression level of SRSF1 was positively correlated with Tr1, T2, and central memory T cells and negatively correlated with macrophage cells (Figure 8(b), all p < 0.05). We also used the ESTIMATE algorithm to evaluate the TME between the two groups. Notably, patients with a high SRSF1 expression presented a lower ESTIMATE score, immune score, and stromal score (all p < 0.05) and a higher level of tumor purity(p < 0.05) (Figure 8(c)). We observed that immune checkpoint markers, consisting of PD-L1, LAG3, and PDCD1LG2, were remarkably downregulated in the SRSF1 high group. Moreover, genes associated with immune response activation, including CD163, CD27, CD40, CXCL12, IDO1, LAMP3, LGALS9, NKG7, NOS1, TIMD4, TNFSF9, and TREM2, were downregulated in SRSF1 high expression MM patients, while genes related to immune response limitation such as LAIR1 and TNFRSF8 were upregulated in SRSF1 low expression MM patients. Taken together, these data suggested that SRSF1 was related to tumor immune infltrating cells and may have participated in tumor immune escape in MM.

Validation of the Expression and Function of SRSF1 in MM.
In order to test whether SRSF1 is dispensable for the survival of cancer cells, we extracted the cancer dependency score of SRSF1 from a genome-wide CRISPR screening database, the DEPMAP portal. A lower score means that a gene is more likely to be dependent in a given tumor cell line. A median score of 0 means that a gene is not essential for tumor cells, whereas a median score of −1 is equivalent to a gene that is essential for tumor cell lines. Te dependency score of SRSF1 is −1.113, which means that SRSF1 is a common essential gene for tumor cell lines (Figure 9(a)). Across more than 500 lines representing 28 diferent cancer cell lineages, the dependency scores of SRSF1 in hematological malignancies, especially myeloma, were signifcantly lower than those in solid tumors, suggesting SRSF1 might play an essential role in hematological malignancies (Figure 9(b)). Additionally, the expression level of SRSF1 had a positive correlation with MKI67 expression in MM patients (Figure 9(c), r � 0.3567, and p < 0.0001), indicating the role of SRSF1 in myeloma development.
To validate our fndings, we frst tested the expression level of SRSF1 in a cohort of three control donors and eight MM patients using RT-qPCR analysis (Figure 9(d)). Te result showed that SRSF1 expression was signifcantly higher in MM patients than that in normal honors. Western blot showed that SRSF1 was commonly expressed in MM cell lines (Figure 9(e)). To investigate the role of SRSF1 in MM cell proliferation, we used short hairpin RNA (shRNA) to reduce SRSF1 expression in H929 and U266 cell lines. Te proliferation assay showed that SRSF1 knockdown signifcantly inhibited the growth of H929 and U266 (Figures 9(f)-9(g)).
Tese results were consistent with our previous fndings, indicating that SRSF1 plays an essential role in the development and progression of multiple myeloma.

Discussion
In recent years, treatments for MM patients have achieved signifcant advances, while drug resistance is still a critical feature of the disease and contributes to disease relapses and  poor overall survival. Tus, fnding predictive biomarkers is essential for improving treatment results in MM patients.
In this study, we acquired RNA expression data and clinical information of MM patients from the GEO database. Firstly, we investigated the role of SRSF family members in MM and screened SRSF1 as the most potential factor for further analysis. SRSF1 was upregulated in newly diagnosed and relapsed MM patients. Furthermore, SRSF1 overexpression was associated with several adverse clinical parameters, including old age, high levels of B2M and LDH, low ALB level, and 1q21 amplifcation. Univariate and multivariate Cox regression was conducted to identify whether SRSF1 was an independent prognostic factor for MM. Survival analysis revealed that patients in the SRSF1 high group had a much worse prognosis than those in the SRSF1 low group. Tese results turned out that the SRSF1 expression level can be used as a potential predictor in MM prognosis.
Te high expression level of SRSF1 has been shown to confer poor prognosis in a variety of cancers, and the underlying mechanisms were characterized. For example, SRSF1 overexpression was reported to increase tumor invasion and metastasis in hepatocellular carcinoma [18]. Du et al. suggested that SRSF1 promotes the progression of breast cancer through oncogenic splice switching of PTPMT1 [19]. Additionally, SRSF1 involves in both normal and malignant hematopoiesis. To protect T cell intrathymic maturation, SRSF1 regulates various cellular processes, such as cell diferentiation, proliferation, apoptosis, and type I interferon signaling pathway [21]. NSrp70, a splicing factor, regulates thymocyte development via partial alternative processing of SRSF1 [22]. Sinnakannu et al. reported that high SRSF1 expression in chronic myeloid leukemia was associated with imatinib resistance, which was mediated by the SRSF1/PRKCH/PLCH1 axis [24]. In AML, SRSF1 was responsible for the generation of alternative isoforms of proapoptotic and antiapoptotic genes, including BCL-x, MCLs, and capsase9b [41]. In pediatric acute lymphoblastic leukemia (ALL), SRSF1 was upregulated in clinical samples from de novo or relapsed patients and decreased when complete remission was achieved [23]. In our study, we also found that the expression level of SRSF1 was signifcantly higher in the relapsed MM compared with newly diagnosed MM and increased with disease recurrences. Alternative splicing events can produce tumor-related splice variants and proteins [42]. Tus, it is tempting to investigate whether increased SRSF1 expression afects alternative splicing to drive MM progression.
To explore the potential biological mechanism of SRSF1 overexpression, GO, KEGG, and GSEA analyses were performed. It turned out that pathways were enriched in cell division, infammatory response, cytokine-cytokine receptor interaction, RNA metabolism, and transcriptional misregulation, specifcally P53, MAPK4/6, NOTCH, and MYC pathways, which are critical regulators involved in myeloma initiation and progression. SRSF1 is important for spliceosome formation and RNA metabolism, and dysregulation of RNA stability could promote MM progression [43]. Moreover, SRSF1 has been identifed as an MYC-sensitive oncogenic protein [13], suggesting that abnormal SRSF1 expression might afect MYC-related pathways. It has been established that p53, NOTCH, and MAPK6/4 signaling pathways play important roles in MM initiation and progression [44,45]. Moreover, IL-12 and B cell receptor (BCR) signaling are essential for an immune response [46][47][48]. Terefore, it is necessary to further investigate whether SRSF1 promotes MM development through immune modulation.
Bone marrow microenvironment (BMME) is important for MM initiation and progression. Components of BMME, such as immune efector cells and immune molecules, can be abnormally edited, which further promote MM progression    CRIM1  ZFP30  ZNF569  ICAM4  SYNGR3  GBA3  MLIP  KIT  SULF2  ELOVL7  PTPRZ1  SERPINI1  BIRC3  PLEKHO1  QPCT  ADTRP  CTSW  IL18R1  PRG2  CPVL  HLA−DRA  CD5L  C1QB  C1QA  VCAM1  MAST1  IGHV3−54  LOC100293211  IGK  PLAT  ATXN8OS  Igk   by enhancing initial immunotolerance and subsequent tumor cell escape from immune surveillance [49]. We found that Tr1, T2, and TCM exhibited a higher degree of infltration in the SRSF1 high group, while the degree of macrophage infltration was higher in the SRSF1 low group. Correlation analysis showed that the SRSF1 expression level was positively correlated with Tr1, T2, and TCM and negatively associated with macrophages. Tr1 cells play a role in infammatory responses and immune tolerance; however, dysfunction of Tr1 cells may limit antitumor immunity [50,51]. Studies have shown that T2 cells are closely associated with MM progression. Increased T2 cells lead to a closer myeloma cell interaction, which subsequently contributes to MM development [52][53][54][55]. Recently, immune checkpoint therapy has achieved great breakthroughs in the treatment of hematological malignancies. Finding reliable biomarkers and potential targets can provide new sights for immunotherapy in MM. Terefore, we compared checkpoint markers and immune-related genes between the SRSF1 high -and SRSF1 low groups. We observed that checkpoint markers such as PD-L1, LAG3, and PDCD1LG2 were downregulated in the SRSF1 high group. PD-L1 inhibitors, such as durvalumab and pembrolizumab, have been reported to be efective in the treatment of relapsed or refractory MM [56][57][58]. SRSF1 expression might provide a new idea for immune checkpoint inhibitor therapy. In MM patients with high SRSF1 expression, immune-related genes for immune response activation were remarkably downregulated, whereas immunosuppressive genes were increased, indicating SRSF1 might play a role in modulating the expression of immune-related genes. Additionally, alternative processing of mRNA has been reported to have the potential to provide new therapeutic targets for cancer immunotherapy [59]. Tus, splicing variants and proteins produced by alternative splicing caused by abnormal expression of SRSF1 may provide a new insight for immunotherapy in MM patients. Altogether, our fndings showed that the SRSF1 expression level could afect tumor immune characteristics via infltrating immune cells, TME, checkpoint markers, and immune-related genes, thereby determining the prognosis of patients with MM. SRSF1 has been widely reported as an oncogene in many tumors. We identifed SRSF1 as an essential cancer-dependent gene in tumorigenesis by using the DepMap database, especially in multiple myeloma. To validate the bioinformatic results, frst, we performed RT-qPCR on clinical samples and found that SRSF1 expression was signifcantly increased in MM patients compared with controls. Ten, we found that the knockdown of SRSF1 led to growth inhibition of MM cell lines. Combined with Figures 3 and5, patients with the high expression level of SRSF1 were associated with worse outcomes, indicating that SRSF1 can be a promising biomarker and target in MM diagnosis and treatment.
Although we briefy profled the SRSF1-induced gene expression, this study has some limitations. Firstly, whether or how SRSF1 afects the splicing events of target genes in MM needs to be explored. Secondly, the number of MM patients enrolled for validation was small. We are collecting more primary MM samples to further detect and correlate SRSF1 expression with the clinical outcomes of MM patients. In our study, we identifed the SRSF1 expression in MM with diferent ages, ISS stages, amplifcation of 1q21, and relapse statuses in newly diagnosed and relapsed MM patients. Since MM is a heterogeneous disease with diferent patterns of clonal evolution [60], to better evaluate SRSF1 as an important prognostic factor, MM patients with various genetic alterations and disease statuses should be enrolled in the future study.  -L1  LAG3  PDCD1LG2  CD163  CD27  CD40  CXCL12  IDO1  LAMP3  LGALS9  NKG7  NOS1  TIMD4  TNFSF9  TREM2  LAIR1  TNFRSF8 (d) Figure 8: Analysis of tumor-infltrating immune cells and immune-related genes in the SRSF1 high and SRSF1 low groups: (a) the fraction of 4 types of tumor immune infltrating cells in two groups, (b) correlation of SRSF1 expression with 4 tumor-infltrating immune cell subtypes, (c) tumor microenvironment characteristics in the SRSF1 low and SRSF1 high groups, and (d) the expression levels of immune-related genes between the SRSF1 high and SRSF1 low groups. * p < 0.05, * * p < 0.01, and * * * p < 0.001.

Conclusion
In conclusion, our study revealed that SRSF1 expression is upregulated in MM patients. Notably, SRSF1 overexpression was associated with worse clinical characteristics in MM patients (age, ISS stage, amplifcation of 1q21, relapse statuses as well as beta-2 microglobulin) and predicts poor OS and EFS of MM patients. Additionally, the knockdown of SRSF1 repressed the growth of MM cell lines. Tus, our results demonstrated that SRSF1 may promote MM growth with prognostic signifcance and can potentially be used as a novel biomarker in the future. Moreover, more research studies need to be carried out to explore the complicated mechanisms of SRSF1 in MM development and progression.

Data Availability
Publicly available datasets were used in this study. Tese data can be found here: Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/). Te data that support the fndings of this study are available from the corresponding author upon reasonable request.

Ethical Approval
Tis study was approved by the Ethics Committee of the Second Afliated Hospital of Zhejiang University School of Medicine (Ethics Approval No. 2020977).

Supplementary Materials
Supplemental   Figure 9: Expression of SRSF1 in MM patients and SRSF1 is essential for MM cell proliferation. (a) Te dependency score of SRSF1 in tumor cell lines. A lower score means a gene is more likely to be dependent in a given cell line. A median score of 0 means a gene that is not essential, whereas a median score of −1 means a gene that is a common essential gene for tumor cell lines. (b) SRSF1 dependency score between hematological malignancies and solid tumors. * p < 0.05, * * p < 0.01, and * * * p < 0.001.