STC2/FPR1 dual genes signature works as a prognosis indicator with implication in macrophages dominated and TIM-3 related osteosarcoma immune landscape

STC2/FPR1 dual genes signature works as a prognosis indicator with implication in macrophages dominated and TIM-3 related osteosarcoma immune landscape | 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 Research Article STC2/FPR1 dual genes signature works as a prognosis indicator with implication in macrophages dominated and TIM-3 related osteosarcoma immune landscape Wenxia Ma, Lei Miao, Siying Liu, Zixin Zeng, Jiayao Li, Fei Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5651928/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Osteosarcoma has been a common bone malignancy occurring in children and adolescents. Attributing to high tumor heterogeneity, none specific breakthrough has been received in targeted gene therapy for osteosarcoma, although it’s still of great potential for immunotherapy in clinical application. In the study, 5 GEO profiles containing transcriptome information of 109 osteosarcoma samples, single cell sequencing data composed of 6 cases of samples, as well as 43 cases of local hospital tissue samples were combine used to identify the promising immune related candidate genes in osteosarcoma. Methods Based on osteosarcoma transcriptome microarrays from GEO database as well as immune related gene profile from IMMPORT database, differently expressed meanwhile immune related gene candidates in osteosarcoma comparing to normal control samples were identified. Then, protein-protein interaction network (PPI), survival analysis followed by LASSO analysis were in succession applied to construct a gene signature based on the selected candidate genes. After understanding the basic genetic physicochemical properties and evaluating the prognosis risk association of the gene signature using local hospital cancer samples, its association with immune microenvironment features including macrophages included various immune cells infiltration, different immune checkpoints expression, immune related signaling pathways involvement were next step assessed. Results From GEO transcriptome datasets which contains a total of 109 osteosarcoma samples, a total of 108 high level differently expressed meanwhile immune related gene candidates were identified. Then, PPI network and LASSO analysis highlighted a 6 genes containing cluster from the 108 candidate genes. Further, ROC curve as well as Cox regression analysis assisted scaled the 6 hub genes down to 2 key genes, namely STC2 and FPR1, and a gene signature was constructed based on them. After understanding the basic genetic physicochemical properties of STC2 and FPR1, double staining immunochemistry (IHC) experiment based on 43 cases of local hospital samples and single cell sequencing date of 6 tissue samples revealed that STC2 was mainly expressed in osteosarcoma cancer cells, meanwhile, FPR1 was mostly enriched in macrophages focused immune cells which has also been the main immune cell type in osteosarcoma microenvironment. Moreover, the combining STC2/FPR1 dual genes signature was also associated with distribution of multiple immune checkpoints, especially TIM-3. Further, the correlation between the signature and other immune features including immune related cell death (ICD) and ESTIMATE immune score were additionally evaluated. Conclusions Based on osteosarcoma transcriptome genes analysis, a dual genes containing signature composed of STC2 and FPR1 genes was constructed. Immune correlation analysis indicated the signature was associated with the macrophages infiltration which has been a main immune cell type in osteosarcoma, ans it was also related with TIM-3 included multiple immune checkpoints expression. The results shall benefit further osteosarcoma immune researches and assist revealing promising prediction markers for clinical immunotherapy. Osteosarcoma STC2 FPR1 immune gene signature TIM-3 immunotherapy prediction marker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Osteosarcoma has been a common bone malignancy with high invasion and metastasis ability, occurring in firstly children and adolescents (average age 15 ~ 19) and secondly over 60 years old elderly, and the lesions were mainly in the metaphyseal of long bones[ 1 ]. The incidence of osteosarcoma is around 1.7–3.3 per million annually worldwide, threateningly, the tumor is severely health affecting, the 5-year survival rate is nearly 70% in patients with non metastatic osteosarcoma, and for patients with advanced and recurrent osteosarcoma, the 5-year survival is less than 20%[ 2 ]. As for the clinical treatment of osteosarcoma, surgery combining with neoadjuvant and adjuvant chemotherapy have been the main strategy for decades[ 3 , 4 ]. Benefiting from the increasingly understanding of the prognosis association of microvessel density and angiogenic factors in cancers, anti-angiogenesis tyrosine kinase inhibitors (AA-TKIs) have also been used in clinical treatment which indeed seem to prolong the progression free survival (RFS) of patients, however, the combination use of AA-TKIs and chemotherapy also raised drugs toxicity and exacerbate patients adverse reactions, the treatment needs to be carefully assessed and managed while being applied[ 5 , 6 ]. Meanwhile, regarding the thriving developing gene targeted therapy which has been receiving promising clinical effect in many other cancers, for instance in lung, breast, gastric and colorectal cancer[ 7 – 11 ], the success has not been replicated in the treatment of osteosarcoma. Since 2014, series of whole genome sequencing analysis based on osteosarcoma samples have been revealing that except for the commonly known sporadic gene mutations including P53, RB1 and other genes for instance MDM2 and NFIB[ 3 , 12 – 15 ], mother frequent gene variations in osteossarcoma including copy number variation and complex chromosome rearrangement, for instance, chromosome division (extensive genome rearrangement and DNA copy number level mutations on one or several chromosomes)[ 16 ], kataegis mutation (local strand specific hypermutation)[ 17 , 18 ] and break fusion bridge cycle (fusion of broken ends from different chromatids or chromosomes, resulting in unstable bicentric chromosomes)[ 3 , 19 , 20 ]. The complex DNA variations make it difficult to identify the genome initial biological processes that drive the cancer development and to explore effective gene targets. It’s of clinical significance to keep exploring promising clinical treatment strategies thus benefiting the suffering patients. Immune escape has been one of the major hallmarks of cancers besides uncontrolled cell proliferation, resisting cell death, evading growth suppressor, metastasis, angiogenesis, promoting inflammation and so on[ 21 , 22 ]. Meanwhile, immunotherapy which is aiming to reactivate body immune system to attack cancer cells has also been showing potential in increasing number of cancer types. Even though the commonly applied anti PD-1/PD-L1 immune checkpoint inhibitors (ICIs) only received limited effect in osteosarcoma patients, it is still of great potential for immunotherapy to be applied in osteosarcoma clinical treatment. For instance, in 2019, Le Cesne.et al released the result of a clinical trial which was based on 17 advanced osteosarcoma patients, and the result revealed that 13.3% of cases achieved 6 month of progress free survival[ 23 ], and in a multicenter phase II clinical trial (SARC028) on the efficacy of pembrolizumab, 18% of soft tissue sarcoma patients and 5% of osteosarcoma patients showed clinical response to pembrolizumab blocking of immune checkpoints[ 24 ]. And also, a clinical analysis revealed that more that 25% of osteosarcoma patients response to Ipilimumab which is a checkpoint blockade for CTLA-4[ 25 ], and in another experiment based on animal modes, the combination of CTLA-4 and PD-L1 antibody blockade immunotherapy in K7M2 mouse models of metastatic osteosarcoma resulted in complete control of most tumors[ 26 ]. Meanwhile, CAR-T cell therapy has also showed clinical potential in the treatment of osteosarcoma included soft tissue sarcomas, these results still indicated the potential of immunotherapy to be applied in osteosarcoma treatment[ 27 ]. It’s of great significance to keep exploring osteosarcoma genome and identifying promising immune response biomarkers thus aiding more precise understanding of the disease and shedding light on further clinical immunotherapy application. In the study, public online datasets and local hospital tissues were combine used to explore osteosarcoma genome data for analyzing osteosarcoma immune landscape. The genes that were differently expressed, prognosis associated meanwhile immune regulation related were highly focused. The selected genes would then be used to construct promising gene signatures followed by exploring their association with osteosarcoma immune features including different immune cells infiltration and expression of PD-L1, CTLA-4, TIGIT included immune checkpoints. The results shall benefit identifying potential new immune biomarkers and shed promising light on further clinical application of immunotherapies in clinical treatment of osteosarcoma. Materials and Methods Public data: GEO profiles and TARGET database information of osteosarcoma cases From GEO online database, the osteosarcoma related profiles were widely screened for exploring the genome information in osteosarcoma comparing to normal control samples. The selection criteria of GEO transcriptome profiles and the detailed information of four selected GEO profiles, namely GSE12865, GSE16088, GSE28424 and GSE42352 had been reported previously[ 28 ]. Besides the four transcriptome profiles, another GEO profile which is based on osteosarcoma single cell sequencing data namely GSE162454 was also included[ 29 ]. Besides above profiles, TARGET database[ 30 ] which is short for Therapeutically Applicable Research To Generate Effective Treatments has also been an open accessed and researchers friendly genome database aiming for children tumors including acute lymphoblastic leukemia, acute myeloid leukemia, kidney tumors, neuroblastoma and osteosarcoma. In the study, the selected GEO profiles and osteosarcoma data in TARGET database were together used in different sections to explore the genome landscape of osteosarcoma. Difference of immunogenic cell death (ICD) related genes in osteosarcoma comparing to normal control samples To preliminary validating the immune status difference between osteosarcoma and normal control samples, ICD which has been well accepted as an immune related form of regulated cell death meanwhile supported by evidence-based medicine to be able to trigger cellular adaptive immune response and contribute to clinical immunotherapy was evaluated in osteosarcoma cases. The first step of the study was to evaluate the expression difference of 32 well accepted ICD related genes[ 31 ] in osteosarcoma comparing to corresponding normal tissues based on TARGET data. Identifying differently expressed meanwhile immune related genes in osteosarcoma comparing to normal bone samples After preliminary validating the potential of immune research in osteosarcoma based on ICD related genes expression analysis, GEO transcriptome data were next step applied to analyze osteosarcoma genome information with the purpose of 1. exploring the differently expressed genes in osteosarcoma comparing to normal control samples, 2. combining with IMMPORT immune database to identify the aberrant differently expressed meanwhile immune regulation associated genes. Firstly, four GEO transcriptome profiles, namely GSE12865, GSE16088, GSE28424 and GSE42352 were in succession analyzed with GEO2R which was provided in each GEO profile to compare the differently expressed genes between osteosarcoma and normal control. The identified genes would then be classified into different groups based on expression difference, namely expression difference level 8 fold ( analysis criteria: P < 0.05 meanwhile |log2FC|<1, 1≤|log2FC|<2, 2≤|log2FC| 8 fold) were mainly focused. Then, based on IMMPORT immune database[ 32 ], the gene list that were well acknowledged as immune regulation related would be recognized followed by Venn diagram analysis to screen the genes that were shared by above high level differently expressed (> 8 fold) genes and immune genes list, thus identifying promising candidate genes for next step analysis. Protein-protein interaction (PPI) network construction and immune enrichment validation of the candidate genes To preliminary validate the immune association of above selected candidate genes, the protein-protein interaction (PPI) network of the candidate genes was constructed using Search Tool for the Retrieval of Interacting Genes (STRING)[ 33 ], followed by Gene ontology analysis (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)[ 34 ] annotating the basic biological attributes these genes, especially the biological processes and signaling pathways they mainly enriched in. The immune system related biological processes would be mainly focused. LASSO algorithm to construct an immune related multi-genes containing signature based on candidate genes To maximum the clinical utilization of the selected expression changed meanwhile immune related gene candidates, LASSO algorithm performed with glmnet R package was applied to construct a multi-genes containing signature. Based on the signature, the best gene combination that represents the candidate genes were selected, and a regression coefficient was assigned to each gene, the final score of the gene signature in each osteosarcoma case equals the multiplication of the regression coefficient and detected gene expression. And based on the calculated median score of the constructed gene signature, the 89 osteosarcoma cases in TARGET database were classified as high-risk and low-risk groups followed by next step estimating the clinical features association of the gene signature. Preliminary prognosis validation of the gene signature To explore the prognosis association of the constructed gene signature, multiple analysis were performed. Firstly, Kaplan-Meier survival difference was estimated between the high-risk and low-risk groups of 89 osteosarcoma cases in TARGET database, then, AUC curve was drawn for observing the 1, 3 and 5 years survival prediction ability of the signature. Further, univariate together with multivariate Cox regression analysis were in succession applied to test the independence survival prediction of the signature. Furthermore, a nomogram consisted of the newly constructed gene signature and other clinical features was drawn, all for together evaluating the prognosis risk in clinical osteosarcoma patients. Immunohistochemistry (IHC) experiment detection of the expression pattern of signature hub genes The gene signature was constructed based on public open accessed GEO and TARGET database information, to validate the clinical application value of the signature in real world osteosarcoma samples, local hospital cancer samples were applied to observe the expression mode of the signature genes and validate the clinical features association of the signature. Considering immunohistochemistry (IHC) has been a common and effectively used method for observing the expression as well as cellular distribution of specific genes, in the study, 43 cases of local hospital osteosarcoma samples (detailed information of the samples have been reported in a previous research)[ 28 ] were used for IHC experiment. IHC experiment Tissue samples All the local hospital osteosarcoma samples used for IHC experiment were from our hospital Biobank which were originally donated by orthopaedics department patients, informed consent of the potential scientific application of the samples have been obtained by the Biobank staff at the same time patients made donations, and use of the tissues in this study was approved by both Biobank committee and Hospital Institutional Board (Second Hospital of ShanXi Medical University, China). In the study, 43 cases of osteosarcoma samples were picked from hospital Biobank for IHC experiment after reconfirmation of the disease diagnosis and cancer percentage by registered pathologists in hospital pathology department. Given the high heterogeneity of osteosarcoma, instead of making the samples into tissue microarrays, the whole section of each tissue paraffin wax was used for examination. Equipment and Regents The whole procedure of IHC experiment was conducted using local hospital Pathology Department instrument and equipment, and the experiment was performed on VENTANA platform (Roche), the primary antibody to STC2 gene was purchased from proteintech (Cat No.60063-1-IG), and FPR1 antibody was from abclonal (Cat No.A20455). As for the secondary antibody (Envision /HRP kit) and DAB detection kit, two different sets of regents were used, the traditional single color (brown) set of regent were purchased from ZSBG-Bio, and another multi-colors set of regents were from abcarta (Jiang Su, China). As for the other IHC experiment needed regents for instance phosphate-buffered saline (PBS), EDTA antigen retrieval citrate solution, H2O2 and glass slides were all from hospital Pathology Department. IHC experimental protocol IHC experiment procedures were same as previously reported, firstly, the FFPE slides were deparaffinized and rehydrated by gradient ethanol, then being incubated with 0.3% H2O2 for inhibiting endogenous peroxidase activity followed by boiling in 10mmol/l EDTA citrate buffer for antigen retrieval. Further, the slides were soaked in bovine serum albumin for 20min followed by incubating with primary antibodies overnight at 4°C. Then, the slides would be incubated with specific secondary antibody at 37°C for 1 hour and further being processed with horseradish peroxidase (HRP) and visualized using DAB by local hospital registered pathologists. IHC results evaluation IHC experiment staining results were evaluated by registered local hospital pathologists (Second Hospital of ShanXi Medical University, China) based on both the staining intensity and staining area. The score criteria was same as previously reported, that the staining intensity was scored as: None (0), mild (1), moderate (2) and strong (3), meanwhile, the staining area was classified as: 75% (4). The final score of each slide equals the multiplication of staining intensity and staining area, the eventual result would be regarded as negative with the multiplication value < 2, and the result would be defined as positive if the multiplication value ≥ 2 . Quantitative real-time PCR (QPCR) experiments detecting the signature genes expression To detect the hub genes expression in the signature, local hospital osteosarcoma tissue samples (the same as before IHC experiments) were applied to extracted mRNA following QPCR experiment. The total mRNA of the selected osteosarcoma samples were extracted using RNAiso-Plus (TAKARA, DaLian, China). And then1 µg extracted mRNA was used for cDNA synthesis with commercial cDNA synthesis kit (TAKARA, DaLian, China) based on the kit operating instruction. Further, qPCR was performed on Roche z 480 and the primers used were listed as below : STC2: Former: TGAAATGTAAGGCCCACGCT Reverse: CGAGGTGCAGAAGCTCAAGA FPR1: Former: CTGTCAGTTATGGGCTTATTGC Reverse: GCAATAACTCACGGATTCTGAC GAPDH: Former: AGAAGGCTGGGGCTCATTTG Reverse: AGGGGCCATCCACAGTCTTC The PCR cycling condition was set as: 95°C 5 min for 1 cycle; 95°C 10 s, 58°C 30 s, and 72°C 30 s for 40 cycles followed by the melting curve stage. And the relative STC2 and FPR1 genes expression in each sample was recorded as the average 2^−ΔΔCT calculation result of three replicates. The constructed STC2/FPR1 dual genes signature risk score was calculated based on the signature formula, namely firstly the detected expression of STC2 and FPR1 gene multiplies its regression coefficient respectively, and then the sum of last step calculated dual genes score which would be recorded as the final risk score of each sample. Single cell sequencing for validating the cellular location of signature genes meanwhile exploring cellular cross talks After IHC experiment detecting the cellular location and relative expression of signature hub genes in clinical samples, osteosarcoma single cell sequencing data which was also obtained based on GEO database, namely GSE162454 was used to 1. explore the cellular distribution of signature hub genes in osteosarcoma tissues, and 2. preliminary analyze the cellular cross talks between STC2 positive and FPR1 positive cells in osteosarcoma samples. Association between the signature and osteosarcoma tumor infiltrating immune cells (TICs) Immune cells are an important part of cancer microenvironment, for evaluating the immune association of the gene signature in osteosarcoma samples, the immune cell infiltration difference between high-risk and low-risk groups of osteosarcoma cases was analyzed. Firstly, CIBERSORT algorithm was performed to preliminary calculate the relative contents of 22 different TICs in osteosaorcoma samples based on TARGET genes expression profile followed by exploring the relationship between the gene signature and different TICs infiltration. Then, for identifying the dominate immune cells in osteosarcoma microenvironment and analyzing the potential key cell type that was associated with the signature, multi-colors IHC experiment was conducted on hospital osteosarcoma samples, and all the sample tissues, experiment equipment were the same as above described. As for the regents, LCA, MPO and CD68 antibodies in this experiment which were all purchased from ZSBG-Bio were combine used to represent the major immune cell types, namely the lymphocytes, myeloid cells as well as macrophages in osteosarcoma. The major cell type which was supported by IHC experiment to be high level infiltrated into osteosarcoma tumor cells would be mainly focused for further analysis. Correlation between the constructed gene signature and expression of immune checkpoints in osteosarcoma After evaluating the association with immune cells infiltration, considering the critical effect immune checkpoints have on cancers immune status, the expression of immune checkpoints including PD-L1, CTLA4, TIGIT, TIM-3 and LAG-3, the blockade of which have been showing to be able to reverse the tumor immune suppressive microenvironment and benefit patients from immunotherapy were detected in osteosarcoma using IHC experiment. The detailed information of the 5 antibodies were listed in Supplementary table 1 , and the secondary antibodies, IHC equipment, regents and experiment procedures were all same as previously stated. Furthermore, the association between the gene signature and these immune checkpoints expression were assessed, as well as the comparison of expression difference of these immune checkpoints between high-risk and low-risk groups of osteosarcoma patients. Association between the gene signature and osteosarcoma estimated environment immune score Besides immune cells infiltration and immune checkpoints expression, ESTIMATE, which is short for Estimation of Stromal and Immune cells in Malignant Tumors using Expression data has been increasingly applied in worldwide researches as an effective cancer immune evaluation tool in various cancers[ 35 ]. In the study, it was applied to estimate the immune score of osteosarcoma samples based on TARGET genes data, and the correlation between the gene signature and ESTIMATE algorithm based osteosarcoma immune score, stromal score as well as tumor purity were evaluated using R package. Difference of immunogenic cell death (ICD) between high-risk and low-risk groups of osteosarcoma patients Considering the importance of ICD related genes in triggering cancers cellular adaptive immune response through the emission of damage associated molecular patterns (DAMPs), besides previous evaluating the expression difference of 32 commonly known ICD related genes in osteosarcoma tumor comparing to corresponding normal tissues, the genes’ expression difference were also explored in high-risk and low-risk groups of osteosarcoma patients for assisting the validation of immune association of the constructed gene signature. Gene set enrichment analysis (GSEA) of the high-risk and low-risk groups of osteosarcoma patients Additionally, GSEA which has also been widely used in exploring the enrichment of signaling pathways was also applied to explore the immunological related signaling difference between high-risk and low-risk groups of osteosarcoma patients, the analysis threshold was set as P < 0.05. Statistical Analysis Most of the bioinformatics analyses were performed on corresponding online databases. As for the processing of local hospital data, statistical analysis were performed using SPSS 26.0. For enumeration data for instance when comparing genes expression difference between cancer and corresponding normal samples, the data were analyzed using t test. As for the measurement data including the association between gene expression and cancer pathological parameters, the data were analyzed by χ2 test. And for correlation analysis, the data were analyzed by Spearman analysis. p < 0.05 was considered statistically significant. (For all analysis results, * represents p < 0.05, ** represents p < 0.01, *** represents p < 0.001). Results ICD related genes express differently between osteosarcoma and normal tissues indicating potential immune association Osteosarcoma has been a long recognized complex tumor with high heterogeneity and complicated microenvironment, for preliminary exhibiting the potential of immune research in the tumor, the 32 ICD related genes expression were compared between osteosarcoma and corresponding normal bone tissues, and the results revealed serve difference existed between the two groups of samples indicating diverse immune status in respective microenvironment (Figure S1 A). The result supported the potential value of detailed immune research in osteosarcoma. Transcriptome data identified 108 high level differently expressed meanwhile immune related candidate genes in osteosarcoma versus normal bone samples Four GEO transcriptome profiles containing a total of 90 osteosarcoma tissue samples and 19 osteosarcoma cancer cell lines were combine applied to explore the differently expressed genes in osteosarcoma comparing to normal bone samples, and a total of 12168, 17090, 9173 and 3062 genes were identified in GSE12865 (Figure S1 B), GSE16088 (Figure S1 C), GSE28424 (Figure S1 D) and GSE42352(Figure S1 E) respectively. Besides the genes that were shared among profiles, the expression of 815 genes were revealed to be high level (> 8 fold) different in osteosarcoma comparing to normal samples (detailed information of the genes has been previously reported)[ 28 ]. Meanwhile, based on IMMPORT immune database, the commonly known immune related genes list was obtained, and further Venn diagram analysis result revealed that 108 genes from the 811 genes were both high level expression changed and immune regulation related (Figure S1 F, Table S2 ), the 108 genes were identified as candidate genes for further analysis. Big portion of the candidate genes were interrelated and enriched in immune associated biological processes and signaling pathways To preliminary understand the potential inner connection and validate immune association among the selected candidate genes, the PPI network of the 108 differently expressed meanwhile potentially immune related genes was constructed followed by enrichment analysis (Fig. 1 A). And the results revealed that nearly 92% of the candidate genes (100 genes) were related with cellular response to stimulus (Fig. 1 B), 53% were associated with immune system processes (56 genes, Fig. 1 C) including innate immune system (30%, 33genes, Fig. 1 D) and adaptive immune system (21%, 23 genes, Fig. 1 E). Meanwhile, 18% of genes were involved in cytokine signaling in immune system (19 genes, Fig. 1 F), 17% of genes were interrelate to lymphocyte activation regulation (18 genes, Fig. 1 G), and 10% of genes were correlated with immune receptor activity as well as natural killer cell mediated cytotoxicity. The involvement of the candidate genes in various immune related signaling assisted firstly the worthy of immune research in osteosarcoma, and secondly the potential of above selected candidate genes in further application. Construction of a STC2/FPR1 dual genes containing signature based on immune related candidate genes To maximum the clinical value of the selected candidate genes and construct a reliable meanwhile efficient multi-genes signature for predicting the immune status of osteosarcoma sample, cox-proportional hazards analysis based on LASSO algorithm was applied to calculate the representative gene equation. And the result firstly revealed a 6 genes containing signature, including STC2, FPR1, VCMA1, PTN, IL13RA2 and HCST (Fig. 2 A, 2 B), the signature weights the normalized expression level of each gene to the regression coefficient of multivariate Cox regression analysis, revealing the final gene equation as: Risk Score = 0.0928* expression (STC2)- 0.1685* expression (FPR1)- 0.0654* expression (VCAM1)- 0.0182* expression(PTN)- 0.0384* expression (IL13RA2)- 0.0677* expression (HCST) (Fig. 2 C- 2 E). Both the Kaplan-Meier survival analysis (Fig. 2 F) and ROC curve (Fig. 2 G) showed great prognosis risk association of the signature. Disturbing by the complex of experimental validation and considering the feasibility of further clinical application of multi-genes containing signature, to further optimize and scale down the amount of signature hub genes, the signature equation was carefully analyzed. And the following points supported two of the signature genes namely STC2 and FPR1 working as the main hub genes in the equation, firstly, STC2 was as the only gene that positively correlates with signature risk score, meanwhile, all the other five genes are negatively related indicating the unique role of STC2. Secondly, the regression coefficient value of FPR1 was the highest among all five negatively correlated genes supporting the relatively higher value. Thirdly, the ROC curve based on different genes combination indicated better survival prediction of STC2/FPR1 combination than other dual-genes combination (data not shown). Above results supported the construction of a new STC2/FPR1 dual genes signature for replacing the complicated six genes equation. Thus, after understanding the basic physicochemical properties of STC2 and FPR1 including their molecular Weight, theoretical pI, hydrophobic value, estimated protein half life and so on (Table 1 ), to maximum the value of the two genes in the signature, LASSO algorithm was again applied to recalculate the coefficient for the genes, and the final dual genes signature was: Risk Score= (0.5663)* STC2 + (-1.3968)* FPR1 (Fig. 3 A- 3 C). An inspiring fact is that although the prediction value of the STC2/FPR1 dual genes signature seemed lower than the original six genes containing signature, the difference was not statistical significant (Fig. 3 D) supporting its clinical value. Table 1 Basic physicochemical properties of STC2 and FPR1 genes applied for constructing the dual genes signature Gene Property STC2 FPR1 Formula C 1425 H 2269 N 439 O 441 S 20 C 1781 H 2796 N 444 O 467 S 17 Molecular Weight 33.2KD 38.4KD Number of amino acids 302AA 350AA Theoretical pI 6.93 9.23 Aliphatic index 70.79 113.06 Hydrophobic value −0.518 0.682 Estimated protein half life 30h 30h Instability index 38.87 (Stable) 26.33 (Stable) STC2/FPR1 works as an independent prognostic indicator in osteosarcoma samples Both STC2 and FPR1 were previously supported to be high level differently expressed in osteosarcoma comparing to normal bone samples, but changed gene expression doesn’t necessarily mean survival association. To explore the direct prognosis risk indication ability of the dual genes signature, series of analysis was conducted. Firstly, based on the calculated median risk score of STC2/FPR1 genes signature, the 89 osteosarcoma cases from TARGET database were categorized into high-risk and low-risk groups, Kaplan Meier survival analysis revealed the high-risk group of patients had both a statistical significantly worse overall survival (OS) and shorter recurrence free survival (RFS) than the low-risk group (Fig. 3 E, 3 G). Meanwhile, ROC curve showed promising 1 year, 3 years and 5 years OS and RFS prediction value of the dual genes signature (Fig. 3 F, 3 H). Moreover, univariate Kapkan-Meier survival, multivariate Cox regression analysis as well as nomogram construction were in succession performed. Combine analysis of Kapkan-Meier survival and Cox regression analysis revealed that although multiple factors including the dual genes signature risk score, tumor metastasis status and tumor soft tissue invasion were associated with patients prognosis, only the signature risk score and tumor metastasis status work as independent prognosis indicators in osteosarcoma (Table 2 ). Meanwhile, a nomogram was drawn combining the prognosis risk variables, and in the nomogram, a point scale was assigned for each variable, the sum of all the variable points equal to the final score of each clinical case, thus the survival risk of the case would be predicted by drawing a vertical line from the total point axis downward to the outcome axis (Fig. 3 I). Table 2 Univariate combine with multivariate Cox Regression analysis result of STC2/FPR1 dual genes signature and other osteosarcoma clinical parameters Clinical parameters P Value Exp (B) Univariate analysis Multivariate analysis Gene Signature score < 0.001 < 0.001 10.405(3.972–27.266) Gender 0.454 - - Race 0.963 - - Histologic response 0.048 - - Primary tumor site 0.016 - - Surgery pattern 0.411 - - Metastasis status < 0.001 < 0.001 8.662(3.700−20.278) Metastasis site < 0.001 - - Further, the association between STC2/FPR1 dual genes signature and osteosarcoma clinical features were together analyzed, and the result revealed that although no significant correlation was discovered between the gene signature and patients age, gender, primary tumor site or tumor recurrence, the metastasis was higher in the high-risk group of patients than the low-risk group patients, even though the difference was not statistical significant partly due to the limited patients number in each group (Table 3 ). Table 3 Association between the STC2/FPR1 dual genes signature and osteosarcoma clinical features Parameters STC2/FPR1 gene signature P Value Low-risk group High-risk group Gender female 22 (57.9%) 16 (42.1%) 0.168 male 22 (43.1%) 29 (56.9%) Age < 15 27 (56.2%) 21 (43.8%) 0.153 15 ~ 20 11 (35.5%) 20 (64.5%) ≥ 20 6 (60.0%) 4 (40.0%) Metastasis status No 36 (55.4%) 29 (44.6%) 0.065 Yes 8 (33.3%) 16 (66.7%) Metastasis site Lung 7 (41.2%) 10 (58.8%) 0.423 Bone 0 (0) 1 (100%) Others 1 (16.7%) 5 (83.3%) Primary tumor site Upper limbs 2 (33.3%) 4 (66.7%) 0.251 Lower limbs 42 (51.9%) 39 (48.1%) Other bones 0 (0) 2 (100%) Primary tumor progression No 9 (39.1%) 14 (60.9%) 0.389 Yes 8 (53.3%) 7 (46.7%) Recurrence No 14 (42.4%) 19 (57.6%) 0.593 Yes 19 (48.7%) 20 (51.3%) Chemotherapy necrosis < 90% 10 (52.6%) 9 (47.4%) 0.492 ≥ 90% 7 (41.2%) 10 (58.8%) Multi-colors IHC experiment observing the cellular location and expression pattern of STC2/FPR1 in osteosarcoma To validate the clinical application value of the STC2/FPR1 dual genes signature in real world osteosarcoma samples, firstly, we compared the prognosis prediction value of the STC2/FPR1 dual genes signature than single STC2 or FPR1 genes, and the result supported the genes were of higher clinical value when they were combined together (Fig. 4 A). And also the patients with high STC2 expression meanwhile low FPR1 expression posses the worst survival, and the survival of patients with the opposite low STC2 and high FPR1 expression were the best, meanwhile the survival of others with either STC2 or FPR1 high expression were intermediate (Fig. 4 B). Both results supporting the value of combination analysis of STC1+/FPR1- dual genes signature. However, an interestingly fact is that no significant correlation has been found between STC2 and FPR1 mRNA expression (Fig. 4 C), indicating a different cooperation system between them. Thus, to preliminary explore the working patterns of STC2 and FPR1 genes, IHC experiment was conducted using local hospital patients tissues to observe the genes’ expression. And the result revealed that STC2 and FPR1 genes located in different cellular components and regions in osteosarcoma tissue, STC2 was mostly enriched in the tumor cells (Fig. 4 D), meanwhile, FPR1 was more observed in monocytes and macrophages especially the ones that were locating in the periphery of tumor region (Fig. 4 E). And a double staining IHC experiment revealed that as a heterogeneity tumor, in the less differentiated region of osteosarcoma with almost all solid tumor cells and barely bone formation, the tumor cells posses more STC2 expression and little FPR1 positive immune cells exist. In opposite, in a more differentiated region with higher percent of osteogenesis, more FPR1 positive immune cells were observed and STC2 expression was much less (Fig. 4 F). The result partly explains the difference of prognosis association among above STC2+/FPR1-, STC2-/FPR1+, STC2+/FPR1 + and STC2-/FPR1- groups of patients. Single cell sequencing analyzing the potential osteosarcoma cellular cross talks After IHC experiment observing the cellular location and expression of STC2 and FPR1 in clinical osteosarcoma tissues, a single cell sequencing GEO profile, namely GSE162454 was next analyzed for better understanding the potential function modes of STC2/FPR1 genes in osteosarcoma. A total of 6 osteosarcoma cases samples were included in the profile, and unbiased clustering of the cells that were included in the profile identified 8 main clusters in parallel using t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), namely fibroblasts, mesenchymal stem cells (MSC), cancer cells, osteoclasts (OC), B lymphocytes, Nature Killer and T lymphocytes (NK/T cells), myeloid cells and endothelial cells (EC) (Fig. 5 A). Expression patterns of the representative markers genes in each cell cluster were demonstrated (Fig. 5 B). Based on the profile, we observed the relative distribution of STC2 and FPR1 genes in each cell cluster, and the result supported that STC2 was mainly focused in cancer cells and some MSC, meanwhile, FPR1 was mostly enriched in myeloid cell region (Fig. 5 C). Further, the primary cellular connection between different cell clusters was analyzed and a strong connection was found between cancer cells and myeloid cell clusters indicating the potential cooperative network between the cancer and immune cells (Fig. 5 D). Meanwhile, the potential receptor and ligand pairs between cells analysis revealed that although STC2-FPR1 was not a direct connection signaling between cancer and macrophages, there were multiple other potential signaling between the two cell types, and FPR1 was a promising receptor protein in myeloid cells (Fig. 5 E). The indirect interaction between STC2 and FPR1 proteins indeed partly explains above phenomenon that the genes play different roles in osteosarcoma microenvironment and posses opposite survival association. STC2/FPR1 signature was associated with macrophages dominated immune cells infiltration in osteosarcoma The main purpose of constructing the STC2/FPR1 dual genes signature is for immune indication, and cancer immune environment has been a complex system containing various cellular types including cancer cells, surrounding immune cells, cancer-associated fibroblasts (CAFs), endothelial cells and other tissue specific cell types. Although both STC2 and FPR1 were indicated by previous analysis to be immune related, the immune association of the constructed gene signature was still unclear. Thus, the correlation between the dual genes signature and osteosarcoma immune cells distribution was next step analyzed. Firstly, the well known CIBERSORT algorithm was used to calculate the relative contents of 22 TICs difference between osteosarcoma high-risk and low-risk groups of patients, and the results revealed multiple immune cells including B cells naive, B cells memory, CD8 + T cells, CD4 + T cells, M1 macrophages, M2 macrophages, mast cells and neutrophils were differently distributed between two groups (Fig. 6 A, Table 4 ). Combing with correlation analysis which was calculated based on the direction association between the gene signature score and different immune cells infiltration level as well as another differentiation analysis which was conducted according to the signature score difference between high and low immune cell infiltration groups, a total of 6 immune cells including B cells naive, B cells memory, M1 macrophages, M2 macrophages, mast cells and neutrophils were supported to correlate with STC2/FPR1 gene signature (Fig. 6 B). Table 4 Correlation between STC2/FPR1 gene signature risk score and different immune cells infiltration Immune cell type Correlation Ratio P value B cells naive 0.331 0.003* B cells memory −0.338 0.002* Plasma cells 0.006 0.958 T cells CD8 0.155 0.168 T cells CD4 naive 0.181 0.105 T cells CD4 memory resting −0.195 0.081 T cells CD4 memory activated −0.091 0.420 T cells follicular helper 0.048 0.673 T cells regulatory (Tregs) 0.121 0.283 T cells gamma delta −0.084 0.455 NK cells resting 0.001 0.990 NK cells activated 0.263 0.017* Monocytes −0.145 0.096 Macrophages M0 0.272 0.014* Macrophages M1 −0.258 0.020* Macrophages M2 −0.380 < 0.001* Dendritic cells resting 0.065 0.562 Dendritic cells activated 0.001 0.995 Mast cells resting −0.127 0.258 Mast cells activated 0.240 0.031* Neutrophils −0.309 0.005* *P < 0.05 was referenced as statistical significant. To maximum the clinical value of the gene signature and select the most important immune cell type that was associated, multi-colors IHC experiment was conducted using hospital osteosarcoma samples (same samples as previous IHC experiment), and an inspiring result was that barely LCA labeled lymphocytes or MPO stained granulocytes were observed in osteosarcoma tissues, meanwhile, CD68 stabled macrophages have been the main immune cell type in the tumor. And also the amount of macrophages was statistical significantly high in high grade osteosarcoma than low grade samples (Fig. 6 E, 6 F), above result all supporting the importance of macrophages in immune regulation of osteosarcoma microenvironment. More importantly, based on previous single cell sequencing data, we compared the distribution of commonly used macrophages M1/M2 polarization indicators in osteosarcoma cell clusters, and the results revealed that among all the CD14 positive macrophages (Fig. 7A), more M2 indicators including CD163, CD206 (also known as MRC1) and IL10 (Fig. 7D-7F) were observed than M1 indicators for instance CD80 and IL6 (Fig. 7B, 7C). Moreover, the IHC experiment based on local hospital osteosarcoma samples also supported that most macrophages in osteosarcoma microenvironment were M2 type (Fig. 7I). Meanwhile, for validating the correlation between the STC2/FPR1 dual genes signature and osteosarcoma macrophages distribution in local hospital samples, firstly, based on TARGET database, we calculated the correlation between STC2/FPR1 dual genes signature risk score and the expression of multiple macrophages M1/M2 polarization indicators including CD80, CD86, CD40, IDO1, CD163, IL10 and IL6, and the result revealed that the correlation between the signature and two most commonly used M2 polarization markers CD163 as well as IL10 expression were the strongest among all connections (Fig. 7G). Meanwhile, both CD163 and IL10 turned to express higher in the high-risk group of osteosarcoma patients, the results supported the correlation between STC2/FPR1 signature and macrophages M2 polarization (Fig. 7H). Besides above analysis, QPCR experiment conducted using local hospital samples also revealed a statistical significant positive correlation between the gene signature score and CD163 positive M2 macrophages infiltration. STC2 gene expression was much higher in high grade samples with more M2 macrophages infiltration, meanwhile, opposite trend was observed with FPR1 expression (Fig. 6 C, 6 D), supporting the potential value of STC2/FPR1 dual genes signature in the regulation of M2 macrophages dominated osteosarcoma immune microenvironment. TIM-3 included multi immune checkpoints expression were associated with STC2/FPR1 gene signature score Besides the estimation of association with immune cells infiltration, the correlation between the gene signature and clinical promising immune checkpoints including PD-L1, CTLA4, TIGIT, TIM-3 and LAG-3 were evaluated based on TARGET genes expression profile. And a median association was revealed between the gene signature and PD-L1 as well as TIM-3 expression (Fig. 8 A, 8 B), and both checkpoints tend to express higher in low-risk group of patients (Fig. 8 C). Meanwhile, no significant relation was found between the signature and CTLA4, TIGIT or LAG-3 expression. In accordance with the correlation between STC2/FPR1 signatures and the immune checkpoints expression, based on TARGET database, we discovered that expect for CTLA4, all the other four immune checkpoints including TIM-3, PD-L1, LAG-3 and TIGIT were correlated with better patients overall survival, the results supported the immune targeting potential in the low-risk group of patients based on STC2/FPR1 signature (Figure S2 A-S2E). However, an interesting fact based on local hospital IHC experiment was that very low PD-L1 expression was observed in osteosarcoma samples explaining part of the reason that only small percent of current PD-L1 blockade cases was proved effective in osteosarcoma clinical therapy (Fig. 8 E). Meanwhile, moderate amount of CTLA4, TIGIT and TIM-3 expression were detected in the tissues. A phenomenon worth of notice is that of all the immune checkpoints, the expression of TIM-3 was much higher and complete than the others (Fig. 8 D, 8 F- 8 H), combining with the fact that it was statistical significantly associate with the dual genes signature indicating a potential value of TIM-3 being developed as an immune drug target in further clinical treatment. High-risk and low-risk groups of osteosarcoma patients based on STC2/FPR1 gene signature revealed disparate ICD related genes expression level Considering the significant roles of ICD in anti-tumor immunological responses, the connection between STC2/FPR1 gene signature and ICD genes were additionally evaluated. And the results revealed that the expression of a large portion of 32 selected ICD genes were statistical significantly different between not only previously detected osteosarcoma and normal bone samples, but also the high-risk and low-risk groups groups of patients which were classified based on the STC2/FPR1 genes signature risk score (Fig. 9 A). The result additionally supported the diverse immune status in the two groups of oateosarcoma patients. STC2/FPR1 signature correlated with osteosarcoma estimated environment immune score and immune related signaling pathways For further validating the immune association of STC2/FPR1 gene signature, firstly, GSEA was utilized to analyze the immune-related biological processes linked to the STC2/FPR1 gene signature, and the result indicated that the low-risk group cases were more enriched in immune related processes, including POSITIVE REGULATION OF IMMUNE RESPONSE, HUMORAL IMMUNE RESPONSE, LYMPHCYTE MEDIATED IMMUNITY, T CELL MEDIATED IMMUNITY as well as MYELOID CELL DEVELOPMENT (all NES <-1, Nominal p value < 0.05) (Fig. 9 B). Meanwhile, none specific immune related enrichment was indicated in high-risk group cases (Figure S3 ). The result shall be an additional support for indicating the immune targeting potential for low-risk group of osteosarcoma patients. Moreover, ESTIMATE algorithm was additionally performed to evaluate the immune, stromal score and tumor purity of osteosarcoma samples based on TARGET data, and the result revealed statistical significant correlation between the gene signature and all three parameters including the tumor stromal score, immune score as well as tumor purity. Meanwhile, as tumor purity was higher in high-risk group of patients, the low-risk group patients were tend to posses both higher immune and stromal score (Fig. 9 C). Further, TIDE analysis also indicated that low-risk group of patients were more likely to response to immune therapy (Fig. 9 D), all results supporting the immune targeting potential of this group of osteosarcoma patients in further clinical treatment. Discussion Cancer has been a major health threat for people, the development of cancer involves not only the genetic alterations of oncogenes and tumor inhibiting genes, but also interaction between cancer cells and surrounding microenvironment. Besides uncontrolled proliferation, resisting cell death, activating metastasis and so on, evading immune surveillance has been one of the well acknowledged major cancers hallmarks. Based on increasing understanding of the immune regulation in cancers, immune checkpoint inhibitors (ICIs) has been a promising pillar of nowadays cancer clinical treatment[ 36 , 37 ]. Osteosarcoma has been a common bone malignancy affecting mainly children and adolescents, and the immunotherapy for osteosarcoma can be traced back to 1891, when William Coley, the well known “father” of cancer immunotherapy, discovered that Coley toxin (an inactivated toxin from bacteria) has therapeutic effects on osteosarcoma included multiple cancers[ 38 ]. Although Coley toxin wasn’t widely used because of its toxic effects and instability in clinical therapy, it did lay a great foundation for the development of immune researches in cancers[ 39 ]. Over time, increasing attention was drawn on various immune activators, for instance muramyl tripeptide which is a synthetic derivative of bacteria. At first, clinical trials did not show any therapeutic effects, but follow-up studies reported that combining mifomotide to chemotherapy could increase patients overall survival[ 40 , 41 ]. Although it hasn’t been approved by US Food and Drug Administration (FDA) for clinical use, mifamotide was licensed by the European Medicines Agency in 2009 and is currently undergoing clinical studies in advanced osteosarcoma cases in Europe and Mexico[ 3 ]. As for the clinical use of ICIs in osteosarcoma, the current clinical trials were mostly focused on PD-1/PD-L1 and CTLA-4. Although the treatment did achieve great therapeutic effects in multiple cancers such as melanoma and lung cancer[ 42 ], the effectiveness was not replicated in osteosarcoma. Especially for PD-1/PD-L1 blockade, although studies found that blocking PD-1/PD-L1 interaction improves the responsiveness to CTL[ 43 ], reducing tumor burden and enhancing the chemotherapy effect of cisplatin on osteosarcoma[ 44 ], the effectiveness of these studies were only observed in animal model stage, and the clinical trials in patients have not received same therapeutic response. However, despite the fact that none specific breakthrough has been received in osteosarcoma immunotherapy, the well treatment effect in animal models and even just small part of patients indeed reveal the potential of immune researches in the tumor. Better understanding of the genetic landscape and regulation mechanism behind osteosarcoma immune microenvironment shall benefit identifying new potential immune biomarkers and aiding further clinical application of ICI therapy in the tumor. In recent years, various genes containing signatures representative of caner immune status have been identified in different types of cancers, and they have been showing inspiring clinical effects[ 45 – 49 ], meanwhile, multiple meaningful studies involving the transcriptome and genome data of osteosarcoma, have also been released[ 50 – 52 ]. It’s of clinical feasibility to construct meaningful immune prediction models based on osteosarcoma genome information, thus benefiting evaluating the cancer immune status for potential clinical ICIs therapy. In the study, GEO osteosarcoma transcriptome profiles as well as single cell sequencing data were together applied for analyzing the potentially immune related gene candidates and exploring their function mode. Based on four GEO osteosarcoma transcriptome profiles which have been previously selected and applied to analyze potential tumor development related key genes, the differently expressed genes in osteosarcoma comparing to normal samples were selected and divided into 4 groups including difference level 8 fold genes clusters, the detailed information of the genes have also been previously discussed. In the study, we mainly focused on the high level (> 8 fold) differently expressed genes for the convenience of further IHC experiment, considering that the genes shall harbor more chance for clinical use if they are suitable to be tested by IHC which has been one of the most common clinical pathology experimental methods. Further, by intersection with the immune related gene list from IMMPORT database, a total of 108 high level expression changed meanwhile immune related gene candidates were identified for next step analysis. After PPI network following GO/KEGG analyzing the immune enrichment of the candidate genes, for constructing a clinical effective multi-genes signature based on the genes, LASSO algorithm which has been a widely used tool suitable for building gene models basing on large numbers of genes expression profile was used. At first, a six genes containing signature composed of STC2, FPR1, VCMA1, PTN, IL13RA2 and HCST genes was proposed, however, despite the fact that both KM survival analysis and ROC curve showed great prognosis indication ability of the signature, a six genes signature is over complicated and hard to be applied in real world clinical experiments. The main purpose of the study was to construct a signature that was suitable for further clinical application, so we next step scale the signature hub genes down to two genes, namely STC2 and FPR1 which was mainly based on the coefficient of signature genes together with survival analysis. STC2 is short for stanniocalcin2, it locates in 5q35.2 and encodes a protein consisting of 302 amino acids including 36 negatively charged amino acid residues (ASP + Glu) and 35 positively charged amino acid residues (Arg + Lys), meanwhile, the molecular weight of the protein is estimated to be 33.2KD with the theoretical isoelectric point computed as 6.96. As for FPR1, the formula of the protein is C 1781 H 2796 N 444 O 467 S 17, which is composed of 350 amino acids weighing 38.4KD, and the estimated protein half life is 30 hours with the theoretical isoelectric point computed to be 9.23. Both STC2 and FPR1 proteins were predicted to be stable in cells with the instability index estimated to be 38.87 and 26.33 respectively. Based on the two genes, a newly STC2/FPR1 dual genes signature was constructed, although the prognosis indication value of the signature was lower than the original six genes containing signature, KM survival as well as Cox regression analysis still supported the dual genes signature as an independent survival indicator. Further, a nomogram combining the STC2/FPR1 dual genes signature and tumor metastasis which has also been an important clinical survival risk indicator was constructed, which shall benefit the clinical evaluation of osteosarcoma patients prognosis. The main aim of the signature was for potential immune status prediction in osteosarcoma samples, and even though both STC2 and FPR1 were indicated by previous IMMPORT data to be included in the immune related genes list, the detailed association between the dual genes signature and ossteosarcoma immune microenvironment regulation should be carefully explored. Considering the vital function that various immune cells play in cancer immune microenvironment, for instance, the increasing immune suppressive cells including Treg cells and tumour-associated macrophages (TAM), as well as inactivation of tumor killing CD8 + T cells have been reported to contribute to immune escape in osteosarcoma included various cancers[ 53 – 57 ], the correlation between the signature and immune cells distribution was firstly evaluated. And the result analyzed based on genes expression profile indicated that STC2/FPR1 gene signature was associated with 6 immune cell types including B cells naive, B cells memory, M1 macrophages, M2 macrophages, mast cells and neutrophils. Interestingly, the IHC experiment conducted on local hospital patients samples revealed that very few amount of lymphocytes or granulocytes infiltrated in osteosarcoma tissues, and the macrophages were actually the main immune cell type in the tumor which phenomenon has been proposed by multiple other researches. Thus, given the dominated role of macrophages in osteosarcoma microenvironment, the association between the STC2/FPR1 signature and macrophages was mainly focused, and further QPCR result indeed supported a statistical significant positive correlation between them. Considering the critical function of immune checkpoints in the development of ICIs therapy, the expression of immune checkpoints including PD-L1, CTLA4, TIGIT, TIM-3 and LAG-3 between the high-risk and low-risk groups of osteosarcoma patients which were divided based on the STC2/FPR1 gene signature risk score were evaluated. And although moderate correlation has been detected with both PD-L1 and TIM-3 expression, local hospital IHC experiment revealed that very low PD-L1 expression was observed in osteosarcoma samples, even in the low-risk group of patients, this might be part of the reason that PD-L1 blockade hasn’t been receiving satisfactory clinical effect. And another inspiring discovery is that as for CTLA4 gene, although the checkpoint expression was not statistical significantly correlated with the gene signature, its expression was broadly high in a big portion of samples, partly explaining the reason that CTLA4 blockade hes been receiving response in some patients. And of the other three immune checkpoints, interestingly, the expression of TIM-3 was much higher and complete than the TIGIT and LAG3, indicating a potential value of TIM-3 being developed as an immune drug target in further clinical treatment Moreover, other analysis including ICD related genes expression, GSEA immune related signaling enrichment and ESTIMATE immune scores evaluation were additionally performed. And the different expression of ICD related genes not only between osteosarcoma and normal bone samples, but also between high-risk and low-risk groups of patients supported the different immune status in corresponding groups of osteosarcoma patients. Meanwhile, the more enriched immune related signaling pathways revealed by GSEA analysis as well as the higher ESTIMATE immune and stromal score in low-risk group of patients than their high-risk group of counterparts, and also the TIDE analysis results all indicated the immune targeting potential for this group of patients, highlighting the potential of developing further new clinical immunotherapy strategies. Conclusion The study constructed a STC2/FPR1 dual genes signature based on osteosarcoma genome information, the newly constructed signature not only works as an independent prognosis indicator, but also related with osteosarcoma immune microenvironment including macrophages dominated immune cells infiltration and TIM-3 included immune checkpoints expression. Although the current result is not yet enough to support the clinical application of the signature, rigorous prospective studies performed on animal models as well as clinical trials are still needed, the results shall provide meaningful insight into immune researches in osteosarcoma, thus benefiting further development of new immunotherapy strategies. Abbreviations AA-TKIs Anti-angiogenesis tyrosine kinase inhibitors GEO Gene Expression Omnibus TARGET Therapeutically Applicable Research to Generate Effective Treatments ICD Immunogenic cell death PPI Protein-protein interaction network IHC Immunohistochemistry ICIs Immune checkpoint inhibitors QPCR Quantative Real Time PCR LASSO Least absolute shrinkage and selection operator GO Gene ontology analysis KEGG Kyoto Encyclopedia of Genes and Genomes GSEA Gene set enrichment analysis Declarations Acknowledgements We sincerely appreciate the researchers for providing their GEO databases information online which were important data resources for the study, it is our pleasure to acknowledge their contributions. Authors' contributions WM and LM designed the study and together drafting the manuscript, contributed equally to the whole study. ZZ, SL and JL performed the data collecting and analysis. FW, NS , ZY and LG participated in the data interpretation and study design, LG and CW were involved in the drafting and critical revision of the manuscript. As the corresponding author, CW has full access to all data of the manuscript and made the final decision to submit the article for publication. All authors read and approved the final manuscript. Funding The work was supported by the China central government funds for guiding local scientific and technological development (YDZJSX2021A042), the fund of Science project from Health Commission of ShanXi Province (2023103) and three grants of Natural Science Foundation of ShanXi Province in China (202203021222393, 202303021222333, 202403021211135). Availability of data and materials Publicly available datasets were analyzed in this study. The data can be found here: GSE12865:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12865. GSE16088:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16088. GSE28424:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28424. GSE42352:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42352. GSE162454: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162454. TARGET database: https://ocg.cancer.gov/programs/target/data-matrix All data generated or analyzed based on the online datasets and other experiments during this study are included in this published article. Human ethics and consent to participate This is a retrospective study involving human samples, the samples were obtained from the hospital Biobank (Second Hospital of Shanxi Medical University), the utility of the samples in the study was approved by the ethics committees of Second Hospital of ShanXi Medical University (ethics approval number: (2023)YX(179)). As a retrospective analysis using patient postoperative samples which were stored permanently in hospital Biobank, the study was approved for exemption from the requirement of informed consent. 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Validation of endoplasmic reticulum stress-related gene signature to predict prognosis and immune landscape of patients with non-small cell lung cancer. Technol Health Care 2024. Gui Z, Du J, Wu N, Shen N, Yang Z, Yang H, Wang X, Zhao N, Zeng Z, Wei R, et al. Immune regulation and prognosis indicating ability of a newly constructed multi-genes containing signature in clear cell renal cell carcinoma. BMC Cancer. 2023;23(1):649. Ho XD, Phung P, V QL, Reimann VHN, Prans E, Koks E, Maasalu G, Le K, L HT. Whole transcriptome analysis identifies differentially regulated networks between osteosarcoma and normal bone samples. Exp Biol Med (Maywood). 2017;242(18):1802–11. Poudel BH, Koks S. The whole transcriptome analysis using FFPE and fresh tissue samples identifies the molecular fingerprint of osteosarcoma. Exp Biol Med (Maywood). 2024;249:10161. Rothzerg E, Ho XD, Xu J, Wood D, Martson A, Maasalu K, Koks S. Alternative splicing of leptin receptor overlapping transcript in osteosarcoma. Exp Biol Med (Maywood). 2020;245(16):1437–43. Moukengue B, Lallier M, Marchandet L, Baud'huin M, Verrecchia F, Ory B, Lamoureux F. Origin and Therapies of Osteosarcoma. Cancers (Basel) 2022, 14(14). Mitra A, Kumar A, Amdare NP, Pathak R. Current Landscape of Cancer Immunotherapy: Harnessing the Immune Arsenal to Overcome Immune Evasion. Biology (Basel) 2024, 13(5). Gerashchenko T, Frolova A, Patysheva M, Fedorov A, Stakheyeva M, Denisov E, Cherdyntseva N. Breast Cancer Immune Landscape: Interplay Between Systemic and Local Immunity. Adv Biol (Weinh). 2024;8(7):e2400140. Alzamami A. Implications of single-cell immune landscape of tumor microenvironment for the colorectal cancer diagnostics and therapy. Med Oncol. 2023;40(12):352. Heymann MF, Lezot F, Heymann D. The contribution of immune infiltrates and the local microenvironment in the pathogenesis of osteosarcoma. Cell Immunol. 2019;343:103711. Additional Declarations No competing interests reported. Supplementary Files FigureS1.pdf FigureS2.pdf FigureS3.pdf SupplementaryTablesandfigurelegends.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-5651928","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431157330,"identity":"5f18287b-0b0c-4045-a99a-5aaef9c93120","order_by":0,"name":"Wenxia Ma","email":"","orcid":"","institution":"Second Hospital of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenxia","middleName":"","lastName":"Ma","suffix":""},{"id":431157332,"identity":"ad0726e1-28e2-4472-9b57-8d4be1f5c464","order_by":1,"name":"Lei Miao","email":"","orcid":"","institution":"Basic school of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Miao","suffix":""},{"id":431157336,"identity":"89cb9cdd-1198-422c-a00d-3c5311211bf0","order_by":2,"name":"Siying Liu","email":"","orcid":"","institution":"Second Clinical Medical College of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Siying","middleName":"","lastName":"Liu","suffix":""},{"id":431157337,"identity":"7c52cc5d-0b18-405a-b7d7-b438f46910e3","order_by":3,"name":"Zixin Zeng","email":"","orcid":"","institution":"Basic school of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zixin","middleName":"","lastName":"Zeng","suffix":""},{"id":431157338,"identity":"983226c5-2536-469c-b080-82a8521d6394","order_by":4,"name":"Jiayao Li","email":"","orcid":"","institution":"Second Clinical Medical College of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiayao","middleName":"","lastName":"Li","suffix":""},{"id":431157339,"identity":"7f37df0f-8cee-4873-b97c-c0e6e63055ae","order_by":5,"name":"Fei Wang","email":"","orcid":"","institution":"Second Clinical Medical College of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Wang","suffix":""},{"id":431157341,"identity":"10959666-600b-495f-ba57-bab31754e196","order_by":6,"name":"Ningning Shen","email":"","orcid":"","institution":"Second Hospital of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ningning","middleName":"","lastName":"Shen","suffix":""},{"id":431157342,"identity":"10e2ebd4-d1d0-40cc-add1-975c79a7bcc6","order_by":7,"name":"Zhiqing Yang","email":"","orcid":"","institution":"Second Hospital of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhiqing","middleName":"","lastName":"Yang","suffix":""},{"id":431157343,"identity":"4e761b66-98c3-4b47-beef-9c8bf4c549b5","order_by":8,"name":"Lifang Gao","email":"","orcid":"","institution":"Second Hospital of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lifang","middleName":"","lastName":"Gao","suffix":""},{"id":431157344,"identity":"303fb49c-e435-4547-a92e-e27f44345c9b","order_by":9,"name":"Chen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYLACxgYGBn4kNpFaJBtI1mJwgFgtBsfPHn7xc4dNnvH5M6abeRhsZDccYH72AK+WM3lplr1n0orNbqSl3eZhSDPecIDN3ACfFrMDOWbGjG2HE7fdYD4G1HI4ccMBHjYJvFrOvwFp+Z+4uf9gG1DLfyK03MgxfszYdiBxA0MyyJYDhLXY33hjxtjblpw4A+iXm3MMko1nHmYzw6tFsj/H+MPPNrvE/v4zZjfeVNjJ9h1vfoZXCxAgOwMUVMwE1IOUfCCsZhSMglEwCkY0AABzwE9dc3zLowAAAABJRU5ErkJggg==","orcid":"","institution":"Second Hospital of ShanXi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-12-16 08:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5651928/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5651928/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79093327,"identity":"8e8aa027-d262-43f8-bd9b-c53d3f405cdf","added_by":"auto","created_at":"2025-03-24 10:27:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network construction of the 108 candidate genes and immune association validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The PPI network of the 108 high level differential expression meanwhile immune related genes in osteosarcoma which were identified based on GEO and IMMPORT datasets. Immune association validation of the 108 genes, and (B) 92% of the genes (100 genes, red colored spots in the network) were related with cellular response to stimulus, (C) 53% of the genes (56 genes, red colored spots in the network) were associated with immune system processes, (D) 30% of the genes (33 genes, red colored spots in the network) were related with innate immune system and (E) 21% of them (23 genes, red colored spots in the network) were related with adaptive immune system. (F) 18% of the 108 candidate genes were involved in cytokine signaling in immune system and (G) 17% of genes were interrelate to lymphocyte activation regulation (red colored spots in each network).\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/68ef9e458901fb61987c1613.png"},{"id":79093526,"identity":"2c7439f7-d1b2-4f05-a2f6-b9537acf7bab","added_by":"auto","created_at":"2025-03-24 10:35:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":610499,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of a preliminary multi genes containing meanwhile immune and prognosis related gene signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) LASSO analysis to calculate the coefficient and likelihood deviance for constructing a immune meanwhile prognosis related signature based on the 108 selected candidate genes. (C) The 89 osteosarcoma cases in TARGET database were divided into high-risk and low-risk groups based on the calculated signature score. (D) Survival status of the high-risk and low-risk groups of osteosarcoma patients in TARGET database. (E) Relative expression of the 6 signature containing genes in osteosarcoma samples. (F) Survival analysis of the high-risk and low-risk groups of osteosarcoma patients which were divided based on the constructed signature. (G) ROC curve of the gene signature to predict patients survival of 1 year, 3 years and 5 years respectively.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/7e00cefa5d25a89ec205b142.jpg"},{"id":79093528,"identity":"b6a88c12-9d4f-4214-88b3-93c5213f1c45","added_by":"auto","created_at":"2025-03-24 10:35:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":701932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOptimize the multi genes signature to a STC2/FPR1 dual genes signature and following prognosis prediction ability validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) LASSO analysis to calculate the coefficient and likelihood deviance for STC2/FPR1 dual genes signature. (C) The 89 osteosarcoma cases in TARGET database were divided into high-risk and low-risk groups based on the calculated STC2/FPR1 signature score. (D) ROC curve for comparing the prognosis prediction ability of STC2/FPR1 dual genes signature and previous multi-genes containing signature. (E) Overall survival analysis of the STC2/FPR1 dual genes signature and (F) ROC curve of the gene signature to predict patients overall survival of 1 year, 3 years and 5 years. (G) Recurrence free survival analysis of the STC2/FPR1 dual genes signature and (H) ROC curve of the gene signature to predict patients recurrence free survival of 1 year, 3 years and 5 years. (I) Osteosarcoma patients prognosis prediction nomogram constructed based on the dual genes signature and clinical parameters that were supported by Cox Regression to be independently related with patients survival.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/5ae272113f4325a0614296f8.jpg"},{"id":79093329,"identity":"2afbcd81-aeed-4f8b-bb0b-1ee53a55b990","added_by":"auto","created_at":"2025-03-24 10:27:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1084139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIHC experiment analysis of STC2 and FPR1 genes expression patterns in real world osteosarcoma tissue samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) ROC curve for comparing the prognosis prediction ability of the STC2/FPR1 dual genes signature and single STC2 as well as FPR1 gene respectively.\u003c/p\u003e\n\u003cp\u003e(B) Survival analysis among the four groups of osteosarcoma patients that were divided based on STC2 and FPR1 expression status.\u003c/p\u003e\n\u003cp\u003e(C) Correlation analysis between STC2 and FPR1 mRNA expression level based on TARGET database information.\u003c/p\u003e\n\u003cp\u003e(D) IHC experiment using local hospital samples reveals STC2 was mainly enriched in osteosarcoma cancer and some surrounding mesenchymal cells, the left panel is a magnified graph of the red boxed region in the middle panel for showing the STC2 positive cancer cells, and the right panel is a magnified graph of the blue boxed region in the middle panel for showing the STC2 positive mesenchymal cells.\u003c/p\u003e\n\u003cp\u003e(E) IHC experiment using local hospital samples reveals FPR1 was mainly enriched in macrophages that were locating mostly locating in the peripheral of cancer, the left panel is a magnified graph of the red boxed region in the middle panel for showing FPR1 positive multi-nuclear macrophages, and the right panel is a magnified graph of the blue boxed region in the middle panel for showing FPR1 positive monocytes.\u003c/p\u003e\n\u003cp\u003e(F) Double staining of STC2 (brown color) and FPR1 (red color) based on multi colors IHC experiment using local hospital samples, the middle graph is the main staining area, and the left panel is a magnified graph of the red boxed region in the middle panel, the area is to show a relatively low differentiated region with more STC2 positive cancer cells and less bone formation. Meanwhile, the right panel is a magnified graph of the blue boxed region in the middle panel, the purpose of this area is to show a relatively higher differentiated region with more bone formation, more FPR1 positive macrophages could be observed in this area. (magnification: 100X in middle graph, and 200X in left and right graphs, error bars represent 80um and 40um in corresponding graphs).\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/d769ed3bdc9fd40aadae56f2.jpg"},{"id":79093341,"identity":"d0800280-536c-41cc-8c97-b4061b730952","added_by":"auto","created_at":"2025-03-24 10:27:01","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":687692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle cell sequencing analysis of the potential cross talks among STC2 and FPR1 positive cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Based on an online single cell sequencing data, namely GSE162454 profile, the main cell cell clusters were identified in osteosarcoma samples which were displayed in the umap graph. (B) The representative marker genes that were identified in each cell cluster in osteosarcoma samples. (C) Relative distribution of STC2 and FPR1 genes in the cell clusters. (D) Estimated cellular crosstalk among different cell clusters, especially between cancer cell and other microenvironment cell types. (E) The computed ligand and receptors that convey the cellular communications between cancer cells and other cell clusters for instance myeloid cells, the ligand and receptor pair that FPR1 gene was involved was highly focused.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/da1421e97d46b4b429634409.jpg"},{"id":79093530,"identity":"fe87a37e-677c-4db4-877c-0e9c98645f08","added_by":"auto","created_at":"2025-03-24 10:35:00","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":942645,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between the STC2/FPR1 dual genes signature and immune cells infiltration in osteosarcoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Relative distribution of 22 immune cells in high-risk and low-risk groups of oateosarcoma patients. (B) Venn diagram for showing the immune cells that were shared by different analysis to be associated with STC2/FPR1 gene signature. QPCR experiment for detecting relative (C) STC2 and (D) FPR1 expression in low grade and high grade osteosarcoma samples. Multi colors staining IHC for revealing the relative distribution of immune cells in (E) low grade and (F) high grade of osteosarcoma samples, the green color represent LCA labeled lymphocytes, red color were MPO stained granulocytes, and brown color were CD68 stabled macrophages. The red and green box as well as arrows were for showing the relatively small amount of lymphocyte and granulocyte in osteosarcoma samples comparing to abundant macrophages.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/6028deea7014033c48ec7563.jpg"},{"id":79093531,"identity":"f93ff072-e490-4014-b880-ad8380879f52","added_by":"auto","created_at":"2025-03-24 10:35:00","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":940253,"visible":true,"origin":"","legend":"\u003cp\u003e(A) \u003cstrong\u003eAnalyzing the macrophages M1/M2 polarization features in osteosarcoma samples and their correlation with STC2/FPR1 signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the single cell sequencing GSE162454 data, the myeloid cell cluster was identified, and (A) CD14 positive macrophages distribution were analyzed in the cluster, and M1 macrophages which were marked by (B) CD80 and (C) IL6 expression, as well as M2 macrophages that were marked by (D) CD163, (E) CD206 (also known as MRC1) and (F) IL10 were observed, the results were to display that more M2 than M1 macrophages were observed in the samples. (F) The correlation between STC2/FPR1 dual genes signature risk score and the commonly used M1/M2 indicator genes expression, the analysis was based on TARGET database information. (H) Relative CD163 and IL10 expression which were two commonly used M2 macrophages indicators in the high-risk and low-risk groups of osteosarcoma samples. (I) IHC experiment using local hospital samples to display that a big portion of the macrophages (indicated by CD68) in osteosarcoma were M2 type (indicated by CD163).\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/f32912d903230c03533f4110.jpg"},{"id":79093334,"identity":"49a1783a-5974-4ae1-96f2-4c8e85963013","added_by":"auto","created_at":"2025-03-24 10:27:00","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1047056,"visible":true,"origin":"","legend":"\u003cp\u003e(A) \u003cstrong\u003eCorrelation between the STC2/FPR1 dual genes signature and immune checkpoints expression in osteosarcoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Association between the STC2/FPR1 gene signature and different immune checkpoints expression. (B) Correlation between gene signature and TIM-3, PD-L1, CALT-4, LAG-3 and TIGIT expression respectively. (C) Relative expression of five immune checkpoints including PD-L1, LAG-3, TIGIT, TIM3 and CALT-4 expression in high-risk and low-risk osteosarcoma groups respectively. IHC experiment for detection the relative expression of (D) TIM-3, (E) PD-L1, (F) CTLA-4, (G) TIGIT and (H) LAG-3 in high grade osteosarcoma tissue samples.\u003c/p\u003e","description":"","filename":"17.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/5b1835b88ee0df2c89d47b70.jpg"},{"id":79093532,"identity":"33215251-b9e2-4c29-b3b0-ec3ef44647be","added_by":"auto","created_at":"2025-03-24 10:35:01","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":856174,"visible":true,"origin":"","legend":"\u003cp\u003e(A) \u003cstrong\u003eCorrelation between STC2/FPR1 dual genes signature and osteosarcoma immune microenvironment landscape\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Heatmap for revealing the expression difference of 32 ICD related genes in high-risk and low-risk groups of osteosarcoma samples respectively. (B) GSEA analysis for revealing the immune related signaling that were enriched in the low-risk groups of osteosarcoma samples. (C) Estimated immune score, stromal score and tumor purity distribution in high-risk and low-risk osteosarcoma groups respectively. (D) TIDE analysis for revealing the potential response difference between high-risk and low-risk groups of osteosarcoma patients to immune therapy.\u003c/p\u003e","description":"","filename":"18.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/0b931313256ca983ba160fbe.jpg"},{"id":96917766,"identity":"9f1eaaa0-8185-4f0a-93e4-94b2391f0cd9","added_by":"auto","created_at":"2025-11-27 14:10:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9550823,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/7422a330-e019-4126-b55d-6d754d1b0b6d.pdf"},{"id":79093337,"identity":"0fa1d754-0e1d-4544-838e-ed1d2e5fa5bc","added_by":"auto","created_at":"2025-03-24 10:27:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1635070,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/8730b71ce309eb2795bb401b.pdf"},{"id":79094918,"identity":"72153962-1bcf-4f97-9947-a52988082212","added_by":"auto","created_at":"2025-03-24 10:43:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":523698,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/487e4e3da75b3933097f7a59.pdf"},{"id":79093328,"identity":"ffa6bf45-0cfe-47a1-b1b2-899a22a5eb09","added_by":"auto","created_at":"2025-03-24 10:27:00","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":342581,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/73c1a60fc169f2950d18103f.pdf"},{"id":79093335,"identity":"b126fb78-3004-44c6-947e-079cea777f92","added_by":"auto","created_at":"2025-03-24 10:27:00","extension":"doc","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":65536,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesandfigurelegends.doc","url":"https://assets-eu.researchsquare.com/files/rs-5651928/v1/d70366fd6830369867404758.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"STC2/FPR1 dual genes signature works as a prognosis indicator with implication in macrophages dominated and TIM-3 related osteosarcoma immune landscape","fulltext":[{"header":"Background","content":"\u003cp\u003eOsteosarcoma has been a common bone malignancy with high invasion and metastasis ability, occurring in firstly children and adolescents (average age 15\u0026thinsp;~\u0026thinsp;19) and secondly over 60 years old elderly, and the lesions were mainly in the metaphyseal of long bones[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The incidence of osteosarcoma is around 1.7\u0026ndash;3.3 per million annually worldwide, threateningly, the tumor is severely health affecting, the 5-year survival rate is nearly 70% in patients with non metastatic osteosarcoma, and for patients with advanced and recurrent osteosarcoma, the 5-year survival is less than 20%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs for the clinical treatment of osteosarcoma, surgery combining with neoadjuvant and adjuvant chemotherapy have been the main strategy for decades[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Benefiting from the increasingly understanding of the prognosis association of microvessel density and angiogenic factors in cancers, anti-angiogenesis tyrosine kinase inhibitors (AA-TKIs) have also been used in clinical treatment which indeed seem to prolong the progression free survival (RFS) of patients, however, the combination use of AA-TKIs and chemotherapy also raised drugs toxicity and exacerbate patients adverse reactions, the treatment needs to be carefully assessed and managed while being applied[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeanwhile, regarding the thriving developing gene targeted therapy which has been receiving promising clinical effect in many other cancers, for instance in lung, breast, gastric and colorectal cancer[\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the success has not been replicated in the treatment of osteosarcoma. Since 2014, series of whole genome sequencing analysis based on osteosarcoma samples have been revealing that except for the commonly known sporadic gene mutations including P53, RB1 and other genes for instance MDM2 and NFIB[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], mother frequent gene variations in osteossarcoma including copy number variation and complex chromosome rearrangement, for instance, chromosome division (extensive genome rearrangement and DNA copy number level mutations on one or several chromosomes)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], kataegis mutation (local strand specific hypermutation)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and break fusion bridge cycle (fusion of broken ends from different chromatids or chromosomes, resulting in unstable bicentric chromosomes)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The complex DNA variations make it difficult to identify the genome initial biological processes that drive the cancer development and to explore effective gene targets. It\u0026rsquo;s of clinical significance to keep exploring promising clinical treatment strategies thus benefiting the suffering patients.\u003c/p\u003e \u003cp\u003eImmune escape has been one of the major hallmarks of cancers besides uncontrolled cell proliferation, resisting cell death, evading growth suppressor, metastasis, angiogenesis, promoting inflammation and so on[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Meanwhile, immunotherapy which is aiming to reactivate body immune system to attack cancer cells has also been showing potential in increasing number of cancer types. Even though the commonly applied anti PD-1/PD-L1 immune checkpoint inhibitors (ICIs) only received limited effect in osteosarcoma patients, it is still of great potential for immunotherapy to be applied in osteosarcoma clinical treatment. For instance, in 2019, Le Cesne.et al released the result of a clinical trial which was based on 17 advanced osteosarcoma patients, and the result revealed that 13.3% of cases achieved 6 month of progress free survival[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and in a multicenter phase II clinical trial (SARC028) on the efficacy of pembrolizumab, 18% of soft tissue sarcoma patients and 5% of osteosarcoma patients showed clinical response to pembrolizumab blocking of immune checkpoints[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. And also, a clinical analysis revealed that more that 25% of osteosarcoma patients response to Ipilimumab which is a checkpoint blockade for CTLA-4[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and in another experiment based on animal modes, the combination of CTLA-4 and PD-L1 antibody blockade immunotherapy in K7M2 mouse models of metastatic osteosarcoma resulted in complete control of most tumors[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Meanwhile, CAR-T cell therapy has also showed clinical potential in the treatment of osteosarcoma included soft tissue sarcomas, these results still indicated the potential of immunotherapy to be applied in osteosarcoma treatment[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It\u0026rsquo;s of great significance to keep exploring osteosarcoma genome and identifying promising immune response biomarkers thus aiding more precise understanding of the disease and shedding light on further clinical immunotherapy application.\u003c/p\u003e \u003cp\u003eIn the study, public online datasets and local hospital tissues were combine used to explore osteosarcoma genome data for analyzing osteosarcoma immune landscape. The genes that were differently expressed, prognosis associated meanwhile immune regulation related were highly focused. The selected genes would then be used to construct promising gene signatures followed by exploring their association with osteosarcoma immune features including different immune cells infiltration and expression of PD-L1, CTLA-4, TIGIT included immune checkpoints. The results shall benefit identifying potential new immune biomarkers and shed promising light on further clinical application of immunotherapies in clinical treatment of osteosarcoma.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePublic data: GEO profiles and TARGET database information of osteosarcoma cases\u003c/h2\u003e \u003cp\u003eFrom GEO online database, the osteosarcoma related profiles were widely screened for exploring the genome information in osteosarcoma comparing to normal control samples. The selection criteria of GEO transcriptome profiles and the detailed information of four selected GEO profiles, namely GSE12865, GSE16088, GSE28424 and GSE42352 had been reported previously[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Besides the four transcriptome profiles, another GEO profile which is based on osteosarcoma single cell sequencing data namely GSE162454 was also included[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBesides above profiles, TARGET database[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] which is short for Therapeutically Applicable Research To Generate Effective Treatments has also been an open accessed and researchers friendly genome database aiming for children tumors including acute lymphoblastic leukemia, acute myeloid leukemia, kidney tumors, neuroblastoma and osteosarcoma. In the study, the selected GEO profiles and osteosarcoma data in TARGET database were together used in different sections to explore the genome landscape of osteosarcoma.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifference of immunogenic cell death (ICD) related genes in osteosarcoma comparing to normal control samples\u003c/h3\u003e\n\u003cp\u003eTo preliminary validating the immune status difference between osteosarcoma and normal control samples, ICD which has been well accepted as an immune related form of regulated cell death meanwhile supported by evidence-based medicine to be able to trigger cellular adaptive immune response and contribute to clinical immunotherapy was evaluated in osteosarcoma cases. The first step of the study was to evaluate the expression difference of 32 well accepted ICD related genes[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] in osteosarcoma comparing to corresponding normal tissues based on TARGET data.\u003c/p\u003e\n\u003ch3\u003eIdentifying differently expressed meanwhile immune related genes in osteosarcoma comparing to normal bone samples\u003c/h3\u003e\n\u003cp\u003eAfter preliminary validating the potential of immune research in osteosarcoma based on ICD related genes expression analysis, GEO transcriptome data were next step applied to analyze osteosarcoma genome information with the purpose of 1. exploring the differently expressed genes in osteosarcoma comparing to normal control samples, 2. combining with IMMPORT immune database to identify the aberrant differently expressed meanwhile immune regulation associated genes.\u003c/p\u003e \u003cp\u003eFirstly, four GEO transcriptome profiles, namely GSE12865, GSE16088, GSE28424 and GSE42352 were in succession analyzed with GEO2R which was provided in each GEO profile to compare the differently expressed genes between osteosarcoma and normal control. The identified genes would then be classified into different groups based on expression difference, namely expression difference level\u0026thinsp;\u0026lt;\u0026thinsp;2 fold, 2\u0026thinsp;~\u0026thinsp;4 fold, 4\u0026thinsp;~\u0026thinsp;8 fold and \u0026gt;\u0026thinsp;8 fold ( analysis criteria: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 meanwhile |log2FC|\u0026lt;1, 1\u0026le;|log2FC|\u0026lt;2, 2\u0026le;|log2FC|\u0026lt;3 and |log2FC|\u0026ge;3 respectively). The genes with highest expression difference (\u0026gt;\u0026thinsp;8 fold) were mainly focused.\u003c/p\u003e \u003cp\u003eThen, based on IMMPORT immune database[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], the gene list that were well acknowledged as immune regulation related would be recognized followed by Venn diagram analysis to screen the genes that were shared by above high level differently expressed (\u0026gt;\u0026thinsp;8 fold) genes and immune genes list, thus identifying promising candidate genes for next step analysis.\u003c/p\u003e\n\u003ch3\u003eProtein-protein interaction (PPI) network construction and immune enrichment validation of the candidate genes\u003c/h3\u003e\n\u003cp\u003eTo preliminary validate the immune association of above selected candidate genes, the protein-protein interaction (PPI) network of the candidate genes was constructed using Search Tool for the Retrieval of Interacting Genes (STRING)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], followed by Gene ontology analysis (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] annotating the basic biological attributes these genes, especially the biological processes and signaling pathways they mainly enriched in. The immune system related biological processes would be mainly focused.\u003c/p\u003e\n\u003ch3\u003eLASSO algorithm to construct an immune related multi-genes containing signature based on candidate genes\u003c/h3\u003e\n\u003cp\u003eTo maximum the clinical utilization of the selected expression changed meanwhile immune related gene candidates, LASSO algorithm performed with glmnet R package was applied to construct a multi-genes containing signature. Based on the signature, the best gene combination that represents the candidate genes were selected, and a regression coefficient was assigned to each gene, the final score of the gene signature in each osteosarcoma case equals the multiplication of the regression coefficient and detected gene expression. And based on the calculated median score of the constructed gene signature, the 89 osteosarcoma cases in TARGET database were classified as high-risk and low-risk groups followed by next step estimating the clinical features association of the gene signature.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePreliminary prognosis validation of the gene signature\u003c/h2\u003e \u003cp\u003eTo explore the prognosis association of the constructed gene signature, multiple analysis were performed. Firstly, Kaplan-Meier survival difference was estimated between the high-risk and low-risk groups of 89 osteosarcoma cases in TARGET database, then, AUC curve was drawn for observing the 1, 3 and 5 years survival prediction ability of the signature. Further, univariate together with multivariate Cox regression analysis were in succession applied to test the independence survival prediction of the signature. Furthermore, a nomogram consisted of the newly constructed gene signature and other clinical features was drawn, all for together evaluating the prognosis risk in clinical osteosarcoma patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmunohistochemistry (IHC) experiment detection of the expression pattern of signature hub genes\u003c/h3\u003e\n\u003cp\u003eThe gene signature was constructed based on public open accessed GEO and TARGET database information, to validate the clinical application value of the signature in real world osteosarcoma samples, local hospital cancer samples were applied to observe the expression mode of the signature genes and validate the clinical features association of the signature.\u003c/p\u003e \u003cp\u003eConsidering immunohistochemistry (IHC) has been a common and effectively used method for observing the expression as well as cellular distribution of specific genes, in the study, 43 cases of local hospital osteosarcoma samples (detailed information of the samples have been reported in a previous research)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] were used for IHC experiment.\u003c/p\u003e\n\u003ch3\u003eIHC experiment Tissue samples\u003c/h3\u003e\n\u003cp\u003e All the local hospital osteosarcoma samples used for IHC experiment were from our hospital Biobank which were originally donated by orthopaedics department patients, informed consent of the potential scientific application of the samples have been obtained by the Biobank staff at the same time patients made donations, and use of the tissues in this study was approved by both Biobank committee and Hospital Institutional Board (Second Hospital of ShanXi Medical University, China).\u003c/p\u003e \u003cp\u003eIn the study, 43 cases of osteosarcoma samples were picked from hospital Biobank for IHC experiment after reconfirmation of the disease diagnosis and cancer percentage by registered pathologists in hospital pathology department. Given the high heterogeneity of osteosarcoma, instead of making the samples into tissue microarrays, the whole section of each tissue paraffin wax was used for examination.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEquipment and Regents\u003c/h2\u003e \u003cp\u003e The whole procedure of IHC experiment was conducted using local hospital Pathology Department instrument and equipment, and the experiment was performed on VENTANA platform (Roche), the primary antibody to STC2 gene was purchased from proteintech (Cat No.60063-1-IG), and FPR1 antibody was from abclonal (Cat No.A20455). As for the secondary antibody (Envision /HRP kit) and DAB detection kit, two different sets of regents were used, the traditional single color (brown) set of regent were purchased from ZSBG-Bio, and another multi-colors set of regents were from abcarta (Jiang Su, China). As for the other IHC experiment needed regents for instance phosphate-buffered saline (PBS), EDTA antigen retrieval citrate solution, H2O2 and glass slides were all from hospital Pathology Department.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIHC experimental protocol\u003c/h2\u003e \u003cp\u003eIHC experiment procedures were same as previously reported, firstly, the FFPE slides were deparaffinized and rehydrated by gradient ethanol, then being incubated with 0.3% H2O2 for inhibiting endogenous peroxidase activity followed by boiling in 10mmol/l EDTA citrate buffer for antigen retrieval. Further, the slides were soaked in bovine serum albumin for 20min followed by incubating with primary antibodies overnight at 4\u0026deg;C. Then, the slides would be incubated with specific secondary antibody at 37\u0026deg;C for 1 hour and further being processed with horseradish peroxidase (HRP) and visualized using DAB by local hospital registered pathologists.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIHC results evaluation\u003c/h2\u003e \u003cp\u003eIHC experiment staining results were evaluated by registered local hospital pathologists (Second Hospital of ShanXi Medical University, China) based on both the staining intensity and staining area. The score criteria was same as previously reported, that the staining intensity was scored as: None (0), mild (1), moderate (2) and strong (3), meanwhile, the staining area was classified as: \u0026lt;1% (0), 1\u0026ndash;25% (1), 26\u0026ndash;50% (2), 51\u0026ndash;75% (3) and \u0026gt;\u0026thinsp;75% (4). The final score of each slide equals the multiplication of staining intensity and staining area, the eventual result would be regarded as negative with the multiplication value\u0026thinsp;\u0026lt;\u0026thinsp;2, and the result would be defined as positive if the multiplication value\u0026thinsp;\u0026ge;\u0026thinsp;2 .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative real-time PCR (QPCR) experiments detecting the signature genes expression\u003c/h2\u003e \u003cp\u003eTo detect the hub genes expression in the signature, local hospital osteosarcoma tissue samples (the same as before IHC experiments) were applied to extracted mRNA following QPCR experiment. The total mRNA of the selected osteosarcoma samples were extracted using RNAiso-Plus (TAKARA, DaLian, China). And then1 \u0026micro;g extracted mRNA was used for cDNA synthesis with commercial cDNA synthesis kit (TAKARA, DaLian, China) based on the kit operating instruction. Further, qPCR was performed on Roche z 480 and the primers used were listed as below :\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSTC2:\u003c/h2\u003e \u003cp\u003eFormer: TGAAATGTAAGGCCCACGCT\u003c/p\u003e \u003cp\u003eReverse: CGAGGTGCAGAAGCTCAAGA\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFPR1:\u003c/h2\u003e \u003cp\u003eFormer: CTGTCAGTTATGGGCTTATTGC\u003c/p\u003e \u003cp\u003eReverse: GCAATAACTCACGGATTCTGAC\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGAPDH:\u003c/h2\u003e \u003cp\u003eFormer: AGAAGGCTGGGGCTCATTTG\u003c/p\u003e \u003cp\u003eReverse: AGGGGCCATCCACAGTCTTC\u003c/p\u003e \u003cp\u003eThe PCR cycling condition was set as: 95\u0026deg;C 5 min for 1 cycle; 95\u0026deg;C 10 s, 58\u0026deg;C 30 s, and 72\u0026deg;C 30 s for 40 cycles followed by the melting curve stage. And the relative STC2 and FPR1 genes expression in each sample was recorded as the average 2^\u0026minus;ΔΔCT calculation result of three replicates.\u003c/p\u003e \u003cp\u003eThe constructed STC2/FPR1 dual genes signature risk score was calculated based on the signature formula, namely firstly the detected expression of STC2 and FPR1 gene multiplies its regression coefficient respectively, and then the sum of last step calculated dual genes score which would be recorded as the final risk score of each sample.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle cell sequencing for validating the cellular location of signature genes meanwhile exploring cellular cross talks\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter IHC experiment detecting the cellular location and relative expression of signature hub genes in clinical samples, osteosarcoma single cell sequencing data which was also obtained based on GEO database, namely GSE162454 was used to 1. explore the cellular distribution of signature hub genes in osteosarcoma tissues, and 2. preliminary analyze the cellular cross talks between STC2 positive and FPR1 positive cells in osteosarcoma samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between the signature and osteosarcoma tumor infiltrating immune cells (TICs)\u003c/h2\u003e \u003cp\u003eImmune cells are an important part of cancer microenvironment, for evaluating the immune association of the gene signature in osteosarcoma samples, the immune cell infiltration difference between high-risk and low-risk groups of osteosarcoma cases was analyzed.\u003c/p\u003e \u003cp\u003eFirstly, CIBERSORT algorithm was performed to preliminary calculate the relative contents of 22 different TICs in osteosaorcoma samples based on TARGET genes expression profile followed by exploring the relationship between the gene signature and different TICs infiltration.\u003c/p\u003e \u003cp\u003eThen, for identifying the dominate immune cells in osteosarcoma microenvironment and analyzing the potential key cell type that was associated with the signature, multi-colors IHC experiment was conducted on hospital osteosarcoma samples, and all the sample tissues, experiment equipment were the same as above described. As for the regents, LCA, MPO and CD68 antibodies in this experiment which were all purchased from ZSBG-Bio were combine used to represent the major immune cell types, namely the lymphocytes, myeloid cells as well as macrophages in osteosarcoma. The major cell type which was supported by IHC experiment to be high level infiltrated into osteosarcoma tumor cells would be mainly focused for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between the constructed gene signature and expression of immune checkpoints in osteosarcoma\u003c/h2\u003e \u003cp\u003eAfter evaluating the association with immune cells infiltration, considering the critical effect immune checkpoints have on cancers immune status, the expression of immune checkpoints including PD-L1, CTLA4, TIGIT, TIM-3 and LAG-3, the blockade of which have been showing to be able to reverse the tumor immune suppressive microenvironment and benefit patients from immunotherapy were detected in osteosarcoma using IHC experiment. The detailed information of the 5 antibodies were listed in Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and the secondary antibodies, IHC equipment, regents and experiment procedures were all same as previously stated. Furthermore, the association between the gene signature and these immune checkpoints expression were assessed, as well as the comparison of expression difference of these immune checkpoints between high-risk and low-risk groups of osteosarcoma patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between the gene signature and osteosarcoma estimated environment immune score\u003c/h2\u003e \u003cp\u003eBesides immune cells infiltration and immune checkpoints expression, ESTIMATE, which is short for Estimation of Stromal and Immune cells in Malignant Tumors using Expression data has been increasingly applied in worldwide researches as an effective cancer immune evaluation tool in various cancers[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In the study, it was applied to estimate the immune score of osteosarcoma samples based on TARGET genes data, and the correlation between the gene signature and ESTIMATE algorithm based osteosarcoma immune score, stromal score as well as tumor purity were evaluated using R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDifference of immunogenic cell death (ICD) between high-risk and low-risk groups of osteosarcoma patients\u003c/h2\u003e \u003cp\u003eConsidering the importance of ICD related genes in triggering cancers cellular adaptive immune response through the emission of damage associated molecular patterns (DAMPs), besides previous evaluating the expression difference of 32 commonly known ICD related genes in osteosarcoma tumor comparing to corresponding normal tissues, the genes\u0026rsquo; expression difference were also explored in high-risk and low-risk groups of osteosarcoma patients for assisting the validation of immune association of the constructed gene signature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eGene set enrichment analysis (GSEA) of the high-risk and low-risk groups of osteosarcoma patients\u003c/h2\u003e \u003cp\u003eAdditionally, GSEA which has also been widely used in exploring the enrichment of signaling pathways was also applied to explore the immunological related signaling difference between high-risk and low-risk groups of osteosarcoma patients, the analysis threshold was set as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eMost of the bioinformatics analyses were performed on corresponding online databases. As for the processing of local hospital data, statistical analysis were performed using SPSS 26.0. For enumeration data for instance when comparing genes expression difference between cancer and corresponding normal samples, the data were analyzed using t test. As for the measurement data including the association between gene expression and cancer pathological parameters, the data were analyzed by χ2 test. And for correlation analysis, the data were analyzed by Spearman analysis. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. (For all analysis results, * represents p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** represents p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** represents p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eICD related genes express differently between osteosarcoma and normal tissues indicating potential immune association\u003c/h2\u003e \u003cp\u003eOsteosarcoma has been a long recognized complex tumor with high heterogeneity and complicated microenvironment, for preliminary exhibiting the potential of immune research in the tumor, the 32 ICD related genes expression were compared between osteosarcoma and corresponding normal bone tissues, and the results revealed serve difference existed between the two groups of samples indicating diverse immune status in respective microenvironment (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). The result supported the potential value of detailed immune research in osteosarcoma.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTranscriptome data identified 108 high level differently expressed meanwhile immune related candidate genes in osteosarcoma versus normal bone samples\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFour GEO transcriptome profiles containing a total of 90 osteosarcoma tissue samples and 19 osteosarcoma cancer cell lines were combine applied to explore the differently expressed genes in osteosarcoma comparing to normal bone samples, and a total of 12168, 17090, 9173 and 3062 genes were identified in GSE12865 (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB), GSE16088 (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC), GSE28424 (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD) and GSE42352(Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE) respectively. Besides the genes that were shared among profiles, the expression of 815 genes were revealed to be high level (\u0026gt;\u0026thinsp;8 fold) different in osteosarcoma comparing to normal samples (detailed information of the genes has been previously reported)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeanwhile, based on IMMPORT immune database, the commonly known immune related genes list was obtained, and further Venn diagram analysis result revealed that 108 genes from the 811 genes were both high level expression changed and immune regulation related (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), the 108 genes were identified as candidate genes for further analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBig portion of the candidate genes were interrelated and enriched in immune associated biological processes and signaling pathways\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo preliminary understand the potential inner connection and validate immune association among the selected candidate genes, the PPI network of the 108 differently expressed meanwhile potentially immune related genes was constructed followed by enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). And the results revealed that nearly 92% of the candidate genes (100 genes) were related with cellular response to stimulus (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), 53% were associated with immune system processes (56 genes, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) including innate immune system (30%, 33genes, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) and adaptive immune system (21%, 23 genes, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Meanwhile, 18% of genes were involved in cytokine signaling in immune system (19 genes, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), 17% of genes were interrelate to lymphocyte activation regulation (18 genes, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG), and 10% of genes were correlated with immune receptor activity as well as natural killer cell mediated cytotoxicity. The involvement of the candidate genes in various immune related signaling assisted firstly the worthy of immune research in osteosarcoma, and secondly the potential of above selected candidate genes in further application.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eConstruction of a STC2/FPR1 dual genes containing signature based on immune related candidate genes\u003c/h2\u003e \u003cp\u003eTo maximum the clinical value of the selected candidate genes and construct a reliable meanwhile efficient multi-genes signature for predicting the immune status of osteosarcoma sample, cox-proportional hazards analysis based on LASSO algorithm was applied to calculate the representative gene equation. And the result firstly revealed a 6 genes containing signature, including STC2, FPR1, VCMA1, PTN, IL13RA2 and HCST (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), the signature weights the normalized expression level of each gene to the regression coefficient of multivariate Cox regression analysis, revealing the final gene equation as: Risk Score\u0026thinsp;=\u0026thinsp;0.0928* expression (STC2)- 0.1685* expression (FPR1)- 0.0654* expression (VCAM1)- 0.0182* expression(PTN)- 0.0384* expression (IL13RA2)- 0.0677* expression (HCST) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Both the Kaplan-Meier survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) and ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG) showed great prognosis risk association of the signature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDisturbing by the complex of experimental validation and considering the feasibility of further clinical application of multi-genes containing signature, to further optimize and scale down the amount of signature hub genes, the signature equation was carefully analyzed. And the following points supported two of the signature genes namely STC2 and FPR1 working as the main hub genes in the equation, firstly, STC2 was as the only gene that positively correlates with signature risk score, meanwhile, all the other five genes are negatively related indicating the unique role of STC2. Secondly, the regression coefficient value of FPR1 was the highest among all five negatively correlated genes supporting the relatively higher value. Thirdly, the ROC curve based on different genes combination indicated better survival prediction of STC2/FPR1 combination than other dual-genes combination (data not shown).\u003c/p\u003e \u003cp\u003eAbove results supported the construction of a new STC2/FPR1 dual genes signature for replacing the complicated six genes equation. Thus, after understanding the basic physicochemical properties of STC2 and FPR1 including their molecular Weight, theoretical pI, hydrophobic value, estimated protein half life and so on (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), to maximum the value of the two genes in the signature, LASSO algorithm was again applied to recalculate the coefficient for the genes, and the final dual genes signature was: Risk Score= (0.5663)* STC2 + (-1.3968)* FPR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). An inspiring fact is that although the prediction value of the STC2/FPR1 dual genes signature seemed lower than the original six genes containing signature, the difference was not statistical significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) supporting its clinical value.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic physicochemical properties of STC2 and FPR1 genes applied for constructing the dual genes signature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Property\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFPR1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e1425\u003c/sub\u003eH\u003csub\u003e2269\u003c/sub\u003eN\u003csub\u003e439\u003c/sub\u003eO\u003csub\u003e441\u003c/sub\u003eS\u003csub\u003e20\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003csub\u003e1781\u003c/sub\u003eH\u003csub\u003e2796\u003c/sub\u003eN\u003csub\u003e444\u003c/sub\u003eO\u003csub\u003e467\u003c/sub\u003eS\u003csub\u003e17\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.2KD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.4KD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of amino acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e302AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350AA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheoretical pI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAliphatic index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrophobic value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated protein half life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstability index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.87 (Stable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.33 (Stable)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eSTC2/FPR1 works as an independent prognostic indicator in osteosarcoma samples\u003c/h2\u003e \u003cp\u003eBoth STC2 and FPR1 were previously supported to be high level differently expressed in osteosarcoma comparing to normal bone samples, but changed gene expression doesn\u0026rsquo;t necessarily mean survival association. To explore the direct prognosis risk indication ability of the dual genes signature, series of analysis was conducted.\u003c/p\u003e \u003cp\u003eFirstly, based on the calculated median risk score of STC2/FPR1 genes signature, the 89 osteosarcoma cases from TARGET database were categorized into high-risk and low-risk groups, Kaplan Meier survival analysis revealed the high-risk group of patients had both a statistical significantly worse overall survival (OS) and shorter recurrence free survival (RFS) than the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Meanwhile, ROC curve showed promising 1 year, 3 years and 5 years OS and RFS prediction value of the dual genes signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eMoreover, univariate Kapkan-Meier survival, multivariate Cox regression analysis as well as nomogram construction were in succession performed. Combine analysis of Kapkan-Meier survival and Cox regression analysis revealed that although multiple factors including the dual genes signature risk score, tumor metastasis status and tumor soft tissue invasion were associated with patients prognosis, only the signature risk score and tumor metastasis status work as independent prognosis indicators in osteosarcoma (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Meanwhile, a nomogram was drawn combining the prognosis risk variables, and in the nomogram, a point scale was assigned for each variable, the sum of all the variable points equal to the final score of each clinical case, thus the survival risk of the case would be predicted by drawing a vertical line from the total point axis downward to the outcome axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate combine with multivariate Cox Regression analysis result of STC2/FPR1 dual genes signature and other osteosarcoma clinical parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExp (B)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Signature score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.405(3.972\u0026ndash;27.266)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic\u0026nbsp;response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u0026nbsp;tumor\u0026nbsp;site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u0026nbsp;status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.662(3.700\u0026minus;20.278)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis\u0026nbsp;site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurther, the association between STC2/FPR1 dual genes signature and osteosarcoma clinical features were together analyzed, and the result revealed that although no significant correlation was discovered between the gene signature and patients age, gender, primary tumor site or tumor recurrence, the metastasis was higher in the high-risk group of patients than the low-risk group patients, even though the difference was not statistical significant partly due to the limited patients number in each group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between the STC2/FPR1 dual genes signature and osteosarcoma clinical features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSTC2/FPR1 gene signature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-risk group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh-risk group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (43.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (56.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (56.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026thinsp;~\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMetastasis status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (55.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMetastasis site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrimary tumor site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper limbs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower limbs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (51.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther bones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrimary tumor progression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRecurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChemotherapy necrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eMulti-colors IHC experiment observing the cellular location and expression pattern of STC2/FPR1 in osteosarcoma\u003c/h2\u003e \u003cp\u003eTo validate the clinical application value of the STC2/FPR1 dual genes signature in real world osteosarcoma samples, firstly, we compared the prognosis prediction value of the STC2/FPR1 dual genes signature than single STC2 or FPR1 genes, and the result supported the genes were of higher clinical value when they were combined together (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). And also the patients with high STC2 expression meanwhile low FPR1 expression posses the worst survival, and the survival of patients with the opposite low STC2 and high FPR1 expression were the best, meanwhile the survival of others with either STC2 or FPR1 high expression were intermediate (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Both results supporting the value of combination analysis of STC1+/FPR1- dual genes signature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, an interestingly fact is that no significant correlation has been found between STC2 and FPR1 mRNA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), indicating a different cooperation system between them. Thus, to preliminary explore the working patterns of STC2 and FPR1 genes, IHC experiment was conducted using local hospital patients tissues to observe the genes\u0026rsquo; expression. And the result revealed that STC2 and FPR1 genes located in different cellular components and regions in osteosarcoma tissue, STC2 was mostly enriched in the tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), meanwhile, FPR1 was more observed in monocytes and macrophages especially the ones that were locating in the periphery of tumor region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). And a double staining IHC experiment revealed that as a heterogeneity tumor, in the less differentiated region of osteosarcoma with almost all solid tumor cells and barely bone formation, the tumor cells posses more STC2 expression and little FPR1 positive immune cells exist. In opposite, in a more differentiated region with higher percent of osteogenesis, more FPR1 positive immune cells were observed and STC2 expression was much less (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The result partly explains the difference of prognosis association among above STC2+/FPR1-, STC2-/FPR1+, STC2+/FPR1\u0026thinsp;+\u0026thinsp;and STC2-/FPR1- groups of patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eSingle cell sequencing analyzing the potential osteosarcoma cellular cross talks\u003c/h2\u003e \u003cp\u003eAfter IHC experiment observing the cellular location and expression of STC2 and FPR1 in clinical osteosarcoma tissues, a single cell sequencing GEO profile, namely GSE162454 was next analyzed for better understanding the potential function modes of STC2/FPR1 genes in osteosarcoma. A total of 6 osteosarcoma cases samples were included in the profile, and unbiased clustering of the cells that were included in the profile identified 8 main clusters in parallel using t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), namely fibroblasts, mesenchymal stem cells (MSC), cancer cells, osteoclasts (OC), B lymphocytes, Nature Killer and T lymphocytes (NK/T cells), myeloid cells and endothelial cells (EC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Expression patterns of the representative markers genes in each cell cluster were demonstrated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the profile, we observed the relative distribution of STC2 and FPR1 genes in each cell cluster, and the result supported that STC2 was mainly focused in cancer cells and some MSC, meanwhile, FPR1 was mostly enriched in myeloid cell region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Further, the primary cellular connection between different cell clusters was analyzed and a strong connection was found between cancer cells and myeloid cell clusters indicating the potential cooperative network between the cancer and immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Meanwhile, the potential receptor and ligand pairs between cells analysis revealed that although STC2-FPR1 was not a direct connection signaling between cancer and macrophages, there were multiple other potential signaling between the two cell types, and FPR1 was a promising receptor protein in myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). The indirect interaction between STC2 and FPR1 proteins indeed partly explains above phenomenon that the genes play different roles in osteosarcoma microenvironment and posses opposite survival association.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSTC2/FPR1 signature was associated with macrophages dominated immune cells infiltration in osteosarcoma\u003c/h3\u003e\n\u003cp\u003eThe main purpose of constructing the STC2/FPR1 dual genes signature is for immune indication, and cancer immune environment has been a complex system containing various cellular types including cancer cells, surrounding immune cells, cancer-associated fibroblasts (CAFs), endothelial cells and other tissue specific cell types. Although both STC2 and FPR1 were indicated by previous analysis to be immune related, the immune association of the constructed gene signature was still unclear. Thus, the correlation between the dual genes signature and osteosarcoma immune cells distribution was next step analyzed.\u003c/p\u003e \u003cp\u003eFirstly, the well known CIBERSORT algorithm was used to calculate the relative contents of 22 TICs difference between osteosarcoma high-risk and low-risk groups of patients, and the results revealed multiple immune cells including B cells naive, B cells memory, CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, M1 macrophages, M2 macrophages, mast cells and neutrophils were differently distributed between two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Combing with correlation analysis which was calculated based on the direction association between the gene signature score and different immune cells infiltration level as well as another differentiation analysis which was conducted according to the signature score difference between high and low immune cell infiltration groups, a total of 6 immune cells including B cells naive, B cells memory, M1 macrophages, M2 macrophages, mast cells and neutrophils were supported to correlate with STC2/FPR1 gene signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between STC2/FPR1 gene signature risk score and different immune cells infiltration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune cell type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u0026nbsp;cells\u0026nbsp;naive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u0026nbsp;cells\u0026nbsp;memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u0026nbsp;cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u0026nbsp;cells\u0026nbsp;CD8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u0026nbsp;cells\u0026nbsp;CD4\u0026nbsp;naive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u0026nbsp;cells\u0026nbsp;CD4\u0026nbsp;memory\u0026nbsp;resting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u0026nbsp;cells\u0026nbsp;CD4\u0026nbsp;memory\u0026nbsp;activated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u0026nbsp;cells\u0026nbsp;follicular\u0026nbsp;helper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u0026nbsp;cells\u0026nbsp;regulatory\u0026nbsp;(Tregs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u0026nbsp;cells\u0026nbsp;gamma\u0026nbsp;delta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNK\u0026nbsp;cells\u0026nbsp;resting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNK\u0026nbsp;cells\u0026nbsp;activated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrophages\u0026nbsp;M0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrophages\u0026nbsp;M1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrophages\u0026nbsp;M2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDendritic\u0026nbsp;cells\u0026nbsp;resting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDendritic\u0026nbsp;cells\u0026nbsp;activated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMast\u0026nbsp;cells\u0026nbsp;resting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMast\u0026nbsp;cells\u0026nbsp;activated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was referenced as statistical significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo maximum the clinical value of the gene signature and select the most important immune cell type that was associated, multi-colors IHC experiment was conducted using hospital osteosarcoma samples (same samples as previous IHC experiment), and an inspiring result was that barely LCA labeled lymphocytes or MPO stained granulocytes were observed in osteosarcoma tissues, meanwhile, CD68 stabled macrophages have been the main immune cell type in the tumor. And also the amount of macrophages was statistical significantly high in high grade osteosarcoma than low grade samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF), above result all supporting the importance of macrophages in immune regulation of osteosarcoma microenvironment.\u003c/p\u003e \u003cp\u003eMore importantly, based on previous single cell sequencing data, we compared the distribution of commonly used macrophages M1/M2 polarization indicators in osteosarcoma cell clusters, and the results revealed that among all the CD14 positive macrophages (Fig.\u0026nbsp;7A), more M2 indicators including CD163, CD206 (also known as MRC1) and IL10 (Fig.\u0026nbsp;7D-7F) were observed than M1 indicators for instance CD80 and IL6 (Fig.\u0026nbsp;7B, 7C). Moreover, the IHC experiment based on local hospital osteosarcoma samples also supported that most macrophages in osteosarcoma microenvironment were M2 type (Fig.\u0026nbsp;7I).\u003c/p\u003e \u003cp\u003e Meanwhile, for validating the correlation between the STC2/FPR1 dual genes signature and osteosarcoma macrophages distribution in local hospital samples, firstly, based on TARGET database, we calculated the correlation between STC2/FPR1 dual genes signature risk score and the expression of multiple macrophages M1/M2 polarization indicators including CD80, CD86, CD40, IDO1, CD163, IL10 and IL6, and the result revealed that the correlation between the signature and two most commonly used M2 polarization markers CD163 as well as IL10 expression were the strongest among all connections (Fig.\u0026nbsp;7G). Meanwhile, both CD163 and IL10 turned to express higher in the high-risk group of osteosarcoma patients, the results supported the correlation between STC2/FPR1 signature and macrophages M2 polarization (Fig.\u0026nbsp;7H).\u003c/p\u003e \u003cp\u003e Besides above analysis, QPCR experiment conducted using local hospital samples also revealed a statistical significant positive correlation between the gene signature score and CD163 positive M2 macrophages infiltration. STC2 gene expression was much higher in high grade samples with more M2 macrophages infiltration, meanwhile, opposite trend was observed with FPR1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), supporting the potential value of STC2/FPR1 dual genes signature in the regulation of M2 macrophages dominated osteosarcoma immune microenvironment.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eTIM-3 included multi immune checkpoints expression were associated with STC2/FPR1 gene signature score\u003c/h2\u003e \u003cp\u003eBesides the estimation of association with immune cells infiltration, the correlation between the gene signature and clinical promising immune checkpoints including PD-L1, CTLA4, TIGIT, TIM-3 and LAG-3 were evaluated based on TARGET genes expression profile. And a median association was revealed between the gene signature and PD-L1 as well as TIM-3 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), and both checkpoints tend to express higher in low-risk group of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Meanwhile, no significant relation was found between the signature and CTLA4, TIGIT or LAG-3 expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn accordance with the correlation between STC2/FPR1 signatures and the immune checkpoints expression, based on TARGET database, we discovered that expect for CTLA4, all the other four immune checkpoints including TIM-3, PD-L1, LAG-3 and TIGIT were correlated with better patients overall survival, the results supported the immune targeting potential in the low-risk group of patients based on STC2/FPR1 signature (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-S2E).\u003c/p\u003e \u003cp\u003eHowever, an interesting fact based on local hospital IHC experiment was that very low PD-L1 expression was observed in osteosarcoma samples explaining part of the reason that only small percent of current PD-L1 blockade cases was proved effective in osteosarcoma clinical therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). Meanwhile, moderate amount of CTLA4, TIGIT and TIM-3 expression were detected in the tissues. A phenomenon worth of notice is that of all the immune checkpoints, the expression of TIM-3 was much higher and complete than the others (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eD, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eF-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eH), combining with the fact that it was statistical significantly associate with the dual genes signature indicating a potential value of TIM-3 being developed as an immune drug target in further clinical treatment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHigh-risk and low-risk groups of osteosarcoma patients based on STC2/FPR1 gene signature revealed disparate ICD related genes expression level\u003c/b\u003e \u003c/p\u003e \u003cp\u003eConsidering the significant roles of ICD in anti-tumor immunological responses, the connection between STC2/FPR1 gene signature and ICD genes were additionally evaluated. And the results revealed that the expression of a large portion of 32 selected ICD genes were statistical significantly different between not only previously detected osteosarcoma and normal bone samples, but also the high-risk and low-risk groups groups of patients which were classified based on the STC2/FPR1 genes signature risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). The result additionally supported the diverse immune status in the two groups of oateosarcoma patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eSTC2/FPR1 signature correlated with osteosarcoma estimated environment immune score and immune related signaling pathways\u003c/h2\u003e \u003cp\u003eFor further validating the immune association of STC2/FPR1 gene signature, firstly, GSEA was utilized to analyze the immune-related biological processes linked to the STC2/FPR1 gene signature, and the result indicated that the low-risk group cases were more enriched in immune related processes, including POSITIVE REGULATION OF IMMUNE RESPONSE, HUMORAL IMMUNE RESPONSE, LYMPHCYTE MEDIATED IMMUNITY, T CELL MEDIATED IMMUNITY as well as MYELOID CELL DEVELOPMENT (all NES \u0026lt;-1, Nominal p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Meanwhile, none specific immune related enrichment was indicated in high-risk group cases (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The result shall be an additional support for indicating the immune targeting potential for low-risk group of osteosarcoma patients.\u003c/p\u003e \u003cp\u003eMoreover, ESTIMATE algorithm was additionally performed to evaluate the immune, stromal score and tumor purity of osteosarcoma samples based on TARGET data, and the result revealed statistical significant correlation between the gene signature and all three parameters including the tumor stromal score, immune score as well as tumor purity. Meanwhile, as tumor purity was higher in high-risk group of patients, the low-risk group patients were tend to posses both higher immune and stromal score (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Further, TIDE analysis also indicated that low-risk group of patients were more likely to response to immune therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eD), all results supporting the immune targeting potential of this group of osteosarcoma patients in further clinical treatment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer has been a major health threat for people, the development of cancer involves not only the genetic alterations of oncogenes and tumor inhibiting genes, but also interaction between cancer cells and surrounding microenvironment. Besides uncontrolled proliferation, resisting cell death, activating metastasis and so on, evading immune surveillance has been one of the well acknowledged major cancers hallmarks. Based on increasing understanding of the immune regulation in cancers, immune checkpoint inhibitors (ICIs) has been a promising pillar of nowadays cancer clinical treatment[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOsteosarcoma has been a common bone malignancy affecting mainly children and adolescents, and the immunotherapy for osteosarcoma can be traced back to 1891, when William Coley, the well known \u0026ldquo;father\u0026rdquo; of cancer immunotherapy, discovered that Coley toxin (an inactivated toxin from bacteria) has therapeutic effects on osteosarcoma included multiple cancers[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Although Coley toxin wasn\u0026rsquo;t widely used because of its toxic effects and instability in clinical therapy, it did lay a great foundation for the development of immune researches in cancers[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Over time, increasing attention was drawn on various immune activators, for instance muramyl tripeptide which is a synthetic derivative of bacteria. At first, clinical trials did not show any therapeutic effects, but follow-up studies reported that combining mifomotide to chemotherapy could increase patients overall survival[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Although it hasn\u0026rsquo;t been approved by US Food and Drug Administration (FDA) for clinical use, mifamotide was licensed by the European Medicines Agency in 2009 and is currently undergoing clinical studies in advanced osteosarcoma cases in Europe and Mexico[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs for the clinical use of ICIs in osteosarcoma, the current clinical trials were mostly focused on PD-1/PD-L1 and CTLA-4. Although the treatment did achieve great therapeutic effects in multiple cancers such as melanoma and lung cancer[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], the effectiveness was not replicated in osteosarcoma. Especially for PD-1/PD-L1 blockade, although studies found that blocking PD-1/PD-L1 interaction improves the responsiveness to CTL[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], reducing tumor burden and enhancing the chemotherapy effect of cisplatin on osteosarcoma[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], the effectiveness of these studies were only observed in animal model stage, and the clinical trials in patients have not received same therapeutic response.\u003c/p\u003e \u003cp\u003eHowever, despite the fact that none specific breakthrough has been received in osteosarcoma immunotherapy, the well treatment effect in animal models and even just small part of patients indeed reveal the potential of immune researches in the tumor. Better understanding of the genetic landscape and regulation mechanism behind osteosarcoma immune microenvironment shall benefit identifying new potential immune biomarkers and aiding further clinical application of ICI therapy in the tumor. In recent years, various genes containing signatures representative of caner immune status have been identified in different types of cancers, and they have been showing inspiring clinical effects[\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], meanwhile, multiple meaningful studies involving the transcriptome and genome data of osteosarcoma, have also been released[\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. It\u0026rsquo;s of clinical feasibility to construct meaningful immune prediction models based on osteosarcoma genome information, thus benefiting evaluating the cancer immune status for potential clinical ICIs therapy. In the study, GEO osteosarcoma transcriptome profiles as well as single cell sequencing data were together applied for analyzing the potentially immune related gene candidates and exploring their function mode.\u003c/p\u003e \u003cp\u003eBased on four GEO osteosarcoma transcriptome profiles which have been previously selected and applied to analyze potential tumor development related key genes, the differently expressed genes in osteosarcoma comparing to normal samples were selected and divided into 4 groups including difference level\u0026thinsp;\u0026lt;\u0026thinsp;2 fold, 2\u0026thinsp;~\u0026thinsp;4 fold, 4\u0026thinsp;~\u0026thinsp;8 fold and \u0026gt;\u0026thinsp;8 fold genes clusters, the detailed information of the genes have also been previously discussed. In the study, we mainly focused on the high level (\u0026gt;\u0026thinsp;8 fold) differently expressed genes for the convenience of further IHC experiment, considering that the genes shall harbor more chance for clinical use if they are suitable to be tested by IHC which has been one of the most common clinical pathology experimental methods. Further, by intersection with the immune related gene list from IMMPORT database, a total of 108 high level expression changed meanwhile immune related gene candidates were identified for next step analysis.\u003c/p\u003e \u003cp\u003eAfter PPI network following GO/KEGG analyzing the immune enrichment of the candidate genes, for constructing a clinical effective multi-genes signature based on the genes, LASSO algorithm which has been a widely used tool suitable for building gene models basing on large numbers of genes expression profile was used. At first, a six genes containing signature composed of STC2, FPR1, VCMA1, PTN, IL13RA2 and HCST genes was proposed, however, despite the fact that both KM survival analysis and ROC curve showed great prognosis indication ability of the signature, a six genes signature is over complicated and hard to be applied in real world clinical experiments. The main purpose of the study was to construct a signature that was suitable for further clinical application, so we next step scale the signature hub genes down to two genes, namely STC2 and FPR1 which was mainly based on the coefficient of signature genes together with survival analysis.\u003c/p\u003e \u003cp\u003eSTC2 is short for stanniocalcin2, it locates in 5q35.2 and encodes a protein consisting of 302 amino acids including 36 negatively charged amino acid residues (ASP\u0026thinsp;+\u0026thinsp;Glu) and 35 positively charged amino acid residues (Arg\u0026thinsp;+\u0026thinsp;Lys), meanwhile, the molecular weight of the protein is estimated to be 33.2KD with the theoretical isoelectric point computed as 6.96. As for FPR1, the formula of the protein is C\u003csub\u003e1781\u003c/sub\u003eH\u003csub\u003e2796\u003c/sub\u003eN\u003csub\u003e444\u003c/sub\u003eO\u003csub\u003e467\u003c/sub\u003eS\u003csub\u003e17,\u003c/sub\u003e which is composed of 350 amino acids weighing 38.4KD, and the estimated protein half life is 30 hours with the theoretical isoelectric point computed to be 9.23. Both STC2 and FPR1 proteins were predicted to be stable in cells with the instability index estimated to be 38.87 and 26.33 respectively.\u003c/p\u003e \u003cp\u003eBased on the two genes, a newly STC2/FPR1 dual genes signature was constructed, although the prognosis indication value of the signature was lower than the original six genes containing signature, KM survival as well as Cox regression analysis still supported the dual genes signature as an independent survival indicator. Further, a nomogram combining the STC2/FPR1 dual genes signature and tumor metastasis which has also been an important clinical survival risk indicator was constructed, which shall benefit the clinical evaluation of osteosarcoma patients prognosis.\u003c/p\u003e \u003cp\u003eThe main aim of the signature was for potential immune status prediction in osteosarcoma samples, and even though both STC2 and FPR1 were indicated by previous IMMPORT data to be included in the immune related genes list, the detailed association between the dual genes signature and ossteosarcoma immune microenvironment regulation should be carefully explored. Considering the vital function that various immune cells play in cancer immune microenvironment, for instance, the increasing immune suppressive cells including Treg cells and tumour-associated macrophages (TAM), as well as inactivation of tumor killing CD8\u0026thinsp;+\u0026thinsp;T cells have been reported to contribute to immune escape in osteosarcoma included various cancers[\u003cspan additionalcitationids=\"CR54 CR55 CR56\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], the correlation between the signature and immune cells distribution was firstly evaluated. And the result analyzed based on genes expression profile indicated that STC2/FPR1 gene signature was associated with 6 immune cell types including B cells naive, B cells memory, M1 macrophages, M2 macrophages, mast cells and neutrophils. Interestingly, the IHC experiment conducted on local hospital patients samples revealed that very few amount of lymphocytes or granulocytes infiltrated in osteosarcoma tissues, and the macrophages were actually the main immune cell type in the tumor which phenomenon has been proposed by multiple other researches. Thus, given the dominated role of macrophages in osteosarcoma microenvironment, the association between the STC2/FPR1 signature and macrophages was mainly focused, and further QPCR result indeed supported a statistical significant positive correlation between them.\u003c/p\u003e \u003cp\u003eConsidering the critical function of immune checkpoints in the development of ICIs therapy, the expression of immune checkpoints including PD-L1, CTLA4, TIGIT, TIM-3 and LAG-3 between the high-risk and low-risk groups of osteosarcoma patients which were divided based on the STC2/FPR1 gene signature risk score were evaluated. And although moderate correlation has been detected with both PD-L1 and TIM-3 expression, local hospital IHC experiment revealed that very low PD-L1 expression was observed in osteosarcoma samples, even in the low-risk group of patients, this might be part of the reason that PD-L1 blockade hasn\u0026rsquo;t been receiving satisfactory clinical effect. And another inspiring discovery is that as for CTLA4 gene, although the checkpoint expression was not statistical significantly correlated with the gene signature, its expression was broadly high in a big portion of samples, partly explaining the reason that CTLA4 blockade hes been receiving response in some patients. And of the other three immune checkpoints, interestingly, the expression of TIM-3 was much higher and complete than the TIGIT and LAG3, indicating a potential value of TIM-3 being developed as an immune drug target in further clinical treatment\u003c/p\u003e \u003cp\u003eMoreover, other analysis including ICD related genes expression, GSEA immune related signaling enrichment and ESTIMATE immune scores evaluation were additionally performed. And the different expression of ICD related genes not only between osteosarcoma and normal bone samples, but also between high-risk and low-risk groups of patients supported the different immune status in corresponding groups of osteosarcoma patients. Meanwhile, the more enriched immune related signaling pathways revealed by GSEA analysis as well as the higher ESTIMATE immune and stromal score in low-risk group of patients than their high-risk group of counterparts, and also the TIDE analysis results all indicated the immune targeting potential for this group of patients, highlighting the potential of developing further new clinical immunotherapy strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study constructed a STC2/FPR1 dual genes signature based on osteosarcoma genome information, the newly constructed signature not only works as an independent prognosis indicator, but also related with osteosarcoma immune microenvironment including macrophages dominated immune cells infiltration and TIM-3 included immune checkpoints expression. Although the current result is not yet enough to support the clinical application of the signature, rigorous prospective studies performed on animal models as well as clinical trials are still needed, the results shall provide meaningful insight into immune researches in osteosarcoma, thus benefiting further development of new immunotherapy strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAA-TKIs \u0026nbsp; \u0026nbsp; Anti-angiogenesis tyrosine kinase inhibitors \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGEO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eTARGET \u0026nbsp; \u0026nbsp; Therapeutically Applicable Research to Generate Effective Treatments\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Immunogenic cell death\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Protein-protein interaction network\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIHC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Immunohistochemistry\u003c/p\u003e\n\u003cp\u003eICIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Immune checkpoint inhibitors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQPCR \u0026nbsp; \u0026nbsp; \u0026nbsp; Quantative Real Time PCR\u003c/p\u003e\n\u003cp\u003eLASSO \u0026nbsp; \u0026nbsp; \u0026nbsp;Least absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Gene ontology analysis\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp; \u0026nbsp; Kyoto Encyclopedia of Genes and Genomes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; \u0026nbsp; \u0026nbsp; Gene set enrichment analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely appreciate the researchers for providing their GEO databases information online which were important data resources for the study, it is our pleasure to acknowledge their contributions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWM and LM designed the study and together drafting the manuscript, contributed equally to the whole study. ZZ, SL and JL performed the data collecting and analysis. FW, NS , ZY and LG participated in the data interpretation and study design, LG and CW were involved in the drafting and critical revision of the manuscript. As the corresponding author, CW has full access to all data of the manuscript and made the final decision to submit the article for publication. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported by the China central government funds for guiding local scientific and technological development (YDZJSX2021A042), the fund of Science project from Health Commission of ShanXi Province (2023103) and three grants of Natural Science Foundation of ShanXi Province in China (202203021222393, 202303021222333, 202403021211135).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. The data can be found here: GSE12865:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12865. GSE16088:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16088. GSE28424:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28424. GSE42352:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42352.\u003c/p\u003e\n\u003cp\u003eGSE162454: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162454.\u003c/p\u003e\n\u003ch4\u003eTARGET database: https://ocg.cancer.gov/programs/target/data-matrix\u003c/h4\u003e\n\u003cp\u003eAll data generated or analyzed based on the online datasets and other experiments during this study are included in this published article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman ethics and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a retrospective study involving human samples, the samples were obtained from the hospital Biobank (Second Hospital of Shanxi Medical University), the utility of the samples in the study was approved by the ethics committees of Second Hospital of ShanXi Medical University (ethics approval number: (2023)YX(179)). As a retrospective analysis using patient postoperative samples which were stored permanently in hospital Biobank, the study was approved for exemption from the requirement of informed consent. All the experiments were carried out in accordance with relevant guidelines and regulations or declaration of Helsinki.\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\u003eAll of the authors agreed the publication of the paper and declare no conflicts of interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71(1):7\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGianferante DM, Mirabello L, Savage SA. 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Cell Immunol. 2019;343:103711.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Osteosarcoma, STC2, FPR1, immune gene signature, TIM-3, immunotherapy prediction marker","lastPublishedDoi":"10.21203/rs.3.rs-5651928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5651928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eOsteosarcoma has been a common bone malignancy occurring in children and adolescents. Attributing to high tumor heterogeneity, none specific breakthrough has been received in targeted gene therapy for osteosarcoma, although it’s still of great potential for immunotherapy in clinical application. In the study, 5 GEO profiles containing transcriptome information of 109 osteosarcoma samples, single cell sequencing data composed of 6 cases of samples, as well as 43 cases of local hospital tissue samples were combine used to identify the promising immune related candidate genes in osteosarcoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eBased on osteosarcoma transcriptome microarrays from GEO database as well as immune related gene profile from IMMPORT database, differently expressed meanwhile immune related gene candidates in osteosarcoma comparing to normal control samples were identified. Then, protein-protein interaction network (PPI), survival analysis followed by LASSO analysis were in succession applied to construct a gene signature based on the selected candidate genes. After understanding the basic genetic physicochemical properties and evaluating the prognosis risk association of the gene signature using local hospital cancer samples, its association with immune microenvironment features including macrophages included various immune cells infiltration, different immune checkpoints expression, immune related signaling pathways involvement were next step assessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eFrom GEO transcriptome datasets which contains a total of 109 osteosarcoma samples, a total of 108 high level differently expressed meanwhile immune related gene candidates were identified. Then, PPI network and LASSO analysis highlighted a 6 genes containing cluster from the 108 candidate genes. Further, ROC curve as well as Cox regression analysis assisted scaled the 6 hub genes down to 2 key genes, namely STC2 and FPR1, and a gene signature was constructed based on them. After understanding the basic genetic physicochemical properties of STC2 and FPR1, double staining immunochemistry (IHC) experiment based on 43 cases of local hospital samples and single cell sequencing date of 6 tissue samples revealed that STC2 was mainly expressed in osteosarcoma cancer cells, meanwhile, FPR1 was mostly enriched in macrophages focused immune cells which has also been the main immune cell type in osteosarcoma microenvironment. Moreover, the combining STC2/FPR1 dual genes signature was also associated with distribution of multiple immune checkpoints, especially TIM-3. Further, the correlation between the signature and other immune features including immune related cell death (ICD) and ESTIMATE immune score were additionally evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eBased on osteosarcoma transcriptome genes analysis, a dual genes containing signature composed of STC2 and FPR1 genes was constructed. Immune correlation analysis indicated the signature was associated with the macrophages infiltration which has been a main immune cell type in osteosarcoma, ans it was also related with TIM-3 included multiple immune checkpoints expression. The results shall benefit further osteosarcoma immune researches and assist revealing promising prediction markers for clinical immunotherapy.\u003c/p\u003e","manuscriptTitle":"STC2/FPR1 dual genes signature works as a prognosis indicator with implication in macrophages dominated and TIM-3 related osteosarcoma immune landscape","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 10:26:55","doi":"10.21203/rs.3.rs-5651928/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"601948ee-8c9f-4a3e-a177-94964e76527d","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T11:24:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-24 10:26:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5651928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5651928","identity":"rs-5651928","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}