Integrative Proteogenomic Analyses Identify TOPs as Prognostic Markers and Therapeutic Targets in Sarcoma

doi:10.21203/rs.3.rs-139866/v1

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Integrative Proteogenomic Analyses Identify TOPs as Prognostic Markers and Therapeutic Targets in Sarcoma

https://doi.org/10.21203/rs.3.rs-139866/v1

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Background: Soft-tissue sarcomas (STSs) are uncommon malignant tumors with varied highly aggressive and histological presentations. They comprise approximately 1% of newly diagnosed adult malignancies each year, but the sarcoma's accurate diagnosis prediction remains a challenge. TOP1/1MT/2A/2B/3A/3B were aberrantly expressed in tumors and associated with tumor progression, but the roles of the six TOP members in sarcoma in clinical trials remains uncertain.

Method: Relationship between the mRNA levels of the TOPs and the clinicopathological parameters evaluated by Oncomine, GEPIA, cBioPortal, TIMER, Metascape, TCGAportal, Kaplan-Meier plotter, String, and Linkedomics. We focused on the potential mechanism of TOPs in the occurrence and development of SARC. We were investigating the prognostic value of TOPs expression in patients with SARC and correlations with clinicopathological features.

Results: In patients with SARC, TOPs mRNA expression level upregulated. Increased TOP family members' expression was associated with different subtypes, FNCLCC grades, Local recurrence, DNA methylation, different miRNA clusters, and poor prognosis in SARC patients. We further found that TOPs showed distinct immune infiltration patterns, TOP gene mutation rate and that TOP somatic copy number variations were negatively related to the survival of sarcoma patients. Survival data analyzed that high transcription levels of TOPs were associated with low overall survival and disease-free survival in sarcoma. Enrichment analyses for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) were conducted to reveal TOPs enriched in DNA topological change, Chromosome segregation, DNA, replication signaling pathway. BUB1B, CENPF, SPC25, NUF2, KNL1SGO1, RPS20, SNRPD3, and MED9 were significantly associated with TOP mutations. These Hub gene expressions in SARC were increased and were associated with unfavorable prognosis.

Conclusion: The results show that multiple TOP family members are involved in the development and progression of sarcoma and suggest that TOPs can be used as potential targets for the diagnosis and treatment of sarcoma. TOP1/1MT/2A/2B/3A/3B may affect the prognosis of SARC through DNA topological change, Chromosome segregation, DNA, replication signaling pathway.

Translational Medicine

bioinformatic analysis

soft-tissue sarcoma

DNA topoisomerase

immune infiltration

mutation

Adult soft-tissue sarcomas are diverse mesenchymal malignancies that account for 1% of solid adult tumors. Many are highly aggressive, accounting for a disproportionate share of cancer mortality among young adults. DNA topoisomerase (TOP) is responsible for DNA unlinking, and these ubiquitous enzymes play critical roles in many biological processes involving DNA(1)(2). There are two types of DNA TOPs, type I and type II.

Currently, there are only a few diagnostic and prognostic markers, and the cellular origin of several sarcoma subtypes is unknown. Therefore, the accurate diagnosis and prediction of many of these tumors' clinical behavior remain a challenge. High-grade sarcomas have a high rate of local recurrence, frequent metastases, and poor prognosis. For sarcoma patients with isolated, resectable, local recurrences, surgery is the sole potentially curative treatment. However, the oncological results remain worse than those in patients with the locally recurrent disease even after macroscopic resection. Unfortunately, these surgeries are often extensive, with significant postoperative morbidity and mortality.

Therefore, different strategies are needed to treat patients with soft-tissue sarcoma. The heterogeneity of sarcomas in molecular genesis, histology, clinical features, and treatment response regulates these rare but diverse neoplasms and is particularly challenging. Often, search results in one subtype do not translate to others. These limitations increase in the context that sarcomas are among the rarest cancers(3).

Meanwhile, the histopathologic type of sarcoma is sometimes tricky to identify, and morphological evaluation based on the microscopic examination of histologic sections remains the gold standard for sarcoma diagnosis. Several types of adjuvant methods are emerging in support of the morphological diagnosis. In recent years, scientific research on sarcoma based on genetics has made great strides. Considering the underlying pathogenesis and etiology of sarcoma will facilitate the discovery of advanced treatments and diagnostic biomarkers. Studies have shown that specific cancer genetics, such as mutations, single nucleotide polymorphisms (SNPs), translocations, deletions, and insertions, may help regulate cancer genes.

To date, 6 DNA TOPs have been detected and numbered in the order of their detection (TOP1, TOP1MT, TOP2Α, TOP2Β, TOP3Α, and TOP3Β), and each TOP enzyme has a specific function. The integrity of the DNA helix during the DNA transaction processes includes replication, transcription, and recombination. DNA TOPs have maintained a domain and are a natural tool in addressing DNA complexity by local transformation, controlling the structure of the DNA chromosome. Increasing experimental evidence indicates that TOPs are involved in breast cancer tumorigenesis. TOP1MT was overexpressed in cancerous tissue, and TOP1MT deficiency has been demonstrated to attenuate tumor growth in human and mouse models of colon cancer and liver cancer(4). Top3α is a crucial enzyme, and null homozygous mice die in the womb. The phenotypes highlight the critical role of Top3α in maintaining genomic stability during development(5). The expression of Top3β in breast ductal carcinomas was a significant predictor of survival, indicating that Top3β plays a significant role in suppressing tumorigenesis and is a significant predictor of survival in ductal breast cancer(6). TOP1-targeted and TOP2-targeted anticancer drugs are commonly used in gastrointestinal malignancies (colorectal and gastroesophageal malignancies). To our knowledge, no bioinformatic analysis has been performed to study the role of TOPs in sarcoma. However, the potential clinical and prognostic value of TOPs and the underlying mechanism of TOPs are inconsistent and even contradictory. Microarray-based comparative genomic hybridization (CGH) and the differential expression of mRNAs and miRNAs have revealed genomic changes, candidates for genes and miRNAs that can be utilized to differentiate sarcoma subtypes and to confirm disease progression, and that is therapeutic targets(7)(8)(9)⁽¹⁰⁾⁽¹¹⁾. Based on multiplatform sarcoma databases, we analyzed the expression level of the TOP family members, the relationship between TOPs and clinicopathological features and prognosis of patients with sarcoma, identified prognostic and predictive biomarkers using genomics and proteomics approaches in the present study. TOPs were demonstrated to have therapeutic potential for sarcoma.

2.1 Oncomine analysis (https://www.oncomine.org/resource/login.html)

The expression levels of TOPs in sarcoma patient tumor samples were determined from the Oncomine Compendium of Expression Array data. Oncomine gene expression array records were used to analyze the transcription levels of TOPs in different types of cancer. The mRNA expression of TOPs in clinical cancer samples was comparable to that in normal controls using Student's t-test to generate a P-value. The threshold values of the P-value and fold change were set as 0.01 and 1.5, respectively.

2.2 GEPIA dataset

Gene Expression Profiling Interactive Analysis (GEPIA, https://www.gepia.cancer-pku.cn) is an open-source database used for performing in-depth analyses of The Cancer Genome Atlas (TCGA) gene expression data and was employed to analyze the expression of TOPs and hub genes in sarcoma and normal samples. We sought to determine the correlation between TOPs and the clinicopathological features of sarcoma (cancer stage, tumor grade, race, weight, stage.) and its potential prognostic value. An analysis of disease-free survival (DFS, also known as recurrence-free survival (RFS)) based on gene expression, was carried out via GEPIA(12). ANOVA and nonparametric tests were used to determine the association between the clinicopathological parameters and the co-expression eigenvector modules were accurate.

2.3 The Kaplan-Meier Plotter

Kaplan-Meier Plotter (www.kmplot.com) was used to analyze patients' gene expression data and survival data with sarcoma. For the analysis of overall survival (OS) and DFS, the samples were divided into two groups based on the mean expression level. The relationship with biomarker values was evaluated by the log-rank test (P <0.05)(13).

2.4 cBioPortal

cBioPortal is a web resource for researching, visualizing, and analyzing multidimensional cancer genome data and mapping the hub gene network(14).

2.5 TIMER

The relationship between the frequency of tumor immune infiltrates (B cells, CD4+ T cells, CD8+ T cells, dendritic cells, macrophages, and neutrophils) was analyzed using the Tumor IMmune Estimation Resource (TIMER) online platform (http://cistrome.shinyapps.io/timer/). This web service contains 10897 samples of various cancers included in TCGA. In short, this platform uses a computational method to estimate the infiltration of different immune cells in 23 types of tumors based on public data from TCGA and their correlation with various factors, such as gene expression levels, mutations, or prognosis. Immunohistochemistry (IHC) was used to confirm the predictions in several tumor types, including sarcoma. Correlation graphs show the partial Spearman correlation corrected for purity and its statistical significance, where purity is defined as the percentage of malignant cells in tumor tissue. TCGA contains both sequencing and pathological data on 30 different types of cancer. The sarcoma dataset (TCGA, Provisional), including data with pathology reports, was selected for further TOP analysis with cBioPortal. Genome profiles included mutations, putative copy number changes (copy number alterations [CNAs]) through genomic identification of significant cancer targets (GISTIC 2.0), mRNA expression z-scores (RNA-seq v.2 RSEM), and z-scores of protein expression (reversed-phase protein array [RPPA]). The co-expression of genes and the network calculated according to the online instructions from cBioPortal(15).

2.6 Metascape

We first identified all statistically enriched terms(16) and cumulative hypergeometric P-values and enrichment factors calculated and used for filtering. The remaining significant terms were then grouped hierarchically in a tree based on the Kappa-statistical similarities between their genetic memberships (similar to that used on the National Cancer Institute [NCI] Database for Annotation, Visualization, and Integrated Discovery [DAVID] website). A kappa score of 0.3 was used as the threshold for placing the tree in term clusters(17).

3.1. Transcription levels of TOPs in patients with sarcoma

The development of microarray and RNA sequencing technology has made RNA research an integral part of biomedical research. Therefore, in this study, we analyzed TOP members' expression based on diverse databases and examined their correlations with clinicopathological features, prognosis, and local tumor recurrence, and explored the possible regulatory mechanism in patients with sarcoma. Six TOP family members have been found in mammalian cells. We compared the transcription levels of TOPs in 20 different types of cancer diseases with those in normal samples utilizing the Oncomine database (Figure 1). Student's t-test compared differences in transcriptional expression. The cutoff of the P-value was 0.01, and that of fold change was 1.5.

3.2. Significant changes in TOP expression at the transcription level between sarcoma and normal tissues (Oncomine)

To date, six DNA TOPs have been identified in mammalian cells. The Oncomine database was used to compare the mRNA levels of TOP1, TOP1MT, TOP2Α, TOP2Β, TOP3Α, and TOP3Β between sarcoma and normal tissues (Table 1). It was found that the mRNA levels of TOP1, TOP2Α, TOP2Β, TOP3A, and TOP3Β were upregulated in patients with sarcoma. Further analysis showed that TOP1 was overexpressed in sarcoma patients in the Detwiller Sarcoma dataset, with a fold change of 2.164 and a P-value of 4.5E-4. In addition, Top2A mRNA was overexpressed in fibrosarcoma (fold change=27.421), pleomorphic liposarcoma (fold change=17.927), LMS (fold change=12.112), malignant fibrous histiocytoma (fold change= 21.095), round cell liposarcoma (fold change= 13.321), and SS (fold change= 26.646). TOP2Β was highly expressed in DDLPS (fold change=2.002), fibrosarcoma (fold change= 2.938), round cell liposarcoma (fold change= 2.491), and SS (fold change= 3.108). In the Barretina Sarcoma dataset, TOP2Α was found to have higher expression in myxofibrosarcoma (fold change=12.264), pleomorphic liposarcoma (fold change=9.788), DDLPS (fold change=7.746), and myxoid/round cell liposarcoma (fold change=4.104). Top2b was overexpressed in myxoid/round cell liposarcoma (fold change = 2.318), DDLPS (fold change= 1.65), and pleomorphic liposarcoma (fold change = 1.505). Top3α was overexpressed in myxofibrosarcoma (fold change=1.758), pleomorphic liposarcoma (fold change=1.676), and LMS (fold change =1.757) in the Quade Uterus dataset, and high TOP2Α expression was found in uterine corpus leiomyosarcoma (fold change= 3.894) compared to normal samples.

3.3. Relationship between the mRNA levels of the TOPs and the clinicopathological parameters of patients with sarcoma

Using GEPIA (http://gepia.cancer-pku.cn/), we compared the mRNA expression of TOP family members between sarcoma and adjacent normal tissues. The expression levels of TOP1, TOP1MT, TOP2Α, TOP2Β, TOP3Α, and TOP3Β were higher in sarcoma tissues than in normal tissues (Figure 2A, 2B, and 2C).

3.4. Relationship between TOP mRNA expression and sarcoma grade

There are differences in the expression of TOPs in distinctive grades of sarcomas. We analyzed the expression of TOPs using the classification system of the Fédération Nationale does Centers de Lutte Contre le Cancer (FNCLCC) for sarcomas (Figure 3A). The revision of the American Joint Committee on Cancer (AJCC) Cancer Staging Manual, 8th Edition (2017) built on TNM stage and tumor grade. The AJCC system follows the classification system of the sarcoma groups of the FNCLCC, a 3-level system founded on the differentiation of tumor cells, mitotic activity, and the extent of necrosis. The panel recommends determining histological grade using the FNCLCC or AJCC/NCI system or an appropriate diagnostic-specific scoring system. The FNCLCC grading system can better reflect the patient's prognosis and predict distant metastasis and death in cancer patients. Postoperative chemotherapy can improve the metastasis-free survival and OS times in patients graded with the FNCLCC system(18). The mRNA expression level of TOPs in different FNCLCC grades correlated significantly with the individual tumor grade. In sarcoma, TOP1, TOP2Α, and TOP3Β differed considerably between FNCLCC grades 2 and 3, while TOP1 and TOP2Α differed between grades 1 and 3. TOP mRNA expression correlated with the individual tumor grade (P-value <0.05). TOP1, TOP2Α, and TOP3Β can be used to identify soft-tissue sarcoma patients with different grades, and the local treatment options (low expression) can thus be discussed.

Month to distant Recurrence harms long-term survival and quality of life in several malignancies(19). Despite improvements in local tumor control through surgery, radiation therapy, and chemotherapy, distant metastases and high tumor-related mortality remain problems with current treatment strategies(20). After surgical resection, Month to distant Recurrence plays a major role in reducing patients' survival and quality of life with sarcoma. The patterns and risk factors for Month to distant Recurrence have been highlighted(21). Important prognostic factors for predicting Month to distant recurrence are patient age, tumor size, completeness of the resection, grade, tumor rupture, multiplicity, histological subtype, and previous radiation therapy(22). We divided the data of 206 patients from TCGA into two groups based on the presence or absence of TOP mutations. The mutation group contained at least one TOP gene mutation to compare the effects of TOP mutations on tumor recurrence (P-value=5.238e-3, P<0.05; q-value = 0.0489). The results suggest that the expression level of TOPs can use as a risk factor for assessing the Month to distant Recurrence of sarcoma (Figure 3B).

3.5. The relationship between the expression of TOPs in sarcoma and tumor subtypes

Soft-tissue sarcomas are malignant mesenchymal tumors, with an estimated 12,390 new sarcoma cases and 4990 deaths in the United States in 2017 and a 5-year OS rate of 64%(23). The completeness of resection, histologic subtype, and tumor grade are the main determinants of survival(24)(25). We considered the expression of TOPs in different sarcoma subtypes. A cumulative histogram of 100% multiple classifications (100% stacked bar graph) (Figure 4A) was made, and different volumes represented the percentage of gene expression in different subtypes. The chi-square test was used to assess the relationship between TOP expression and the six major sarcoma subtypes. The expression of TOPs was significantly different among the six main sarcoma subtypes. The results showed that TOP1 has the highest expression ratio in DDLPS, with a ratio of 31.82%. TOP1MT and TOP3Β have the highest expression ratios in UPS, with 30% and 36.84% ratios, respectively. TOP2Α and TOP3Α have the highest expression ratios in STLMS, with 30% and 36.84% ratios, respectively. TOP2Β has the highest expression in ULMS, with a ratio of 36.84%. There were considerable differences in the expression proportions among these six sarcoma subtypes (P-value=8.540e-3, q-value=0.0368). The results showed that there were differences in the expression of TOPs between different subtypes. These results provide important insights into TOPs that can be used to diagnose different subtypes of sarcoma.

3.6. The relationships between the expression of TOPs and DNA methylation and miRNA clusters

Because the binary definition of hypermethylation is based entirely on the DNA methylation signal and DNA methylation profiles, sarcoma subtype information could not be separated at the DNA methylation level. Thus, we used the Partition Around Medoids (PAM) clustering method. The dendrogram from divisive hierarchical clustering revealed five main subsets of sarcomas. We integrated the methylation data and the mRNA expression data to identify the DNA methylation changes in different sarcoma subtypes and function-related genes (including potential tumor suppressor candidates). Previous studies have examined DNA methylation in soft-tissue sarcoma and based on genome-level DNA methylation studies. There are different patterns of DNA methylation in pediatric embryos and alveolar rhabdomyosarcoma. We found that Ewing's sarcoma euphemistically inactivates potential target genes. We further explored the correlation between TOPs and methylation, dividing data from TCGA into two groups based on TOP mutations' presence or absence. The mutation group contained TOP gene mutations to compare the effects of TOP mutations on DNA methylation. The results suggested that there are group differences between genes in separate clusters (P-value < 0.05).

Sarcomas were divided into distinct miRNA clusters, and different miRNA clusters were associated with DFS. The results showed that there were significant differences in TOP1, TOP2Α, TOP2Β, TOP3Α, and TOP3Β expression between cluster 1 and cluster 2 by the Wilcoxon signed-rank test.

3.7. Gene mutations of the TOP family and their relationship with OS and DFS in sarcoma (cBioPortal)

We next proceeded from the genome to disease intervention, rather than judging by tumor subtype. To better understand the relationship between TOP mutations and sarcoma, we elaborated and standardized RNA-Seq V2 data in the TCGA database. RNA-Seq V2 data in RSEM corresponded to RSEM genes from TCGA. The Z-score determined a threshold of ±2.0 missense mutations. The results show that the TOP gene mutation rate was higher in sarcoma patients. The mutation rates were 11% for TOP1, 10% for TOP1MT, 10% for TOP2Α, 9% for TOP2Β, 28% for TOP3Α and 16% for TOP3Β (Figure 5A and 5B). High expression of TOPs in sarcoma was associated with shorter OS and DFS. We believe this finding may be related to TOP gene mutations.

3.8. Mutation and copy number variation analysis

There are somatic copy number variations of TOPs in sarcomas, and we examined the variation in the somatic copy numbers of the TOP genes in sarcoma. Molecular genetic testing has been suggested to be an efficient approach to additional testing because various types of sarcoma have specific genetic aberrations, including substitutions, deletions, amplifications, and single base pair translocations. Moreover, the accumulation of somatic mutations is one of the main reasons for tumor development and contributes to the expression of neoantigens(26). We sought to determine whether mutations in TOP genes are associated with sarcoma. We used a multivariate Cox proportional hazards model to correct clinical factors, including age, sex, race, and tumor stage, to show the clinical correlation between gene expression related to sarcoma and immune subgroups. We identified somatic mutations in sarcoma using the International Cancer Genome Consortium (ICGC) dataset and the TCGA dataset. We further investigated the association of gene mutations with prognosis. Transcription signatures associated with selected mutations were identified and analyzed in terms of immune cell infiltration and outcome. Finally, we examined whether gene mutations affect immunity. The results of this study may reveal novel biomarkers and suggest potential immunotherapy for patients with sarcoma.

We used the somatic copy number alteration (SCNA) module to obtain the copy number variations. The SCNA module provides an opportunity to compare the somatic copy number of a given gene with tumor invasion level between tumor subtypes. Sus is certified by GISTIC 2.0 and includes deep deletion (-2), arm-level deletion (-1), diploid/normal (0), arm-level gain (1), and high gain (2). Graphs were used to display each immune subset's distribution in each copy number of the selected cancer types. The two-sided Wilcoxon rank-sum test was used to compare each SCNA category's penetration level with the normal level. We found that TOP1 has highly amplified somatic copy number variations in neutrophils and dendritic cells and is an immunization promoter; TOP1MT has highly amplified somatic copy number variations in CD4+ T cell and neutrophil immune subsets; TOP2Β has high amplification in the B cell immune subset, arm-level gain in the CD4+ T cell immune subset, and somatic copy number variation with an arm-level deletion in dendritic cells; TOP3Α has arm-level and high gain amplification, the presence or absence of arm-level variations in neutrophils, and somatic copy number variations in CD4 + T cells and neutrophils immune subsets; TOP3Β has high amplification in the immune subset of B cells, CD4+ T cells, CD8+ T cells, and dendritic cells, as well as arm-level somatic copy number variations in CD4 +T cells (Figure 5C).

3.9. Immune cell infiltration of TOPs in patients with sarcoma

Immunotherapy has become a promising treatment option for the treatment of solid tumors(27), but there is no research on the immune correlation between TOPs and sarcoma development. Our report describes the outcome and recruitment of TOPs and immune cells in sarcoma for the first time. We utilized the TIMER database to initiate a comprehensive study of the correlation between TOP and immune cell infiltration. Since GBM/OV microarray data contain more samples than RNA-seq data, we used GBM/OV microarray expression values to determine whether genes were available. By studying their relationship with immune infiltration, we observed that TOP-expressing tumors have low purity in the entire sinusoidal and basic subtypes and upper and lower cancer populations (as described in the Materials and Methods section, tumor tissue immune population). We found that the high expression of TOP1 in sarcoma was negatively correlated with neutrophils (Cor = 0.1996, P = 0.001) and B cells (Cor = 0, 1611, P = 0.012). TOP1MT expression was negatively correlated with macrophages (Cor = -0.236, P = 0.0002) and neutrophils (Cor = -0.144, P = 0.024). TOP2Α expression was negatively correlated with CD4+ T cell infiltration (Cor = -0.2322, P = 0.0002) and macrophages (Cor = -0.1713, P = 0.0084). Similarly, TOP2Β expression was negatively correlated with CD4+ T cell infiltration (Cor = -0.2554, P = 6.48E-05), macrophages (Cor = -0.226594498, P = 0.00046395) and dendritic cells (Cor = -0.294179571, P = 3.38E-06). TOP3Α expression was negatively correlated with CD4+ T cell infiltration (Cor = -0.2522, P = 8.07E-05). TOP3Β expression was negatively correlated with macrophage infiltration (Cor = -0.14316, P = 0.028219) (Figure 6). We describe the TOP and immune cell recruitment as well as changes in the tumor microenvironment in sarcoma. The results showed that the transcriptional characteristics associated with TOP mutations are related to immune cell populations' infiltration into tumors. The high expression of TOPs in CD4+ T cells and neutrophils is positively correlated with sarcoma prognosis. In contrast, the high expression of TOPs in other immune cells is negatively correlated with sarcoma prognosis. The elevated expression of TOP1, TOP1MT, TOP2Α, TOP2Β, TOP3Α, and TOP3Β in CD4+ T cells and neutrophils increased the OS rate and improved the immune response (Figure 7). TOPs are associated with inflammatory responses and immune cell infiltration, influencing the clinical outcome of SARC patients. TOP mutations are a risk factor for prognosis and have a predictive effect on sarcoma prognosis. After considering age, sex, and race (Table 2), TOP mutations were considered significantly different based on age.

We explored the clinical relevance of one or more tumor immune subsets, with the flexibility to correct multiple covariates in a multivariable Cox proportional hazards model. The covariates included clinical factors (age, sex, ethnicity, and tumor stage) and gene expression. For the Cox model outputs, survival (cancer type)~variables was the formula of the user-defined Cox regression model. This model was fitted by the function in the R package 'survival.' Coef is the regression coefficient. HR is the hazard ratio. Its lower and upper 95% confidence intervals are denoted as 95%CI_l and 95%CI_u.

3.10. Association of the increased mRNA expression of TOPs with the improved prognosis of patients with sarcoma

The existing evidence suggests that TOPs would improve DFS in selected patients at high risk of recurrence. We used public datasets (2015 version; http://kmplot.com/analysis/index.php?) to analyze the correlation between the TOP mRNA levels and survival time of patients with sarcoma. The Kaplan-Meier curve and log-rank test showed that elevated TOP mRNA levels were significantly related to the OS and DFS times of all patients with sarcoma (P<0.05). Compared to normal tissues, high TOP expression in sarcoma tissues was negatively correlated with the OS and DFS of patients with sarcoma. The results showed that the high expression of TOP1 (hazard ratio (HR) 2.11, confidence interval (CI) 1.41-3.17; P = 2.3e-4), TOP1MT (HR 1.83, CI 1.2-2.79; P = 4.1e-3), TOP2Α (HR 2.2, CI 1.39-3.47; P = 5.2e-4), TOP2Β (HR 2.11, CI 1.36-3.27; P = 6.4e-4), TOP3Α (HR 1.62, CI 1.07-2.46; P = 2.2e-2) and TOP3Β (HR 1.57, CI 1.05-2.33; P = 2.5e-2) was associated with lower OS. The high expression of TOP2Α (HR 2.41, CI 1.38-4.2; P = 1.5e-3), TOP2Β (HR 2.03, CI 1.2-3.44; P = 6.9e-3) and TOP3Α (HR 1.76, CI 1.06-2.93; P = 2.7e-2) was correlated with lower DFS. We also divided the sarcoma patients into two groups: one group was defined as the TOP mutation group, and the other group was defined as the TOP non-mutation group. The survival curve analysis of OS and DFS using cBioPortal showed that when the TOP family is regarded as a whole, the increased expression of TOPs in sarcoma is related to lower OS, and the result is of scientific and statistical significance. Simultaneously, the medium-to-high expression of the TOP family in sarcoma is not completely correlated with lower DFS, and the result is statistically significant. The result shows that each TOP member plays a different role in the prediction of DFS. (additional file shows this in more detail [see Additional file 3])

3.11 Functional enrichment analysis of TOP co-expression modules

GSEA is a genomic expression profile micro class data analysis tool used to collect genes composed of numerous genes in an entire transcript modification group to perform a simple and straightforward correlation analysis(28). The functions of TOPs and the genes significantly related to TOP expression changes were predicted by performing GO and the KEGG analyses in the database. We analyzed the enriched biological processes (BPs), cellular components (CCs), and molecular functions (MFs) of TOPs as well as the main BPs regulated by the signing of the TOPs, classified by the combination of the results. We found that GO:0006265 (DNA topological change), GO:0007059 (chromosome segregation), and GO:0006260 (DNA replication) (Figure 9A, 9B) were significantly different, and TOP alterations in sarcoma significantly regulated these changes. (additional file shows this in more detail [see Additional file 1])

3.12 Co-expressed genes of TOPs (Metascape)

The co-expressed genes of TOP1, TOP1MT, TOP2Α, TOP2Β, TOP3Α, and TOP3Β were tested by Metascape (Figure 10A and Table 3) and network topology-based analysis (Figure 10B). We set the top-ranking neighbors as 10 to identify the adjacent genes associated with TOPs with differential expression(additional file shows this in more detail [see Additional file 2]).

3.13. Predicted cellular functions and pathways of TOP genes and TOP-related neighboring genes in sarcoma

A network of TOP mutations and their 120 frequently changed neighboring genes was created. The results showed that BUB1B, CENPF, SPC25, NUF2, KNL1SGO1, RPS20, SNRPD3, and MED9 were significantly associated TOP mutations (Figure 11A and 11B). GO functional enrichment analysis predicted the three main mutation functions of TOPs and their 120 frequently changed neighboring genes, including BPs, CCs, and MFs. KEGG pathway analysis revealed the pathways of TOPs and their 120 most frequently changed neighboring genes (Figure 11C).

3.14. Protein interaction network of TOPs (Metascape)

For a given list of TOP genes, an enrichment analysis of protein-protein interactions was performed using the following databases: BioGrid, InWeb_IM, and OmniPath. The resulting network contains a subset of proteins with physical interaction with at least one additional member of the list(29)(30)(31)(32)(33)(34). GO and KEGG results were obtained and showed that in sarcoma, the neighboring proteins of TOPs are mainly expressed on the GO: 0000775 chromosomes, the centromeric region (log10P: -12.7); GO: 0005819 spindles (log10P: -12.0); and GO: 0000819 sister (log10P: -11.2). There were four protein clusters with apparent interaction relationships (Table 4)(35)(36)(additional file shows this in more detail [see Additional file 4]).

There are six topoisomerases (TOP1, TOP1MT, TOP2α, TOP2β, TOP3α, and TOP3β) in human cells, which mainly operate in a wide range of DNA and RNA substrates in the nuclear and mitochondrial genomes. Previous studies have shown that their catalytic intermediate DNA topoisomerase cleavage complex (TOPcc) is a therapeutic target for numerous anticancer drugs(37). Eukaryotic DNA TOPs play a significant role in transcription, replication, genome stability, and previous studies focused on TOPs in cancer, immune disorders, and the nervous system's diseases have shown that they have therapeutic relevance(38). Although the role of TOPs in the onset and prognosis of various cancers has been partially confirmed, there is no additional bioinformatics analysis on the relationship between TOPs and the development of sarcoma. Since transcriptase sequencing and other methods can help identify molecular events, these molecular events may help in the differential diagnosis of sarcomas. In the past year, research teams have reviewed the influence of molecular detection on histological diagnosis internationally, showing that molecular marker detection can provide a more diagnostic basis. This study took the lead in studying the mRNA expression of TOPs in sarcoma. It explored the relationships between TOPs and gene mutation, immune infiltration, sarcoma typing, DNA methylation, miRNA grouping, and prognosis (OS and DFS). We hope our findings will help enrich existing knowledge, improve existing treatment designs, and improve patients' prognostic accuracy with sarcoma.

Studies have shown that the lack of TOP activity or TOP mutations is related to neurodegenerative genomic instability syndrome and autism spectrum disorder(39). Top1, as a molecular target of SN38, is overexpressed in 43% to 51% of colon cancers(40)(41). Elevated TOP1 expression was found to be associated with a low OS rate in colon cancer. According to the literature, TOP1 inhibitors are associated with a good prognosis in sarcoma. After clarifying other cellular and genetic factors related to these drugs' susceptibility, it can be reasonably predicted that TOP inhibitors could treat at least some tumors with specific chromosomal changes in the cell cycle and DNA repair genes. These drugs are actively involved and have an antitumor effect. TOP2Α is a marker related to the prognosis of STS proliferation, and its expression is related to the poor prognosis of peripheral nerve sheath malignancies and SS(42). As expected, LASTS with high expression of Top2α has a higher recurrence rate, and the annual risk of recurrence increases as the percentage of Top2α-positive cells increases(43). In liver cancer, the increase in TOP1 and TOP2Α expression levels are associated with a worse OS rate and is closely related to the DFS rate(44). Research reports indicate that the lack of TOP1MT dramatically reduces the proliferation and regeneration of liver cells(45). Besides, TOP1MT deficiency can impair tumor growth in colon cancer and liver cancer in human and mouse models. The importance of TOP1MT in tumor development provides a theoretical basis for the development of anticancer drugs targeting TOP1MT. Research has also revealed that androgen signaling promotes the co-recruitment of androgen receptor and topoisomerase II beta (TOP2Β)(46). TOP2Β is associated with distinctive genome rearrangements and copy number changes in prostate cancer(47). Little is known about the expression and role of TOP1MT, TOP3Α, and TOP3Β in sarcoma. This study found that the expression of TOP1, TOP1MT, TOP2Α, TOP2Β, TOP3Α, and TOP3Β in sarcoma was higher than that in normal tissues.

The FNCLCC scoring system can reflect the prognosis of patients with sarcoma and the metastasis and death of cancer patients. This study showed that in sarcoma, TOP1, TOP2Α, and TOP3Β were significantly altered in patients with FNCLCC grades 2 and 3. There were noteworthy differences in TOP1 and TOP2Α expression between FNCLCC grades 1 and 3. Regarding the relationship between the local recurrence of sarcoma and TOPs, studies have shown that the most critical factor affecting local recurrence is the resectability of recurrent disease. The patients most likely to benefit from resection are disease-free and have a longer course, no primary tumor rupture, low-grade tumors, and unidirectional recurrence. The results showed that the increase in TOP mutations caused an increase in distant tumors' recurrence rate. Therefore, it is believed that the expression level of TOPs can be used as a risk factor for predicting the recurrence of local sarcoma.

We used the chi-square test to explore the expression of TOPs in different sarcoma subtypes and determine the relationship between the TOP expression level and the six main sarcoma subtypes. We found that among the six main sarcoma subtypes, TOP1, TOP1MT, TOP2Α, TOP2Β, TOP3Α, and TOP3Β all had expression differences, and different TOP members had altered expression levels in different subtypes. We also explored the relationship between the DNA methylation of TOPs and miRNA typing in sarcoma. The results showed that TOPs were expressed differently in diverse DNA methylation clusters and miRNA clusters in sarcoma. The relative rarity of each subtype of sarcoma and the potential genetic differences between sarcomas can complicate the evaluation of the efficacy of any drug61. Some researchers believe that the treatment of sarcoma should be based on genotype rather than subtype(48). Cambodia et al. (49) combined SNP sequences with global and target exon sequencing to characterize the genome patterns of 86 liposarcomas in all major subtypes.

It should be noted that the Food and Drug Administration (FDA) has approved treatments for metastatic liposarcoma, and this study focused on three different subtypes (dedifferentiated, mucinous and pleomorphic liposarcoma) of liposarcoma. The prognosis of these subtypes is the same. There are still challenges in developing targeted therapies for sarcomas because drugs target secondary mutations rather than triggers, and sarcoma genome instability can promote drug resistance. In this sense, even though many sarcomas are similar to other cancers, at least in some sarcoma subtypes, the sarcoma gene's triggering mutation is more prominent, and the genetics are more straightforward than other tumors. In the future, sarcoma diagnostic strategies should pay more attention to genomic changes rather than sarcoma subtypes. To this end, the study described the somatic mutations of TOPs in sarcomas, explored the clinical correlation between sarcoma-related gene mutations and immune subgroups, and determined that TOP gene mutations are associated with higher inflammation and immune cell infiltration, thus affecting the poor prognosis of SARC patients. These results suggest that TOP mutations are a negative risk factor for prognosis.

Moreover, the functions and pathways of TOPs and their 120 frequently changed adjacent gene mutations in sarcoma patients were explored. The results showed that DNA repair and mitochondrial fusion genes were closely linked to TOP mutations. Through multivariate analysis, we have reason to believe that the abnormal expression of TOP1, TOP1MT, TOP2Α, TOP2Β, TOP3Α and TOP3Β in sarcoma is an independent prognostic factor that affects OS and DFS times of sarcoma patients. The article's shortcoming is the lack of sufficient clinical samples and unable to perform immunohistochemical detection of TOPs on clinical samples and tumor formation experiments in animals.

Through Oncomine, TCGA, and other databases, we found that TOPs are highly expressed in sarcoma tissues at the transcriptome level compared to normal tissues. As an international standard for determining the histological grade of sarcoma, the FNCLCC system can better predict the distant metastasis and prognosis of cancer patients. We studied the relationship between TOPs and FNCLCC grades of sarcoma and found that TOP1/TOP2Α/TOP2Β/TOP3Β can be used to predict different sarcoma grades. Local recurrence harms the long-term survival of patients with malignant tumors. The study results suggest that TOPs can be used as a risk factor for assessing sarcoma's local recurrence. There are differences in the expression of TOPs in different sarcoma subtypes, DNA methylation clusters, and miRNA clusters. We also studied the relationships between TOP gene mutations and immune infiltration, copy number variations, and survival curves. The results showed that in sarcoma, there are differences in the immune infiltration and somatic copy numbers of different TOPs, which indicates different prognoses. The above studies predict that TOPs can be used as a therapeutic target for the treatment of sarcoma. Therefore, we further performed enrichment analysis to understand the enriched BPs, CCs, and MFs of TOPs and discussed TOP-like co-expressed genes and the protein interaction network of these genes.

TOPs, DNA topoisomerase; STS, Soft-tissue sarcoma; DNA Topoisomerase I; TOP1MT,DNA Topoisomerase I Mitochondrial; TOP2α, DNA Topoisomerase II Alpha;TOP2β, DNA Topoisomerase II Beta;TOP3α, DNA Topoisomerase III Alpha;TOP3β, DNA Topoisomerase III Beta; FNCLCC, Fédération Nationale does Centers de Lutte Contre le Cancer; SNP, single nucleotide polymorphisms; DDLPS, dedifferentiated liposarcoma; LMS, leiomyosarcoma; UPS, undifferentiated pleomorphic sarcoma; MFS ,mucin fibroblast sarcoma malignant peripheral nerve sheath tumor MPNST; SS, synovial sarcoma; ULMS，uterine leiomyosarcoma; PD, pharmacologic biomarker; TCGA, The Cancer Genome Atlas; GO, Gene Ontology; GEPIA, Gene Expression Profiling Interactive Analysis; DFS, disease-free survival; OS, overall survival; TIMER, Tumor Immune Estimation Resource; IHC, Immunohistochemistry; CNAs, copy number alterations; RPPA, reversed-phase protein array; KEGG, Kyoto Encyclopedia of Genes and Genomes; DAVID, Database for Annotation, Visualization and Integrated Discovery; PAM, Partition Around Medoids; ICGC, International Cancer Genome Consortium; SCNA, somatic copy number alteration; BP, biological processes, CC ;cellular components; MF，molecular functions; NTA, Network Traffic Analysis;

Acknowledgement:

We thank "Springer Nature Author Services" for help in editing grammar, spelling, and other writing errors.

Funding information

This research was funded by the National Natural Science Foundation of China (Grant Nos. 81874020);

Author information

Affiliations

Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.

Genchun wang, Kai Sun, Jiamin Lin, Xudong Yao, Jiachao Guo, Zhou Guo, Liang Cai Hou, jingTing Xu, and Fengjing Guo

Department of Obstetrics and Gynecology Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.

Li Ting Liu

Contributions

GW composed the manuscript and proposed the experimental design. GW, KS, and LL contributed to data interpretation and bioinformatic analysis. JL, XY, JG, ZG, LH, JH contributed with data collection. The final manuscript was reviewed and approved by all authors before submission.

ethics approval and consent to participate

not applicable

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Each author approved the manuscript before submission for publication.

conflict of interest

The authors declare no conflict of interest

Availability of data and material

The data used to support the findings of this study are available from the corresponding author upon request.

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Table 1

Significant changes in TOP transcription levels between different types of sarcoma and normal tissues. *P<0.05.
	Type of Sarcoma versus Normal Tissue	Fold Change	P-value	t-test	Source and/or Reference
TOP1	Sarcoma vs. Normal	2.164	0.000457	3.895	Detwiller Sarcoma Statistics
TOP1MT	NA	NA	NA	NA	NA
TOP2Α	Myxofibrosarcoma vs. Normal	12.264	3.47E-24	24.318	Barretina Sarcoma Statistics
	Pleomorphic Liposarcoma vs. Normal	9.788	3.33E-16	18.002	Barretina Sarcoma Statistics
	Dedifferentiated Liposarcoma vs. Normal	7.746	4.15E-19	14.12	Barretina Sarcoma Statistics
	Leiomyosarcoma vs. Normal	12.112	1.92E-12	11.088	Barretina Sarcoma Statistics
	Myxoid/Round Cell Liposarcoma vs. Normal	4.104	2.79E-11	11.745	Barretina Sarcoma Statistics
	Fibrosarcoma vs. Normal	27.421	1.14E-08	8.906	Detwiller Sarcoma Statistics
	Pleomorphic Liposarcoma vs. Normal	17.927	5.88E-08	9.384	Detwiller Sarcoma Statistics
	Leiomyosarcoma vs. Normal	48.841	3.46E-08	8.485	Detwiller Sarcoma Statistics
	Malignant Fibrous Histiocytoma vs. Normal	21.095	1.79E-08	8.258	Detwiller Sarcoma Statistics
	Round Cell Liposarcoma vs. Normal	13.321	1.91E-07	8.171	Detwiller Sarcoma Statistics
	Synovial Sarcoma vs. Normal	26.646	2.55 E-06	6.884	Detwiller Sarcoma Statistics
	Uterine Corpus Leiomyosarcoma vs. Normal	3.894	7 E-03	2.898	Quade Uterus Statistics
TOP2Β	Dedifferentiated Liposarcoma vs. Normal	2.002	1.02 E-04	4.767	Detwiller Sarcoma Statistics
	Fibrosarcoma vs. Normal	2.938	2.24 E-06	6.429	Detwiller Sarcoma Statistics
	Round Cell Liposarcoma vs. Normal	2.491	5.58 E-05	5.581	Detwiller Sarcoma Statistics
	Synovial Sarcoma vs. Normal	3.108	1.22 E-04	6.033	Detwiller Sarcoma Statistics
	Myxoid/Round Cell Liposarcoma vs. Normal	2.318	1.25E-10	10.793	Barretina Sarcoma Statistics
	Dedifferentiated Liposarcoma vs. Normal	1.65	4.71E-07	7.352	Barretina Sarcoma Statistics
	Pleomorphic Liposarcoma vs. Normal	1.505	1.41 E-05	5.151	Barretina Sarcoma Statistics
TOP3Α	Myxofibrosarcoma vs. Normal	1.758	7.09E-11	10.087	Barretina Sarcoma Statistics
	Pleomorphic Liposarcoma vs. Normal	1.676	3.19E-08	7.126	Barretina Sarcoma Statistics
	Leiomyosarcoma vs. Normal	1.757	1.75E-09	8.012	Barretina Sarcoma Statistics
TOP3Β	NA	NA	NA	NA	NA
NA, not available; TCGA, The Cancer Genome Atlas.

Table 2

Cox proportional hazards model. *P<0.05.
	COEF	HR	95%CI_l	95%CI_u	p-value	sig
Age	0.022	1.022	1.006	1.039	0.006	**
Gender male	0.091	1.095	0.693	1.732	0.697
Race black	0.229	1.258	0.144	10.969	0.836
Race white	-0.075	0.927	0.121	7.077	0.942
Purity	0.713	0.713	0.632	6.589	0.233
TOP1	0.1	1.105	0.767	1.591	0.591
TOP1MT	0.227	1.255	0.951	1.657	0.109
TOP2Α	0.075	1.078	0.879	1.322	0.472
TOP2Β	0.37	1.448	0.888	2.361	0.138
TOP3Α	-0.043	0.957	0.702	1.306	0.784
TOP3Β	-0.104	0.901	0.569	1.425	0.655

Table 3

Functional enrichment analysis of TOP co-expression modules. *P-value < 0.05.
Network	Annotation
Input ID	GO:0006265\| DNA topological change\|-17.7 GO:0071103\| DNA conformation change\|-9.4; R-HSA-4615885\| SUMOylation of DNA replication proteins\|-7.2
Input ID_MCODE_ALL	GO:0006265\| DNA topological change\|-14.1; R-HSA-4615885\| SUMOylation of DNA replication proteins\|-7.6; GO:0071103\| DNA conformation change\|-7.5
Input ID_SUB1_MCODE_1	GO:0006265\| DNA topological change\|-14.1; R-HSA-4615885\| SUMOylation of DNA replication proteins\|-7.6; GO:0071103\| DNA conformation change\|-7.5
Network construction method: network expansion; set number of top-ranking neighbors: 10; *P-value < 0.05

Table 4

Protein interaction network of TOP genes. *P-value < 0.05.
Network	Annotation
Input ID	GO:0000775\|chromosome, centromeric region\|-12.7;GO:0005819\|spindle\|-12.0;GO:0000819\|sister chromatid segregation\|-11.2
Input ID_MCODE_ALL	GO:0000777\|condensed chromosome kinetochore\|-9.6;GO:0000779\|condensed chromosome, centromeric region\|-9.3;GO:0000776\|kinetochore\|-9.0
Input ID_SUB1_MCODE_1	GO:0000777\|condensed chromosome kinetochore\|-14.2;GO:0000779\|condensed chromosome, centromeric region\|-13.9;GO:0000776\|kinetochore\|-13.5
Input ID_SUB1_MCODE_2	GO:0006413\|translational initiation\|-12.6;GO:0006412\|translation\|-9.2;GO:0043043\|peptide biosynthetic process\|-9.1
Input ID_SUB1_MCODE_3	GO:0000398\|mRNA splicing, via spliceosome\|-9.2;GO:0000377\|RNA splicing, via transesterification reactions with bulged adenosine as nucleophile\|-9.2; GO:0000375\|RNA splicing, via transesterification reactions\|-9.2
Input ID_SUB1_MCODE_4	GO:0003712\|transcription coregulator activity\|-4.8

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Integrative Proteogenomic Analyses Identify TOPs as Prognostic Markers and Therapeutic Targets in Sarcoma

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Abstract

Figures

1. Background

2. Materials And Methods

2.1 Oncomine analysis (https://www.oncomine.org/resource/login.html)

2.2 GEPIA dataset

2.3 The Kaplan-Meier Plotter

2.4 cBioPortal

2.5 TIMER

2.6 Metascape

3. Results

3.1. Transcription levels of TOPs in patients with sarcoma

3.2. Significant changes in TOP expression at the transcription level between sarcoma and normal tissues (Oncomine)

3.3. Relationship between the mRNA levels of the TOPs and the clinicopathological parameters of patients with sarcoma

3.4. Relationship between TOP mRNA expression and sarcoma grade

3.5. The relationship between the expression of TOPs in sarcoma and tumor subtypes

3.6. The relationships between the expression of TOPs and DNA methylation and miRNA clusters

3.7. Gene mutations of the TOP family and their relationship with OS and DFS in sarcoma (cBioPortal)

3.8. Mutation and copy number variation analysis

3.9. Immune cell infiltration of TOPs in patients with sarcoma

3.10. Association of the increased mRNA expression of TOPs with the improved prognosis of patients with sarcoma

3.11 Functional enrichment analysis of TOP co-expression modules

3.12 Co-expressed genes of TOPs (Metascape)

3.13. Predicted cellular functions and pathways of TOP genes and TOP-related neighboring genes in sarcoma

3.14. Protein interaction network of TOPs (Metascape)

4. Discussion

5. Conclusions

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