Integrative bioinformatics analysis and experimental validation identify CHEK1 as an unfavorable prognostic biomarker related to immunosuppressive phenotypes in soft tissue sarcomas

Integrative bioinformatics analysis and experimental validation identify CHEK1 as an unfavorable prognostic biomarker related to immunosuppressive phenotypes in soft tissue sarcomas | 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrative bioinformatics analysis and experimental validation identify CHEK1 as an unfavorable prognostic biomarker related to immunosuppressive phenotypes in soft tissue sarcomas Chao Rong, Yun Liu, Fang Xiang, Xin Zhao, Jinjin Zhang, Zuorun Xiao, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5923386/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Aug, 2025 Read the published version in npj Precision Oncology → Version 1 posted 15 You are reading this latest preprint version Abstract Rhabdomyosarcoma (RMS) represents one of the most common soft tissue sarcoma (STS) in children and adolescents. Transforming growth factor beta 1 (TGFβ1) is a potent inhibitor of myogenic differentiation in RMS and plays a significant function in the tumour immune microenvironment. Currently, unsupervised tumor immune phenotype based on multi-omics expression profiling has been less studied. To reveal the tumour immune phenotype of STS and identify promising therapeutic targets, multi-omics expression profiling in 363 tumours across subtypes of STS was investigated. Here, we validated the TGFβ1 signal function in RMS myogenic differentiation and established a novel molecular classifier based on immune cell subsets related to TGFβ1 and Interferon-γ (IFNγ) to identify distinct immune phenotypes with higher or lower cytotoxic contents. Moreover, we compared multi-omics expression profiling across subgroups of RMS and STS to identify CHEK1 as an unfavourable prognostic biomarker related to immunosuppressive phenotypes. In situ analysis of independent validation cohorts addresses the correlation between CHEK1 and tumour-infiltrating immune cells. Collectively, our data validate the TGFβ1 signal function in RMS myogenic differentiation and establish a novel risk assessment strategy for RMS and STS patients. This work potentially improves risk assessment for STS patients and offers a new therapeutic strategy to increase antitumor immunity through the combined targeting of CHEK1 inhibition. Biological sciences/Cancer Health sciences/Biomarkers Health sciences/Health care Health sciences/Medical research Health sciences/Molecular medicine Health sciences/Oncology Soft tissue sarcomas Rhabdomyosarcoma multi-omics profiling analysis prognostic biomarker immune phenotypes checkpoint kinase 1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Soft tissue sarcoma (STS) is a heterogeneous group of mesenchymal tumors encompassing more than 60 histological subtypes. Rhabdomyosarcoma (RMS) is one of the most common STS in children and adolescents, representing 5% of all childhood cancers 1 , 2 . RMS is differentiated from primitive mesenchymal stem cells, which cannot fully differentiate into skeletal muscle. It can occur anywhere in the human body, with the head and neck being the most common primary site 3 – 5 . RMS is divided into four subtypes based on its clinical and pathological characteristics: embryonic rhabdomyosarcoma (ERMS), alveolar rhabdomyosarcoma (ARMS), pleomorphic rhabdomyosarcoma (PRMS) and sclerosing rhabdomyosarcoma (SSRMS) 6 – 9 . ERMS and ARMS are two major histological subtypes of RMS. ERMS occurs more commonly in younger children and has a more favorable prognosis. Histologically, ERMS showed primitive oval to spindle cells with minimal cytoplasm, resembling immature skeletal myoblasts. ERMS has a wide range of genetic alterations, termed PAX-fusion-negative or fusion-negative RMS 7 , 10 , 11 . ARMS occurs mainly in adolescents with poorer prognoses and characteristically exhibits an alveolar pattern with cells distributed around an open central space. The importance of the PAX-FOXO1 fusion has been highlighted in the ARMS pathomechanism 12 , 13 . Despite advances in multi-disciplinary treatment for RMS and STS, consisting of surgery, irradiation, chemotherapy, and targeted therapy, the clinical prognosis of patients has only improved slightly, and promising curative treatment remains a significant challenge 14 – 16 . The ability to complete muscle differentiation is impaired in RMS. Clinically, RMS tumors are diagnosed based on the expression of skeletal markers, such as Myogenin, MyoD, and desmin, as well as skeletal α-actin and vimentin 17 , 18 . Transforming growth factor beta 1 (TGFβ1) is the most potent inhibitor of myogenic differentiation in RMS and is central to immune suppression within the tumor microenvironment 4 , 19 – 22 . Although immune checkpoint inhibition has demonstrated promise in improving clinical outcomes for certain cancers, soft tissue sarcomas remain limited in effectiveness in immune checkpoint blockade (ICB) based on current clinical trials 23 , 24 . The current revolution in understanding the molecular landscape clarifies that STS is ‘non-immunogenic’ with a low tumor mutation burden (TMB) and PD-L1 expression 25 . Interferonγ (IFNγ) is a cytokine pivotal in regulating PD-L1 expression and antitumor immunity 26 . Owing to the rarity and heterogeneity of STS, few studies have investigated the tumor microenvironment (TME) and tumor-infiltrating immune cells (TIIC) in different STS histologies, including RMS. A recent study used global gene expression data to define molecular immune signatures stratify STS into distinct immune phenotypes and identified a subpopulation of patients with improved survival and a high response rate to PD1 inhibitor therapy 27 . In the interim, immune checkpoint blockade has few therapeutic benefits in STS patients. Therefore, it is urgent to explore combinations for a more efficient immunomodulator. Here, we started this study with the expression and differentiation regulation of TGFβ1 in RMS. We developed a novel molecular classification of RMS and STS based on immune cell subsets related to TGFβ1 and IFNγ expression, revealing distinct immune phenotypes. Moreover, we compared multi-omics expression profiles across subgroups of RMS and STS to identify CHEK1 as an unfavorable prognostic biomarker related to immunosuppressive phenotypes. We also used multiple immunohistochemistry (mIHC) staining assays to assess the correlation between CHEK1 and tumor-infiltrating immune cells. Main deliverables can potentially improve risk assessment for STS patients and increase antitumor immunity from a combined targeting of CHEK1 therapy. Materials and Methods Patient cohorts and samples A total of patients with RMS were enrolled, including 33 real-world samples from our institute (titled the Hefei-RMS cohort). The tissue samples used in this study were obatined from patients with rhabdomyosarcoma diagnosed between 2016–2019, who were diagnosed with RMS according to the World Health Organization (WHO) guidelines. Paraffin-embedded RMS tissues were collected from The First Affiliated Hospital of University of Science and Technology of China (USTC) for immunohistochemistry and immunofluorescence staining. Written informed consent was obtained from all participants or family members in the study. The research protocol was approved by the Ethics Committee of the First Affiliated Hospital of USTC (Ethic No 2024/RE256 ) in accordance with the Declaration of Helsinki. Expression profiling and clinical datasets RNA-seq data for 106 samples, including five normal muscles and 101 Rhabdomyosarcomas, were downloaded from the Gene Expression Omnibus (GSE108022). The TCGA-SARC cohort RNA, protein expression, and clinical data were downloaded from https://www.cbioportal.org/ in December 2022. The curated gene sets for TGFBETA_SIGNALING_PATHWAY were obtained from https://www.gsea-msigdb.org/gsea/msigdb . Immune checkpoint, IFNγ-related gene sets, and the tumor inflammation signature (TIS) were obtained from the published literature 27 – 29 . Fraction genomic alterations and mutation frequencies were analyzed using the cBioportal tool. Cell culture, treatment and transfection Human RMS cell line RD, was cultured in DMEM supplemented (HyClone, Utah, USA) with 10% fetal bovine serum (FCS, Gibco Life Technologies, Carlsbad, CA, USA) and 1% penicillin/streptomycin (Corning, Discovery Boulevard Manassas, VA, USA) at 37°C in a 5% CO 2 atmosphere (Thermo Fisher Scientific, Woodward St, Austin). Short Tandem Repeat (STR) genotyping was used to validate cell line authenticity prior to performing the described experiments. Mycoplasma testing was done every three months and no mycoplasma was detected. siRNA for TGFβ1, SMAD2, ( Supplementary Table 1 , Zorin, Shanghai, China) were transfected by using Lipofectamine™ RNAiMAX Transfection Reagent (Life Technologies). Cells transfected with nonsense siRNA served as controls. RNAs and proteins were extracted 48 post-transfection and used for further analyses. For TGFβ1 stimulation, RD cells were stimulated by TGFβ1 cytokine (10 ng/mL, Peprotech, 5 Cedarbrook Drive, Cranbury, NJ, USA) to each well for 48h, and then RNAs and proteins were extracted. The SMAD2 transient transfection plasmid was transfected into RD cells using Lipofectamine® 3000 (Life Technologies) to mimic SMAD2. RNAs and proteins were extracted 48h post-transfect and used for further analysis. Histology and Immunohistochemical staining Tissue microarrays were purchased from Bioaitech Company (Xi’an, China), comprised 91 soft tissue sarcomas. RMS and soft tissue sarcoma tumors were fixed in 4% PFA, processed, and embedded in paraffin. Histological sections were stained with Hematoxylin and Eosin or immunohistochemical staining. The tissue sections were deparaffinized and rehydrated using the following steps: melting the wax at 65°C for 2 h, 3 × 5 min xylene, 2 × 3 min 100% ethanol, 3 min 95% ethanol, 3 min 75% ethanol, and finally rinsed with water. Tissue sections were incubated with 10 mM sodium citrate buffer (pH 6.0) (Boster, Wuhan, China) in a microwave twice for 15 min each. After antigen retrieval, use 3% peroxidase solution to block endogenous enzymes (Chemical Technology, Jiangsu Yonghua, China) for 10 minutes, block with 5% BSA (Boster) for 20 min, and incubate the primary antibody overnight at 4°C. The sections were incubated with biotinylated anti-rabbit secondary antibody (Boster) for 2 h. A solution of streptavidin-HRP (Boster) and peroxidase substrate (DAB) (MXB Biotechnologies, Fuzhou, China) was used to generate signals in tissue sections. CHEK1 staining scores were automatically determined using QuPath 30 (version 0.3.2.), reflecting positive cells and staining intensity. Specific antibodies are indicated and outlined in Supplementary table 2. Real-time PCR Total RNA was extracted from the cells using the NucleoSpin RNA extraction kit (Macherey-Nagel, Düren Neumann Neander Str, Germany). miRNA and total RNA were determined according to the kit instructions. RNA was isolated from human tissues using the MagMAX™ FFPE DNA/RNA Ultra Kit (Thermo Fisher Scientific). Using Reverted 1st cDNA synth kit (Thermo Fisher Scientific) for reverse transcription. Quantitative real-time PCR was carried out using the FS Essential DNA Green Master (Roche, F. Hoffmann-La Roche AG Konzern-Hauptsitz Grenzacherstrasse 124 CH-4070 Basel, Swiss) and the following cycling condition: 95°C for 10 min, 40 cycles of 95°C for 20s, 60°C for 20s and 72°C for 20s, 37°C for 30s. The mRNA expression levels of detected genes were standardized to GAPDH. The relative quantification was calculated using the 2 −△△CT method. Primers designed for RT-PCR in this research are listed in Supplementary table 3 . Western blotting 10x cell lysis buffer (Cell Signaling Technology, MA, USA) was used to extract proteins from cultured cells and supplement them with protease inhibitors (Cell Signaling Technology). For western blotting, 20 µg of total protein was measured by BCA analysis (Beyotime, Shanghai, China) was separated by sodium dodecyl sulfate (SDS) polyacrylamide gel electrophoresis and transferred to a PVDF membrane (Merck Millipore, MA, USA). The membrane was blocked with 5% skimmed milk (Tris-buffered saline/0.1% Tween 20) in TBST. The membrane was incubated with the primary antibody overnight at 4°C, and then the HRP-conjugated secondary antibody (1:5000; Boster, Wuhan, China) was incubated in skimmed milk. Electronic Chemistry Laboratory (ECL) test kits were used for signal development (EpiZyme, Shanghai, China). Specific antibodies are indicated and outlined in Supplementary table 4. Immune cell scores deconvolution Absolute immune cell infiltration levels from gene expression were predicted by CIBERSORTx using the website server ( https://cibersortx.stanford.edu ). The TPM-normalized expression matrix datasets from GSE108022 and TCGA-SARC were used as the input mixture files, and the relative levels for the 22 immune cells were computed by LM22 gene signature. Unsupervised Hierarchical Clustering Transcriptome count data of genes were ln(x + 1)-transformed and clustered using correlation distance and average linkage. ClustVis, a web tool for visualizing multivariate data, was utilized for unsupervised hierarchical clustering and to visualize data in a heatmap [63] . Multiple Immunohistochemistry staining and image analysis We designed a 5-plex immunofluorescence panel for RMS tissue and an 8-plex panel for sarcoma TMA to characterize the tumor immune microenvironment. Candidate commercial antibodies intended for mIF staining were first validated by IHC using RMS FFPE tissue to confirm optimal staining intensity, specificity, and signal-to-noise ratio. mIF was performed according to the Opal Multiplex IHC assay protocol (Akoya Biosciences) as previously described (31) . The antibody panel was then stained in the following order. Each primary antibody was incubated for 60 min, followed by 10-min incubation with a secondary antibody (Opal Polymer Anti-Rabbit HRP Kit, Akoya Biosciences), application of the Opal fluorophore (OPAL Fluor, Akoya Biosciences), and incubation for 10 min at room temperature. Detailed information is provided in the supplementary Table. mIF images were scanned using a Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences). The fluorescent images were unmixed and analyzed to quantify the mean fluorescent intensity for each marker using inForm Advanced Image software (inForm: 2.5.1, Akoya Biosciences). Statistical analysis All statistical analyses were performed using R software 4.2 and GraphPad Prism 10.2 (ID: GPS-1928733-EJSL-94BFE, San Diego, CA, USA). The two-tailed Wilcoxon-Mann-Whitney non-parametric test was performed to compare quantitative variables across two groups or subclusters. Kaplan-Meier estimation and log-rank tests were used for survival analysis. Differences between groups were compared using the chi-squared or Fisher’s exact tests for categorical variables. Correlations were evaluated using non-parametric Spearman analysis. * P values < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. Results TGFβ/SMAD signaling is highly expressed in human rhabdomyosarcoma and regulates myogenic transcription factors. We previously identified a role for activated TGFβ signaling in blocking the differentiation of human rhabdomyosarcoma 32 – 34 . The canonical TGFβ/SMAD signaling exerts significant functions in cancer progression by remodeling the architecture of the carcinomas and by suppressing antitumor immunity 35 , 36 . This study analyzed publicly available RNA sequencing (RNA-seq) data (GSE108022) from primary RMS samples. A total of 133 genes from the GSEAsig database (WP_TGFBETA_SIGNALING_PATHWAY) were collected, and the relative gene expression levels were presented in RMS subtypes. We identified 90 differentially expressed genes that were significantly upregulated in RMS as compared with muscle tissues (Fig. 1 A). The canonical TGFβ/SMAD signaling members were highly expressed in RMS regardless of the subtype (Fig. 1 B). IHC staining of RMS samples revealed that TGF-beta1 was highly expressed in the majority of primary tumors (n = 9/11 ERMS, n = 7/10 ARMS, and n = 8/9 PRMS) (Fig. 1 C), which was consistent with the findings of the previous studies 32 – 34 and confirmed in an independent fraction of sarcomas within fibrosarcoma, gastrointestinal stromal tumors, and synovial sarcoma (Supplementary Fig. 2). Next, we evaluated whether TGFβ/SMAD signaling plays a role in RMS myogenic differentiation. TGFβ1 was knocked down using small interfering RNA (siRNA) transfection and up-regulated using the exogenous cytokine TGFβ1 in RD cells (Supplementary Fig. 3A). Transient TGFβ1 silencing significantly increased mRNA levels of myogenic differentiation genes MyoD1 , Myogenin , Myosin , and Desmin. When RD cells were treated with exogenous cytokine TGFβ1, mRNA levels of MyoD1 , Myogenin , Myosin , and Desmin were significantly decreased (Fig. 1 D-E). At the protein level, loss of TGFβ1 resulted in a striking increased expression of MyoD1, Myogenin, and Myosin. In contrast, exogenous TGFβ1 reduced the protein levels of MyoD1, Myogenin, and Myosin. We did not observe significant changes in Desmin protein expression regardless of the loss or gain function of TGFβ1 (Fig. 1 F-H). To further evaluate the association between TGFβ/SMAD signaling and RMS differentiation block, we investigated the transcript and protein levels of myogenic differentiation markers after knockdown or overexpression of SMAD2 in RD cells (Supplementary Fig. 3C-G). Upon SMAD2 knockdown or overexpression, MyoD1, Myogenin, Myosin, and Desmin increased or decreased, respectively, at both transcript and protein levels, indicating a similar trend as the effect of TGFβ1 on myogenic differentiation. Our results suggest that highly expressed TGFβ/SMAD signaling significantly regulates RMS myogenic differentiation. In terms of the mechanism, our previous study showed that TGFβ1 interacts with the miRNA network to regulate the growth, apoptosis, and malignant behaviors of RMS tumor cells. However, TGFβ, as an immunoregulatory master of the tumor microenvironment has been less studied in soft tissue sarcomas, including RMS. Together, these data prompted us to further investigate the immune regulatory roles of TGFβ1 in the tumour microenvironment. Distinct immune cell types related to TGFβ1 and IFNγ expression in RMS IFNγ, an important cytokine, is critical for coordinating the antitumor immune response 37 . Activated IFNγ signaling upregulates PD-L1 expression and immune cell infiltration, which may improve the response to anti-PD-1 immunotherapy 38 . Next, we analyzed the correlation between TGFβ1 and IFNγ across 33 TCGA tumor types (Pan-cancer cohort). A statistically significant positive correlation was observed in 24 TCGA cohorts, including TCGA-SARC. Only the TCGA-HNSC cohort revealed a negative association. Publicly available gene expression data (GSE108022) from primary RMS samples were analyzed using the CIBERSORTx deconvolution algorithm to assess the relative immune fraction scores of distinct immune cell subtypes. Statistically significant positive or negative associations between TGFβ1 ( TGFB1 ) or IFNγ ( IFNG ) transcript levels and individual immune cell scores were assessed by Spearman correlation analysis. Our results revealed that two immune cell subtypes (naïve B cells and M1 Macrophages) had a significant positive correlation with TGFB1 , and four immune cell subtypes (activated NK cells, monocytes, resting mast cells, eosinophils) were negatively correlated with TGFB1 . Meanwhile, we found that IFNγ ( IFNG ) expression level was positively correlated with three subtypes (B cells naïve, M1 Macrophages, CD8 T cells) and negatively correlated with three immune cell subtypes (monocytes, resting mast cells, and M0 Macrophages). All significantly relevant immune cell subtypes (CD8 T cells, naïve B cells, M1&M0 Macrophages, activated NK cells, resting Mast cells, Monocytes, and Eosinophils) were selected for further analyses. Unsupervised hierarchical cluster analysis of the RMS cohort based on the eight selected immune cell subtypes revealed two RMS immune clusters, A and B (Fig. 2 C). Cases in cluster A were enriched for CD8 T cells, naïve B cells, and M1 Macrophages and had higher transcript levels of TGFB1 and IFNG (Fig. 2 D, E). Cluster B was divided into four subclusters (B1, B2, B3, and B4) due to the significant differences in M0 Macrophages and activated NK cells. To evaluate whether stratification into molecular immune clusters A and B is also applicable to all sarcomas, transcriptome datasets of the TCGA-SARC cohort were analyzed using CIBERSORTx. Unsupervised hierarchical clustering revealed a similar pattern (Supplementary Fig. 4A). Cluster A was significantly correlated with higher TGFB1 and IFNG (Supplementary Fig. 4B, C). We compared cluster A cases with cluster B2 in the RMS cohort or cluster B3 in the TCGA-SARC cohort. This was particularly common evidence that CD8 T cells and M1 macrophages were enriched in cluster A from both the RMS and TCGA-SARC cohorts (Fig. 2 F, Supplementary Fig. 4D). The other selected immune cell subsets revealed more heterogeneous characterizations in the various subclusters. Interestingly, an inverse finding was observed in activated NK cells and monocytes among clusters A and B2 or B3, indicating a heterogeneous immune niche in the tumor microenvironment of the RMS and SARC cohorts. Differences in immune gene signature and survival patterns related to immune phenotypes Immune-related gene expression signatures are associated with immune cell infiltration and clinical response to immunotherapy agents targeting immune checkpoints 39 . Hence, we evaluated the transcript levels of the immune checkpoint and IFNγ-related genes in the subclusters of the RMS and TCGA-SARC cohorts. Cases in cluster A from the RMS and SARC cohorts were enriched for the majority of the selected immune checkpoint and IFNγ-related genes. A 25-gene signature ( CD274, PDCD1, PDCD1LG2, CTLA4, HAVCR2, LAG3, IDO1, CXCL10, CXCL9, HLA_DRA, STAT1, IFNG, CD3D, IL2RG, NKG7, CIITA, HLA_E, CD3E, CXCR6, CCL5, GZMK, TAGAP, CD2, CXCL13 , and GZMB ) was shown in the hierarchical clustering heatmap (Fig. 3 A, B). The expression of each selected immune checkpoint gene and IFNγ immune signature scores were compared between clusters A and B2 or B3 in RMS and SARC cohorts (Fig. 3 C, D). In the RMS dataset, the higher gene expression of CD274, PDCD1, PDCD1LG2, and CTLA4 was observed in cluster A tumors as compared to cluster B2. Similarly, PDCD1, PDCD1LG2, CTLA4, HAVCR2 , and LAG3 were expressed at high levels in cluster A of the SARC cohort as compared to other clusters. Interestingly, CD274 (which encodes PDL1) was heterogeneously expressed in various clusters, which was also found in a previous study using another immune classification tool 27 . As expected, cluster A tumors have higher IFNγ immune signature scores in RMS and SARC cohorts. Recently, a “tumor inflammation signature” (TIS) was reported to predict the clinical benefit of anti-PD-1 therapy in several clinical trials 29 , 40 . TIS scores were also compared among the subclusters from RMS and TCGA-SARC cohorts. A high TIS score was observed in cluster A as compared to B2 or B3 in RMS and SARC cohorts (Supplementary Fig. 5A and B). In terms of clinical relevance, patients in cluster A with available survival data (TCGA-SARC) exhibited a favorable overall survival as compared to cluster B3 and B4 ( P = 0.048 and P < 0.0001, respectively) (Fig. 3 E). In addition, patients in cluster A had better disease-free survival than patients in cluster B4 ( P = 0.032) (Fig. 3 F). These data suggested that our newly established stratification for distinct molecular immune phenotypes can predict clinical outcomes in a soft tissue sarcoma cohort. Differences in gene and protein expression related to the immune phenotype To unravel relevant genetic alterations, we investigated the fraction of genome altered (FGA) and tumor mutational count (TMC) using cBioportal web-tool. The lowest FGA was found in cluster A as compared to the other clusters (P = 0.0012 Kruskal Wallis Test , Supplementary Fig. 4C ). The top 20 genes with the highest mutation frequency and most significant difference among the four clusters are listed (Supplementary Fig. 5D-F). Next, we identified 7485 and 5474 differentially expressed genes (DEGs) among clusters A and B2 or B3 in the TCGA-SARC and RMS cohorts, respectively. In addition, 21 differentially expressed proteins were also screened in the TCPA-SARC database. In total, nine genes were observed differentially expressed at the transcript and protein levels (Fig. 4 A). Analysis of protein-protein interaction according to the STRING database indicated that these DEGs were closely related to each other (Fig. 4 B). Significantly higher CHEK1 expression was found in subcluster B2 and B3 than in cluster A. The transcript level of CHEK1 was significantly elevated in both fusion-negative and fusion-positive tumors RMS compared to that in normal muscle tissue (Fig. 4 C). CHEK1 (Checkpoint kinase 1) is a serine/threonine-specific protein kinase encoded in humans by the CHEK1 gene. The transcript level of CHEK1 was positively correlated with the protein levels in the TCGA-SARC cohort (Fig. 4 D). Significantly higher CHEK1 transcript and protein expression were found in clusters B3 and B4 in TCGA-SARC cohorts, which exhibited unfavorable overall survival (Fig. 4 E, F). To further investigate the clinical relevance of CHEK1 in sarcoma, patients were divided into CHEK1 expression low and high groups. We performed Kaplan-Meier analysis for patients' disease-free survival (PFS) and overall survival (OS). We found that high expression of CHEK1 served as an unfavorable prognostic biomarker at both the transcript and protein levels (Fig. 4 G-J). Association between CHEK1 and tumor immune microenvironment in soft tissue sarcomas Next, we explored the associations between CHEK1 expression levels and tumor-infiltrating immune cells and immune-related genes in the TCGA-SARC and RMS cohorts. The correlation coefficient of 22 immune cells with CHEK1 gene and protein expression is shown in Fig. 5 A and B. For CHEK1 transcript and protein levels, Spearman correlation analysis revealed nine immune cell subtypes with significant negative associations (M2 macrophages, Monocytes, resting CD4 memory T cells, naïve B cells, CD8 T cells, activated NK cells, gamma delta T cells, regulatory T cells, and M1 Macrophages). Three subtypes (M0 macrophages, Plasma cells and activated dendritic cells) were found to have a significant positive correlation with CHEK1 transcript level. Regarding CHEK1 protein level, three subtypes (M0 macrophages, Plasma cells and activated CD4 memory T cells) were positively correlated. The transcript and protein levels of CHEK1 were also found to have statistically significant negative associations with the majority of selected IFNγ-related and expanded immune gene signatures (Fig. 5 C, D). We divided the patients in the TCGA-SARC and RMS cohorts into two groups depending on the CHEK1 transcript or protein levels. In the RMS cohort, significantly higher scores for naïve B cells and follicular T helper cells were detected in the CHEK1 high group. Statistically significant low scores for resting CD4 + memory T cells and resting Mast cells were observed in the CHEK1 high group (Fig. 5 E). In the TCGA-SARC cohort, the scores for six subtypes of immune cells (resting CD4 memory T cells, regulatory T cells, gamma delta T cells, Monocytes, M1 Macrophages, and M2 macrophages) were significantly different between the CHEK1 transcript low and high levels (Fig. 5 F). In terms of CHEK1 protein levels, nine immune cell subtypes (naïve B cells, memory B cells, resting CD4 memory cells, regulatory T cells, activated NK cells, Monocytes, M1 Macrophages, M2 macrophages, and resting Mast cells) were significantly different between the CHEK1 low and high group (Fig. 5 G). All relevant immune cell subtypes were enriched in the CHEK1 low group. We also compared the relative expression of IFNγ-related and expanded immune gene signatures between the CHEK1 low and high groups (Fig. 5 H, I). The results revealed that patients with CHEK1 low expression had significantly higher IFNγ-related and expanded immune gene signature scores, which predicts the clinical response to PD-1 target therapy in various cancers. CHEK1 predicted clinical prognosis and molecular subtypes for RMS To validate the expression and clinical relevance of CHEK1 for RMS in situ , we analyzed CHEK1 expression in an independent cohort of 33 cases (Hefei-cohort; Supplemental Table 2) by immunohistochemistry. A heterogeneous staining pattern ranging from absence and moderate to prominent staining in tumor cells was observed in the RMS tissues (Fig. 6 A). High CHEK1 protein levels were detected in the 14 patients. We did not observe any significant difference between CHEK1 low and high subgroups concerning histological types and clinicopathological characteristics except for disease recurrent status (Fig. 6 B, C). We performed Kaplan-Meier analysis for relapse-free survival of patients to confirm that the CHEK1 high expression subgroup was significantly associated with unfavorable clinical outcomes (Fig. 6 D). To complement the transcriptional analysis of the TIME between subgroups with different CHEK1 expressions, we analyzed the densities of CD4+, CD8+, T-regulatory cells (Foxp3+), tumor-associated macrophages (CD163 + TAM), and PD-L1 expression in RMS specimens. (Fig. 6 E, Supplementary Fig. 5A, B) Using digital image analysis, we found higher densities of main tumor-infiltrating immune cells (CD4+, CD8+, T-reg cells) in the CHEK1 low tumor compared to the CHEK1 high expression. Similar to the immune-checkpoint gene CD274 detected through our analysis of publicly available data, mIHC analysis revealed that the mean PD-L1 expression was not significantly different between the subgroups with low and high CHEK1 expression (Fig. 6 F). In line with a previous study 41 , TAMs predominated the sarcoma immune microenvironment with the highest intratumor density in the RMS specimens. However, we did not observe any significant differences between the CD163 + positive cells of the two RMS subgroups. We also discovered a very small population of CD4 + Foxp3 + Tregs in both subgroups of the RMS specimens. Together, these data suggested that CHEK1 expression predicted the clinical outcome and was correlated with tumor-infiltrating immune cells in RMS in our real-world cohort. CHEK1 expression evaluation in soft tissue sarcomas and correlated with immune cell densities To clarify more precisely the associations between CHEK1 expression and tumor-infiltrating immune cells in soft tissue sarcomas, we examined tumor samples of formalin-fixed paraffin-embedded tissue microarray (TMA) from 91 patients with soft tissue sarcomas. We observed a range of undetectable to prominent staining intensity in tumor cells by IHC staining (Fig. 7 A). CHEK1 staining intensity was quantified using QuPath software for patients. Patients were divided into two subgroups according to the staining intensity with CHEK1 high (n = 50) and CHEK low (n = 41) for further analysis. We compared the distribution of CHEK1 expression across histological subtypes and found that most synoviosarcoma tumors revealed higher CHEK1 expression (Fig. 7 B). All available clinico-pathological features were compared between CHEK high and CHEK low groups. In line with our results from the RMS cohort data, Chi-square analysis showed that high CHEK1 expression was significantly associated with advanced clinical stage ( P = 0.006) (Fig. 7 C). Unfortunately, follow-up survival records of the TMA cohort were unavailable for further survival analysis. To complement the transcriptional analysis of associations between CHEK1 expression levels and tumor-infiltrating immune cells, we performed an Opal multiplex IHC tissue staining assay to characterize the soft tissue sarcoma TME (Fig. 7 D). The densities of T cells (CD3+), CD4 + T cells (CD4+), CD8 + T cells (CD8+), CD19 + B cells (CD19+), T-regulatory cells (Foxp3+), pan-macrophages (CD68+), M2-like macrophage (CD163+), and PD-L1 in each competition for tumor sections were calculated (Fig. 7 E). A significant increase in the overall density of Foxp3, CD19+, CD68+, CD163 + and CD68 + CD163 + cells was observed in the CHEK low group tumors. However, infiltration of T cells (CD3+, CD4+, and CD8+) and PD-L1 densities were not significantly different between the CHEK high and CHEK low groups. Altogether, our digital image analysis strongly supports the immune deconvolution results that CHEK1 serves as an unfavorable prognostic biomarker related to diminished cytotoxic immune cell infiltration in STS. Discussion We previously reported that the TGFβ1 signaling pathway contributes to the growth and differentiation of RMS 32 , immune cell subsets based on TGFβ1 and IFNγ expression in RMS have not been investigated. Here, utilizing a public oncogenic database, we revealed that TGFβ/SMAD signaling is highly expressed in human rhabdomyosarcoma and regulates myogenic transcription factors. In addition, we established a new stratification model based on RNA expression profiling for RMS with distinct molecular immune phenotypes, which was validated in the TCGA-SARC cohort. Predefined immune gene signatures related to ICB responses were evaluated as statistically significant among distinct subgroups. Higher IFNγ-related and expanded immune gene signatures contribute to an improved prognosis. Importantly, the assessment of the variation in transcript and protein expression of DEGs with higher or lower cytotoxic immune phenotypes highlighted that CHEK1 served as an unfavorable biomarker and is related to reduced cytotoxic immune cell infiltration in soft tissue sarcomas. Finally, the density and distribution of tumor-infiltrating immune cells in CHEK1 low- and high-expression soft tissue sarcomas were evaluated using multiplex immunofluorescence staining. Elevated expression of CHEK1 in sarcomas was correlated with lower infiltrating immune cells, indicating that CHEK1 is a predictor of clinical prognosis and a potential novel adjuvant immunotherapy target for sarcoma. TGFβ has three isoforms, TGFβ1, TGFβ2, and TGFβ3, which belong to a 33-member cytokine superfamily. TGFβ signaling has widespread and diverse effects on cell proliferation, differentiation, adhesion, migration, metabolism, and immune homeostasis. TGFβ1, as a modulator of RMS cell differentiation, plays a significant role in tumor growth and progression 35 , 42 , 43 . In the present study, RNA-seq data and IHC staining of tumor tissues from RMS revealed that canonical TGFβ/SMAD signaling was highly expressed in human RMS. Using gain or loss of function experiments, we first verified the effects of TGF-β1/SMAD2 on myogenic differentiation in RMS-RD cells. The interaction between TGFβ1 and IFNγ plays a pivotal role in regulating antitumor host immunity. Activated IFNγ signaling upregulates PD-L1 expression and immune cell infiltration, which may improve the response to anti-PD-1 immunotherapy. RMS is a type of sarcoma with a heterogeneous group of soft-tissue tumors. Immunotherapy in RMS has limited effectiveness so far. Several previous studies have investigated the tumor microenvironment (TME) composition in different soft tissue sarcomas to understand the immune niche that maintains the tumor and how to mediate cancer immune escape. In 2020, Chen and colleagues 41 delineated the immune characteristics of specific TMEs in RMS and undifferentiated pleomorphic sarcomas (UPS) and revealed an immunosuppressive TME dominated by tumor-associated macrophages. They speculated that in situ T-cell distribution in the STS TME could overcome the immunosuppressive niche and play a predominant role in ICB responsiveness. Interestingly, another study established an immune-based classification of TME composition using the microenvironment cell populations-counter method in sarcoma. They reported three main immune phenotypes in soft tissue sarcomas: immune-low, immune-high, and highly vascularized subgroups, with different clinical outcomes and response rates to ICB. Their study defined a subgroup of sarcoma patients who benefited from ICB, marked by at high density of B cells and the presence of tertiary lymphoid structures (TLS) 27 . The present study differs from previous reports in that we conducted an integrative deconvolution analysis of multi-omics data from RMS to cluster the patients into subgroups depending on the immune cells related to TGFβ1 and IFNγ. In addition, we used the public dataset TCGA-SARC to validate our new stratification model and evaluate the prognostic outcomes of subgroups with lower or higher cytotoxic immune phenotypes. Consistently, the immune gene signature and tumor inflammation signature were enriched in cluster A, which is an accurate and independent predictive biomarker for ICB clinical outcome. We identified CHEK1 as a critical predictive biomarker in distinct TIME and as a potential therapeutic target to increase TILs and improve the clinical efficacy of ICB therapy in RMS and STS. CHEK1 is a crucial mediator of cell cycle progression in response to DNA damage. The critical function of CHEK1 in normal and germinal center B-cell development, lymphomagenesis, and survival has been reported 44 , 45 . Therapeutic CHEK1 inhibition combined with BCR-signalling blockade in patients might improve the efficacy in eradicating B-cell lymphoma and leukemia cells 45 . Recently, several efforts have tested CHEK1 signaling drugs in combination with ICB in other human malignancies 46 – 48 . One study by Sato et al. 49 highlights the critical role of CHK1 in regulating PD-L1 expression and immune response, indicating the translational application of CHEK1 agents in combination with ICB therapy. Most studies have demonstrated that the cell-cycle-related kinase function of CHEK1 is a promising therapeutic strategy for STS 50 – 53 . However, few studies reported the association between CHEK1 expression and immune cell infiltration in STS. Notably, the immune landscape of STS exhibits considerable heterogeneity across different histological subtypes, with implications for the treatment response and prognosis. By applying an integrative analysis of multi-omic expression profiling, our findings unraveled the heterogeneity and complexity of the immune microenvironment across distinct subclusters and significantly correlated with CHEK1 expression. In addition, we provide strong experimental evidence that highly infiltrating immune cells in STS were observed in the subgroup of patients with CHEK1 low expression, which revealed a favorable clinical prognosis. Nevertheless, the prognostic significance of the immune cell density and spatial location in RMS and STS after ICB therapy is not completely understood. Therefore, further studies, including a larger cohort with a more extended follow-up period, ideally from prospective clinical trials, are required to confirm our findings. Conclusion In this report, we validated TGFβ1 signal function in RMS myogenic differentiation and established a novel risk assessment strategy for RMS and STS patients. Multi-omics expression profiling and independent cohort data demonstrated that CHEK1 is an unfavorable prognostic biomarker related to immunosuppressive phenotypes in soft tissue sarcomas. We speculate that CHEK1 may be a promising therapeutic target alone or in combination with ICB immunotherapy. Abbreviations Differentially expressed genes DEGs Disease-free survival DFS Electronic Chemistry Laboratory ECL Fraction of genome altered FGA Gene set enrichment analysis GSEA Immune checkpoint blockade ICB Immunohistochemistry IHC Interferon-γ IFNγ multiple immunohistochemistry mIHC Overall survival OS Rhabdomyosarcoma RMS RNA sequencing RNA-seq Short Tandem Repeat STR Soft tissue sarcoma STS Transforming growth factor beta 1 TGFβ1 Tumor inflammation signature TIS Tumor microenvironment TME Tumor mutation burden TMB Tumor mutational count TMC Tumor-infiltrating immune cells TIIC Undifferentiated pleomorphic sarcomas UPS World Health Organization WHO Declarations Data availability The genomic data and survival information used in this study are publicly available from the GDC Portal (portal.dc.cancer.gov, cohort TCGA-SARC) and the GEO database (GSE108022). The proteomics data are publicly available from The Cancer Proteome Atlas (TCPA). All the other data are available from the corresponding authors upon reasonable request. Author contributions C.R., Y.L., and F.X. analyzed multi-omics data and performed statistical analyses. C.R. and S.W. conceived the study and designed the experiments. X.Z., J.Z., Z.X.,J.W., L.C., Z.G., Z.Z, and J.A. performed the experiments. Y.L. and X.Y. collected clinical samples and information. C.R., Y.L., and F.X. wrote the manuscript, J.S. and J.H. contributed to manuscript review and conceptual advice. C.R., Q.Z. and S.W. directed the project and were responsible for funding acquisition. All authors read and approved the final manuscript. Fundings This work was supported by the National Natural Science Foundation of China (82103121), the Natural Science Foundation of Jiangsu Province (BK20200878), Key Projects of Students Academic Research Foundation of Soochow University (KY2023093A, KY2024273B), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We thank all the patients and their families at The First Affiliated Hospital of University of Science and Technology of China (USTC) for their contribution of biological specimens and clinical information used in this study. Ethics approval and consent to participate The research protocol was approved by the Ethics Committee of the First Affiliated Hospital of USTC (Ethic No 2024/RE256) in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants or family members in the study. Consent for publication Not applicable. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-5923386","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":411035721,"identity":"fbfcb2c4-2311-4d43-aa31-bbf761616998","order_by":0,"name":"Chao 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Zhang","suffix":""},{"id":411035754,"identity":"56109e70-7fa6-4562-875a-0d8949cdcc55","order_by":15,"name":"Shouli Wang","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Shouli","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-29 10:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5923386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5923386/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41698-025-01064-8","type":"published","date":"2025-08-01T16:20:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75705569,"identity":"fc541f60-fb97-4263-8513-3d10329ffc48","added_by":"auto","created_at":"2025-02-07 10:11:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1723419,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHighly expressed TGFβ1 regulates the myogenic transcription factors in RMS. (A) \u003c/strong\u003eHeatmap showing normalized expression for TGFβ signalling genes from RNA sequencing of normal muscle, fusion-negative (Fusion N), and fusion-positive （Fusion P）RMS samples. \u003cstrong\u003e(B) \u003c/strong\u003eNormalized expression for canonical TGFβ/SMAD signalling associated genes of normal muscle, Fusion N, and Fusion P RMS samples. \u003cstrong\u003e(C)\u003c/strong\u003eImmunohistochemistry showing TGF-β1 expression and H\u0026amp;E stained sections of representative primary ERMS, ARMS, and PRMS samples. Scale bar, 100 μm. Quantitative real-time PCR analysis of the myogenic transcription factors (MyoD1, Myogenin, Myosin, and Desmin) in RD cells with Transient TGFβ1 silencing \u003cstrong\u003e(D)\u003c/strong\u003e and with exogenous cytokine TGFβ1 \u003cstrong\u003e(E).\u003c/strong\u003e \u003cstrong\u003e(F-G) \u003c/strong\u003eWestern blot and quantification analysis of the myogenic transcription factors in RD cells with Transient TGFβ1 silencing and with exogenous cytokine TGFβ1. Data are presented as mean values ± SEM. Two-sided t test（**p \u0026lt; 0.01, ***p \u0026lt; 0.001,）\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5923386/v1/fbd7a724a91dc136d3a53bb9.png"},{"id":75705814,"identity":"b7c337b2-381f-4e03-b200-d0ef4ce9b627","added_by":"auto","created_at":"2025-02-07 10:19:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1106455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstablishment of distinct immune cell types related to TGFβ1 and IFNγ expression in RMS. (A)\u003c/strong\u003e Spearman correlation analysis between TGFβ1 and IFNγ across 33 TCGA tumour types (Pan-cancer cohort). (\u003cstrong\u003eB)\u003c/strong\u003e Heatmap showed the significantly positive and negative spearman correlation coefficients between either TGFβ1 (TGFB1) or IFNγ（IFNG）transcript levels and relative abundance of eight immune cell types assessed by the CIBERSORTx deconvolution algorithm. (\u003cstrong\u003eC)\u003c/strong\u003e Unsupervised hierarchical cluster analysis revealed two main RMS immune clusters and five subclusters. (\u003cstrong\u003eD-E)\u003c/strong\u003e Violin plots described the transcript levels of TGFB1 and IFNG among the five subclusters. F. Relative fraction scores of selected eight immune cell types were compared between Cluster A and B2 in box-whisker plots. In all graphs box-whisker plots shows individual samples, group median and min-max values. Two-sided t test（*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001）\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5923386/v1/1589eb4d281fca2d476d2377.png"},{"id":75706983,"identity":"8573862e-dd41-4258-a179-fb5a7bdb2832","added_by":"auto","created_at":"2025-02-07 10:27:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1758494,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStratification for distinct molecular immune phenotypes correlated with immune gene signature and patient survival. \u003c/strong\u003eHeatmaps show expression\u003cstrong\u003e \u003c/strong\u003eof\u003cstrong\u003e \u003c/strong\u003ea 25-gene signature related to immune checkpoints among distinct subclusters from RMS cohort \u003cstrong\u003e(A) \u003c/strong\u003eand TCGA-SARC \u003cstrong\u003e(B)\u003c/strong\u003e. \u003cstrong\u003eC\u003c/strong\u003e Relative expression scores of selected immune genes and signatures were compared between cluster A and B2 from RMS cohort in box-whisker plots.\u003cstrong\u003e D\u003c/strong\u003e Relative expression scores of selected immune genes and signatures were compared among cluster A, B2, B3, and B4 from TCGA-SARC cohort in box-whisker plots. Overall survival \u003cstrong\u003e(E)\u003c/strong\u003e and disease-free survival \u003cstrong\u003e(F)\u003c/strong\u003e of cluster A, B2, B3, and B4 from TCGA-SARC cohort was estimated by Kaplan-Meier plots and two-sided log-rank test.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5923386/v1/2aa14640f65b502ddca1d723.png"},{"id":75705572,"identity":"b6044f27-45e8-4321-94f6-fe1c750598ad","added_by":"auto","created_at":"2025-02-07 10:11:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":968323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression profiling analysis revealed CHEK1 was highly expression in\u003c/strong\u003e \u003cstrong\u003esubcluster with unfavourable survival. (A)\u003c/strong\u003e Venn diagrams show the amount of DEGs and DEPs between cluster A and subcluster B2 (RMS cohort) or B3 (SARC cohort). \u003cstrong\u003e(B)\u003c/strong\u003eSchematic presentation of protein-protein interaction network according to the STRING database. \u003cstrong\u003e(C)\u003c/strong\u003e Normalized expression for CHEK1 of normal muscle, Fusion N, and Fusion P RMS samples are compared in box-whisker plot. \u003cstrong\u003e(D)\u003c/strong\u003eDot plot illustrates the significantly positive correlation between CHEK1 protein and transcript levels in tumours of TCGA-SARC cohort. \u003cstrong\u003e(E-F) \u003c/strong\u003eBox-whisker plots show the transcript and protein levels among cluster A, B2, B3, and B4 from TCGA-SARC cohort. Kaplan-Meier plots show an unfavourable overall survival \u003cstrong\u003e(G, I)\u003c/strong\u003e and disease-free survival \u003cstrong\u003e(H, J) \u003c/strong\u003efor patients with higher transcript and protein level of CHEK1 from TCGA-SARC cohort. Survival analyses were performed with Kaplan-Meier estimates and two-sided log-rank tests.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5923386/v1/809bbc1bd6a587e4c4a88cc8.png"},{"id":75705574,"identity":"66e602db-5528-4dfc-80d8-95ac334ec8f2","added_by":"auto","created_at":"2025-02-07 10:11:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1477211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between CHEK1 expression levels and tumour-infiltrating immune cells and immune-related genes in TCGA-SARC and RMS cohorts\u003c/strong\u003e. Heatmap showing the spearman correlation coefficients between CHEK1 transcript (A) and protein (B) expression levels and relative abundance of 22 immune cell types. Heatmap showing the spearman correlation coefficients between CHEK1 transcript (C) and protein (D) expression levels with IFNγ-related genes as well as expanded immune gene signatures. E Violin plot showing the significant differences of four immune cell types between CHEK1 transcript low and high groups in RMS cohort. Significant differences of immune cell types are compared between CHEK1\u003csup\u003elow\u003c/sup\u003e and CHEK1\u003csup\u003ehigh\u003c/sup\u003e group in transcript (F) and protein (G) levels. Violin plots showing the significant expression differences of IFNγ-related genes as well as expanded immune gene signatures between CHEK1\u003csup\u003elow\u003c/sup\u003e and CHEK1\u003csup\u003ehigh\u003c/sup\u003e group in transcript (H) and protein (I) levels. Two-sided\u003cem\u003e t\u003c/em\u003e test（*p \u0026lt; 0.05, **p \u0026lt; 0.01,\u0026nbsp; ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001）\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5923386/v1/e284466d8afdbc8a990ad38e.png"},{"id":75705581,"identity":"c119a0ce-866e-460b-b7ee-08e5395279e9","added_by":"auto","created_at":"2025-02-07 10:11:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1840709,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCHEK1 predicted clinical prognosis and correlated with a low infiltration immune subtype in Hefei-RMS cohort. A \u003c/strong\u003eRepresentative images of\u003cstrong\u003e \u003c/strong\u003eImmunohistochemical (IHC) staining for CHEK1 in RMS tumour tissues with the low and high Immunoreactivity. The distribution of histology types (\u003cstrong\u003eB\u003c/strong\u003e) and recurrence status (\u003cstrong\u003eC\u003c/strong\u003e) are compared between CHEK1 expression low and high groups. \u003cstrong\u003eC\u003c/strong\u003e Kaplan-Meier plot shows an unfavourable relapse free survival for patients with high CHEK1 expression from Hefei-RMS cohort. D Multiplex IHC staining for RMS tumours with CHEK1 low and high expression, CD4 (green), CD8 (cyan), Foxp3 (orange), CD163 (red), PDL1 (yellow), DAPI staining is shown in blue. E Densities of immune cells and PD-L1 are compared between CHEK1 expression low and high groups. Data are presented as mean values ± SEM. Two-sided t test (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ns, nonsignificant).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5923386/v1/e651e3f7c19ba0470d22d229.png"},{"id":75705815,"identity":"6976da70-ab55-425c-9951-4a878ab8b147","added_by":"auto","created_at":"2025-02-07 10:19:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2128342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCHEK1 expression evaluation by Immunohistochemical staining and correlated with immune cell densities in soft tissue sarcomas\u003c/strong\u003e \u003cstrong\u003etissue microarrays (A)\u003c/strong\u003e Representative images of Immunohistochemical (IHC) staining for CHEK1 in RMS tumour tissues with the low and high Immunoreactivity. The distribution of distinct histology types \u003cstrong\u003e(B)\u003c/strong\u003e and clinical stages \u003cstrong\u003e(C)\u003c/strong\u003e are compared between CHEK1 expression low and high groups. \u003cstrong\u003e(D)\u003c/strong\u003e A representative multiplex IHC staining images for tissue microarrays. The densities (immune cells/mm\u003csup\u003e2\u003c/sup\u003e) of T cells (CD3+), CD4+ T cells (CD4+), CD8+ T cells (CD8+), CD19+ B cells (CD19+), T-regulatory cells (Foxp3+), pan-macrophage (CD68+)， M2-like macrophage (CD163+), and PD-L1 in each compete for tumour sections were calculated.\u0026nbsp; \u003cstrong\u003e(E)\u003c/strong\u003e Densities of immune cells and PD-L1 are compared between CHEK1 expression low and high groups. Data are presented as mean values ± SEM. Two-sided t test (*p \u0026lt; 0.05,\u0026nbsp; ns, no significant).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5923386/v1/18e009cf09a41ecaa9982ab1.png"},{"id":88268097,"identity":"f4febded-0c07-43cc-9777-0842fe96f7c1","added_by":"auto","created_at":"2025-08-04 16:48:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14868984,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5923386/v1/7d0c6caa-2fbf-4f85-a37f-5af9373169c5.pdf"},{"id":75705587,"identity":"a317e8fd-6126-4958-9de7-119e33c955d5","added_by":"auto","created_at":"2025-02-07 10:11:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2928242,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYINFORMATION.docx","url":"https://assets-eu.researchsquare.com/files/rs-5923386/v1/47d805891143c07a56a15216.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative bioinformatics analysis and experimental validation identify CHEK1 as an unfavorable prognostic biomarker related to immunosuppressive phenotypes in soft tissue sarcomas","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoft tissue sarcoma (STS) is a heterogeneous group of mesenchymal tumors encompassing more than 60 histological subtypes. Rhabdomyosarcoma (RMS) is one of the most common STS in children and adolescents, representing 5% of all childhood cancers \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. RMS is differentiated from primitive mesenchymal stem cells, which cannot fully differentiate into skeletal muscle. It can occur anywhere in the human body, with the head and neck being the most common primary site \u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. RMS is divided into four subtypes based on its clinical and pathological characteristics: embryonic rhabdomyosarcoma (ERMS), alveolar rhabdomyosarcoma (ARMS), pleomorphic rhabdomyosarcoma (PRMS) and sclerosing rhabdomyosarcoma (SSRMS) \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eERMS and ARMS are two major histological subtypes of RMS. ERMS occurs more commonly in younger children and has a more favorable prognosis. Histologically, ERMS showed primitive oval to spindle cells with minimal cytoplasm, resembling immature skeletal myoblasts. ERMS has a wide range of genetic alterations, termed PAX-fusion-negative or fusion-negative RMS \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. ARMS occurs mainly in adolescents with poorer prognoses and characteristically exhibits an alveolar pattern with cells distributed around an open central space. The importance of the PAX-FOXO1 fusion has been highlighted in the ARMS pathomechanism \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Despite advances in multi-disciplinary treatment for RMS and STS, consisting of surgery, irradiation, chemotherapy, and targeted therapy, the clinical prognosis of patients has only improved slightly, and promising curative treatment remains a significant challenge \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ability to complete muscle differentiation is impaired in RMS. Clinically, RMS tumors are diagnosed based on the expression of skeletal markers, such as Myogenin, MyoD, and desmin, as well as skeletal α-actin and vimentin \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Transforming growth factor beta 1 (TGFβ1) is the most potent inhibitor of myogenic differentiation in RMS and is central to immune suppression within the tumor microenvironment \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Although immune checkpoint inhibition has demonstrated promise in improving clinical outcomes for certain cancers, soft tissue sarcomas remain limited in effectiveness in immune checkpoint blockade (ICB) based on current clinical trials \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The current revolution in understanding the molecular landscape clarifies that STS is \u0026lsquo;non-immunogenic\u0026rsquo; with a low tumor mutation burden (TMB) and PD-L1 expression \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Interferonγ (IFNγ) is a cytokine pivotal in regulating PD-L1 expression and antitumor immunity \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Owing to the rarity and heterogeneity of STS, few studies have investigated the tumor microenvironment (TME) and tumor-infiltrating immune cells (TIIC) in different STS histologies, including RMS. A recent study used global gene expression data to define molecular immune signatures stratify STS into distinct immune phenotypes and identified a subpopulation of patients with improved survival and a high response rate to PD1 inhibitor therapy \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In the interim, immune checkpoint blockade has few therapeutic benefits in STS patients. Therefore, it is urgent to explore combinations for a more efficient immunomodulator.\u003c/p\u003e \u003cp\u003eHere, we started this study with the expression and differentiation regulation of TGFβ1 in RMS. We developed a novel molecular classification of RMS and STS based on immune cell subsets related to TGFβ1 and IFNγ expression, revealing distinct immune phenotypes. Moreover, we compared multi-omics expression profiles across subgroups of RMS and STS to identify CHEK1 as an unfavorable prognostic biomarker related to immunosuppressive phenotypes. We also used multiple immunohistochemistry (mIHC) staining assays to assess the correlation between CHEK1 and tumor-infiltrating immune cells. Main deliverables can potentially improve risk assessment for STS patients and increase antitumor immunity from a combined targeting of CHEK1 therapy.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient cohorts and samples\u003c/h2\u003e \u003cp\u003eA total of patients with RMS were enrolled, including 33 real-world samples from our institute (titled the Hefei-RMS cohort). The tissue samples used in this study were obatined from patients with rhabdomyosarcoma diagnosed between 2016\u0026ndash;2019, who were diagnosed with RMS according to the World Health Organization (WHO) guidelines. Paraffin-embedded RMS tissues were collected from The First Affiliated Hospital of University of Science and Technology of China (USTC) for immunohistochemistry and immunofluorescence staining. Written informed consent was obtained from all participants or family members in the study. The research protocol was approved by the Ethics Committee of the First Affiliated Hospital of USTC \u003cem\u003e(Ethic No 2024/RE256\u003c/em\u003e) in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExpression profiling and clinical datasets\u003c/h3\u003e\n\u003cp\u003eRNA-seq data for 106 samples, including five normal muscles and 101 Rhabdomyosarcomas, were downloaded from the Gene Expression Omnibus (GSE108022). The TCGA-SARC cohort RNA, protein expression, and clinical data were downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e in December 2022. The curated gene sets for TGFBETA_SIGNALING_PATHWAY were obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Immune checkpoint, IFNγ-related gene sets, and the tumor inflammation signature (TIS) were obtained from the published literature \u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Fraction genomic alterations and mutation frequencies were analyzed using the cBioportal tool.\u003c/p\u003e\n\u003ch3\u003eCell culture, treatment and transfection\u003c/h3\u003e\n\u003cp\u003eHuman RMS cell line RD, was cultured in DMEM supplemented (HyClone, Utah, USA) with 10% fetal bovine serum (FCS, Gibco Life Technologies, Carlsbad, CA, USA) and 1% penicillin/streptomycin (Corning, Discovery Boulevard Manassas, VA, USA) at 37\u0026deg;C in a 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere (Thermo Fisher Scientific, Woodward St, Austin). Short Tandem Repeat (STR) genotyping was used to validate cell line authenticity prior to performing the described experiments. Mycoplasma testing was done every three months and no mycoplasma was detected. siRNA for TGFβ1, SMAD2, (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e, Zorin, Shanghai, China) were transfected by using Lipofectamine\u0026trade; RNAiMAX Transfection Reagent (Life Technologies). Cells transfected with nonsense siRNA served as controls. RNAs and proteins were extracted 48 post-transfection and used for further analyses. For TGFβ1 stimulation, RD cells were stimulated by TGFβ1 cytokine (10 ng/mL, Peprotech, 5 Cedarbrook Drive, Cranbury, NJ, USA) to each well for 48h, and then RNAs and proteins were extracted. The SMAD2 transient transfection plasmid was transfected into RD cells using Lipofectamine\u0026reg; 3000 (Life Technologies) to mimic SMAD2. RNAs and proteins were extracted 48h post-transfect and used for further analysis.\u003c/p\u003e\n\u003ch3\u003eHistology and Immunohistochemical staining\u003c/h3\u003e\n\u003cp\u003eTissue microarrays were purchased from Bioaitech Company (Xi\u0026rsquo;an, China), comprised 91 soft tissue sarcomas. RMS and soft tissue sarcoma tumors were fixed in 4% PFA, processed, and embedded in paraffin. Histological sections were stained with Hematoxylin and Eosin or immunohistochemical staining. The tissue sections were deparaffinized and rehydrated using the following steps: melting the wax at 65\u0026deg;C for 2 h, 3 \u0026times; 5 min xylene, 2 \u0026times; 3 min 100% ethanol, 3 min 95% ethanol, 3 min 75% ethanol, and finally rinsed with water. Tissue sections were incubated with 10 mM sodium citrate buffer (pH 6.0) (Boster, Wuhan, China) in a microwave twice for 15 min each. After antigen retrieval, use 3% peroxidase solution to block endogenous enzymes (Chemical Technology, Jiangsu Yonghua, China) for 10 minutes, block with 5% BSA (Boster) for 20 min, and incubate the primary antibody overnight at 4\u0026deg;C. The sections were incubated with biotinylated anti-rabbit secondary antibody (Boster) for 2 h. A solution of streptavidin-HRP (Boster) and peroxidase substrate (DAB) (MXB Biotechnologies, Fuzhou, China) was used to generate signals in tissue sections. CHEK1 staining scores were automatically determined using QuPath \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e (version 0.3.2.), reflecting positive cells and staining intensity. Specific antibodies are indicated and outlined in \u003cb\u003eSupplementary table 2.\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eReal-time PCR\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from the cells using the NucleoSpin RNA extraction kit (Macherey-Nagel, D\u0026uuml;ren Neumann Neander Str, Germany). miRNA and total RNA were determined according to the kit instructions. RNA was isolated from human tissues using the MagMAX\u0026trade; FFPE DNA/RNA Ultra Kit (Thermo Fisher Scientific). Using Reverted 1st cDNA synth kit (Thermo Fisher Scientific) for reverse transcription. Quantitative real-time PCR was carried out using the FS Essential DNA Green Master (Roche, F. Hoffmann-La Roche AG Konzern-Hauptsitz Grenzacherstrasse 124 CH-4070 Basel, Swiss) and the following cycling condition: 95\u0026deg;C for 10 min, 40 cycles of 95\u0026deg;C for 20s, 60\u0026deg;C for 20s and 72\u0026deg;C for 20s, 37\u0026deg;C for 30s. The mRNA expression levels of detected genes were standardized to GAPDH. The relative quantification was calculated using the 2\u003csup\u003e\u0026minus;△△CT\u003c/sup\u003e method. Primers designed for RT-PCR in this research are listed in \u003cb\u003eSupplementary table 3\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003e10x cell lysis buffer (Cell Signaling Technology, MA, USA) was used to extract proteins from cultured cells and supplement them with protease inhibitors (Cell Signaling Technology). For western blotting, 20 \u0026micro;g of total protein was measured by BCA analysis (Beyotime, Shanghai, China) was separated by sodium dodecyl sulfate (SDS) polyacrylamide gel electrophoresis and transferred to a PVDF membrane (Merck Millipore, MA, USA). The membrane was blocked with 5% skimmed milk (Tris-buffered saline/0.1% Tween 20) in TBST. The membrane was incubated with the primary antibody overnight at 4\u0026deg;C, and then the HRP-conjugated secondary antibody (1:5000; Boster, Wuhan, China) was incubated in skimmed milk. Electronic Chemistry Laboratory (ECL) test kits were used for signal development (EpiZyme, Shanghai, China). Specific antibodies are indicated and outlined in \u003cb\u003eSupplementary table 4.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmune cell scores deconvolution\u003c/h3\u003e\n\u003cp\u003eAbsolute immune cell infiltration levels from gene expression were predicted by CIBERSORTx using the website server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersortx.stanford.edu\u003c/span\u003e\u003cspan address=\"https://cibersortx.stanford.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The TPM-normalized expression matrix datasets from GSE108022 and TCGA-SARC were used as the input mixture files, and the relative levels for the 22 immune cells were computed by LM22 gene signature.\u003c/p\u003e\n\u003ch3\u003eUnsupervised Hierarchical Clustering\u003c/h3\u003e\n\u003cp\u003eTranscriptome count data of genes were ln(x\u0026thinsp;+\u0026thinsp;1)-transformed and clustered using correlation distance and average linkage. ClustVis, a web tool for visualizing multivariate data, was utilized for unsupervised hierarchical clustering and to visualize data in a heatmap \u003csup\u003e[63]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultiple Immunohistochemistry staining and image analysis\u003c/h2\u003e \u003cp\u003eWe designed a 5-plex immunofluorescence panel for RMS tissue and an 8-plex panel for sarcoma TMA to characterize the tumor immune microenvironment. Candidate commercial antibodies intended for mIF staining were first validated by IHC using RMS FFPE tissue to confirm optimal staining intensity, specificity, and signal-to-noise ratio. mIF was performed according to the Opal Multiplex IHC assay protocol (Akoya Biosciences) as previously described \u003csup\u003e(31)\u003c/sup\u003e. The antibody panel was then stained in the following order. Each primary antibody was incubated for 60 min, followed by 10-min incubation with a secondary antibody (Opal Polymer Anti-Rabbit HRP Kit, Akoya Biosciences), application of the Opal fluorophore (OPAL Fluor, Akoya Biosciences), and incubation for 10 min at room temperature. Detailed information is provided in the supplementary Table. mIF images were scanned using a Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences). The fluorescent images were unmixed and analyzed to quantify the mean fluorescent intensity for each marker using inForm Advanced Image software (inForm: 2.5.1, Akoya Biosciences).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software 4.2 and GraphPad Prism 10.2 (ID: GPS-1928733-EJSL-94BFE, San Diego, CA, USA). The two-tailed Wilcoxon-Mann-Whitney non-parametric test was performed to compare quantitative variables across two groups or subclusters. Kaplan-Meier estimation and log-rank tests were used for survival analysis. Differences between groups were compared using the chi-squared or Fisher\u0026rsquo;s exact tests for categorical variables. Correlations were evaluated using non-parametric Spearman analysis. * \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eTGFβ/SMAD signaling is highly expressed in human rhabdomyosarcoma and regulates myogenic transcription factors.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe previously identified a role for activated TGFβ signaling in blocking the differentiation of human rhabdomyosarcoma \u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The canonical TGFβ/SMAD signaling exerts significant functions in cancer progression by remodeling the architecture of the carcinomas and by suppressing antitumor immunity \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This study analyzed publicly available RNA sequencing (RNA-seq) data (GSE108022) from primary RMS samples. A total of 133 genes from the GSEAsig database (WP_TGFBETA_SIGNALING_PATHWAY) were collected, and the relative gene expression levels were presented in RMS subtypes. We identified 90 differentially expressed genes that were significantly upregulated in RMS as compared with muscle tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The canonical TGFβ/SMAD signaling members were highly expressed in RMS regardless of the subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). IHC staining of RMS samples revealed that TGF-beta1 was highly expressed in the majority of primary tumors (n\u0026thinsp;=\u0026thinsp;9/11 ERMS, n\u0026thinsp;=\u0026thinsp;7/10 ARMS, and n\u0026thinsp;=\u0026thinsp;8/9 PRMS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), which was consistent with the findings of the previous studies \u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and confirmed in an independent fraction of sarcomas within fibrosarcoma, gastrointestinal stromal tumors, and synovial sarcoma (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eNext, we evaluated whether TGFβ/SMAD signaling plays a role in RMS myogenic differentiation. TGFβ1 was knocked down using small interfering RNA (siRNA) transfection and up-regulated using the exogenous cytokine TGFβ1 in RD cells (Supplementary Fig.\u0026nbsp;3A). Transient TGFβ1 silencing significantly increased mRNA levels of myogenic differentiation genes \u003cem\u003eMyoD1\u003c/em\u003e, \u003cem\u003eMyogenin\u003c/em\u003e, \u003cem\u003eMyosin\u003c/em\u003e, and \u003cem\u003eDesmin.\u003c/em\u003e When RD cells were treated with exogenous cytokine TGFβ1, mRNA levels of \u003cem\u003eMyoD1\u003c/em\u003e, \u003cem\u003eMyogenin\u003c/em\u003e, \u003cem\u003eMyosin\u003c/em\u003e, and \u003cem\u003eDesmin\u003c/em\u003e were significantly decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-E). At the protein level, loss of TGFβ1 resulted in a striking increased expression of MyoD1, Myogenin, and Myosin. In contrast, exogenous TGFβ1 reduced the protein levels of MyoD1, Myogenin, and Myosin. We did not observe significant changes in Desmin protein expression regardless of the loss or gain function of TGFβ1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF-H). To further evaluate the association between TGFβ/SMAD signaling and RMS differentiation block, we investigated the transcript and protein levels of myogenic differentiation markers after knockdown or overexpression of SMAD2 in RD cells (Supplementary Fig.\u0026nbsp;3C-G). Upon SMAD2 knockdown or overexpression, MyoD1, Myogenin, Myosin, and Desmin increased or decreased, respectively, at both transcript and protein levels, indicating a similar trend as the effect of TGFβ1 on myogenic differentiation. Our results suggest that highly expressed TGFβ/SMAD signaling significantly regulates RMS myogenic differentiation. In terms of the mechanism, our previous study showed that TGFβ1 interacts with the miRNA network to regulate the growth, apoptosis, and malignant behaviors of RMS tumor cells. However, TGFβ, as an immunoregulatory master of the tumor microenvironment has been less studied in soft tissue sarcomas, including RMS. Together, these data prompted us to further investigate the immune regulatory roles of TGFβ1 in the tumour microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDistinct immune cell types related to TGFβ1 and IFNγ expression in RMS\u003c/h2\u003e \u003cp\u003eIFNγ, an important cytokine, is critical for coordinating the antitumor immune response \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Activated IFNγ signaling upregulates PD-L1 expression and immune cell infiltration, which may improve the response to anti-PD-1 immunotherapy \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Next, we analyzed the correlation between TGFβ1 and IFNγ across 33 TCGA tumor types (Pan-cancer cohort). A statistically significant positive correlation was observed in 24 TCGA cohorts, including TCGA-SARC. Only the TCGA-HNSC cohort revealed a negative association. Publicly available gene expression data (GSE108022) from primary RMS samples were analyzed using the CIBERSORTx deconvolution algorithm to assess the relative immune fraction scores of distinct immune cell subtypes. Statistically significant positive or negative associations between TGFβ1 (\u003cem\u003eTGFB1\u003c/em\u003e) or IFNγ (\u003cem\u003eIFNG\u003c/em\u003e) transcript levels and individual immune cell scores were assessed by Spearman correlation analysis. Our results revealed that two immune cell subtypes (na\u0026iuml;ve B cells and M1 Macrophages) had a significant positive correlation with \u003cem\u003eTGFB1\u003c/em\u003e, and four immune cell subtypes (activated NK cells, monocytes, resting mast cells, eosinophils) were negatively correlated with \u003cem\u003eTGFB1\u003c/em\u003e. Meanwhile, we found that IFNγ (\u003cem\u003eIFNG\u003c/em\u003e) expression level was positively correlated with three subtypes (B cells na\u0026iuml;ve, M1 Macrophages, CD8 T cells) and negatively correlated with three immune cell subtypes (monocytes, resting mast cells, and M0 Macrophages).\u003c/p\u003e \u003cp\u003eAll significantly relevant immune cell subtypes (CD8 T cells, na\u0026iuml;ve B cells, M1\u0026amp;M0 Macrophages, activated NK cells, resting Mast cells, Monocytes, and Eosinophils) were selected for further analyses. Unsupervised hierarchical cluster analysis of the RMS cohort based on the eight selected immune cell subtypes revealed two RMS immune clusters, A and B (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Cases in cluster A were enriched for CD8 T cells, na\u0026iuml;ve B cells, and M1 Macrophages and had higher transcript levels of \u003cem\u003eTGFB1\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E). Cluster B was divided into four subclusters (B1, B2, B3, and B4) due to the significant differences in M0 Macrophages and activated NK cells. To evaluate whether stratification into molecular immune clusters A and B is also applicable to all sarcomas, transcriptome datasets of the TCGA-SARC cohort were analyzed using CIBERSORTx. Unsupervised hierarchical clustering revealed a similar pattern (Supplementary Fig.\u0026nbsp;4A). Cluster A was significantly correlated with higher \u003cem\u003eTGFB1\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e (Supplementary Fig.\u0026nbsp;4B, C). We compared cluster A cases with cluster B2 in the RMS cohort or cluster B3 in the TCGA-SARC cohort. This was particularly common evidence that CD8 T cells and M1 macrophages were enriched in cluster A from both the RMS and TCGA-SARC cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, Supplementary Fig.\u0026nbsp;4D). The other selected immune cell subsets revealed more heterogeneous characterizations in the various subclusters. Interestingly, an inverse finding was observed in activated NK cells and monocytes among clusters A and B2 or B3, indicating a heterogeneous immune niche in the tumor microenvironment of the RMS and SARC cohorts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in immune gene signature and survival patterns related to immune phenotypes\u003c/h2\u003e \u003cp\u003eImmune-related gene expression signatures are associated with immune cell infiltration and clinical response to immunotherapy agents targeting immune checkpoints \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Hence, we evaluated the transcript levels of the immune checkpoint and IFNγ-related genes in the subclusters of the RMS and TCGA-SARC cohorts. Cases in cluster A from the RMS and SARC cohorts were enriched for the majority of the selected immune checkpoint and IFNγ-related genes. A 25-gene signature (\u003cem\u003eCD274, PDCD1, PDCD1LG2, CTLA4, HAVCR2, LAG3, IDO1, CXCL10, CXCL9, HLA_DRA, STAT1, IFNG, CD3D, IL2RG, NKG7, CIITA, HLA_E, CD3E, CXCR6, CCL5, GZMK, TAGAP, CD2, CXCL13\u003c/em\u003e, and \u003cem\u003eGZMB\u003c/em\u003e) was shown in the hierarchical clustering heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). The expression of each selected immune checkpoint gene and IFNγ immune signature scores were compared between clusters A and B2 or B3 in RMS and SARC cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). In the RMS dataset, the higher gene expression of \u003cem\u003eCD274, PDCD1, PDCD1LG2, and CTLA4\u003c/em\u003e was observed in cluster A tumors as compared to cluster B2. Similarly, \u003cem\u003ePDCD1, PDCD1LG2, CTLA4, HAVCR2\u003c/em\u003e, and \u003cem\u003eLAG3\u003c/em\u003e were expressed at high levels in cluster A of the SARC cohort as compared to other clusters. Interestingly, CD274 (which encodes PDL1) was heterogeneously expressed in various clusters, which was also found in a previous study using another immune classification tool \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. As expected, cluster A tumors have higher IFNγ immune signature scores in RMS and SARC cohorts. Recently, a \u0026ldquo;tumor inflammation signature\u0026rdquo; (TIS) was reported to predict the clinical benefit of anti-PD-1 therapy in several clinical trials \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. TIS scores were also compared among the subclusters from RMS and TCGA-SARC cohorts. A high TIS score was observed in cluster A as compared to B2 or B3 in RMS and SARC cohorts (Supplementary Fig.\u0026nbsp;5A and B). In terms of clinical relevance, patients in cluster A with available survival data (TCGA-SARC) exhibited a favorable overall survival as compared to cluster B3 and B4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). In addition, patients in cluster A had better disease-free survival than patients in cluster B4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). These data suggested that our newly established stratification for distinct molecular immune phenotypes can predict clinical outcomes in a soft tissue sarcoma cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in gene and protein expression related to the immune phenotype\u003c/h2\u003e \u003cp\u003eTo unravel relevant genetic alterations, we investigated the fraction of genome altered (FGA) and tumor mutational count (TMC) using cBioportal web-tool. The lowest FGA was found in cluster A as compared to the other clusters (P\u0026thinsp;=\u0026thinsp;0.0012 \u003cem\u003eKruskal Wallis Test\u003c/em\u003e, Supplementary Fig.\u0026nbsp;4C ). The top 20 genes with the highest mutation frequency and most significant difference among the four clusters are listed (Supplementary Fig.\u0026nbsp;5D-F). Next, we identified 7485 and 5474 differentially expressed genes (DEGs) among clusters A and B2 or B3 in the TCGA-SARC and RMS cohorts, respectively. In addition, 21 differentially expressed proteins were also screened in the TCPA-SARC database. In total, nine genes were observed differentially expressed at the transcript and protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Analysis of protein-protein interaction according to the STRING database indicated that these DEGs were closely related to each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Significantly higher CHEK1 expression was found in subcluster B2 and B3 than in cluster A. The transcript level of CHEK1 was significantly elevated in both fusion-negative and fusion-positive tumors RMS compared to that in normal muscle tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). CHEK1 (Checkpoint kinase 1) is a serine/threonine-specific protein kinase encoded in humans by the \u003cem\u003eCHEK1\u003c/em\u003e gene. The transcript level of CHEK1 was positively correlated with the protein levels in the TCGA-SARC cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Significantly higher CHEK1 transcript and protein expression were found in clusters B3 and B4 in TCGA-SARC cohorts, which exhibited unfavorable overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, F). To further investigate the clinical relevance of CHEK1 in sarcoma, patients were divided into CHEK1 expression low and high groups. We performed Kaplan-Meier analysis for patients' disease-free survival (PFS) and overall survival (OS). We found that high expression of CHEK1 served as an unfavorable prognostic biomarker at both the transcript and protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-J).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between CHEK1 and tumor immune microenvironment in soft tissue sarcomas\u003c/h2\u003e \u003cp\u003eNext, we explored the associations between CHEK1 expression levels and tumor-infiltrating immune cells and immune-related genes in the TCGA-SARC and RMS cohorts. The correlation coefficient of 22 immune cells with CHEK1 gene and protein expression is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and B. For CHEK1 transcript and protein levels, Spearman correlation analysis revealed nine immune cell subtypes with significant negative associations (M2 macrophages, Monocytes, resting CD4 memory T cells, na\u0026iuml;ve B cells, CD8 T cells, activated NK cells, gamma delta T cells, regulatory T cells, and M1 Macrophages). Three subtypes (M0 macrophages, Plasma cells and activated dendritic cells) were found to have a significant positive correlation with CHEK1 transcript level. Regarding CHEK1 protein level, three subtypes (M0 macrophages, Plasma cells and activated CD4 memory T cells) were positively correlated. The transcript and protein levels of CHEK1 were also found to have statistically significant negative associations with the majority of selected IFNγ-related and expanded immune gene signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D). We divided the patients in the TCGA-SARC and RMS cohorts into two groups depending on the CHEK1 transcript or protein levels. In the RMS cohort, significantly higher scores for na\u0026iuml;ve B cells and follicular T helper cells were detected in the CHEK1 high group. Statistically significant low scores for resting CD4\u0026thinsp;+\u0026thinsp;memory T cells and resting Mast cells were observed in the CHEK1 high group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). In the TCGA-SARC cohort, the scores for six subtypes of immune cells (resting CD4 memory T cells, regulatory T cells, gamma delta T cells, Monocytes, M1 Macrophages, and M2 macrophages) were significantly different between the CHEK1 transcript low and high levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). In terms of CHEK1 protein levels, nine immune cell subtypes (na\u0026iuml;ve B cells, memory B cells, resting CD4 memory cells, regulatory T cells, activated NK cells, Monocytes, M1 Macrophages, M2 macrophages, and resting Mast cells) were significantly different between the CHEK1 low and high group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). All relevant immune cell subtypes were enriched in the CHEK1 low group. We also compared the relative expression of IFNγ-related and expanded immune gene signatures between the CHEK1 low and high groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH, I). The results revealed that patients with CHEK1 low expression had significantly higher IFNγ-related and expanded immune gene signature scores, which predicts the clinical response to PD-1 target therapy in various cancers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCHEK1 predicted clinical prognosis and molecular subtypes for RMS\u003c/h2\u003e \u003cp\u003eTo validate the expression and clinical relevance of CHEK1 for RMS \u003cem\u003ein situ\u003c/em\u003e, we analyzed CHEK1 expression in an independent cohort of 33 cases (Hefei-cohort; Supplemental Table\u0026nbsp;2) by immunohistochemistry. A heterogeneous staining pattern ranging from absence and moderate to prominent staining in tumor cells was observed in the RMS tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). High CHEK1 protein levels were detected in the 14 patients. We did not observe any significant difference between CHEK1 low and high subgroups concerning histological types and clinicopathological characteristics except for disease recurrent status (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C). We performed Kaplan-Meier analysis for relapse-free survival of patients to confirm that the CHEK1 high expression subgroup was significantly associated with unfavorable clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). To complement the transcriptional analysis of the TIME between subgroups with different CHEK1 expressions, we analyzed the densities of CD4+, CD8+, T-regulatory cells (Foxp3+), tumor-associated macrophages (CD163\u0026thinsp;+\u0026thinsp;TAM), and PD-L1 expression in RMS specimens. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, Supplementary Fig.\u0026nbsp;5A, B) Using digital image analysis, we found higher densities of main tumor-infiltrating immune cells (CD4+, CD8+, T-reg cells) in the CHEK1 low tumor compared to the CHEK1 high expression. Similar to the immune-checkpoint gene CD274 detected through our analysis of publicly available data, mIHC analysis revealed that the mean PD-L1 expression was not significantly different between the subgroups with low and high CHEK1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). In line with a previous study \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, TAMs predominated the sarcoma immune microenvironment with the highest intratumor density in the RMS specimens. However, we did not observe any significant differences between the CD163\u0026thinsp;+\u0026thinsp;positive cells of the two RMS subgroups. We also discovered a very small population of CD4\u0026thinsp;+\u0026thinsp;Foxp3\u0026thinsp;+\u0026thinsp;Tregs in both subgroups of the RMS specimens. Together, these data suggested that CHEK1 expression predicted the clinical outcome and was correlated with tumor-infiltrating immune cells in RMS in our real-world cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCHEK1 expression evaluation in soft tissue sarcomas and correlated with immune cell densities\u003c/h2\u003e \u003cp\u003eTo clarify more precisely the associations between CHEK1 expression and tumor-infiltrating immune cells in soft tissue sarcomas, we examined tumor samples of formalin-fixed paraffin-embedded tissue microarray (TMA) from 91 patients with soft tissue sarcomas. We observed a range of undetectable to prominent staining intensity in tumor cells by IHC staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). CHEK1 staining intensity was quantified using QuPath software for patients. Patients were divided into two subgroups according to the staining intensity with CHEK1\u003csup\u003ehigh\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;50) and CHEK\u003csup\u003elow\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;41) for further analysis. We compared the distribution of CHEK1 expression across histological subtypes and found that most synoviosarcoma tumors revealed higher CHEK1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). All available clinico-pathological features were compared between CHEK\u003csup\u003ehigh\u003c/sup\u003e and CHEK\u003csup\u003elow\u003c/sup\u003e groups. In line with our results from the RMS cohort data, Chi-square analysis showed that high CHEK1 expression was significantly associated with advanced clinical stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Unfortunately, follow-up survival records of the TMA cohort were unavailable for further survival analysis. To complement the transcriptional analysis of associations between CHEK1 expression levels and tumor-infiltrating immune cells, we performed an Opal multiplex IHC tissue staining assay to characterize the soft tissue sarcoma TME (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). The densities of T cells (CD3+), CD4\u0026thinsp;+\u0026thinsp;T cells (CD4+), CD8\u0026thinsp;+\u0026thinsp;T cells (CD8+), CD19\u0026thinsp;+\u0026thinsp;B cells (CD19+), T-regulatory cells (Foxp3+), pan-macrophages (CD68+), M2-like macrophage (CD163+), and PD-L1 in each competition for tumor sections were calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). A significant increase in the overall density of Foxp3, CD19+, CD68+, CD163\u0026thinsp;+\u0026thinsp;and CD68\u0026thinsp;+\u0026thinsp;CD163\u0026thinsp;+\u0026thinsp;cells was observed in the CHEK\u003csup\u003elow\u003c/sup\u003e group tumors. However, infiltration of T cells (CD3+, CD4+, and CD8+) and PD-L1 densities were not significantly different between the CHEK\u003csup\u003ehigh\u003c/sup\u003e and CHEK\u003csup\u003elow\u003c/sup\u003e groups. Altogether, our digital image analysis strongly supports the immune deconvolution results that CHEK1 serves as an unfavorable prognostic biomarker related to diminished cytotoxic immune cell infiltration in STS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe previously reported that the TGFβ1 signaling pathway contributes to the growth and differentiation of RMS \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, immune cell subsets based on TGFβ1 and IFNγ expression in RMS have not been investigated. Here, utilizing a public oncogenic database, we revealed that TGFβ/SMAD signaling is highly expressed in human rhabdomyosarcoma and regulates myogenic transcription factors. In addition, we established a new stratification model based on RNA expression profiling for RMS with distinct molecular immune phenotypes, which was validated in the TCGA-SARC cohort. Predefined immune gene signatures related to ICB responses were evaluated as statistically significant among distinct subgroups. Higher IFNγ-related and expanded immune gene signatures contribute to an improved prognosis. Importantly, the assessment of the variation in transcript and protein expression of DEGs with higher or lower cytotoxic immune phenotypes highlighted that CHEK1 served as an unfavorable biomarker and is related to reduced cytotoxic immune cell infiltration in soft tissue sarcomas. Finally, the density and distribution of tumor-infiltrating immune cells in CHEK1 low- and high-expression soft tissue sarcomas were evaluated using multiplex immunofluorescence staining. Elevated expression of CHEK1 in sarcomas was correlated with lower infiltrating immune cells, indicating that CHEK1 is a predictor of clinical prognosis and a potential novel adjuvant immunotherapy target for sarcoma.\u003c/p\u003e \u003cp\u003eTGFβ has three isoforms, TGFβ1, TGFβ2, and TGFβ3, which belong to a 33-member cytokine superfamily. TGFβ signaling has widespread and diverse effects on cell proliferation, differentiation, adhesion, migration, metabolism, and immune homeostasis. TGFβ1, as a modulator of RMS cell differentiation, plays a significant role in tumor growth and progression\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In the present study, RNA-seq data and IHC staining of tumor tissues from RMS revealed that canonical TGFβ/SMAD signaling was highly expressed in human RMS. Using gain or loss of function experiments, we first verified the effects of TGF-β1/SMAD2 on myogenic differentiation in RMS-RD cells. The interaction between TGFβ1 and IFNγ plays a pivotal role in regulating antitumor host immunity. Activated IFNγ signaling upregulates PD-L1 expression and immune cell infiltration, which may improve the response to anti-PD-1 immunotherapy. RMS is a type of sarcoma with a heterogeneous group of soft-tissue tumors. Immunotherapy in RMS has limited effectiveness so far. Several previous studies have investigated the tumor microenvironment (TME) composition in different soft tissue sarcomas to understand the immune niche that maintains the tumor and how to mediate cancer immune escape. In 2020, Chen and colleagues \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e delineated the immune characteristics of specific TMEs in RMS and undifferentiated pleomorphic sarcomas (UPS) and revealed an immunosuppressive TME dominated by tumor-associated macrophages. They speculated that \u003cem\u003ein situ\u003c/em\u003e T-cell distribution in the STS TME could overcome the immunosuppressive niche and play a predominant role in ICB responsiveness. Interestingly, another study established an immune-based classification of TME composition using the microenvironment cell populations-counter method in sarcoma. They reported three main immune phenotypes in soft tissue sarcomas: immune-low, immune-high, and highly vascularized subgroups, with different clinical outcomes and response rates to ICB. Their study defined a subgroup of sarcoma patients who benefited from ICB, marked by at high density of B cells and the presence of tertiary lymphoid structures (TLS) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe present study differs from previous reports in that we conducted an integrative deconvolution analysis of multi-omics data from RMS to cluster the patients into subgroups depending on the immune cells related to TGFβ1 and IFNγ. In addition, we used the public dataset TCGA-SARC to validate our new stratification model and evaluate the prognostic outcomes of subgroups with lower or higher cytotoxic immune phenotypes. Consistently, the immune gene signature and tumor inflammation signature were enriched in cluster A, which is an accurate and independent predictive biomarker for ICB clinical outcome. We identified CHEK1 as a critical predictive biomarker in distinct TIME and as a potential therapeutic target to increase TILs and improve the clinical efficacy of ICB therapy in RMS and STS.\u003c/p\u003e \u003cp\u003eCHEK1 is a crucial mediator of cell cycle progression in response to DNA damage. The critical function of CHEK1 in normal and germinal center B-cell development, lymphomagenesis, and survival has been reported \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Therapeutic CHEK1 inhibition combined with BCR-signalling blockade in patients might improve the efficacy in eradicating B-cell lymphoma and leukemia cells \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Recently, several efforts have tested CHEK1 signaling drugs in combination with ICB in other human malignancies \u003csup\u003e\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. One study by Sato et al. \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e highlights the critical role of CHK1 in regulating PD-L1 expression and immune response, indicating the translational application of CHEK1 agents in combination with ICB therapy. Most studies have demonstrated that the cell-cycle-related kinase function of CHEK1 is a promising therapeutic strategy for STS \u003csup\u003e\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. However, few studies reported the association between CHEK1 expression and immune cell infiltration in STS. Notably, the immune landscape of STS exhibits considerable heterogeneity across different histological subtypes, with implications for the treatment response and prognosis. By applying an integrative analysis of multi-omic expression profiling, our findings unraveled the heterogeneity and complexity of the immune microenvironment across distinct subclusters and significantly correlated with CHEK1 expression. In addition, we provide strong experimental evidence that highly infiltrating immune cells in STS were observed in the subgroup of patients with CHEK1 low expression, which revealed a favorable clinical prognosis. Nevertheless, the prognostic significance of the immune cell density and spatial location in RMS and STS after ICB therapy is not completely understood. Therefore, further studies, including a larger cohort with a more extended follow-up period, ideally from prospective clinical trials, are required to confirm our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this report, we validated TGFβ1 signal function in RMS myogenic differentiation and established a novel risk assessment strategy for RMS and STS patients. Multi-omics expression profiling and independent cohort data demonstrated that CHEK1 is an unfavorable prognostic biomarker related to immunosuppressive phenotypes in soft tissue sarcomas. We speculate that CHEK1 may be a promising therapeutic target alone or in combination with ICB immunotherapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"407\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eDifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eDisease-free survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eElectronic Chemistry Laboratory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eECL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eFraction of genome altered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eFGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eGene set enrichment analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eGSEA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eImmune checkpoint blockade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eICB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eImmunohistochemistry\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eInterferon-\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eIFN\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003emultiple immunohistochemistry\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003emIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eOverall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eRhabdomyosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eRMS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eRNA sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eRNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eShort Tandem Repeat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eSoft tissue sarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eSTS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eTransforming growth factor beta 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eTGF\u0026beta;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eTumor inflammation signature\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eTIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eTumor microenvironment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eTME\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eTumor mutation burden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eTumor mutational count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eTMC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eTumor-infiltrating immune cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eTIIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eUndifferentiated pleomorphic sarcomas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eUPS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80.8354%;\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.1646%;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe genomic data and survival information used in this study are publicly available from the GDC Portal (portal.dc.cancer.gov, cohort TCGA-SARC) and the GEO database (GSE108022). The proteomics data are publicly available from The Cancer Proteome Atlas (TCPA). All the other data are available from the corresponding authors upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eC.R., Y.L., and F.X. analyzed multi-omics data and performed statistical analyses. C.R. and S.W. conceived the study and designed the experiments. X.Z., J.Z., Z.X.,J.W., L.C., Z.G., Z.Z, and J.A. performed the experiments. Y.L. and X.Y. collected clinical samples and information. C.R., Y.L., and F.X. wrote the manuscript, J.S. and J.H. contributed to manuscript review and conceptual advice. C.R., Q.Z. and S.W. directed the project and were responsible for funding acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eFundings\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82103121), the Natural Science Foundation of Jiangsu Province (BK20200878), Key Projects of Students Academic Research Foundation of Soochow University (KY2023093A, KY2024273B), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We thank all the patients and their families at The First Affiliated Hospital of University of Science and Technology of China (USTC) for their contribution of biological specimens and clinical information used in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research protocol was approved by the Ethics Committee of the First Affiliated Hospital of USTC (Ethic No 2024/RE256) in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants or family members in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSkapek SX, Ferrari A, Gupta AA et al. Rhabdomyosarcoma. Nat Rev Dis Primers 2019; 5 (1): 1.\u003c/li\u003e\n\u003cli\u003eDagher R, Helman L. Rhabdomyosarcoma: an overview. The oncologist 1999; 4 (1): 34-44.\u003c/li\u003e\n\u003cli\u003eReilly BK, Kim A, Pena MT et al. Rhabdomyosarcoma of the head and neck in children: review and update. 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Clinical cancer research : an official journal of the American Association for Cancer Research 2024.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-precision-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjprecisiononcology","sideBox":"Learn more about [npj Precision Oncology](http://www.nature.com/npjprecisiononcology/)","snPcode":"41698","submissionUrl":"https://submission.springernature.com/new-submission/41698/3","title":"npj Precision Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Soft tissue sarcomas, Rhabdomyosarcoma, multi-omics profiling analysis, prognostic biomarker, immune phenotypes, checkpoint kinase 1","lastPublishedDoi":"10.21203/rs.3.rs-5923386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5923386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRhabdomyosarcoma (RMS) represents one of the most common soft tissue sarcoma (STS) in children and adolescents. Transforming growth factor beta 1 (TGFβ1) is a potent inhibitor of myogenic differentiation in RMS and plays a significant function in the tumour immune microenvironment. Currently, unsupervised tumor immune phenotype based on multi-omics expression profiling has been less studied. To reveal the tumour immune phenotype of STS and identify promising therapeutic targets, multi-omics expression profiling in 363 tumours across subtypes of STS was investigated. Here, we validated the TGFβ1 signal function in RMS myogenic differentiation and established a novel molecular classifier based on immune cell subsets related to TGFβ1 and Interferon-γ (IFNγ) to identify distinct immune phenotypes with higher or lower cytotoxic contents. Moreover, we compared multi-omics expression profiling across subgroups of RMS and STS to identify CHEK1 as an unfavourable prognostic biomarker related to immunosuppressive phenotypes. \u003cem\u003eIn situ\u003c/em\u003e analysis of independent validation cohorts addresses the correlation between CHEK1 and tumour-infiltrating immune cells. Collectively, our data validate the TGFβ1 signal function in RMS myogenic differentiation and establish a novel risk assessment strategy for RMS and STS patients. This work potentially improves risk assessment for STS patients and offers a new therapeutic strategy to increase antitumor immunity through the combined targeting of CHEK1 inhibition.\u003c/p\u003e","manuscriptTitle":"Integrative bioinformatics analysis and experimental validation identify CHEK1 as an unfavorable prognostic biomarker related to immunosuppressive phenotypes in soft tissue sarcomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-07 10:11:28","doi":"10.21203/rs.3.rs-5923386/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-08T12:39:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T16:38:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298933634219900859834697106072678950463","date":"2025-04-28T10:07:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339130041340631265217201364284873370879","date":"2025-04-26T19:55:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287255029090640447917376832006569112452","date":"2025-04-21T14:35:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-08T23:54:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-06T00:48:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179765710214291306289845064142022232274","date":"2025-03-26T13:56:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95444200139537415144773276622609235340","date":"2025-03-26T10:51:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262848709711974215026818888944216030136","date":"2025-03-25T23:06:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222724089495294725758295971168011569221","date":"2025-03-24T11:33:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-23T22:33:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-11T07:39:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-04T11:53:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Precision Oncology","date":"2025-01-29T10:02:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-precision-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjprecisiononcology","sideBox":"Learn more about [npj Precision Oncology](http://www.nature.com/npjprecisiononcology/)","snPcode":"41698","submissionUrl":"https://submission.springernature.com/new-submission/41698/3","title":"npj Precision Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a27bbc8-bcd8-47ee-9d10-f2fda4a60f3a","owner":[],"postedDate":"February 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43824136,"name":"Biological sciences/Cancer"},{"id":43824137,"name":"Health sciences/Biomarkers"},{"id":43824138,"name":"Health sciences/Health care"},{"id":43824139,"name":"Health sciences/Medical research"},{"id":43824140,"name":"Health sciences/Molecular medicine"},{"id":43824141,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-08-04T16:39:09+00:00","versionOfRecord":{"articleIdentity":"rs-5923386","link":"https://doi.org/10.1038/s41698-025-01064-8","journal":{"identity":"npj-precision-oncology","isVorOnly":false,"title":"npj Precision Oncology"},"publishedOn":"2025-08-01 16:20:52","publishedOnDateReadable":"August 1st, 2025"},"versionCreatedAt":"2025-02-07 10:11:28","video":"","vorDoi":"10.1038/s41698-025-01064-8","vorDoiUrl":"https://doi.org/10.1038/s41698-025-01064-8","workflowStages":[]},"version":"v1","identity":"rs-5923386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5923386","identity":"rs-5923386","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}