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

doi:10.21203/rs.3.rs-3299827/v1

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

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.

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 < 0.05, |LogFC|>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.

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.

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.

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.

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/).

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

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