Access the full text.
Sign up today, get DeepDyve free for 14 days.
Hindawi Journal of Oncology Volume 2023, Article ID 9998927, 20 pages https://doi.org/10.1155/2023/9998927 Research Article Increased Expression of SRSF1 Predicts Poor Prognosis in Multiple Myeloma 1,2 3 2 1 1 1 Jiawei Zhang , Zanzan Wang, Kailai Wang, Dijia Xin, Luyao Wang, Yili Fan, 1,4 and Yang Xu Department of Hematology, Te Second Afliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China Zhejiang University Cancer Institute, Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Te Second Afliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China Department of Hematology, Ningbo First Hospital, Ningbo 315010, China Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, China Correspondence should be addressed to Yang Xu; firstname.lastname@example.org Received 3 October 2022; Revised 11 December 2022; Accepted 19 January 2023; Published 10 May 2023 Academic Editor: Liren Qian Copyright © 2023 Jiawei Zhang et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Multiple myeloma (MM) is a clonal plasma cell disorder which still lacks sufcient prognostic factors. Te 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 high low abundance of immune infltrating cells between the SRSF1 and SRSF1 groups. Te ESTIMATE algorithm was used to evaluate the tumor microenvironment in MM. Te 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 amplifcation 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 confrmed that SRSF1 takes part in the myeloma progression via tumor-associated and immune-related pathways. Several checkpoints and immune- high activating genes were signifcantly downregulated in the SRSF1 groups. Furthermore, we detected that SRSF1 expression was signifcantly higher in MM patients than that in control donors. SRSF1 knockdown resulted in proliferation arrest in MM cell lines. Conclusion. Te expression value of SRSF1 is positively associated with myeloma progression, and high SRSF1 expression might be a poor prognostic biomarker in MM patients. undetermined signifcance (MGUS) is a premalignant stage, 1. Introduction and approximately, 0.5–1% of MGUS can transform into Multiple myeloma (MM) is characterized by abnormal MM per year . Between MGUS and MM, smoldering proliferation of clonal plasma cells that produce monoclonal multiple myeloma (SMM) represents an intermediate, immunoglobulin or M protein in bone marrow, leading to asymptomatic condition without the SLiM features, which organ dysfunctions, such as hypercalcemia, renal failure, stand for sixty, light chain ratio, MRI, but at a higher risk of anemia, and bone lesions [1, 2]. Monoclonal gammopathy of progression to MM . Plasma cell leukemia (PCL) is an 2 Journal of Oncology aggressive MM variant defned by the presence of 5% or and 403 relapsed patients, respectively; while the remaining more circulating plasma cells in peripheral blood smears in datasets include only patients with newly diagnosed MM. patients with symptomatic MM [5–7]. Te cancer dependency score of SRSF1 was acquired from Alternative splicing generates diferent RNA isoforms the DEPMAP portal. DEPMAP portal is a genome-wide and increases protein diversity to ensure normal develop- CRISPR screening database that identifes essential genes for ment. Tus, abnormal regulation of mRNA splicing may tumorigenesis (https://depmap.org/portal) . A lower produce altered proteins with oncogenic potential that score means a gene is more likely to be dependent in a given contribute to cancer development [8, 9]. Previous studies tumor cell line. A median score of 0 means a gene is not have demonstrated that alternative splicing mediated by essential for tumor cells, whereas a median score of −1 is mutant splicing regulators could drive the initiation and equivalent to a gene that is essential for tumor cell lines. progression of hematological malignancies [10–13]. Te serine/arginine-rich splicing factor (SRSF) family has 12 2.2. Exploring the Role of SRSF1 in MM. Te protein-protein members that share the conserved serine/arginine (SR) interaction (PPI) network of the SRSF family was analyzed domain and have been reported to be essential for devel- through the Search Tool for Interaction Genes (STRING) opment . Loss of SRSF genes leads to embryonic lethality database . Pearson’s correlation test was applied to and organ failure [15–17]. Previous studies have shown that evaluate the relationship between members of the SRSF SRSF1 is a potent oncogene and upregulated in many solid family. To defne the prognostic role of the SRSF family, tumors, including breast, lung, and liver cancer [18–20]. univariate Cox regression was conducted in GSE24080. Te Additionally, studies about the role of SRSF1 in normal expression of the SRSF family between healthy donors and hemopoiesis and hematological malignancies have been MM patients was analyzed in the GSE39754 dataset, and emerging as well. For example, SRSF1 is a critical post- SRSF1 was chosen to do further analysis due to its difer- transcriptional regulator in the late stage of thymocyte ential expression and prognostic value. Te relationship development [21, 22]. Upregulated SRSF1, with the co- between SRSF1 expression and the clinical characteristics of operation of PRTM1, acts as an adverse factor in pediatric MM patients was analyzed in GSE24080. Patients were acute lymphoblastic leukemia . In chronic myeloid low high divided into the SRSF1 group and the SRSF1 group leukemia, overexpression of SRSF1 resulted in impaired based on the median expression values of SRSF1. imatinib sensitivity via BCR-ABL1 and cytokine-mediated Kaplan–Meier methods and log-rank test were used for signaling pathways . However, the role of SRSF1 in MM survival analysis. Univariate Cox regression and multivariate is still unclear. Cox regression were constructed for event-free survival In order to explore the role of SRSF1 in MM, we in- (EFS) and overall survival (OS), using the “Backward: LR” vestigated the relationship between SRSF1 expression and procedure. Te confdence interval (CI) was 95%. MM progression, ISS stages, amplifcation of 1q21, relapse status, and prognosis. We also explore the possible un- derlying mechanisms of SRSF1 in MM. To make our results 2.3. Identifcation of Diferentially Expressed Genes and En- more credible, we detected SRSF1 expression in clinical richment Analysis. Diferential gene expression analysis was samples and reduced SRSF1 expression in MM cell lines to performed by the “limma” package . |Fold change | > 1.5, investigate the role of SRSF1 in MM development. Using and p< 0.05 was utilized to determine diferentially a combination of comprehensive bioinformatic and ex- expressed genes (DEGs). Gene Ontology (GO) enrichment perimental analysis, we conclude that SRSF1 is an un- terms and Kyoto Encyclopedia of Genes and Genomes favorable prognostic indicator in MM and essential for MM (KEGG) pathways were performed by “clusterProfler” development. package . Gene set enrichment analysis (GSEA) was performed by GSEA software (https://www.gsea-msigdb. 2. Methods org/gsea/index.jsp) . 2.1. Data Sources. In this study, we selected 11 datasets from the Gene Expression Omnibus (GEO) (http://www.ncbi. 2.4. Analysis of Immune Cell Characteristics and Immune- high low nlm.nih.gov/geo) to explore the role of SRSF1 in MM. A Specifc Gene Expression between SRSF1 and SRSF1 high total of 3928 samples were included, among them were 32 Groups. Immune cell characteristics between the SRSF1 low normal controls, 62 MGUSs, 12 SMMs, 3792 MMs, and 30 and SRSF1 groups were analyzed by ImmuCellAI (Im- PCLs. Te datasets of GSE39754 (n � 176), GSE5900 (n � 78), mune Cell Abundance Identifer), an online tool to provide GSE116294 (n � 69), GSE13591 (n � 162), and GSE2113 the quantitative infltration of immune cells by using gene (n � 52) were used for microarray expression analysis; expression matrix data (http://bioinfo.life.hust.edu.cn/ GSE24080 (n � 559), GSE4204 (n � 538), GSE31161 ImmuCellAI) . ESTIMATE was used to score the tu- (n � 1038), and GSE83503 (n � 602) were analyzed to de- mor microenvironment (TME) of samples, including stro- termine the relationship between SRSF1 expression and age, mal score, immune score, ESTIMATE score, and tumor high ISS staging, 1q21 abnormalities, or disease relapse. For purity . Diferences in the TME between the SRSF1 low survival analysis, GSE24080 (n � 559), GSE4204 (n � 538), and SRSF1 groups were analyzed. Te correlation be- GSE2658 (n � 599), and GSE57317 (n � 55) were used. tween SRSF1 expression and immune cell infltration was Among these datasets, GSE31161 and GSE83503 contain 255 calculated by Pearson correlation analysis. Journal of Oncology 3 2.5. Cell Culture and Primary Cells from Normal Donors and Afymetrix protocols. Tis study was conducted in accor- MM Patients. KM3, U266, 8226, and H929 cells were cul- dance with the International Conference and the Declara- tured in Roswell Park Memorial Institute (RPMI)-1640 tion of Helsinki. Each dataset was frst evaluated for medium (Gibco, USA) with 10% fetal bovine serum (FBS, normality of distribution by the Kolmogorov–Smirnov test Gibco, USA), 100 μ/mL penicillin, and 100 mg/mL strep- to decide whether a nonparametric rank-based analysis or tomycin. All cells were acquired from the American Type a parametric analysis should be used. Te Fisher exact and Culture Collection (ATCC, USA). Eight patients with MM Wilcoxon rank-sum tests were used to compare categorical and three patients with benign diseases were included, and and numerical data, respectively. informed consents were obtained from all the participants. Te mononuclear cells were isolated from the bone marrow 3. Results samples using Ficoll density centrifugation. 3.1. Te Role of SRSF Family in MM. To explore the potential role of the SRSF family in MM, we performed gene ex- 2.6. Reverse Transcription-Quantitative Polymerase Chain pression profling in GSE24080. Te STRING database Reaction (RT-qPCR) and Western Blotting. Total RNA was showed a close PPI network among SRSF family genes extracted from mononuclear cells by TRIZOL reagent (Figure 1(a)). Te RNA expression levels were correlated (Invitrogen, USA) and was then reverse transcribed to with each other among SRSF members, and the correlation cDNA using PrimeScript RT reagent Kit (Takara, Japan) was the strongest between SRSF2 and SRSF10 (r � 0.67, according to the manufacturer’s instruction. Real-time Figure 1(b)). We next investigated whether SRSF members fuorescent quantitative PCR was performed to amplify are prognostic for MM through univariate Cox regression the SRSF1 cDNA fragment by SYBR Green Master Mix analysis, and SRSF1, SRSF2, SRSF7, and SRSF10 were found (Yeasen, China), with β-actin as an internal control. Te to be associated with OS of MM patients (Figure 1(c), all expression level of related lncRNAs was analyzed using p< 0.05, all hazard ratio (HR)> 1). Moreover, we utilized the −ΔΔCT 2 . Each PCR reaction was performed in triplicate. Te GSE39754 dataset to screen diferentially expressed SRSF following primers were used: SRSF1 forward, 5′-GCCGCA genes between healthy donors and MM patients. As shown TCTACGTGGGTAAC-3′; SRSF1 reverse, 5′-GAGGTC in Figure 1(d), SRSF1, SRSF2, and SRSF7 were expressed GATGTCGCGGATAG-3′;β-actin forward, 5′-GATCAT signifcantly higher in MM than those in healthy control, TGCTCCTCCTGAGC-3′;β-actin reverse, 5′- ACTCCT and among these, SRSF1 was top-ranked in terms of HR GCTTGCTGATCCAC-3′. Te expression of SRSF1 in MM value and was selected for further analysis. cell lines was detected by western blotting as previously described . Primary rabbit anti-SRSF1 antibody was 3.2. Te Expression Level of SRSF1 in Normal Donors and purchased from Abcam (ab133689), and primary mouse Multiple Myeloma Patients in Diferent Stages. To charac- anti-β-tubulin antibody was purchased from HUABIO terize the SRSF1 expression pattern related to MM devel- (M1305-2). opment, we employed fve datasets to determine the SRSF1 mRNA levels in diferent stages of MM, including MGUS, 2.7. Knockdown of SRSF1 and Cell Proliferation Assay. SMM, MM, and PCL. In GSE39754, the expression level of We used short hairpin RNA (shRNA) to reduce SRSF1 SRSF1 was signifcantly higher in 170 MM patients than that expression. Te shSRSF1 and shctrl plasmids were con- in 6 normal donors (p � 0.011) (Figure 2(a)). A prominent structed with the PLKO.1 vector (Addgene, US). shSRSF1 trend of increase in SRSF1 expression was found from target sequence: GCTGATGTTTACCGAGATGGC; control normal control (n � 22), MGUS (n � 44) to SMM (n � 12) in target sequence: TTCTCCGAACGTGTCACGT. Te target GSE5900 (p � 0.016, 0.00013, and 0.096, respectively, and control plasmids were separately cotransfected with the Figure 2(b)). Similarly, the GSE116249 dataset showed that lentiviral packaging plasmids (pM2D.G and psPAX2) into SRSF1 expression increased from normal control (n � 4), HEK293 T cells with Liposomal Transfection Reagent MM (n � 50) to PCL (n � 15), even though the diference is (Yeasen, China) to produce lentiviruses. Target cells were not statistically signifcant (p � 0.058, 0.08, and 0.58, re- infected with the virus and 10 μg/ml polybrene (Sigma, US) spectively, Figure 2(c)). Moreover, the expression of SRSF1 for 24 hours, and 2 μg/ml puromycin was added at 72 hours signifcantly increased in MGUS (n � 11), MM (n � 142), and after infection. For the cell proliferation assay, a total of PCL (n � 9) (p � 0.0021, 1.2e − 05, and 7.7e − 05, re- 3 3 5 ×10^ H929 and 2 ×10^ U266 were seeded in 96-well plates spectively, Figure 2(d)). Te same trend was also found in in triplicates and cultured at 37 C. Cell proliferation was the GSE2113 dataset, from MGUS (n � 7) to MM (n � 39) determined by CCK-8 assays (Dojindo Lot.JE603). and PCL (n � 6) (p � 3.5e − 05, 0.0012, and 0.063, re- spectively, Figure 2(e)). Together, these data suggest that SRSF1 was overexpressed during the courses of MM 2.8. Statistical Analysis. SPSS statistical software (SPSS progression. 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 3.3. Te Expression Level of SRSF1 in MM Patients with Human Genome 133 plus 2.0 Array. All experiment design, Diferent Age Groups, ISS Stages, Amplifcation of 1q21, and quality control, and data normalization follow the standard Relapse Statuses. To better understand the clinical 4 Journal of Oncology (a) SRSF12 1 −0.07 −0.13 −0.07 −0.01 0.08 −0.07 −0.19 −0.14 −0.16 0.1 −0.06 0.8 SRSF8 1 0.24 0.11 −0.07 0 0.14 0.08 0.11 0.2 0.21 0.14 0.6 SRSF9 1 −0.12 −0.06 −0.12 0.12 0.21 0.41 0.39 −0.09 0.22 SRSF11 1 0.25 0.3 0.2 0.14 0.08 0.19 0.16 0.03 0.4 SRSF1 1 0.34 0.11 0.24 0.3 0.34 0.23 0.12 0.2 SRSF4 1 0.13 0.25 0.04 0.16 0.23 0.03 SRSF3 1 0.37 0.39 0.39 0.12 0.1 −0.2 SRSF7 1 0.51 0.43 0.14 0.23 −0.4 SRSF2 1 0.63 0.14 0.22 SRSF10 1 0.09 0.2 −0.6 SRSF5 0.2 −0.8 SRSF6 1 −1 (b) pvalue Hazard ratio SRSF1 <0.001 2.602 (1.686−4.017) 0.019 SRSF2 1.669 (1.089−2.557) SRSF7 0.012 1.682 (1.119−2.529) SRSF10 <0.001 2.363 (1.547−3.607) (c) Figure 1: Continued. SRSF12 SRSF8 SRSF9 SRSF11 SRSF1 SRSF4 SRSF3 SRSF7 SRSF2 SRSF10 SRSF5 SRSF6 Journal of Oncology 5 ** * Type Normal MM (d) Figure 1: Analysis of SRSF family in multiple myeloma patients. (a) Te PPI network of SRSF family members. (b) Correlation analysis between the expression of the SRSF family in the GSE24080 dataset. Te size of the dot represents the correlation coefcient, and the larger the dot, the higher the correlation. Red dots represent a positive correlation, while blue dots represent a negative correlation. (c) Univariate Cox regression results for the subunits related to the survival of MM patients in GSE24080. (d) Te expression of SRSF family members between normal donors and MM patients in GSE39754. characteristics associated with SRSF1 expression, MM Ten, we analyzed the clinical and molecular characteristics patients’ age, ISS stages, 1q21 aberrations, and relapse between the two groups (Table 1). Compared with the low high status were analyzed using four independent datasets. In SRSF1 group, the SRSF1 group was more likely related GSE24080, SRSF1 expression in the group of age ≥65 was to race (p � 0.003), advanced ISS stage (p � 0.047), and signifcantly higher than that in the group of age <65 increased beta-2 microglobulin (B2M) (p � 0.005). Te (p � 0.0056, Figure 3(a)). Furthermore, a signifcantly incidence of cytogenetic abnormality was higher in the high low higher SRSF1 expression was observed in ISS stage III SRSF1 group than in the SRSF1 group, although there when compared with stage I or II (p � 0.011, Krus- was no signifcant statistical diference (p � 0.09). Addi- high kal–Wallis test, Figure 3(b)), which specifcally occurs in tionally, the SRSF1 group was associated with high ex- IgG-type MM (p � 0.013 and 0.01, respectively, Krus- pression of NOTCH2NL, MYBL2, and UBE2T and low kal–Wallis test, Figure 3(c)), but not in free light chain expression of CXCR4 and IL18R1 (all p< 0.05). (FLC)- or IgA-type MM (p � 0.878 and 0.123, respectively, NOTCH2NL, MYBL2, and UBE2T were reported to be Figure 3(c)). involved in tumorigenesis [36–38], while CXCR4 and Te 1q21 copy number amplifcation is a common cyto- IL18R1 were associated with immune response and pathway genetic abnormality that is indicative of poor prognosis in activation [39, 40], indicating SRSF1 may take part in MM patients with MM [33–35]. In GSE4204 (n = 538), the expression progression via tumor-related pathways. Between the two of SRSF1 had an upward trend with the amplifcation of 1q21 groups, there were no signifcant diferences in age, gender, (p � 0.024, Kruskal–Wallis test, Figure 3(d)), and a similar isotype, and therapy options. result was obtained in GSE2658 (n = 599) (Supplementary Figure 1). Furthermore, in GSE31161, we found a signifcant 3.5. Univariate and Multivariate Analysis of Possible Prog- increase of SRSF1 expression in relapsed MM patients (n = 258) nostic Factors in MM. To further evaluate the potential when compared with MM patients at diagnosis (n = 780) prognostic value of SRSF1 in MM, age (≥65 vs. <65), gender (p � 0.00028, Figure 3(e)). Besides, we found that SRSF1 ex- (female vs. male), B2M (≥5.5 vs. <5.5), LDH (≥250 vs. <250), pression increased with the duration of relapse in the GSE83530 ALB (≥3.5 vs. <3.5), and lytic bone lesions on MRI (≥2 vs. dataset (p � 0.3, 0.008, and 0.0025, respectively, Figure 3(f)), <2) were enrolled in univariate and multivariate analysis. As suggesting that SRSF1 may contribute to the relapse of MM a result, SRSF1, B2M, and LDH were signifcantly associated patients. with EFS in univariate analysis (all p< 0.05) (Table 2). Furthermore, the multivariate analysis showed that HR 3.4. Clinical and Molecular Characteristics of Patients between values of SRSF1, B2M (≥5.5 vs. 5.5), and LDH (≥250 vs. high low SRSF1 and SRSF1 Groups. Using the GSE24080 <250) were 1.851 (p< 0.001), 1.614 (p � 0.008), and 2.590 dataset, we divided 559 patients into two groups, including (p < 0.001), respectively. Additionally, SRSF1, B2M, LDH, low high the SRSF1 (n � 280) and the SRSF1 (n � 279) groups. ALB, and MRI lesions were identifed to be closely related to SRSF family expression (log2) SRSF1 SRSF2 SRSF3 SRSF4 SRSF5 SRSF6 SRSF7 SRSF9 SRSF10 SRSF11 SRSF12 6 Journal of Oncology p = 0.011 p = 0.096 p = 0.58 p = 0.00013 p = 0.08 11.5 p = 0.016 p = 0.058 11.0 10.5 10.0 9.5 Normal MM Normal MGUS SMM Normal MM PCL GSE39754 GSE5900 GSE116294 Group Group Group Normal Normal Normal MM MGUS MM SMM PCL (a) (b) (c) p = 7.7e-05 p = 0.063 p = 1.2e-05 p = 0.0012 9 9 p = 0.0021 p = 3.5e-05 MGUS MM PCL MGUS MM PCL GSE13591 GSE2113 Group Group MGUS MGUS MM MM PCL PCL (d) (e) Figure 2: Te expression level of SRSF1 in fve datasets of normal donors and myeloma patients in diferent stages: (a) MM patients (n � 170) compared with normal donors (n � 6); (b) the expression value of SRSF1 in normal donors (n � 22) and other diferent stages of 56 myeloma patients. MGUS (n � 44) and SMM (n � 12); (c) the diferent expressions of SRSF1 in normal donors (n � 4), MM , and PCL patients (n � 15); (d) SRSF1 expression levels in diferent subtypes of myeloma patients. MGUS (n � 11), MM (n � 133), and PCL (n � 9); (e) comparison of SRSF1 expression levels in three diferent stages of myeloma patients. MGUS (n � 7), MM (n � 39), and PCL (n � 6). OS in univariate analysis (all p< 0.05) (Table 3). Further- 3.6. SRSF1 Predicted Survival Levels in MM Patients. To more, the multivariate analysis for OS displayed that the HR validate the poor prognosis conferred by high SRFS1 ex- of SRSF1 was 1.720 (p � 0.001), and the HR values of other pression in MM patients, we conducted survival analysis in OS-related factors, including B2M, LDH, ALB, and MRI, GSE24080 and other independent cohorts. In GSE24080, we high were 1.922, 2.909, 0.656, and 1.737 (p< 0.001, <0.001, 0.034, found that the SRSF1 group had signifcantly shorter EFS low and <0.002, respectively). Tese results suggested that SRSF1 and OS than the SRSF1 group, both p< 0.001 expression was an independent risk factor afecting the (Figures 4(a) and 4(b)). A similar prognostic value of SRSF1 survival of MM patients. was demonstrated in GSE4204, GSE2658, and GSE57317 Expression of SRSF1 (log2) Expression of SRSF1 (log2) Expression of SRSF1 (log2) Expression of SRSF1 (log2) Expression of SRSF1 (log2) Journal of Oncology 7 p = 0.1 p = 0.0056 p = 0.011 p = 0.5 Kruskal-Wallis test p = 0.041 <65 ≥65 I II III GSE24080 GSE24080 ISS Stage AGE (year) <65 II ≥65 III (a) (b) IgG IgA FLC p = 0.057 p = 0.013 p = 0.22 p = 0.62 11 p = 0.0071 p = 0.01 p = 0.65 p = 0.89 p = 0.39 p = 0.5 p = 0.051 p = 0.82 Kruskal-wallis test p = 0.024 2 34+ GSE4204 Amplification levels of 1q21 Kruskal-wallis test p = 0.019 Kruskal-wallis test p = 0.123 Kruskal-wallis test p = 0.878 I II III I II III I II III GSE24080 GSE24080 GSE24080 4+ ISS Stage II III (c) (d) Figure 3: Continued. Expression of SRSF1 (log2) Expression of SRSF1 (log2) Expression of SRSF1 (log2) Expression of SRSF1 (log2) 8 Journal of Oncology p = 0.0023 p = 0.008 p = 0.0025 p = 0.3 baseline relapse 0 12 GSE31161 GSE83503 Group Relapse baseline 0 relapse 1 (e) (f) Figure 3: Te expression of SRSF1 in diferent age groups, ISS stages, amplifcation levels of 1q21, and relapse status of MM patients: (a) the expression level of SRSF1 in ages <65 years old (n � 423) and ≥65 years old (n � 136) groups, (b) the expression of SRSF1 in diferent ISS stages, (c) the SRSF1 expression pattern in diferent serotypes, (d) SRSF1 expression levels at diferent 1q21 amplifcation in 246 MM patients of GSE4204, (e) the expression of SRSF1 in baseline (n � 780) and relapse (n � 255) MM groups, and (f) SRSF1 expression levels in 585 MM patients with diferent relapse times: relapse time � 0 (n � 182), relapse time � 1 (n � 391), and relapse time � 2 (n � 12). low high Table 1: Clinical characteristics of 559 MM patients between the SRSF1 and SRSF1 groups in GSE24080. Low High SRSF1 , n � 280 SRSF1 , n � 279 p value Age (mean, range) 57.74 (24.83–75.90) 56.62 (31.82–76.50) 0.161 Male 175 (62.50) 162 (58.06) 0.284 Gender (%) Female 105 (37.50) 117 (41.94) White 238 (85.00) 259 (92.83) 0.003 Race (%) Other 42 (15.00) 20 (7.17) FLC 41 (14.64) 43 (15.41) 0.820 IgA 62 (22.14) 71 (25.45) IgG 164 (58.57) 149 (53.41) Isotype (%) IgD 1 (0.36) 2 (0.72) Nonsecretory 3 (1.07) 5 (1.79) NA 9 (3.21) 9 (3.23) B2M (mean (sd)) 4.177 (4.208) 5.290 (6.285) 0.005 ALB (mean (sd)) 4.093 (0.530) 4.005 (0.628) 0.073 I and II 230 (82.1) 210 (75.3) 0.047 ISS stage (%) III 50 (17.9) 69 (24.7) CRP (mean (sd)) 10.327 (18.391) 12.928 (26.779) 0.270 CREAT (mean (sd)) 1.239 (1.129) 1.407 (1.399) 0.227 LDH (mean (sd)) 170.529 (63.305) 173.430 (68.553) 0.603 HGB (mean (sd)) 11.320 (1.825) 11.186 (1.799) 0.382 ASPC (mean (sd)) 41.413 (24.065) 43.953 (24.570) 0.229 BMPC (mean (sd)) 45.103 (26.527) 47.668 (26.009) 0.256 MRI (mean (sd)) 10.314 (13.934) 11.759 (15.102) 0.257 Yes 94 (33.57) 113 (40.50) 0.090 Cytogenetic abnormality No 186 (66.43) 166 (59.50) TT2 172 (61.43) 173 (62.01) 0.888 Terapy, no (%) TT3 108 (38.57) 106 (38.3) High CCND1, no (%) 140 (50.18) 140 (50.00) 0.966 High FGFR3, no (%) 142 (50.90) 138 (49.29) 0.703 Expression of SRSF1 (log2) Expression of SRSF1 (log2) Journal of Oncology 9 Table 1: Continued. Low High SRSF1 , n � 280 SRSF1 , n � 279 p value High LIG4, no (%) 148 (53.05) 132 (47.14) 0.163 High TP53, no (%) 137 (49.10) 143 (51.07) 0.642 High CDK4, no (%) 140 (50.18) 140 (50.00) 0.966 High KRAS, no (%) 131 (46.95) 149 (53.21) 0.139 High NRAS, no (%) 139 (49.82) 141 (50.36) 0.899 High CXCR4, no (%) 158 (56.42) 122 (43.73) 0.003 High NOTCH2NL, no (%) 114 (40.71) 166 (59.50) <0.001 High UBE2T, no (%) 117 (41.79) 162 (58.06) <0.001 High IL18R1, no (%) 165 (58.93) 115 (41.22) <0.001 HighMYBL2, no (%) 127 (45.36) 153 (54.84) <0.025 AGE: age at registration (years); B2M: beta-2 microglobulin, mg/l; ALB: albumin, 10 g/l; CRP: c-reactive protein, mg/l; CREAT: creatinine, mg/dl; LDH: lactate dehydrogenase, U/l; HGB: hemoglobin, g/dl; ASPC: aspirate plasma cells (%); BMPC: bone marrow biopsy plasma cells (%); MRI: number of magnetic resonance imaging (MRI)-defned focal lesions (skull, spine, and pelvis); cytogenetic abnormality: an indicator of the detection of cytogenetic abnormalities; TT2: total therapy 2; TT3: total therapy 3; no: number of patients. Bold values mean the diferences between two groups are statistically signifcant. (p � 0.013, 0.032, and 0.020, respectively, Figures 4(c)–4(e)). 3.8. Te PPI Network and Correlation Analysis of DEGs. Since B2M and LDH are recognized as important prognostic Te PPI network in the STRING database showed the top biomarkers for MM, we further explored the prognosis of 60 SRSF1-related DEGs’ interaction (Figure 6(a)). Ten, we SRSF1 expression levels in the B2M and LDH subgroups of discovered the subnetwork by using the MCODE in the GSE24080. In the B2M≤ 3.5 mg/l and LDH< 250 U/L Cytoscape (Figure 6(b)). In addition, we used the top of 60 high DEGs to calculate the correlativity between those genes. groups, the SRSF1 group had signifcantly shorter EFS low and OS than the SRSF1 group (EFS: p< 0.001 and Based on the expression heatmap, we found that SRSF1 was p< 0.001; OS: p � 0.0026 and p � 0.001, respectively. positively correlated with SLC20A1, MIR142, and EIF3C Supplementary Figures 2A and 2D; Supplementary and negatively associated with IgK, GPHA2, and VCAM1 Figures 3A and 3C). In the LDH ≥250 U/L group, patients (Figure 6(c), all p< 0.001). In addition, there were positive with high SRSF1 expression tended to have shorter EFS and correlations between SRSF1 and many noncoding RNAs, OS than those with low SRSF1 expression, even though these such as EMC-AS1 and MIR142, and the function of most diferences were not statistically signifcant (EFS: p � 0.073 noncoding RNA in MM is still unknown. and OS: p � 0.113, respectively. Supplementary Figures 3B and 3D). While in the 3.5< B2M< 5.5 mg/l and 3.9. GSEA Analysis Showed a Lot of Gene Sets Enriched in the B2M≥ 5.5 mg/l groups, there were no signifcant diferences high high low SRSF1 Group. Te GSEA analysis showed that spliceo- in OS and EFS between the SRSF1 and the SRSF1 some, metabolism of RNA, protein ubiquitination, P53 expression groups (Supplementary Figures 2B–2F). Te lack signaling pathway, MYC targets, signaling by NOTCH, of diference may suggest that the deleterious impact of interleukin 12 signaling, downstream signaling events of B2M≥ 5.5 mg/l on prognosis may override that of high B cell receptor BCR, and MARK6/4 signaling were signif- SRSF1 expression. high cantly enriched in the SRSF1 group (Figures 7(a)–7(i), all p< 0.01), while hematopoietic cell lineage, B cell receptor signaling pathway, TNFa signaling via NFKB, infammatory 3.7. Diferential Gene Expression and Pathway Enrichment response, complement, and coagulation were signifcantly high low Analysis for SRSF1 versus SRSF1 . To gain insight into low enriched in the SRSF1 group (Supplementary Figure 4, all SRSF1 biological functions, we tried to identify the DEGs by p< 0.01). high low comparing the SRSF1 group with the SRSF1 group in GSE24080. A total of 289 DEGs related to SRSF1 were identifed, of which 162 were upregulated and 124 were 3.10. Analysis of Immune Cell Characteristics and Immune- high low downregulated (p< 0.05, |log2 FC|≥0.378, Figure 5(a), Specifc Gene Expression between the SRSF1 and SRSF1 Supplementary Table 1). Te heatmap showed the top 30 Groups. ImmuCellAI is an online tool to estimate the in- upregulated genes and 30 downregulated genes fltration of immune cells based on the gene expression (Figure 5(b)). Furthermore, we analyzed the top 20 GO matrix data . Te abundance of immune cells with terms and KEGG pathways to identify enriched categories signifcant diferences between the high- and low-risk and signaling pathways (Supplementary Tables 2 and 3). groups is shown in Figure 8(a). Te levels of type 1 regu- DEGs were mainly enriched in cell division, infammatory latory T (Tr1) cells, T helper 2 (T2) cells, and central high response, and positive regulation of immune response memory T (TCM) cells in the SRSF1 group were higher low (Figure 5(c)). In the KEGG pathway analysis, cytokine- than those in the SRSF1 group, while the levels of high cytokine receptor interaction, systemic lupus eryth- macrophage cells in the SRSF1 group were lower than low ematosus, transcriptional misregulation in cancer, and that in the SRSF1 group (all p< 0.05). To determine the complement and coagulation cascades were the most correlation between SRSF1 expression and tumor- enriched pathways (Figure 5(d)). infltrating immune cells, we found that the expression 10 Journal of Oncology Table 2: Univariate and multivariate cox regression analysis of EFS in GSE24080. Univariate analysis Multivariate analysis Prognostic parameters HR (95% CI) p value HR (95% CI) p value SRSF1 (high vs. low) 1.837 (1.347–2.506) <0.001 1.851 (1.353–2.533) <0.001 Age (≥65 vs. <65) 1.047 (0.731–1.500) 0.801 — — Gender (female vs. male) 0.951 (0.697–1.297) 0.751 — — B2M (≥5.5 vs. <5.5) 1.855 (1.310–2.628) 0.001 1.614 (1.132–2.302) 0.008 LDH (≥250 vs. <250) 2.633 (1.664–4.169) <0.001 2.590 (1.619–4.143) <0.001 ALB (≥3.5 vs. <3.5) 0.810 (0.525–1.249) 0.340 — — MRI (≥2 vs. <2) 1.063 (0.773–1.461) 0.709 — — AGE: age at registration (years); B2M: beta-2 microglobulin, mg/l; LDH: lactate dehydrogenase, U/l; ALB: albumin, 10 g/l; MRI: number of magnetic resonance imaging (MRI)-defned focal lesions (skull, spine, and pelvis). HR: hazard ratio and CI: credible interval. Table 3: Univariate and multivariate cox regression analysis of OS in GSE24080. Univariate analysis Multivariate analysis Prognostic parameters HR (95% CI) p value HR (95% CI) p value SRSF1 (high vs. low) 1.660 (1.222–2.253) 0.001 1.720 (1.254–2.358) 0.001 Age (≥65 vs. <65) 1.398 (1.035–1.887) 0.300 — — Gender (female vs. male) 1.030 (0.760–1.397) 0.848 — — B2M (≥5.5 vs. <5.5) 2.563 (1.868–3.517) <0.001 1.922 (1.351–2.733) <0.001 LDH (≥250 vs. <250) 3.845 (2.624–5.633) <0.001 2.909 (1.919–4.410) <0.001 ALB (≥3.5 vs. <3.5) 0.520 (0.359–0.754) 0.001 0.656 (0.444–0.969) 0.034 MRI (≥2 vs. <2) 1.660 (1.222–2.253) 0.001 1.737 (1.223–2.467) 0.002 AGE: age at registration (years); B2M: beta-2 microglobulin, mg/l; LDH: lactate dehydrogenase, U/l; ALB: albumin, 10 g/l; MRI: number of magnetic resonance imaging (MRI)-defned focal lesions (skull, spine, and pelvis). HR: hazard ratio and CI: credible interval. Bold values mean the diferences between two groups (like SRSF1-high vs. SRSF1-low) are statistically signifcant. level of SRSF1 was positively correlated with Tr1, T2, and score of SRSF1 is −1.113, which means that SRSF1 is central memory T cells and negatively correlated with a common essential gene for tumor cell lines (Figure 9(a)). macrophage cells (Figure 8(b), all p< 0.05). We also used the Across more than 500 lines representing 28 diferent cancer ESTIMATE algorithm to evaluate the TME between the two cell lineages, the dependency scores of SRSF1 in hemato- groups. Notably, patients with a high SRSF1 expression logical malignancies, especially myeloma, were signifcantly presented a lower ESTIMATE score, immune score, and lower than those in solid tumors, suggesting SRSF1 might stromal score (all p< 0.05) and a higher level of tumor play an essential role in hematological malignancies purity(p< 0.05) (Figure 8(c)). We observed that immune (Figure 9(b)). Additionally, the expression level of SRSF1 had a positive correlation with MKI67 expression in MM checkpoint markers, consisting of PD-L1, LAG3, and PDCD1LG2, were remarkably downregulated in the patients (Figure 9(c), r � 0.3567, and p< 0.0001), indicating high SRSF1 the role of SRSF1 in myeloma development. group. Moreover, genes associated with immune response activation, including CD163, CD27, CD40, To validate our fndings, we frst tested the expression CXCL12, IDO1, LAMP3, LGALS9, NKG7, NOS1, TIMD4, level of SRSF1 in a cohort of three control donors and eight TNFSF9, and TREM2, were downregulated in SRSF1 high MM patients using RT-qPCR analysis (Figure 9(d)). Te expression MM patients, while genes related to immune result showed that SRSF1 expression was signifcantly higher response limitation such as LAIR1 and TNFRSF8 were in MM patients than that in normal honors. Western blot upregulated in SRSF1 low expression MM patients. Taken showed that SRSF1 was commonly expressed in MM cell lines together, these data suggested that SRSF1 was related to (Figure 9(e)). To investigate the role of SRSF1 in MM cell tumor immune infltrating cells and may have participated proliferation, we used short hairpin RNA (shRNA) to reduce SRSF1 expression in H929 and U266 cell lines. Te pro- in tumor immune escape in MM. liferation assay showed that SRSF1 knockdown signifcantly inhibited the growth of H929 and U266 (Figures 9(f)–9(g)). 3.11. Validation of the Expression and Function of SRSF1 in Tese results were consistent with our previous fndings, MM. In order to test whether SRSF1 is dispensable for the indicating that SRSF1 plays an essential role in the devel- survival of cancer cells, we extracted the cancer dependency opment and progression of multiple myeloma. score of SRSF1 from a genome-wide CRISPR screening database, the DEPMAP portal. A lower score means that 4. Discussion 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 In recent years, treatments for MM patients have achieved for tumor cells, whereas a median score of −1 is equivalent to signifcant advances, while drug resistance is still a critical a gene that is essential for tumor cell lines. Te dependency feature of the disease and contributes to disease relapses and Journal of Oncology 11 GSE24080 GSE24080 GSE4204 GSE2658 100 100 100 100 80 80 80 60 60 60 40 40 40 20 20 HR = 1.532 HR = 1.842 20 HR = 1.681 20 HR = 1.679 p = 0.013 p = 0.032 p < 0.0001 p = 0.007 0 0 0 0 0 20 40 60 80 100 0 20 40 60 80 0 20 40 60 80 100 120 0 20 40 60 80 EFS Time (months) OS Time (months) OS Time (months) OS Time (months) Low-SRSF1 Low-SRSF1 Low-SRSF1 Low-SRSF1 High-SRSF1 High-SRSF1 High-SRSF1 High-SRSF1 (a) (b) (c) (d) GSE57317 HR = 4.164 p = 0.020 0 10 20 30 40 50 OS Time (months) Low-SRSF1 High-SRSF1 (e) high low Figure 4: Survival analysis of the SRSF1 and SRSF1 groups. Te X-axis represents the survival time (month), and the Y-axis represents high low survival probability. (a, b) Analysis of EFS and OS between the SRSF1 and SRSF1 groups in GSE24080 (n � 559). (c) OS between high low SRSF1 and SRSF1 in GSE4204 (n � 538). (d) OS analysis of 559 pretreatment MM patients in the GSE2658 dataset. (e) OS analysis of MM patients after treatment in GSE57315 (n � 55). poor overall survival. Tus, fnding predictive biomarkers is regulates various cellular processes, such as cell diferentia- essential for improving treatment results in MM patients. tion, proliferation, apoptosis, and type I interferon signaling In this study, we acquired RNA expression data and pathway . NSrp70, a splicing factor, regulates thymocyte clinical information of MM patients from the GEO database. development via partial alternative processing of SRSF1 . Firstly, we investigated the role of SRSF family members in Sinnakannu et al. reported that high SRSF1 expression in MM and screened SRSF1 as the most potential factor for chronic myeloid leukemia was associated with imatinib re- further analysis. SRSF1 was upregulated in newly diagnosed sistance, which was mediated by the SRSF1/PRKCH/PLCH1 and relapsed MM patients. Furthermore, SRSF1 over- axis . In AML, SRSF1 was responsible for the generation expression was associated with several adverse clinical pa- of alternative isoforms of proapoptotic and antiapoptotic genes, including BCL-x, MCLs, and capsase9b . In pe- rameters, including old age, high levels of B2M and LDH, low ALB level, and 1q21 amplifcation. Univariate and multi- diatric acute lymphoblastic leukemia (ALL), SRSF1 was variate Cox regression was conducted to identify whether upregulated in clinical samples from de novo or relapsed SRSF1 was an independent prognostic factor for MM. Sur- patients and decreased when complete remission was high vival analysis revealed that patients in the SRSF1 group achieved . In our study, we also found that the expression low had a much worse prognosis than those in the SRSF1 level of SRSF1 was signifcantly higher in the relapsed MM group. Tese results turned out that the SRSF1 expression compared with newly diagnosed MM and increased with level can be used as a potential predictor in MM prognosis. disease recurrences. Alternative splicing events can produce Te high expression level of SRSF1 has been shown to tumor-related splice variants and proteins . Tus, it is confer poor prognosis in a variety of cancers, and the un- tempting to investigate whether increased SRSF1 expression derlying mechanisms were characterized. For example, SRSF1 afects alternative splicing to drive MM progression. To explore the potential biological mechanism of SRSF1 overexpression was reported to increase tumor invasion and metastasis in hepatocellular carcinoma . Du et al. sug- overexpression, GO, KEGG, and GSEA analyses were per- gested that SRSF1 promotes the progression of breast cancer formed. It turned out that pathways were enriched in cell through oncogenic splice switching of PTPMT1 . Addi- division, infammatory response, cytokine-cytokine receptor tionally, SRSF1 involves in both normal and malignant he- interaction, RNA metabolism, and transcriptional mis- matopoiesis. To protect T cell intrathymic maturation, SRSF1 regulation, specifcally P53, MAPK4/6, NOTCH, and MYC Survival Probability (%) Survival Probability (%) Survival Probability (%) Survival Probability (%) Survival Probability (%) 12 Journal of Oncology Type AZGP1 KIF21B INHBE 12 HPDL C1orf112 SKA1 HJURP 0 FOXM1 TACC3 SLC20A1 −2 EMC3−AS1 AC004941.5 RP11−480A16.1 MIR142 −4 RP1−102H19.8 LOC100129917 SRSF1 CTB−181H17.1 BC045784 LOC100134822 SNORA21 EIF3C LINC00328 LINC01021 LOC100132352 RP11−124L9.5 ZNF559 CRIM1 ZFP30 -1.2 -0.8 -0.4 0.0 0.4 0.8 ZNF569 ICAM4 SYNGR3 logFC GBA3 MLIP KIT SULF2 Up (n=162) ELOVL7 PTPRZ1 SERPINI1 Down (n=124) BIRC3 PLEKHO1 QPCT ADTRP CTSW IL18R1 PRG2 CPVL HLA−DRA CD5L C1QB C1QA VCAM1 MAST1 IGHV3−54 LOC100293211 IGK PLAT ATXN8OS Igk GPHA2 Type High Low (a) (b) Cell division Cytokine-cytokine receptor interaction Inflammatory response Systemic lupus erythematosus Positive regulation of immune response Transcriptional misregulation in cancer Chromosome segregation -log10 (P value) -log10 (P value) Complement and coagulation cascades Myeloid leukocyte activation 4.0 Apoptosis Leukocyte activation involved in immune response 3.5 Cell activation involved in immune response Fluid shear stress and atherosclerosis 3.0 Lymphocyte activation 5 Phagosome 2.5 Regulation of cytokine production 4 Inflammatory bowel disease (IBD) 2.0 Regulation of immune effector process Platinum drug resistance Adaptive immune response Count Count Staphylococcus aureus infection Response to bacterium 3 Pertussis 4 Positive regulation of hydrolase activity Regulated exocytosis Rheumatoid arthritis Regulation of protein kinase activity NF-kappa B signaling pathway Nuclear division TNF signaling pathway Organelle fission Asthma Leukocyte migration Prion diseases Regulation of leukocyte activation Legionellosis Microtubule cytoskeleton organization 0.0 2.5 5.0 7.5 10.0 12.5 04 1 2 3 Ratio (%) Ratio (%) (c) (d) Figure 5: Diferently expressed genes (DEGs) and the results of GO and KEGG enrichment analysis. (a) Volcano plot of the DEGs high low expression between the SRSF1 and SRSF1 groups. Green dots represent 126 downregulated genes, red dots represent 163 upregulated genes, and grey dots indicate nonsignifcant genes. (b) Heatmap shows top 30 upregulated genes and the top 30 downregulated genes. Red represents high expression, white represents intermediate expression, and blue represents low expression. (c-d) Top 20 terms of GO and KEGG enrichment analysis for diferential expressed genes. pathways, which are critical regulators involved in myeloma progression [44, 45]. Moreover, IL-12 and B cell receptor initiation and progression. SRSF1 is important for spliceo- (BCR) signaling are essential for an immune response some formation and RNA metabolism, and dysregulation of [46–48]. Terefore, it is necessary to further investigate RNA stability could promote MM progression . More- whether SRSF1 promotes MM development through immune over, SRSF1 has been identifed as an MYC-sensitive onco- modulation. genic protein , suggesting that abnormal SRSF1 Bone marrow microenvironment (BMME) is important expression might afect MYC-related pathways. It has been for MM initiation and progression. Components of BMME, established that p53, NOTCH, and MAPK6/4 signaling such as immune efector cells and immune molecules, can be pathways play important roles in MM initiation and abnormally edited, which further promote MM progression -log10 (adj.P value) Journal of Oncology 13 TACC3 BIRC3 PHF19 AZGP1 KIF21B CD5L SNORA21 C1orf112 FOXM1 PLAT SERPINI1 MLIP HLA-DRA SKA1 RRM2 ANKLE1 C1QA RPL36A SRSF1 EIF3C C1QB NUF2 HJURP ZNF569 MAST1 SULF2 CENPM TOP2A PRG2 VCAM1 ASPM PTPRZ1 KIT ELOVL7 QPCT C1orf112 ZFP30 CENPM CTSW HJURP CPVL NUF2 ZNF559 SKA1 HPDL TACC3 AGAP4 FOXM1 CRIM1 ASPM ICAM4 RRM2 TOP2A (a) (b) AZGP1 KIF21B INHBE HPDL C1orf112 SKA1 0.8 HJURP FOXM1 TACC3 SLC20A1 EMC3−AS1 AC004941.5 0.6 RP11−480A16.1 MIR142 RP1−102H19.8 LOC100129917 SRSF1 CTB−181H17.1 0.4 BC045784 LOC100134822 SNORA21 EIF3C LINC00328 LINC01021 0.2 LOC100132352 RP11−124L9.5 ZNF559 CRIM1 ZFP30 ZNF569 ICAM4 SYNGR3 GBA3 MLIP KIT SULF2 −0.2 ELOVL7 PTPRZ1 SERPINI1 BIRC3 PLEKHO1 QPCT −0.4 ADTRP CTSW IL18R1 PRG2 CPVL HLA−DRA −0.6 CD5L C1QB C1QA VCAM1 MAST1 IGHV3−54 −0.8 LOC100293211 IGK PLAT ATXN8OS Igk GPHA2 −1 (c) Figure 6: Continued. AZGP1 KIF21B INHBE HPDL C1orf112 SKA1 HJURP FOXM1 TACC3 SLC20A1 EMC3−AS1 AC004941.5 RP11−480A16.1 MIR142 RP1−102H19.8 LOC100129917 SRSF1 CTB−181H17.1 BC045784 LOC100134822 SNORA21 EIF3C LINC00328 LINC01021 LOC100132352 RP11−124L9.5 ZNF559 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 GPHA2 14 Journal of Oncology r = -0.142 r = 0.177 16 r = -0.105 13 13 p = 0.027 p <0.001 p = 0.013 3 1 2 8 910 11 8 910 11 8 910 11 Expression of SRSF1 (log2) Expression of SRSF1 (log2) Expression of SRSF1 (log2) 12 r = 0.251 14 r = 0.199 13 r = -0.222 p <0.001 p <0.001 p < 0.001 8 8 4 2 3 8 910 11 8 910 11 8 910 11 Expression of SRSF1 (log2) Expression of SRSF1 (log2) Expression of SRSF1 (log2) (d) Figure 6: Te PPI and correlation analysis of DEGs. (a) PPI network of top 60 DEGs. (b) Te core subnetwork of the PPI network by using MCODE APP in Cytoscape. (c) Te correlation analysis of DEGs with the Pearson correlation coefcient. Te red circle means a positive correlation, while the blue circle means a negative correlation. (d) Te correlation analysis of SRSF1 and DEGs. by enhancing initial immunotolerance and subsequent tu- the expression of immune-related genes. Additionally, al- mor cell escape from immune surveillance . We found ternative processing of mRNA has been reported to have the that Tr1, T2, and TCM exhibited a higher degree of in- potential to provide new therapeutic targets for cancer high fltration in the SRSF1 group, while the degree of mac- immunotherapy . Tus, splicing variants and proteins low rophage infltration was higher in the SRSF1 group. produced by alternative splicing caused by abnormal ex- pression of SRSF1 may provide a new insight for immu- Correlation analysis showed that the SRSF1 expression level was positively correlated with Tr1, T2, and TCM and notherapy in MM patients. Altogether, our fndings showed negatively associated with macrophages. Tr1 cells play a role that the SRSF1 expression level could afect tumor immune in infammatory responses and immune tolerance; however, characteristics via infltrating immune cells, TME, check- dysfunction of Tr1 cells may limit antitumor immunity point markers, and immune-related genes, thereby de- [50, 51]. Studies have shown that T2 cells are closely as- termining the prognosis of patients with MM. sociated with MM progression. Increased T2 cells lead to SRSF1 has been widely reported as an oncogene in many a closer myeloma cell interaction, which subsequently tumors. We identifed SRSF1 as an essential cancer-dependent contributes to MM development [52–55]. Recently, immune gene in tumorigenesis by using the DepMap database, especially checkpoint therapy has achieved great breakthroughs in the in multiple myeloma. To validate the bioinformatic results, frst, treatment of hematological malignancies. Finding reliable we performed RT-qPCR on clinical samples and found that SRSF1 expression was signifcantly increased in MM patients biomarkers and potential targets can provide new sights for immunotherapy in MM. Terefore, we compared check- compared with controls. Ten, we found that the knockdown of point markers and immune-related genes between the SRSF1 led to growth inhibition of MM cell lines. Combined high low SRSF1 - and SRSF1 groups. We observed that check- with Figures 3 and5, patients with the high expression level of point markers such as PD-L1, LAG3, and PDCD1LG2 were SRSF1 were associated with worse outcomes, indicating that high downregulated in the SRSF1 group. PD-L1 inhibitors, SRSF1 can be a promising biomarker and target in MM di- such as durvalumab and pembrolizumab, have been re- agnosis and treatment. ported to be efective in the treatment of relapsed or re- Although we briefy profled the SRSF1-induced gene fractory MM [56–58]. SRSF1 expression might provide expression, this study has some limitations. Firstly, whether a new idea for immune checkpoint inhibitor therapy. In MM or how SRSF1 afects the splicing events of target genes in patients with high SRSF1 expression, immune-related genes MM needs to be explored. Secondly, the number of MM patients enrolled for validation was small. We are collecting for immune response activation were remarkably down- regulated, whereas immunosuppressive genes were in- more primary MM samples to further detect and correlate creased, indicating SRSF1 might play a role in modulating SRSF1 expression with the clinical outcomes of MM Expression of GPHA2 (log2) Expression of SLC20A1 (log2) Expression of EIF3C (log2) Expression of MIR142 (log2) Expression of VCAM1 (log2) Expression of Igk (log2) Journal of Oncology 15 0.4 0.4 p-value =2e-04 p-value =0.0001 p-value =0.0056 0.4 0.3 0.3 0.2 0.2 0.2 0.1 SPLICEOSOME 0.1 METABOLISM_OF_RNA PROTEIN_UBIQUITINATION 0.0 0.0 0.0 SRSF1high SRSF1low SRSF1high SRSF1high SRSF1low SRSF1low 5.0 0.5 0.5 2.5 0.0 0.0 0.0 −0.5 −0.5 −2.5 −1.0 −1.0 5000 10000 15000 20000 2500 5000 7500 10000 5000 10000 15000 20000 Rank in Ordered Dataset Rank in Ordered Dataset Rank in Ordered Dataset (a) (b) (c) 0.4 0.4 p-value =0.0071 p-value = 3e-04 p-value =2e-04 0.3 0.4 0.3 0.2 0.2 0.2 0.1 MYC_TARGETS 0.1 P53_SIGNALING_PATHWAY SIGNALING_BY_NOTCH 0.0 0.0 0.0 SRSF1high SRSF1low SRSF1high SRSF1low SRSF1high SRSF1low 5.0 0.5 0.5 2.5 0.0 0.0 0.0 −0.5 −0.5 −2.5 −1.0 −1.0 5000 10000 15000 20000 5000 10000 15000 20000 2500 5000 7500 10000 Rank in Ordered Dataset Rank in Ordered Dataset Rank in Ordered Dataset (d) (e) (f) 0.5 p-value = 5e-04 p=0.0003 0.4 p-value = 3e-04 0.4 0.4 0.3 0.3 0.2 0.2 0.2 Downstream_ signaling_events 0.1 0.1 INTERLEUKIN_12_SIGNALING MAPK6_MAPK4_SIGNALING _of_B_cell_receptor BCR 0.0 0.0 0.0 SRSF1high SRSF1low SRSF1high SRSF1low 5.0 SRSF1high SRSF1low 5.0 5.0 2.5 2.5 2.5 0.0 0.0 0.0 −2.5 −2.5 −2.5 2500 5000 7500 10000 2500 5000 7500 10000 2500 5000 7500 10000 Rank in Ordered Dataset Rank in Ordered Dataset Rank in Ordered Dataset (g) (h) (i) high low Figure 7: Enrichment analysis of gene signaling pathways between the SRSF1 group and the SRSF1 groups. GSEA showed that spliceosome (a), metabolism of RNA (b), protein ubiquitination (c), P53 signaling pathway (d), MYC targets (e), signaling by NOTCH (f), interleukin 12 signaling (g), downstream signaling events of B cell receptor BCR (h), and MARK6/4 signaling (i) were enriched in the high SRSF1 group. All p < 0.01. patients. In our study, we identifed the SRSF1 expression in patterns of clonal evolution , to better evaluate SRSF1 as MM with diferent ages, ISS stages, amplifcation of 1q21, an important prognostic factor, MM patients with various and relapse statuses in newly diagnosed and relapsed MM genetic alterations and disease statuses should be enrolled in patients. Since MM is a heterogeneous disease with diferent the future study. Ranked list Ranked list Ranked list Running Enrichment Running Enrichment Running Enrichment metric metric metric Score Score Score Ranked list Ranked list Ranked list Running Enrichment Running Enrichment Running Enrichment metric metric metric Score Score Score Ranked list Ranked list Ranked list Running Enrichment Running Enrichment Running Enrichment metric metric metric Score Score Score 16 Journal of Oncology Type 1 regulatory T T helper 2 0.15 0.3 r = 0.133 r = 0.146 0.3 0.15 *** p = 0.002 p < 0.001 0.10 0.2 0.10 0.2 0.05 0.1 0.05 0.1 0.00 0.0 0.0 0.00 8 910 11 8 910 11 Low High Low High SRSF1 expression SRSF1 expression SRSF1 expression SRSF1 expression 0.10 0.15 Central_memory Macrophage r = 0.184 r = -0.171 p < 0.001 p <0.001 0.12 0.20 0.08 ** *** 0.10 0.16 0.06 0.08 0.12 0.04 0.05 0.08 0.04 0.02 0.04 0.00 0.00 0.00 0.00 8 910 11 8 910 11 Low High Low High SRSF1 expression SRSF1 expression SRSF1 expression SRSF1 expression (a) (b) *** ** * *** * ** *** *** *** ** ** ** *** * ** * * *** *** 15 4000 3000 2000 2000 0 1000 -1000 -2000 Low High Low High SRSF1 expression SRSF1 expression ** 1000 ** 1.2 1.0 -1000 0.8 -2000 0.6 Type -3000 0.4 Low High Low High SRSF1-Low SRSF1 expression SRSF1 expression SRSF1-High (c) (d) high low Figure 8: Analysis of tumor-infltrating immune cells and immune-related genes in the SRSF1 and SRSF1 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, low high (c) tumor microenvironment characteristics in the SRSF1 and SRSF1 groups, and (d) the expression levels of immune-related genes high low ∗ ∗∗ ∗∗∗ between the SRSF1 and SRSF1 groups. p< 0.05, p < 0.01, and p< 0.001. ESTIMATEScore StromalScore Fraction Fraction ImmuneScore Fraction Fraction TumorPurity Gene expression Central_memory T cell Type 1 regulatory T PD-L1 LAG3 PDCD1LG2 CD163 CD27 CD40 CXCL12 IDO1 Macrophage T helper 2 LAMP3 LGALS9 NKG7 NOS1 TIMD4 TNFSF9 TREM2 LAIR1 TNFRSF8 Journal of Oncology 17 0.0 myeloma ** -0.5 lymphoma *** -1.0 leukemia -1.5 solid tumor -2.0 -2.5 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 SRSF1 Dependency score (a) (b) GSE39754 (n=170) p <0.001 r = 0.3567 p<0.0001 7 15 8 910 11 0 SRSF1 (log2) (c) (d) H929 U266 KM3 8226 2.5 SRSF1 **** 2.0 H929 1.5 β-Tubulin shscr shSRSF1 1.0 *** SRSF1 0.5 β-Tubulin 0.0 0 1234 Day H929-shscr H929-shSRSF1 (e) (f) Figure 9: Continued. MKI67 (log2) Dependency Score Relative expression of SRSF1 BMSC #1 BMSC #2 PBMC #1 MM #1 OD value MM #2 MM #3 MM #4 MM #5 MM #6 MM #7 MM #8 18 Journal of Oncology 0.8 **** 0.6 U266 shscr shSRSF1 0.4 SRSF1 0.2 β-Tubulin 0.0 0 1234 Days U266-shscr U266-shSRSF1 (g) 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. (c) Correlation of SRSF1 expression and MKI67 expression in GSE39754 (n � 170). (d) Te expression level of SRSF1 between three normal samples and eight MM patients. (e) Te expression of level of SRSF1 in four MM cell lines. (f, g) SRSF1 knockdown inhibits the growth of MM cell lines: H929 and U266. 5. Conclusion Authors’ Contributions In conclusion, our study revealed that SRSF1 expression is Te authors Jiawei Zhang and Zanzan Wang contributed upregulated in MM patients. Notably, SRSF1 overexpression equally in this work. was associated with worse clinical characteristics in MM Acknowledgments patients (age, ISS stage, amplifcation of 1q21, relapse sta- tuses as well as beta-2 microglobulin) and predicts poor OS Tis study was supported by the Natural Science Foundation and EFS of MM patients. Additionally, the knockdown of of Zhejiang Province of China (LY21H080005) and National SRSF1 repressed the growth of MM cell lines. Tus, our Natural Science Foundation of China (81572920). results demonstrated that SRSF1 may promote MM growth Supplementary Materials with prognostic signifcance and can potentially be used as a novel biomarker in the future. Moreover, more research Supplemental Table 1. Diferential expressed genes between the studies need to be carried out to explore the complicated SRSF1high and the SRSF1low groups. Supplementary Table 2. mechanisms of SRSF1 in MM development and progression. Top 20 terms of GO analysis of diferential expressed genes. Supplementary Table 3. Top 20 terms of KEGG pathway Data Availability analysis of diferential expressed genes. Supplementary Figure 1. SRSF1 expression levels at diferent 1q21 amplifcation in 248 Publicly available datasets were used in this study. Tese data MM patients of GSE2658. Supplementary Figure 2. Te can be found here: Gene Expression Omnibus (GEO) re- Kaplan–Meier curve shows the prognostic value of the SRSF1 pository (https://www.ncbi.nlm.nih.gov/geo/). Te data that expression levels for MM patients categorized by B2M≤ 3.5 support the fndings of this study are available from the (mg/l) (A, D), 3.5< B2M< 5.5 (mg/l) (B, E), and B2M≥ 5.5 corresponding author upon reasonable request. (mg/l) (C, F). Supplementary Figure 3. Te Kaplan–Meier curve shows the prognostic value of the SRSF1 expression levels for Ethical Approval MM patients categorized by LDH< 250 (U/l) (A, C) and LDH≥ 250 (U/l) (B, D). Supplementary Figure 4. GSEA showed Tis study was approved by the Ethics Committee of the that hematopoietic cell lineage (A), cell receptor signaling Second Afliated Hospital of Zhejiang University School of pathway (B), TNFa signaling via NFKB (C), infammatory Medicine (Ethics Approval No. 2020977). response (D), complement (E), and coagulation (F) were sig- nifcantly enriched in the SRSF1low group. (Supplementary Consent Materials) Each participant had signed informed consent. References  Y. Furukawa and J. Kikuchi, “Molecular pathogenesis of Conflicts of Interest multiple myeloma,” International Journal of Clinical Oncol- Te authors declare that they have no conficts of interest. ogy, vol. 20, no. 3, pp. 413–422, 2015. OD value Journal of Oncology 19  S. K. Kumar, V. Rajkumar, R. A. Kyle et al., “Multiple my-  S. Lei, B. Zhang, L. Huang et al., “SRSF1 promotes the in- eloma,” Nature Reviews Disease Primers, vol. 3, no. 1, Article clusion of exon 3 of SRA1 and the invasion of hepatocellular ID 17046, 2017. carcinoma cells by interacting with exon 3 of SRA1pre-  O. Landgren, R. A. Kyle, R. M. Pfeifer et al., “Monoclonal mRNA,” Cell death discovery, vol. 7, no. 1, p. 117, 2021. gammopathy of undetermined signifcance (MGUS) consis-  J. X. Du, Y. H. Luo, S. J. Zhang et al., “Splicing factor SRSF1 tently precedes multiple myeloma: a prospective study,” promotes breast cancer progression via oncogenic splice Blood, vol. 113, no. 22, pp. 5412–5417, 2009. switching of PTPMT1,” Journal of Experimental & Clinical  R. A. Kyle, E. D. Remstein, T. M. Terneau et al., “Clinical Cancer Research, vol. 40, no. 1, p. 171, 2021. course and prognosis of smoldering (asymptomatic) multiple  S. Das, O. Anczukow, ´ M. Akerman, and A. R. Krainer, myeloma,” New England Journal of Medicine, vol. 356, no. 25, “Oncogenic splicing factor SRSF1 is a critical transcriptional pp. 2582–2590, 2007. target of MYC,” Cell Reports, vol. 1, no. 2, pp. 110–117, 2012.  C. Fernandez ´ de Larrea, R. A. Kyle, B. G. M. Durie et al.,  Z. Qi, F. Wang, G. Yu et al., “SRSF1 serves as a critical “Plasma cell leukemia: consensus statement on diagnostic posttranscriptional regulator at the late stage of thymocyte requirements, response criteria and treatment recommen- development,” Science Advances, vol. 7, no. 16, Article ID dations by the International Myeloma Working Group,” eabf0753, 2021. Leukemia, vol. 27, no. 4, pp. 780–791, 2013.  C. H. Kim, S. M. Park, S. J. Lee et al., “NSrp70 is a lymphocyte-  R. E. Tiedemann, N. Gonzalez-Paz, R. A. Kyle et al., “Genetic essential splicing factor that controls thymocyte develop- aberrations and survival in plasma cell leukemia,” Leukemia, ment,” Nucleic Acids Research, vol. 49, no. 10, pp. 5760–5778, vol. 22, no. 5, pp. 1044–1052, 2008.  C. Fernandez ´ de Larrea, R. Kyle, L. Rosiñol et al., “Primary  L. Zou, H. Zhang, C. Du et al., “Correlation of SRSF1 and plasma cell leukemia: consensus defnition by the In- PRMT1 expression with clinical status of pediatric acute ternational Myeloma Working Group according to peripheral lymphoblastic leukemia,” Journal of Hematology & Oncology, blood plasma cell percentage,” Blood Cancer Journal, vol. 11, vol. 5, no. 1, p. 42, 2012. no. 12, p. 192, 2021.  J. R. Sinnakannu, K. L. Lee, S. Cheng et al., “SRSF1 mediates  M. Blijlevens, J. Li, and V. W. van Beusechem, “Biology of the cytokine-induced impaired imatinib sensitivity in chronic mRNA splicing machinery and its dysregulation in cancer myeloid leukemia,” Leukemia, vol. 34, no. 7, pp. 1787–1798, providing therapeutic opportunities,” International Journal of Molecular Sciences, vol. 22, no. 10, p. 5110, 2021.  R. M. Meyers, J. G. Bryan, J. M. McFarland et al., “Com-  Z. Dou, D. Zhao, X. Chen et al., “Aberrant Bcl-x splicing in putational correction of copy number efect improves spec- cancer: from molecular mechanism to therapeutic modula- ifcity of CRISPR-Cas9 essentiality screens in cancer cells,” tion,” Journal of Experimental & Clinical Cancer Research, Nature Genetics, vol. 49, no. 12, pp. 1779–1784, 2017. vol. 40, no. 1, p. 194, 2021.  D. Szklarczyk, A. L. Gable, K. C. Nastou et al., “Te string  M. A. Bauer, C. Ashby, C. Wardell et al., “Diferential RNA database in 2021: customizable protein-protein networks, and splicing as a potentially important driver mechanism in functional characterization of user-uploaded gene/measure- multiple myeloma,” Haematologica, vol. 106, no. 3, pp. 736– ment sets,” Nucleic Acids Research, vol. 49, no. D1, 745, 2021. pp. D605–D612, 2021.  V. V. Grinev, F. Barneh, I. M. Ilyushonak et al., “Runx1/  M. E. Ritchie, B. Phipson, D. Wu et al., “Limma powers runx1t1 mediates alternative splicing and reorganises the diferential expression analyses for RNA-sequencing and transcriptional landscape in leukemia,” Nature Communica- microarray studies,” Nucleic Acids Research, vol. 43, no. 7, tions, vol. 12, no. 1, p. 520, 2021. p. e47, 2015.  S. S. Itskovich, A. Gurunathan, J. Clark et al., “MBNL1  G. Yu, L. G. Wang, Y. Han, and Q. Y. He, “ClusterProfler: an r regulates essential alternative RNA splicing patterns in MLL- package for comparing biological themes among gene clus- rearranged leukemia,” Nature Communications, vol. 11, no. 1, ters,” OMICS: A Journal of Integrative Biology, vol. 16, no. 5, p. 2369, 2020. pp. 284–287, 2012.  K. Singh, J. Lin, Y. Zhong et al., “C-MYC regulates mRNA  A. Subramanian, P. Tamayo, V. K. Mootha et al., “Gene set translation efciency and start-site selection in lymphoma,” enrichment analysis: a knowledge-based approach for inter- Journal of Experimental Medicine, vol. 216, no. 7, pp. 1509– preting genome-wide expression profles,” Proceedings of the 1524, 2019. National Academy of Sciences of the U S A, vol. 102, no. 43,  J. L. Manley and A. R. Krainer, “A rational nomenclature for pp. 15545–15550, 2005. serine/arginine-rich protein splicing factors (SR proteins):  Y. R. Miao, Q. Zhang, Q. Lei et al., “ImmuCellAI: a unique Table 1,” Genes &amp; Development, vol. 24, no. 11, method for comprehensive T-cell subsets abundance pre- pp. 1073-1074, 2010. diction and its application in cancer immunotherapy,” Ad-  D. Longman, I. L. Johnstone, and J. F. Caceres, ´ “Functional vanced Science, vol. 7, no. 7, Article ID 1902880, 2020. characterization of SR and SR-related genes in Caenorhabditis  K. Yoshihara, M. Shahmoradgoli, E. Mart´ınez et al., “Inferring elegans,” Te EMBO Journal, vol. 19, no. 7, pp. 1625–1637, tumour purity and stromal and immune cell admixture from expression data,” Nature Communications, vol. 4, no. 1,  X. Xu, D. Yang, J. H. Ding et al., “ASF/SF2-Regulated p. 2612, 2013. CaMKIIδ alternative splicing temporally reprograms  S. Li, X. He, Y. Gan et al., “Targeting miR-21 with NL101 excitation-contraction coupling in cardiac muscle,” Cell, blocks c-Myc/Mxd1 loop and inhibits the growth of B cell vol. 120, no. 1, pp. 59–72, 2005.  Y. Cheng, C. Luo, W. Wu, Z. Xie, X. Fu, and Y. Feng, “Liver- lymphoma,” Teranostics, vol. 11, no. 7, pp. 3439–3451, 2021.  A. Franken, P. Van Mol, S. Vanmassenhove et al., “Single-cell specifc deletion of SRSF2 caused acute liver failure and early death in mice,” Molecular and Cellular Biology, vol. 36, no. 11, transcriptomics identifes pathogenic T-helper 17.1 cells and pp. 1628–1638, 2016. pro-infammatory monocytes in immune checkpoint 20 Journal of Oncology inhibitor-related pneumonitis,” Journal for immunotherapy of  Y. Zhong, J. C. Byrd, and J. A. Dubovsky, “Te B-cell receptor cancer, vol. 10, no. 9, Article ID e005323, 2022. pathway: a critical component of healthy and malignant  R. Zhang, W. Yu, R. Guo et al., “Te amplifcation of 1q21 is immune biology,” Seminars in Hematology, vol. 51, no. 3, an adverse prognostic factor in patients with multiple mye- pp. 206–218, 2014.  V. Desantis, F. D. Savino, A. Scaringella et al., “Te leading loma in a Chinese population,” Onco Targets and Terapy, role of the immune microenvironment in multiple myeloma: vol. 9, pp. 295–302, 2016. a new target with a great prognostic and clinical value,”  J. Burroughs Garc`ıa, R. A. Eufemiese, P. Storti et al., “Role of Journal of Clinical Medicine, vol. 11, no. 9, p. 2513, 2022. 1q21 in multiple myeloma: from pathogenesis to possible  Z. Song, T. Zhang, G. Li, Y. Tang, Y. Luo, and G. Yu, “Tr1 therapeutic targets,” Cells, vol. 10, no. 6, p. 1360, 2021. responses are elevated in asymptomatic H. pylori-infected  T. M. Schmidt, R. Fonseca, and S. Z. Usmani, “Chromosome individuals and are functionally impaired in H. pylori-gastric 1q21 abnormalities in multiple myeloma,” Blood Cancer cancer patients,” Experimental Cell Research, vol. 367, no. 2, Journal, vol. 11, no. 4, p. 83, 2021. pp. 251–256, 2018.  Z. Xin, Y. Li, L. Meng, L. Dong, J. Ren, and J. Men, “Elevated  J. Wang, Y. Hu, H. Hamidi et al., “Immune microenviron- expression of the MYB proto-oncogene like 2 (MYBL2)- ment characteristics in multiple myeloma progression from encoding gene as a prognostic and predictive biomarker in transcriptome profling,” Frontiers Oncology, vol. 12, Article human cancers,” Mathematical Biosciences and Engineering: ID 948548, 2022. MBE, vol. 19, no. 2, pp. 1825–1842, 2022.  F. Tian, B. Lu, Z. Chen et al., “Microbial antigens-loaded  L. Li, J. Liu, and W. Huang, “E2F5 promotes proliferation and myeloma cells enhance T2 cell proliferation and myeloma invasion of gastric cancer through directly upregulating clonogenicity via T2-myeloma cell interaction,” BMC UBE2T transcription,” Digestive and Liver Disease, vol. 54, Cancer, vol. 19, no. 1, p. 1246, 2019. no. 7, pp. 937–945, 2022.  F. Tian, J. Li, Y. Li, and S. Luo, “Enhancement of myeloma  K. Funato, R. C. Smith, Y. Saito, and V. Tabar, “Dissecting the development mediated though myeloma cell-T2 cell in- impact of regional identity and the oncogenic role of human- teractions after microbial antigen presentation by myeloma specifc NOTCH2NL in an hESC model of H3.3G34R-mutant cells and DCs,” Blood Cancer Journal, vol. 2, no. 6, p. e74, glioma,” Cell Stem Cell, vol. 28, no. 5, pp. 894–905.e7, 2021.  X. Wu, L. Qian, H. Zhao et al., “CXCL12/CXCR4: an amazing  S. Hong, J. Qian, J. Yang, H. Li, L. W. Kwak, and Q. Yi, “Roles challenge and opportunity in the fght against fbrosis,” Ageing of idiotype-specifc t cells in myeloma cell growth and sur- Research Reviews, vol. 83, Article ID 101809, 2023. vival: T1 and CTL cells are tumoricidal while T2 cells  P. Zeis, M. Lian, X. Fan et al., “In situ maturation and tissue promote tumor growth,” Cancer Research, vol. 68, no. 20, adaptation of type 2 innate lymphoid cell progenitors,” Im- pp. 8456–8464, 2008. munity, vol. 53, no. 4, pp. 775–792.e9, 2020.  M. H. Young, G. Pietz, E. Whalen et al., “Immunomodulation  Z. Dlamini, B. Shoba, and R. Hull, “Splicing machinery ge- by durvalumab and pomalidomide in patients with relapsed/ nomics events in acute myeloid leukaemia (AML): in search refractory multiple myeloma,” Scientifc Reports, vol. 11, no. 1, for therapeutic targets, diagnostic and prognostic bio- Article ID 16460, 2021. markers,” American journal of cancer research, vol. 10, no. 9,  P. Moreau, R. Ghori, M. Farooqui, P. Marinello, and J. San pp. 2690–2704, 2020. Miguel, “Pembrolizumab combined with carflzomib and  A. Basera, R. Hull, D. Demetriou et al., “Competing endog- low-dose dexamethasone for relapsed or refractory multiple enous RNA (ceRNA) networks and splicing switches in myeloma: cohort 2 of the phase I KEYNOTE-023 study,” cervical cancer: HPV oncogenesis, clinical signifcance and British Journal of Haematology, vol. 194, no. 1, pp. e48–e51, therapeutic opportunities,” Microorganisms, vol. 10, no. 9, p. 1852, 2022.  N. Biran, E. Gourna Paleoudis, R. Feinman et al., “Pem- brolizumab, lenalidomide and dexamethasone post autolo-  F. Jiang, X. Tang, C. Tang et al., “HNRNPA2B1 promotes multiple myeloma progression by increasing AKT3 expres- gous transplant in patients with high-risk multiple myeloma,” American Journal of Hematology, vol. 96, no. 11, pp. E430– sion via m6A-dependent stabilization of ILF3 mRNA,” E433, 2021. Journal of Hematology & Oncology, vol. 14, no. 1, p. 54, 2021.  L. Frankiw, D. Baltimore, and G. Li, “Alternative mRNA  R. Ordoñez, M. Kulis, N. Russiñol et al., “Chromatin acti- splicing in cancer immunotherapy,” Nature Reviews Immu- vation as a unifying principle underlying pathogenic mech- nology, vol. 19, no. 11, pp. 675–687, 2019. anisms in multiple myeloma,” Genome Research, vol. 30, no. 9,  N. H. Ismail, A. Mussa, N. A. Zakaria et al., “Te role of pp. 1217–1227, 2020. epigenetics in the development and progression of multiple  F. Shirazi, R. J. Jones, R. K. Singh et al., “Activating KRAS, myeloma,” Biomedicines, vol. 10, no. 11, p. 2767, 2022. NRAS, and BRAF mutants enhance proteasome capacity and reduce endoplasmic reticulum stress in multiple myeloma,” Proceedings of the National Academy of Sciences of the U S A, vol. 117, no. 33, pp. 20004–20014, 2020.  R. Hunter, K. J. Imbach, C. Zhou et al., “B-cell acute lym- phoblastic leukemia promotes an immune suppressive mi- croenvironment that can be overcome by IL-12,” Scientifc Reports, vol. 12, no. 1, Article ID 11870, 2022.  D. S. Jones 2nd, J. D. Nardozzi, K. L. Sackton et al., “Cell surface-tethered IL-12 repolarizes the tumor immune mi- croenvironment to enhance the efcacy of adoptive T cell therapy,” Science Advances, vol. 8, no. 17, Article ID eabi8075,
Journal of Oncology – Hindawi Publishing Corporation
Published: May 10, 2023
Access the full text.
Sign up today, get DeepDyve free for 14 days.