Identification of differentially expressed genes, signaling pathways and immuneinfiltration in uterine leiomyosarcoma by integrated bioinformatics analysis

Identification of differentially expressed genes, signaling pathways and immuneinfiltration in uterine leiomyosarcoma by integrated bioinformatics analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of differentially expressed genes, signaling pathways and immuneinfiltration in uterine leiomyosarcoma by integrated bioinformatics analysis YiFan Kang, zhihong wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3299827/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: Uterine leiomyosarcoma (ULMS) ranks high among female malignant tumors with poor prognosis. Among them, the underlying molecular mechanism remains uncertain. We aim to use comprehensive bioinformatics to analyze differently expressed genes (DEGs), signaling pathways, and immune infiltration involved in uterine leiomyosarcoma (ULMS) and uterine leiomyoma (UL). Methods: The GSE68295 and GSE64763 datasets were downloaded from the GEO database and contain 56 samples, including 28 ULMS and 28 UL samples. Microarray data sets were integrated to obtain differentially expressed genes (DEGs) and further analyzed by bioinformatics techniques. The GO and KEGG pathway enrichment analyses were performed using R software and the PPI network was built using the STRING database. Cytoscape software was used to screen Hub genes (top 10 genes in terms of severity). Finally, ssGSVA was used to assess immune cell penetration in ULMS. Results: There were 2268 consensus DEGs, of which 83 were revised upwards and 59 downward. GO and KEGG pathway analysis revealed that these DEGs are mainly concentrated in cell cycle and mitotic pathways. CDK1, CCNB1, and BUB1 are potential biomarkers of ULMS. Activated CD10 T cells, activated CD20 T cells, type 20 T helper cells, and type 28 T helper cells are the top four cell types with the largest number of central genes, and they are present in small amounts in sarcoma tissue. Of these, CDK1 has the highest correlation with CD10 activated T cells. Conclusion: This study shows that the use of comprehensive bioinformatics analysis to screen DEGs, pathways and immune infiltrates provides new insights into the molecular mechanism of ULMS pathogenesis and targeted therapy. GEO database uterus leiomyosarcoma bioinformatics analysis differentially expressed genes immune infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Uterine leiomyosarcoma (ULMS) has the highest incidence in postmenopausal women [ 1 ] , accounting for about 2% of all uterine malignancies [ 2 ] . It is highly invasive and has a poor prognosis [ 3 ] , and the possibility of malignant transformation should be vigilant for the enlargement of fibroids in postmenopausal women. Clinical manifestations mainly include postmenopausal bleeding, pelvic mass and pelvic pain [ 4 , 5 ] , which are often unspecific and misdiagnosed as benign uterine leiomyoma (UL), leading to delayed diagnosis and treatment. At present, uterus leiomyosarcoma is mainly diagnosed by imaging examination [ 6 ] . Ultrasound is the preferred method. Leiomyomas are circular lesions with high echo and vascularization. Leiomyosarcoma is an isolated oval lesion with uneven echo and panvascularization [ 7 ] . Further diagnosis is made using magnetic resonance imaging (MRI) to assess tumor stage, depth of invasion, and extent of spread in vivo. Based on this, diffusion-weighted imaging (DWI) can map malignant lesions as areas of high intensity with better tissue contrast and provide quantitative information. PET/CT can also be used to indicate distant region metastasis. In addition, ULMS serologic tests may show elevated levels of lactate dehydrogenase (LDH) and/or CA125. The treatment methods mainly include surgical therapy, chemotherapy, targeted drug therapy and immunotherapy, while surgical therapy may have the risk of hidden spread for ULMS and other malignant tumors [ 8 , 9 ] . Therefore, pre-operative accurate diagnosis and targeted treatment is very important. Currently, there is no characteristic molecular change in the uterine leiomyosarcoma, so there has been no breakthrough in diagnosis and treatment. Mutations in the leiomyoma-driving factors present in ULMS provide growth advantages for highly aggressive cancers, and these mutations occur in certain key genes, which can provide new insights into pathogenesis and potentially develop new therapeutic targets [ 10 ] . Therefore, the use of gene expression microarrays in the GEO database to identify differential genetic targets for ULMS and UL, search for specific molecular markers, and real-time monitoring of tumor occurrence status and cellular characteristics is important for more effective control and prevention. In this study, we aim to analyze gene expression, signaling pathways, and immune infiltration in leiomyosarcoma by integrated bioinformatics analysis. Materials and methods 1.1 Search strategy Using the keywords “Uterine Leiomyosarcoma” to search GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ), from set up to December 31, 2022, the GEO database of the communist party of China 166 “Leiomyosarcoma” results. Items not relevant to this study were excluded by limiting source of tissue (Homo sapiens) and type of study (Expression profiling by array). After further selection of titles, abstracts and samples, two series (GSE68295 and GSE64763) were finally included. The retrieval flow diagram is shown in Fig. 1 . 1.2 Differentially expressed genes Download GSE68295 and GSE64763 from the GEO database and use the organizations "Uterine Leiomyosarcoma" and "Uterine Leiomyoma" in them. Normal uterine fibroid tissue and the uterine leiomyosarcoma SKN cell line from the GSE68295 series were excluded during the analysis. A total of 56 samples were included, including 28 ULMS samples and 28 UL samples. Through the R software (4.2.2) to batch standardization of microarray data set, with P 2 as a condition for screening, using limma R package analysis of two data sets of differentially expressed genes (DEGs), up-regulated or down-regulated genes for further analysis. Volcanic maps were used to display up-regulated and down-regulated genes differently expressed, and the results were visualized using the pheatmap R package. 1.3 Functional enrichment analysis of differentially expressed genes GO and KEGG path enrichment analyses of DEGs were performed using the clusterProfiler R package. The enrichment of DEGs in cellular component (CC), molecular function (MF), biological process (BP) and participating signal pathway was studied. P < 0.05 is considered statistically significant. 1.4 PPI network construction for differentially expressed genes. STRING database ( https://string-db.org ) was used to obtain the DEGs protein-protein interaction (PPI) network. Import Cytoscape 3.7.1 software was used to perform MCODE analysis of functional modules of the PPI network using default parameters. The Cytohubba tool was used for analysis and the top 10 hub genes were screened. 1.5 ssGSVA immune infiltration The percolation matrix of 28 immune cells was obtained from Single sample Gene Set Enrichment Analysis of the normalized data. In this study, immune cells include activated B cells, T cells (11 CD molecules), immature dendritic cell, natural killer cells (6 CD molecules), central memory T cells (10 CD molecules), effector memory T cells (6 CD molecules), eosinophil, gamma delta T cell, macrophages, mast cells, memory B cells, monocytes, plasmacytoid dendritic cell, myeloid derived suppressor cell, suppressor cells, natural killer cells, natural killer T cells, neutrophils, regulatory T cells, T helper cells (27 CD molecules). The corrplot R package was used to calculate the degree and fraction of percolation in the gene expression matrix of the two groups of immune cells, and the relationship between the pivot genes and the immune cells. Results 2.1 Screening for differential expression of genes Two datasets, GSE68295 and GSE64763, were screened from the GEO database. A total of 2268 differentially expressed genes were obtained, of which 59 were up-regulated and 83 were down-regulated. Table 1 shows the up-regulated and down-regulated DEGs. Volcanic maps and heat maps were drawn using R software, as shown in Figs. 2 and 3. 2.2 GO and KEGG enrichment analysis of differentially expressed genes Enrichplot analysis of GO and KEGG paths with P < 0.05 was obtained, as shown in Fig. 4 and Fig. 5 . GO analysis was mainly enriched to cell division related pathways, and the first three significantly enriched pathway terms were regulationofnucleardivisionNA, nuclear division and mitotic cell cycle checkpoint. The gene GO analysis is shown in Table 2 . The main pathway of KEGG enrichment analysis was cell cycle. The KEGG pathway of DEGs is shown in Table 3 . In addition, KEGG pathway enrichment was used to describe the pathway targeting ULMS (Fig. 6 ). Among them, the cell cycle is the main KEGG enrichment pathway (Fig. 7 ). Table 2 The gene GO analysis Biological function GO ID pathway P value Corrected P value Gene ID Score BP GO:0051783 regulationofnucleardivisionNA 5.78E-09 2.03E-05 CCNB1/BUB1/PLK1/AURKB/AURKA/BIRC5/KNTC1/IGF1/ CHEK1/MSX1/WNT4 11 BP GO:0000280 nuclear division 4.32E-08 4.69E-05 TOP2A/CCNB1/BUB1/PLK1/AURKB/AURKA/KIFC1/EPS8/ BIRC5/CHEK2/RMI1/KNTC1/IGF1/ATRX/CHEK1/MSX1/WNT4 17 BP GO:0007093 mitotic cell cycle checkpoint signaling 5.56E-08 4.69E-05 CCNB1/BUB1/CDK1/PLK1/AURKB/DTL/BIRC5/CHEK2/KNTC1 /CHEK1 10 BP GO:0000075 cell cycle checkpoint signaling 7.67E-08 4.69E-05 CCNB1/BUB1/CDK1/PLK1/AURKB/DTL/BIRC5/CHEK2/KNTC1 /WDR76/CHEK1 11 BP GO:1902749 regulation of cell cycle G2/M phase transition 7.68E-08 4.69E-05 CCNB1/CDK1/PLK1/AURKB/AURKA/DTL/CDC25A/CHEK1 /ATF5 9 BP GO:0008406 gonad development 8.79E-08 4.69E-05 WT1/PGR/AKR1C3/PDGFA/ATRX/TFAP2C/SFRP1/PTX3/FST /NUDT1/TNFSF10/WNT4 12 BP GO:0045137 development of primary sexual characteristics 1.11E-07 4.69E-05 WT1/PGR/AKR1C3/PDGFA/ATRX/TFAP2C/SFRP1/PTX3/FST/ NUDT1/TNFSF10/WNT4 12 BP GO:0007548 sex differentiation 1.16E-07 4.69E-05 WT1/PGR/AKR1C3/PDGFA/ATRX/TFAP2C/SFRP1/PTX3/FST /NUDT1/TNFSF10/WNT4/FOXF2 13 CC GO:0098687 chromosomal region 2.50E-06 7.31E-04 TOP2A/CCNB1/BUB1/CDK1/PLK1/AURKB/BIRC5/CHEK2 /HELLS/KNTC1/ATRX/MCM5/CHEK1 13 CC GO:0032133 chromosome passenger complex 6.04E-06 8.82E-04 AURKB/AURKA/BIRC5 3 CC GO:0044853 plasma membrane raft 1.21E-05 1.18E-03 FXYD1/TRPC4/HMOX1/HAS2/CAV1/CD36/KCND2 7 CC GO:0005876 spindle microtubule 2.83E-05 1.75E-03 CDK1/PLK1/AURKB/AURKA/BIRC5/KNTC1 6 CC GO:0031091 platelet alpha granule 3.65E-05 1.75E-03 SELP/VWF/PDGFA/IGF1/SERPING1/CD36 6 CC GO:0097125 cyclin B1-CDK1 complex 4.58E-05 1.75E-03 CCNB1/CDK1 2 CC GO:0071162 CMG complex 4.86E-05 1.75E-03 GINS4/GINS2/MCM5 3 CC GO:0000228 nuclear chromosome 5.23E-05 1.75E-03 TOP2A/PLK1/GINS4/GINS2/BIRC5/ATRX/MCM5/CHEK1/ORC6 9 MF GO:0035173 histone kinase activity 1.1E-05 5.19E-03 CDK1/AURKB/AURKA/CHEK1 4 MF GO:0031732 CCR7 chemokine receptor binding 5.1E-05 1.19E-02 CCL21/CCL19 2 Table 3 The KEGG pathway of DEGs KEGG ID Pathway P value Corrected P value Gene ID Score hsa04110 Cell cycle 8.87E-07 0.000133698 891/699/983/5347/11200/4174/993/1111/23594 9 hsa04914 Progesterone-mediated oocyte maturation 1.75E-06 0.000133698 891/699/983/5347/6790/5241/3479/993 8 hsa04115 p53 signaling pathway 2.88E-05 0.00146664 891/983/11200/3479/1111/64393 6 hsa04114 Oocyte meiosis 9.81E-05 0.00375285 891/699/983/5347/6790/5241/3479 7 hsa05418 Fluid shear stress and atherosclerosis 0.000988684 0.03025372 5154/3162/857/2938/2947/1003 6 hsa05207 Chemical carcinogenesis - receptor activation 0.001789557 0.045633707 5241/332/3838/993/3839/2938/2947 7 hsa01524 Platinum drug resistance 0.003074175 0.067192686 7153/332/2938/2947 4 hsa04068 FoxO signaling pathway 0.004513048 0.086312041 891/5347/3479/5106/8743 5 2.3 Use PPI Network analysis and Cytoscape to analyze hotspot modules The DEGs expression products in ULMS were constructed from the STRING database to construct the PPI network (minimum required interaction score: 0.990). After removing all the isolated and partially disconnected nodes, an integrated network is built, as shown in Fig. 8 . Among them, MCODE is used to obtain 5 important modules, and the module with the highest score is visualized, as shown in Fig. 9 , including 24 nodes and 482 lines. Finally, the MCC algorithm was used to screen the top 10 Hub genes, including CDK1, BUB1, DTL, CCNB1, AURKB, BIRC5, CEP55, GINS2, MCM5 and TOP2A (Fig. 10 ). (The darker the color, the higher the score) 2.4 Analysis of immune cells infiltration The correlation heatmap (Fig. 11 ) summarizes the results obtained from the gene expression matrix of 28 immune cells. The box diagram of immune cells (Fig. 12 ) shows that activated CD28 T cells, activated CD9 T cells, central memory CD11 T cells, central memory CD16 T cells, central memory CD19 T cells, central CD31 T memory cells, central CD5 T memory cells, central CD8 T memory cells, eosinophils, type 11 T helper cell, type 14 T helper cell, type 44 T helper cell, type 44 T helper cell, type 5 T helper cell, type 50T helper cell, type 60 T helper cell, type 69T helper cell, and type 7 T helper cell infiltrate statistically more in uterine leiomyosarcoma tissue. Activated CD10 T cells, activated CD20 T cells, activated CD27 T cells, CD68 bright natural killer cells, CD70 dim natural killer cells, CD81bright natural killer cells, CD87 bright natural killer cells, effector CD17 T cells, effector CD21 T cells, effector CD28 T cells, effector CD29 T cells, immature dendritic cells, mast cells, neutrophils, plasma dendritic cells, type 15 T helper cells, type 20 T helper cells, type 24 T helper cells, type 28 T helper cells, type 29 T helper cells and type 74 T helper cells was statistically higher in the leiomyoma group. We evaluated the association of 10 Hub genes with immune cells infiltration (Fig. 13 ), with activated CD10 T cells, type 28 T helper cells, activated CD20 T cells, and type 20 T helper cells being the top four cell types with the largest number of central genes. The highest correlation was found between CDK1 and activated CD10 T cells, indicating a close interplay between the immune response and the development of uterine leiomyosarcoma. The blue part on the left indicates the UL group, and the red part on the right indicates the ULMS group. Discussion Uterine leiomyosarcoma is a common aggressive malignant tumor [ 11 ] , originating from the muscular layer, characterized by early metastasis, poor prognosis, high distant recurrence rate [ 12 ] , and limited treatment options. Its morphological and molecular characteristics cannot be identified by current clinical diagnostic tests, so it cannot be determined whether it is benign or malignant before surgery, and there is a risk of hidden malignant tumor during surgery [ 13 ] . The diagnosis of ULMS relies primarily on histology, and there is no specific molecular gene marker. Therefore, it is necessary to find specific targets for diagnosis and treatment. In this study, we integrated gene expression profiles from two datasets (GSE68295 and GSE64763) and analyzed these datasets using R (4.2.2). The limma R package was used to identify 2286 DEGs, including 59 up-regulated genes and 83 down-regulated genes. The 10 genes most significantly upregulated at the peak were RUNDC3B, RAMP3, TSPAN7, SLCO2A1, JAM2, WT1, UBL3, PGR, EPS8, and KANK3. The top 10 genes significantly downregulated were TOP2A, ASF1B, CEP55, CCNB1, BUB1, CDK1, PLK1, GGH, AURKB, KIAA0101. The GO and KEGG enrichment analysis was mainly related to cell division and cell cycle, suggesting the potential role of GO and KEGG in inducing tumorigenesis and metastatic behavior in stratified cells. A PPI network of DEGs encoded proteins was constructed using MCODE to screen for hot spot modules, where the highest scoring modules included 24 closely related genes including ARHGAP11A, AURKA, CCNB1, KIAA0101, PLK1, BIRC5, BUB1, AURKB, KNTC1, KPNA2, CHEK1, DTL, HELLS, KIFC1, CEP55, TOP2A, CDK1, ASF1B, MCM5, ORC6. GINS2, CDC25A, TK1 and CHEK2 are genes that are downregulated. The top 10 highest scoring Hub genes were screened, among which CDK1, CCNB1 and BUB1 play unique roles in the pathogenesis of ULMS and are involved in the transition from UL to ULMS. In the UL vs. ULMS comparison, the differentially expressed genes are mainly concentrated in the cell division pathway, highlighting the role of the cell cycle in the myeloma-to-sarcoma transition. The results of this study confirm that the cell cycle can transform these benign myomas into aggressive malignant leiomyosarcoma. The analysis revealed changes in the expression of genes that encode these pathways, leading to abnormal cell proliferation and an increased risk of cancer metastasis. Histological diagnosis of ULMS includes histological atypia, tumor cell necrosis, and increased mitotic rate [ 14 ] . In addition, ADAMS et al. [ 15 ] demonstrated high expression of cell cycle genes in uterine leiomyosarcoma. Meanwhile, WEST et al. [ 16 ] found that leiomyosarcoma showed cell cycle inhibition, cell-cell contact inhibition, and enrichment of DNA replication pathways. These findings are consistent with the results of this study. Activation of cyclin dependent kinase 1 (CDK1) is required for the transition from the S phase to mitosis. CDK1 is essential for cell division by regulating G2/M phase and mitosis [ 17 ] . CDK1 inhibitors induce DNA repair inhibition and cell cycle regulation of cell sensitivity to DNA damage during G2/M [ 18 ] . Cyclin B1/cyclin dependent kinase 1 (CCNB1/CDK1) is a key mediator of mitosis. During mitosis, autophagy is inhibited, and autophagy is involved in cytokinesis and mesomeric degradation [ 19 ] , and the inhibitory effect decreases at the end of cell stage and cytokinesis [ 20 ] . PI3K and mTORC1 involved in CCNB1/CDK1 are also inhibited during mitosis [ 21 ] . Therefore, competing mTORC1/2 inhibitors such as AZD8055 have been used to inhibit mitotic phosphorylation of proteins [ 20 ] . Meanwhile, PI3K (p110CAAX) inhibitors can inhibit CCNB1 transcription, leading to cytokinesis failure [ 22 ] . In addition, YING et al. [ 23 ] confirmed in vitro experiments that inhibition of CDK1 activity could induce apoptosis and cause G2/M phase arrest of the cell cycle of endometrial cancer cells. Overexpression of CDK1 in adrenal cortical cancer cell lines promotes proliferation and induces epithelial-mesenchymal transformation (EMT), while knockdown of CDK1 expression inhibits the growth of ACC cell lines [ 24 ] . Dihydro artemisinin targets are mainly enriched in cell cycle related pathways and can bind to CDK1/CCNB1 complex to inhibit the activation of CDK1/CCNB1 signal transduction and inhibit the progression of colorectal cancer cells [ 25 ] . CDK1 and CCNB1 gene clusters can provide potential therapeutic targets and prognostic biomarkers for breast cancer patients [ 26 ] . BUB1 is a kinase for examining the spindle components, monitoring the attachment of mitotic kinetosomes and maintaining high fidelity of mitotic chromosome separation [ 27 ] . BUB1 induced by CDK1 is a major motorist in mitosis [ 28 ] . It was found that the expression of BUB1 [ 29 ] was increased in neuroblastoma patients, which was negatively correlated with host immune infiltration, and could be used as a prognostic marker. It is therefore suspected that the use of CDK1 and its pathway inhibitors may be effective in reducing the growth and invasion of sarcoma tissue. ​The use of the ssGSVA tool for 28 immune cells in this study is an aspect not previously mentioned. We evaluated the association of 10 Hub genes with immune cells infiltration. Activated CD10 T cells, activated CD20 T cells, type 20 T helper cells, and type 28 T helper cells are the top four cell types that are most closely related to the central genes and exist in small amounts in sarcoma tissues. Of these, CDK1 showed the highest correlation with activated CD10 T cells. ZOU et al. [ 30 ] found that CDK1, CCNB1 and CCNB2 are potential prognostic biomarkers of hepatocellular carcinoma and positively correlate with levels of CD4 T cells, CD8 T cells, neutrophils, macrophages and dendritic cells. BUB1 is highly expressed in the immune microenvironment, and CD4 T cells and macrophages of BUB1 mRNA are associated with good survival outcomes in patients with gastric cancer [ 31 ] . The above results suggest that CDK1 and BUB1 are closely related to immune infiltration, and immune cells infiltration was a favorable prognostic factor. There is a close interplay between the immune inflammatory response and the development of uterine leiomyosarcoma. Now the evolving field of molecular biology has revealed the potential role of a variety of novel therapies, including targeted therapies and immunotherapies, as important components of combination therapies for refractory diseases. This study provides useful insights for proposing clinical strategies for multipoint targeted therapy of tumor cells, such as using CDK1 inhibitors, pathway multipoint gene synergistic inhibitors, and ATP-binding site selective inhibitors [ 32 ] . In addition, immunotherapy strategies for leiomyosarcoma are under active investigation. Conclusion In this study, new genes and pathways affected by differences in leiomyosarcoma and leiomyosarcoma were identified. CDK1, BUB1 and CCNB1 could be used as diagnostic markers. ​At the same time, different levels of immune cells infiltration were found in the ULMS, the most prominent being activated CD10 T cells, activated CD20 T cells, type 20 T helper cells of and type 28 T helper cells. Therefore, it is important to focus on molecular mutation signatures and immune cell percolation of ULMS as potential diagnostic and therapeutic targets in the hope of better precision diagnosis and personalized treatment. Declarations Ethics approval and consent to participate This article is not subject to ethical approval. Consent for publication The authors promises to publish this article in the journal. Competing interests The authors declare that they have no competing interests in this work. Funding This article is not funded. Authors' contributions The authors’ contributions are as follows: Wang conceived and designed the experiments. Kang carried out the experiments, evaluated the data, and wrote and interpreted the paper. Acknowledgements Thanks to all the authors who contributed to this article. Availability of data and material All data generated during this study are included in this published article. All data analysed are included in its supplementary information files (https://www.ncbi.nlm.nih.gov/geo/). References BAGGISH, MICHAEL S J C O, GYNECOLOGY. Mesenchymal Tumors of the Uterus [J]. 2011, 17(2): 51-88. RIZZO A, RICCI A D, SAPONARA M, et al. Recurrent Uterine Smooth-Muscle Tumors of Uncertain Malignant Potential (STUMP): State of The Art [J]. 2020, 40(3): 1229-38. SANEGRE S, ERITJA N, ANDREA C D, et al. Characterizing the Invasive Tumor Front of Aggressive Uterine Adenocarcinoma and Leiomyosarcoma [J]. 2021, 9(670185. TIRUMANI S H, IMAGING V O J A. Current concepts in the imaging of uterine sarcoma [J]. 2013, 38(2): 397-411. AYHAN A, GUNGORDUK K, KHATIB G, et al. Prognostic factors and survival outcomes of women with uterine leiomyosarcoma: A Turkish Uterine Sarcoma Group Study-003 [J]. 2021, Suppl 2): 100712. ELSOKARY H A, ABDULLAH L S, UJAIMI A, et al. Assessing the role of serum prolactin levels and coding region somatic mutations of the prolactin gene in Saudi uterine leiomyoma patients [J]. 2020, 1): MAS A, ALONSO R, ALMAZAN J J, et al. Identification of targetable mutations for differential molecular diagnosis of uterine leiomyomas versus leiomyosarcomas using next generation sequencing [J]. 2018, 110(4): e63. MARTHA, HICKEY, IBRAHEEM, et al. Fibroids: diagnosis and management [J]. 2015, BARRAL M, PLACé V, DAUTRY R, et al. Magnetic resonance imaging features of uterine sarcoma and mimickers [J]. 2017, 42(6): 1-11. MED12 mutations and FH inactivation are mutually exclusive in uterine leiomyomas %J The British journal of cancer [J]. 2016, 114(12): 1405-11. Uterine Leiomyosarcoma Management, Outcome, and Associated Molecular Biomarkers: A Single Institution's Experience %J Annals of Surgical Oncology [J]. 2013, COINDRE J M, TERRIER P, GUILLOU L, et al. Predictive value of grade for metastasis development in the main histologic types of adult soft tissue sarcomas [J]. 2001, HALASKA M J, HAIDOPOULOS D, GUYON F, et al. European Society of Gynecological Oncology Statement on Fibroid and Uterine Morcellation [J]. 2017, 1): 27. LEITAO M M, HENSLEY M L, BARAKAT R R, et al. Immunohistochemical expression of estrogen and progesterone receptors and outcomes in patients with newly diagnosed uterine leiomyosarcoma [J]. 2012, 124(3): 558-62. ADAMS C, DIMITROVA I, POST M, et al. Identification of a novel diagnostic gene expression signature to discriminate uterine leiomyoma from leiomyosarcoma [J]. 2019, 110(104284. WEST J, NEWTON P J P O T N A O S O T U S O A. Cellular interactions constrain tumor growth [J]. 2019, 116(6): 1918-23. LIU X A-O X, WU H A-O, LIU Z. An Integrative Human Pan-Cancer Analysis of Cyclin-Dependent Kinase 1 (CDK1). LID - 10.3390/cancers14112658 [doi] LID - 2658 [J]. 2072-6694 (Print)): SUNADA S, SAITO H, ZHANG D, et al. CDK1 inhibitor controls G2/M phase transition and reverses DNA damage sensitivity [J]. 1090-2104 (Electronic)): A., BELAID, M., et al. Autophagy Plays a Critical Role in the Degradation of Active RHOA, the Control of Cell Cytokinesis, and Genomic Stability [J]. 2013, ODLE R I, WALKER S A, OXLEY D, et al. An mTORC1-to-CDK1 Switch Maintains Autophagy Suppression during Mitosis [J]. 2020, 77(2): 228-40.e7. ODLE R, FLOREY O, KTISTAKIS N, et al. CDK1, the Other 'Master Regulator' of Autophagy [J]. 2021, 31(2): 95-107. ALVAREZ B, MARTíNEZA C, BURGERING B M, et al. Forkhead transcription factors contribute to execution of the mitotic programme in mammals [J]. 2001, 413(6857): 744-7. YING X, CHE X, WANG J, et al. CDK1 serves as a novel therapeutic target for endometrioid endometrial cancer [J]. 1837-9664 (Print)): REN L, YANG Y, LI W, et al. CDK1 serves as a therapeutic target of adrenocortical carcinoma via regulating epithelial-mesenchymal transition, G2/M phase transition, and PANoptosis [J]. 1479-5876 (Electronic)): YI Y C, LIANG R, CHEN X Y, et al. Dihydroartemisinin Suppresses the Tumorigenesis and Cycle Progression of Colorectal Cancer by Targeting CDK1/CCNB1/PLK1 Signaling [J]. 2234-943X (Print)): XING Z, WANG X A-O X, LIU J, et al. Expression and prognostic value of CDK1, CCNA2, and CCNB1 gene clusters in human breast cancer [J]. 1473-2300 (Electronic)): KIM T A-O X, GARTNER A. Bub1 kinase in the regulation of mitosis [J]. 1976-8354 (Print)): SINGH P, PESENTI M E, MAFFINI S, et al. BUB1 and CENP-U, Primed by CDK1, Are the Main PLK1 Kinetochore Receptors in Mitosis [J]. 1097-4164 (Electronic)): SONG J, NI C, DONG X, et al. bub1 as a potential oncogene and a prognostic biomarker for neuroblastoma [J]. 2234-943X (Print)): ZOU Y, RUAN S, JIN L, et al. CDK1, CCNB1, and CCNB2 are Prognostic Biomarkers and Correlated with Immune Infiltration in Hepatocellular Carcinoma [J]. Medical science monitor : international medical journal of experimental and clinical research, 2020, 26(e925289. LI X, HE J, YU M, et al. [BUB1 gene is highly expressed in gastric cancer:analysis based on Oncomine database and bioinformatics] [J]. 1673-4254 (Print)): AHMED B, KHAN S A-O, NOUROZ F, et al. Exploring multi-target inhibitors using in silico approach targeting cell cycle dysregulator-CDK proteins [J]. 1538-0254 (Electronic)): Supplementary Files Supplementary Files are not available with this version Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3299827","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":230089077,"identity":"a5487d5d-37b5-4a66-ab7b-90cda537ec88","order_by":0,"name":"YiFan Kang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACefaGhMN/KiTkgAwitRj2HHj4gOeMhTGQQaw1NxIfG/C2VSQy3EggUgdjz+E0CYkzEgmMMx9vvMFQYxNNUAs7e1uahEGFRB67dFqxBcOxtNwGwracSZNIOCNRzDg7x0yCseEwYS0MN/K/SRxsk0hsuHmGaC0JyYaNIC03eIjUAgzbxMcMZySAgQz0SwIxfgFHJUNFHTAqD2+88aHGhgiHIQEDiQRSlEO0kKpjFIyCUTAKRgYAAHoBQ8fGaPb6AAAAAElFTkSuQmCC","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"YiFan","middleName":"","lastName":"Kang","suffix":""},{"id":230089079,"identity":"1937614a-24ba-4c56-a302-8568d0ea7cb4","order_by":1,"name":"zhihong wang","email":"","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"zhihong","middleName":"","lastName":"wang","suffix":""}],"badges":[],"createdAt":"2023-08-27 07:14:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3299827/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3299827/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42676278,"identity":"e2f3a471-4a39-4b33-9bff-ac67570fc3f1","added_by":"auto","created_at":"2023-09-06 00:58:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33364,"visible":true,"origin":"","legend":"\u003cp\u003eRetrieval flow diagram\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/d69f7d870ff99fc90ecb0caf.png"},{"id":42676279,"identity":"4e504431-6fac-4bc8-9b4a-57be5a2fb186","added_by":"auto","created_at":"2023-09-06 00:58:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43389,"visible":true,"origin":"","legend":"\u003cp\u003eVolcanic maps of genes with different expression\u003c/p\u003e\n\u003cp\u003eGray represents P value \u0026gt; 0.05, green represents down-regulated gene with P value \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003eand red represents up-regulated genes with P value \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/aa050ee1bc4f7f390d5973a4.png"},{"id":42676282,"identity":"e32d9017-f043-4176-aaff-e58e8ecb32df","added_by":"auto","created_at":"2023-09-06 00:58:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":177448,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of the differentially expressed genes\u003c/p\u003e\n\u003cp\u003eRed indicates higher gene expression and blue indicates lower gene expression\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/022fc230fd651ec2df5715bb.png"},{"id":42676285,"identity":"acb0423a-c758-4892-9c03-83ee8df074da","added_by":"auto","created_at":"2023-09-06 00:58:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62621,"visible":true,"origin":"","legend":"\u003cp\u003eGO pathway enrichment analysis of DEGs (P \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/8e7272dd5e63644bfcd1d8ec.png"},{"id":42676946,"identity":"d22ba0fc-caef-4a07-8b31-ab7d6a96662c","added_by":"auto","created_at":"2023-09-06 01:06:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42253,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway enrichment analysis of DEGs (P \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/3ceb3efb873184d3458693f5.png"},{"id":42676949,"identity":"a3652867-abc2-4af1-b58d-fbfe4fc03afd","added_by":"auto","created_at":"2023-09-06 01:06:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":64242,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment pathway diagram of ULMS\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/d8a467cd7199af154c3e4632.png"},{"id":42678061,"identity":"5fea53ec-db37-4dee-9cb3-fe2052ab645c","added_by":"auto","created_at":"2023-09-06 01:14:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":273940,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG path diagram of CELL CYCLE\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/493a341853af2d20c65f476b.png"},{"id":42676951,"identity":"eb6a9f53-7840-47c6-9ca5-4fa69367cb17","added_by":"auto","created_at":"2023-09-06 01:06:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":140788,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network containing 142 nodes DEGs\u003c/p\u003e\n\u003cp\u003e(Filtering 9 edge nodes)\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/622eb1dd07653a7af5155373.png"},{"id":42676287,"identity":"3cb002d8-2d20-4dd0-bd74-82a6320d4caa","added_by":"auto","created_at":"2023-09-06 00:58:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":682261,"visible":true,"origin":"","legend":"\u003cp\u003eSelected Hub gene modules\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/59607b73720532c707bb4fd0.png"},{"id":42676290,"identity":"7fb6b6dd-f8e9-4bda-af75-0ce026a4f45b","added_by":"auto","created_at":"2023-09-06 00:58:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":206079,"visible":true,"origin":"","legend":"\u003cp\u003eSelected the top 10 hub genes by MCC algorithm\u003c/p\u003e\n\u003cp\u003e(The darker the color, the higher the score)\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/7c74f8c8f35859be9e927297.png"},{"id":42678065,"identity":"b3d66976-0e78-448d-a1c5-3e938e779bdc","added_by":"auto","created_at":"2023-09-06 01:14:16","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":120139,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix of the degree of immune cell infiltration in the two groups of samples. Red shows trends consistent with a positive correlation, and blue shows trends consistent with a negative correlation(The deeper the depth, the greater the positive or negative correlation）\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/8c7fbad82307f4e4298b32db.png"},{"id":42679206,"identity":"9b4b390a-5b54-4485-bf1f-4f11a3d6b728","added_by":"auto","created_at":"2023-09-06 01:22:16","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":103064,"visible":true,"origin":"","legend":"\u003cp\u003eBox diagram of the proportion of immune cells in the two groups\u003c/p\u003e\n\u003cp\u003eThe blue part on the left indicates the UL group, and the red part on the right indicates the ULMS group.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/0d66a414a3fbae43d6cc0406.png"},{"id":42678066,"identity":"9d0a8cf6-a839-4b2b-a4f9-87e11e167ea0","added_by":"auto","created_at":"2023-09-06 01:14:16","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":76010,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between immune cells and 10 Hub genes\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/0538a5b4b8203d9c788fc930.png"},{"id":47117839,"identity":"7c67a82f-a6ba-414a-acc0-a1cf3aca8916","added_by":"auto","created_at":"2023-11-27 05:37:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3605212,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3299827/v1/344263c5-ee4c-4ebe-8172-dcb46601bf04.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of differentially expressed genes, signaling pathways and immuneinfiltration in uterine leiomyosarcoma by integrated bioinformatics analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUterine leiomyosarcoma (ULMS) has the highest incidence in postmenopausal women\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, accounting for about 2% of all uterine malignancies \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It is highly invasive and has a poor prognosis \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, and the possibility of malignant transformation should be vigilant for the enlargement of fibroids in postmenopausal women. Clinical manifestations mainly include postmenopausal bleeding, pelvic mass and pelvic pain \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, which are often unspecific and misdiagnosed as benign uterine leiomyoma (UL), leading to delayed diagnosis and treatment.\u003c/p\u003e\n\u003cp\u003eAt present, uterus leiomyosarcoma is mainly diagnosed by imaging examination \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Ultrasound is the preferred method. Leiomyomas are circular lesions with high echo and vascularization. Leiomyosarcoma is an isolated oval lesion with uneven echo and panvascularization \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Further diagnosis is made using magnetic resonance imaging (MRI) to assess tumor stage, depth of invasion, and extent of spread in vivo. Based on this, diffusion-weighted imaging (DWI) can map malignant lesions as areas of high intensity with better tissue contrast and provide quantitative information. PET/CT can also be used to indicate distant region metastasis. In addition, ULMS serologic tests may show elevated levels of lactate dehydrogenase (LDH) and/or CA125. The treatment methods mainly include surgical therapy, chemotherapy, targeted drug therapy and immunotherapy, while surgical therapy may have the risk of hidden spread for ULMS and other malignant tumors \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Therefore, pre-operative accurate diagnosis and targeted treatment is very important.\u003c/p\u003e\n\u003cp\u003eCurrently, there is no characteristic molecular change in the uterine leiomyosarcoma, so there has been no breakthrough in diagnosis and treatment. Mutations in the leiomyoma-driving factors present in ULMS provide growth advantages for highly aggressive cancers, and these mutations occur in certain key genes, which can provide new insights into pathogenesis and potentially develop new therapeutic targets \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Therefore, the use of gene expression microarrays in the GEO database to identify differential genetic targets for ULMS and UL, search for specific molecular markers, and real-time monitoring of tumor occurrence status and cellular characteristics is important for more effective control and prevention. In this study, we aim to analyze gene expression, signaling pathways, and immune infiltration in leiomyosarcoma by integrated bioinformatics analysis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e1.1 Search strategy\u003c/p\u003e\n\u003cp\u003eUsing the keywords \u0026ldquo;Uterine Leiomyosarcoma\u0026rdquo; to search GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e), from set up to December 31, 2022, the GEO database of the communist party of China 166 \u0026ldquo;Leiomyosarcoma\u0026rdquo; results. Items not relevant to this study were excluded by limiting source of tissue (Homo sapiens) and type of study (Expression profiling by array). After further selection of titles, abstracts and samples, two series (GSE68295 and GSE64763) were finally included. The retrieval flow diagram is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e1.2 Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eDownload GSE68295 and GSE64763 from the GEO database and use the organizations \u0026quot;Uterine Leiomyosarcoma\u0026quot; and \u0026quot;Uterine Leiomyoma\u0026quot; in them. Normal uterine fibroid tissue and the uterine leiomyosarcoma SKN cell line from the GSE68295 series were excluded during the analysis. A total of 56 samples were included, including 28 ULMS samples and 28 UL samples. Through the R software (4.2.2) to batch standardization of microarray data set, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |LogFC|\u0026gt;2 as a condition for screening, using limma R package analysis of two data sets of differentially expressed genes (DEGs), up-regulated or down-regulated genes for further analysis. Volcanic maps were used to display up-regulated and down-regulated genes differently expressed, and the results were visualized using the pheatmap R package.\u003c/p\u003e\n\u003cp\u003e1.3 Functional enrichment analysis of differentially expressed genes\u003c/p\u003e\n\u003cp\u003eGO and KEGG path enrichment analyses of DEGs were performed using the clusterProfiler R package. The enrichment of DEGs in cellular component (CC), molecular function (MF), biological process (BP) and participating signal pathway was studied. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered statistically significant.\u003c/p\u003e\n\u003cp\u003e1.4 PPI network construction for differentially expressed genes.\u003c/p\u003e\n\u003cp\u003eSTRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003c/span\u003e) was used to obtain the DEGs protein-protein interaction (PPI) network. Import Cytoscape 3.7.1 software was used to perform MCODE analysis of functional modules of the PPI network using default parameters. The Cytohubba tool was used for analysis and the top 10 hub genes were screened.\u003c/p\u003e\n\u003cp\u003e1.5 ssGSVA immune infiltration\u003c/p\u003e\n\u003cp\u003eThe percolation matrix of 28 immune cells was obtained from Single sample Gene Set Enrichment Analysis of the normalized data. In this study, immune cells include activated B cells, T cells (11 CD molecules), immature dendritic cell, natural killer cells (6 CD molecules), central memory T cells (10 CD molecules), effector memory T cells (6 CD molecules), eosinophil, gamma delta T cell, macrophages, mast cells, memory B cells, monocytes, plasmacytoid dendritic cell, myeloid derived suppressor cell, suppressor cells, natural killer cells, natural killer T cells, neutrophils, regulatory T cells, T helper cells (27 CD molecules). The corrplot R package was used to calculate the degree and fraction of percolation in the gene expression matrix of the two groups of immune cells, and the relationship between the pivot genes and the immune cells.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e2.1 Screening for differential expression of genes\u003c/p\u003e\n\u003cp\u003eTwo datasets, GSE68295 and GSE64763, were screened from the GEO database. A total of 2268 differentially expressed genes were obtained, of which 59 were up-regulated and 83 were down-regulated. Table\u0026nbsp;1 shows the up-regulated and down-regulated DEGs. Volcanic maps and heat maps were drawn using R software, as shown in Figs.\u0026nbsp;2 and 3.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1693821204.png\"\u003e\u003c/p\u003e\n\u003cp\u003e2.2 GO and KEGG enrichment analysis of differentially expressed genes\u003c/p\u003e\n\u003cp\u003eEnrichplot analysis of GO and KEGG paths with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was obtained, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. GO analysis was mainly enriched to cell division related pathways, and the first three significantly enriched pathway terms were regulationofnucleardivisionNA, nuclear division and mitotic cell cycle checkpoint. The gene GO analysis is shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The main pathway of KEGG enrichment analysis was cell cycle. The KEGG pathway of DEGs is shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. In addition, KEGG pathway enrichment was used to describe the pathway targeting ULMS (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Among them, the cell cycle is the main KEGG enrichment pathway (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe gene GO analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBiological function\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epathway\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrected P value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eregulationofnucleardivisionNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.78E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCNB1/BUB1/PLK1/AURKB/AURKA/BIRC5/KNTC1/IGF1/\u003c/p\u003e\n \u003cp\u003eCHEK1/MSX1/WNT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0000280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enuclear division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.69E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOP2A/CCNB1/BUB1/PLK1/AURKB/AURKA/KIFC1/EPS8/\u003c/p\u003e\n \u003cp\u003eBIRC5/CHEK2/RMI1/KNTC1/IGF1/ATRX/CHEK1/MSX1/WNT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emitotic cell cycle checkpoint signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.56E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.69E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCNB1/BUB1/CDK1/PLK1/AURKB/DTL/BIRC5/CHEK2/KNTC1 /CHEK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0000075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecell cycle checkpoint signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.67E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.69E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCNB1/BUB1/CDK1/PLK1/AURKB/DTL/BIRC5/CHEK2/KNTC1 /WDR76/CHEK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:1902749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eregulation of cell cycle G2/M phase transition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.68E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.69E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCNB1/CDK1/PLK1/AURKB/AURKA/DTL/CDC25A/CHEK1 /ATF5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0008406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egonad development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.79E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.69E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT1/PGR/AKR1C3/PDGFA/ATRX/TFAP2C/SFRP1/PTX3/FST /NUDT1/TNFSF10/WNT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0045137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edevelopment of primary sexual characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.69E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT1/PGR/AKR1C3/PDGFA/ATRX/TFAP2C/SFRP1/PTX3/FST/\u003c/p\u003e\n \u003cp\u003eNUDT1/TNFSF10/WNT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0007548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esex differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.69E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT1/PGR/AKR1C3/PDGFA/ATRX/TFAP2C/SFRP1/PTX3/FST /NUDT1/TNFSF10/WNT4/FOXF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0098687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echromosomal region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.31E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOP2A/CCNB1/BUB1/CDK1/PLK1/AURKB/BIRC5/CHEK2 /HELLS/KNTC1/ATRX/MCM5/CHEK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0032133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003echromosome passenger complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.04E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.82E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAURKB/AURKA/BIRC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0044853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eplasma membrane raft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFXYD1/TRPC4/HMOX1/HAS2/CAV1/CD36/KCND2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003espindle microtubule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.83E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDK1/PLK1/AURKB/AURKA/BIRC5/KNTC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0031091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eplatelet alpha granule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.65E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSELP/VWF/PDGFA/IGF1/SERPING1/CD36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0097125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecyclin B1-CDK1 complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCNB1/CDK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0071162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMG complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.86E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGINS4/GINS2/MCM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0000228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enuclear chromosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.23E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOP2A/PLK1/GINS4/GINS2/BIRC5/ATRX/MCM5/CHEK1/ORC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0035173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehistone kinase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.19E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDK1/AURKB/AURKA/CHEK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0031732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCR7 chemokine receptor binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCL21/CCL19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe KEGG pathway of DEGs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKEGG ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePathway\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrected P value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa04110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCell cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.87E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000133698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891/699/983/5347/11200/4174/993/1111/23594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa04914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProgesterone-mediated oocyte maturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000133698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891/699/983/5347/6790/5241/3479/993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa04115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep53 signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00146664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891/983/11200/3479/1111/64393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa04114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOocyte meiosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.81E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00375285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891/699/983/5347/6790/5241/3479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa05418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluid shear stress and atherosclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000988684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03025372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5154/3162/857/2938/2947/1003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa05207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemical carcinogenesis - receptor activation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001789557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045633707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5241/332/3838/993/3839/2938/2947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa01524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlatinum drug resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003074175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.067192686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7153/332/2938/2947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa04068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFoxO signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004513048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086312041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891/5347/3479/5106/8743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e2.3 Use PPI Network analysis and Cytoscape to analyze hotspot modules\u003c/p\u003e\n\u003cp\u003eThe DEGs expression products in ULMS were constructed from the STRING database to construct the PPI network (minimum required interaction score: 0.990). After removing all the isolated and partially disconnected nodes, an integrated network is built, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. Among them, MCODE is used to obtain 5 important modules, and the module with the highest score is visualized, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, including 24 nodes and 482 lines. Finally, the MCC algorithm was used to screen the top 10 Hub genes, including CDK1, BUB1, DTL, CCNB1, AURKB, BIRC5, CEP55, GINS2, MCM5 and TOP2A (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e(The darker the color, the higher the score)\u003c/p\u003e\n\u003cp\u003e2.4 Analysis of immune cells infiltration\u003c/p\u003e\n\u003cp\u003eThe correlation heatmap (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e) summarizes the results obtained from the gene expression matrix of 28 immune cells. The box diagram of immune cells (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e) shows that activated CD28 T cells, activated CD9 T cells, central memory CD11 T cells, central memory CD16 T cells, central memory CD19 T cells, central CD31 T memory cells, central CD5 T memory cells, central CD8 T memory cells, eosinophils, type 11 T helper cell, type 14 T helper cell, type 44 T helper cell, type 44 T helper cell, type 5 T helper cell, type 50T helper cell, type 60 T helper cell, type 69T helper cell, and type 7 T helper cell infiltrate statistically more in uterine leiomyosarcoma tissue. Activated CD10 T cells, activated CD20 T cells, activated CD27 T cells, CD68 bright natural killer cells, CD70 dim natural killer cells, CD81bright natural killer cells, CD87 bright natural killer cells, effector CD17 T cells, effector CD21 T cells, effector CD28 T cells, effector CD29 T cells, immature dendritic cells, mast cells, neutrophils, plasma dendritic cells, type 15 T helper cells, type 20 T helper cells, type 24 T helper cells, type 28 T helper cells, type 29 T helper cells and type 74 T helper cells was statistically higher in the leiomyoma group. We evaluated the association of 10 Hub genes with immune cells infiltration (Fig. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e), with activated CD10 T cells, type 28 T helper cells, activated CD20 T cells, and type 20 T helper cells being the top four cell types with the largest number of central genes. The highest correlation was found between CDK1 and activated CD10 T cells, indicating a close interplay between the immune response and the development of uterine leiomyosarcoma.\u003c/p\u003e\n\u003cp\u003eThe blue part on the left indicates the UL group, and the red part on the right indicates the ULMS group.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUterine leiomyosarcoma is a common aggressive malignant tumor\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, originating from the muscular layer, characterized by early metastasis, poor prognosis, high distant recurrence rate \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, and limited treatment options. Its morphological and molecular characteristics cannot be identified by current clinical diagnostic tests, so it cannot be determined whether it is benign or malignant before surgery, and there is a risk of hidden malignant tumor during surgery\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The diagnosis of ULMS relies primarily on histology, and there is no specific molecular gene marker. Therefore, it is necessary to find specific targets for diagnosis and treatment.\u003c/p\u003e\u003cp\u003eIn this study, we integrated gene expression profiles from two datasets (GSE68295 and GSE64763) and analyzed these datasets using R (4.2.2). The limma R package was used to identify 2286 DEGs, including 59 up-regulated genes and 83 down-regulated genes. The 10 genes most significantly upregulated at the peak were RUNDC3B, RAMP3, TSPAN7, SLCO2A1, JAM2, WT1, UBL3, PGR, EPS8, and KANK3. The top 10 genes significantly downregulated were TOP2A, ASF1B, CEP55, CCNB1, BUB1, CDK1, PLK1, GGH, AURKB, KIAA0101. The GO and KEGG enrichment analysis was mainly related to cell division and cell cycle, suggesting the potential role of GO and KEGG in inducing tumorigenesis and metastatic behavior in stratified cells. A PPI network of DEGs encoded proteins was constructed using MCODE to screen for hot spot modules, where the highest scoring modules included 24 closely related genes including ARHGAP11A, AURKA, CCNB1, KIAA0101, PLK1, BIRC5, BUB1, AURKB, KNTC1, KPNA2, CHEK1, DTL, HELLS, KIFC1, CEP55, TOP2A, CDK1, ASF1B, MCM5, ORC6. GINS2, CDC25A, TK1 and CHEK2 are genes that are downregulated. The top 10 highest scoring Hub genes were screened, among which CDK1, CCNB1 and BUB1 play unique roles in the pathogenesis of ULMS and are involved in the transition from UL to ULMS.\u003c/p\u003e\u003cp\u003eIn the UL vs. ULMS comparison, the differentially expressed genes are mainly concentrated in the cell division pathway, highlighting the role of the cell cycle in the myeloma-to-sarcoma transition. The results of this study confirm that the cell cycle can transform these benign myomas into aggressive malignant leiomyosarcoma. The analysis revealed changes in the expression of genes that encode these pathways, leading to abnormal cell proliferation and an increased risk of cancer metastasis. Histological diagnosis of ULMS includes histological atypia, tumor cell necrosis, and increased mitotic rate \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. In addition, ADAMS et al. \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e demonstrated high expression of cell cycle genes in uterine leiomyosarcoma. Meanwhile, WEST et al. \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e found that leiomyosarcoma showed cell cycle inhibition, cell-cell contact inhibition, and enrichment of DNA replication pathways. These findings are consistent with the results of this study.\u003c/p\u003e\u003cp\u003eActivation of cyclin dependent kinase 1 (CDK1) is required for the transition from the S phase to mitosis. CDK1 is essential for cell division by regulating G2/M phase and mitosis \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. CDK1 inhibitors induce DNA repair inhibition and cell cycle regulation of cell sensitivity to DNA damage during G2/M \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Cyclin B1/cyclin dependent kinase 1 (CCNB1/CDK1) is a key mediator of mitosis. During mitosis, autophagy is inhibited, and autophagy is involved in cytokinesis and mesomeric degradation \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, and the inhibitory effect decreases at the end of cell stage and cytokinesis \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. PI3K and mTORC1 involved in CCNB1/CDK1 are also inhibited during mitosis \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Therefore, competing mTORC1/2 inhibitors such as AZD8055 have been used to inhibit mitotic phosphorylation of proteins \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, PI3K (p110CAAX) inhibitors can inhibit CCNB1 transcription, leading to cytokinesis failure \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In addition, YING et al. \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e confirmed in vitro experiments that inhibition of CDK1 activity could induce apoptosis and cause G2/M phase arrest of the cell cycle of endometrial cancer cells. Overexpression of CDK1 in adrenal cortical cancer cell lines promotes proliferation and induces epithelial-mesenchymal transformation (EMT), while knockdown of CDK1 expression inhibits the growth of ACC cell lines \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Dihydro artemisinin targets are mainly enriched in cell cycle related pathways and can bind to CDK1/CCNB1 complex to inhibit the activation of CDK1/CCNB1 signal transduction and inhibit the progression of colorectal cancer cells \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. CDK1 and CCNB1 gene clusters can provide potential therapeutic targets and prognostic biomarkers for breast cancer patients \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. BUB1 is a kinase for examining the spindle components, monitoring the attachment of mitotic kinetosomes and maintaining high fidelity of mitotic chromosome separation \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. BUB1 induced by CDK1 is a major motorist in mitosis \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. It was found that the expression of BUB1 \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e was increased in neuroblastoma patients, which was negatively correlated with host immune infiltration, and could be used as a prognostic marker. It is therefore suspected that the use of CDK1 and its pathway inhibitors may be effective in reducing the growth and invasion of sarcoma tissue.\u003c/p\u003e\u003cp\u003e​The use of the ssGSVA tool for 28 immune cells in this study is an aspect not previously mentioned. We evaluated the association of 10 Hub genes with immune cells infiltration. Activated CD10 T cells, activated CD20 T cells, type 20 T helper cells, and type 28 T helper cells are the top four cell types that are most closely related to the central genes and exist in small amounts in sarcoma tissues. Of these, CDK1 showed the highest correlation with activated CD10 T cells. ZOU et al. \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e found that CDK1, CCNB1 and CCNB2 are potential prognostic biomarkers of hepatocellular carcinoma and positively correlate with levels of CD4 T cells, CD8 T cells, neutrophils, macrophages and dendritic cells. BUB1 is highly expressed in the immune microenvironment, and CD4 T cells and macrophages of BUB1 mRNA are associated with good survival outcomes in patients with gastric cancer \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The above results suggest that CDK1 and BUB1 are closely related to immune infiltration, and immune cells infiltration was a favorable prognostic factor. There is a close interplay between the immune inflammatory response and the development of uterine leiomyosarcoma.\u003c/p\u003e\u003cp\u003eNow the evolving field of molecular biology has revealed the potential role of a variety of novel therapies, including targeted therapies and immunotherapies, as important components of combination therapies for refractory diseases. This study provides useful insights for proposing clinical strategies for multipoint targeted therapy of tumor cells, such as using CDK1 inhibitors, pathway multipoint gene synergistic inhibitors, and ATP-binding site selective inhibitors \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In addition, immunotherapy strategies for leiomyosarcoma are under active investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, new genes and pathways affected by differences in leiomyosarcoma and leiomyosarcoma were identified. CDK1, BUB1 and CCNB1 could be used as diagnostic markers. ​At the same time, different levels of immune cells infiltration were found in the ULMS, the most prominent being activated CD10 T cells, activated CD20 T cells, type 20 T helper cells of and type 28 T helper cells. Therefore, it is important to focus on molecular mutation signatures and immune cell percolation of ULMS as potential diagnostic and therapeutic targets in the hope of better precision diagnosis and personalized treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; This article is not subject to ethical approval.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; The authors promises to publish this article in the journal.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests in this work.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; This article is not funded.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; The authors\u0026rsquo; contributions are as follows: Wang conceived and designed the experiments. Kang carried out the experiments, evaluated the data, and wrote and interpreted the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Thanks to all the authors who contributed to this article.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eAll data generated during this study are included in this published article. All data analysed are included in its supplementary information files (https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBAGGISH, MICHAEL S J C O, GYNECOLOGY. Mesenchymal Tumors of the Uterus [J]. 2011, 17(2): 51-88.\u003c/li\u003e\n\u003cli\u003eRIZZO A, RICCI A D, SAPONARA M, et al. Recurrent Uterine Smooth-Muscle Tumors of Uncertain Malignant Potential (STUMP): State of The Art [J]. 2020, 40(3): 1229-38.\u003c/li\u003e\n\u003cli\u003eSANEGRE S, ERITJA N, ANDREA C D, et al. Characterizing the Invasive Tumor Front of Aggressive Uterine Adenocarcinoma and Leiomyosarcoma [J]. 2021, 9(670185.\u003c/li\u003e\n\u003cli\u003eTIRUMANI S H, IMAGING V O J A. Current concepts in the imaging of uterine sarcoma [J]. 2013, 38(2): 397-411.\u003c/li\u003e\n\u003cli\u003eAYHAN A, GUNGORDUK K, KHATIB G, et al. Prognostic factors and survival outcomes of women with uterine leiomyosarcoma: A Turkish Uterine Sarcoma Group Study-003 [J]. 2021, Suppl 2): 100712.\u003c/li\u003e\n\u003cli\u003eELSOKARY H A, ABDULLAH L S, UJAIMI A, et al. Assessing the role of serum prolactin levels and coding region somatic mutations of the prolactin gene in Saudi uterine leiomyoma patients [J]. 2020, 1): \u003c/li\u003e\n\u003cli\u003eMAS A, ALONSO R, ALMAZAN J J, et al. Identification of targetable mutations for differential molecular diagnosis of uterine leiomyomas versus leiomyosarcomas using next generation sequencing [J]. 2018, 110(4): e63.\u003c/li\u003e\n\u003cli\u003eMARTHA, HICKEY, IBRAHEEM, et al. Fibroids: diagnosis and management [J]. 2015, \u003c/li\u003e\n\u003cli\u003eBARRAL M, PLAC\u0026eacute; V, DAUTRY R, et al. Magnetic resonance imaging features of uterine sarcoma and mimickers [J]. 2017, 42(6): 1-11.\u003c/li\u003e\n\u003cli\u003eMED12 mutations and FH inactivation are mutually exclusive in uterine leiomyomas %J The British journal of cancer [J]. 2016, 114(12): 1405-11.\u003c/li\u003e\n\u003cli\u003eUterine Leiomyosarcoma Management, Outcome, and Associated Molecular Biomarkers: A Single Institution\u0026apos;s Experience %J Annals of Surgical Oncology [J]. 2013, \u003c/li\u003e\n\u003cli\u003eCOINDRE J M, TERRIER P, GUILLOU L, et al. Predictive value of grade for metastasis development in the main histologic types of adult soft tissue sarcomas [J]. 2001, \u003c/li\u003e\n\u003cli\u003eHALASKA M J, HAIDOPOULOS D, GUYON F, et al. European Society of Gynecological Oncology Statement on Fibroid and Uterine Morcellation [J]. 2017, 1): 27.\u003c/li\u003e\n\u003cli\u003eLEITAO M M, HENSLEY M L, BARAKAT R R, et al. Immunohistochemical expression of estrogen and progesterone receptors and outcomes in patients with newly diagnosed uterine leiomyosarcoma [J]. 2012, 124(3): 558-62.\u003c/li\u003e\n\u003cli\u003eADAMS C, DIMITROVA I, POST M, et al. Identification of a novel diagnostic gene expression signature to discriminate uterine leiomyoma from leiomyosarcoma [J]. 2019, 110(104284.\u003c/li\u003e\n\u003cli\u003eWEST J, NEWTON P J P O T N A O S O T U S O A. Cellular interactions constrain tumor growth [J]. 2019, 116(6): 1918-23.\u003c/li\u003e\n\u003cli\u003eLIU X A-O X, WU H A-O, LIU Z. An Integrative Human Pan-Cancer Analysis of Cyclin-Dependent Kinase 1 (CDK1). LID - 10.3390/cancers14112658 [doi] LID - 2658 [J]. 2072-6694 (Print)): \u003c/li\u003e\n\u003cli\u003eSUNADA S, SAITO H, ZHANG D, et al. CDK1 inhibitor controls G2/M phase transition and reverses DNA damage sensitivity [J]. 1090-2104 (Electronic)): \u003c/li\u003e\n\u003cli\u003eA., BELAID, M., et al. Autophagy Plays a Critical Role in the Degradation of Active RHOA, the Control of Cell Cytokinesis, and Genomic Stability [J]. 2013, \u003c/li\u003e\n\u003cli\u003eODLE R I, WALKER S A, OXLEY D, et al. An mTORC1-to-CDK1 Switch Maintains Autophagy Suppression during Mitosis [J]. 2020, 77(2): 228-40.e7.\u003c/li\u003e\n\u003cli\u003eODLE R, FLOREY O, KTISTAKIS N, et al. CDK1, the Other \u0026apos;Master Regulator\u0026apos; of Autophagy [J]. 2021, 31(2): 95-107.\u003c/li\u003e\n\u003cli\u003eALVAREZ B, MART\u0026iacute;NEZA C, BURGERING B M, et al. Forkhead transcription factors contribute to execution of the mitotic programme in mammals [J]. 2001, 413(6857): 744-7.\u003c/li\u003e\n\u003cli\u003eYING X, CHE X, WANG J, et al. CDK1 serves as a novel therapeutic target for endometrioid endometrial cancer [J]. 1837-9664 (Print)): \u003c/li\u003e\n\u003cli\u003eREN L, YANG Y, LI W, et al. CDK1 serves as a therapeutic target of adrenocortical carcinoma via regulating epithelial-mesenchymal transition, G2/M phase transition, and PANoptosis [J]. 1479-5876 (Electronic)): \u003c/li\u003e\n\u003cli\u003eYI Y C, LIANG R, CHEN X Y, et al. Dihydroartemisinin Suppresses the Tumorigenesis and Cycle Progression of Colorectal Cancer by Targeting CDK1/CCNB1/PLK1 Signaling [J]. 2234-943X (Print)): \u003c/li\u003e\n\u003cli\u003eXING Z, WANG X A-O X, LIU J, et al. Expression and prognostic value of CDK1, CCNA2, and CCNB1 gene clusters in human breast cancer [J]. 1473-2300 (Electronic)): \u003c/li\u003e\n\u003cli\u003eKIM T A-O X, GARTNER A. Bub1 kinase in the regulation of mitosis [J]. 1976-8354 (Print)): \u003c/li\u003e\n\u003cli\u003eSINGH P, PESENTI M E, MAFFINI S, et al. BUB1 and CENP-U, Primed by CDK1, Are the Main PLK1 Kinetochore Receptors in Mitosis [J]. 1097-4164 (Electronic)): \u003c/li\u003e\n\u003cli\u003eSONG J, NI C, DONG X, et al. bub1 as a potential oncogene and a prognostic biomarker for neuroblastoma [J]. 2234-943X (Print)): \u003c/li\u003e\n\u003cli\u003eZOU Y, RUAN S, JIN L, et al. CDK1, CCNB1, and CCNB2 are Prognostic Biomarkers and Correlated with Immune Infiltration in Hepatocellular Carcinoma [J]. Medical science monitor : international medical journal of experimental and clinical research, 2020, 26(e925289.\u003c/li\u003e\n\u003cli\u003eLI X, HE J, YU M, et al. [BUB1 gene is highly expressed in gastric cancer:analysis based on Oncomine database and bioinformatics] [J]. 1673-4254 (Print)): \u003c/li\u003e\n\u003cli\u003eAHMED B, KHAN S A-O, NOUROZ F, et al. Exploring multi-target inhibitors using in silico approach targeting cell cycle dysregulator-CDK proteins [J]. 1538-0254 (Electronic)): \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Files","content":"\u003cp\u003eSupplementary Files are not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GEO database, uterus leiomyosarcoma, bioinformatics analysis, differentially expressed genes, immune infiltration","lastPublishedDoi":"10.21203/rs.3.rs-3299827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3299827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: Uterine leiomyosarcoma (ULMS) ranks high among female malignant tumors with poor prognosis. Among them, the underlying molecular mechanism remains uncertain. We aim to use comprehensive bioinformatics to analyze differently expressed genes (DEGs), signaling pathways, and immune infiltration involved in uterine leiomyosarcoma (ULMS) and uterine leiomyoma (UL).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: The GSE68295 and GSE64763 datasets were downloaded from the GEO database and contain 56 samples, including 28 ULMS and 28 UL samples. Microarray data sets were integrated to obtain differentially expressed genes (DEGs) and further analyzed by bioinformatics techniques. The GO and KEGG pathway enrichment analyses were performed using R software and the PPI network was built using the STRING database. Cytoscape software was used to screen Hub genes (top 10 genes in terms of severity). Finally, ssGSVA was used to assess immune cell penetration in ULMS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: There were 2268 consensus DEGs, of which 83 were revised upwards and 59 downward. GO and KEGG pathway analysis revealed that these DEGs are mainly concentrated in cell cycle and mitotic pathways. CDK1, CCNB1, and BUB1 are potential biomarkers of ULMS. Activated\u003cbr\u003e\nCD10 T cells, activated CD20 T cells, type 20 T helper cells, and type 28 T helper cells are the top four cell types with the largest number of central genes, and they are present in small amounts in sarcoma tissue. Of these, CDK1 has the highest correlation with CD10 activated T cells.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: This study shows that the use of comprehensive bioinformatics analysis to screen DEGs, pathways and immune infiltrates provides new insights into the molecular mechanism of ULMS pathogenesis and targeted therapy.\u003c/p\u003e","manuscriptTitle":"Identification of differentially expressed genes, signaling pathways and immuneinfiltration in uterine leiomyosarcoma by integrated bioinformatics analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-06 00:58:11","doi":"10.21203/rs.3.rs-3299827/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd00ee6f-ceb2-48a9-84d1-5ebfc3b885bc","owner":[],"postedDate":"September 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-27T05:29:31+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-06 00:58:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3299827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3299827","identity":"rs-3299827","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}