Identification and validation of the important role of YAP in the development and progression of Osteosarcoma

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Methods This study integrates multi-omics data to conduct a systematic analysis of osteosarcoma. We obtained whole-exome sequencing (WES) data from 106 patients from the National Genomics Data Center (NGDC) and collected clinical data from 3,115 osteosarcoma patients from the SEER database. Univariate Cox regression analysis was performed using the survival analysis package in R to construct a time-dependent covariate model for chemotherapy efficacy. WES data were analyzed using tools such as HISAT2 and SAMtools to identify copy number variations (CNVs) in genes and genomic regions. KEGG and GO enrichment analyses were conducted using the KOBAS-i and Metascape platforms. Gene-drug interaction data were retrieved from the Drug Gene Interaction (DGI) database and visualized using the igraph package in R. Additionally, RNA sequencing data were obtained to analyze the expression levels of the YAP1 gene, and its expression was further validated by immunohistochemical staining, with phosphate-buffered saline as the negative control and a known positive marker as the positive control. Results Clinical data analysis indicates that both surgery and chemotherapy significantly reduce mortality rates, with chemotherapy demonstrating significant early efficacy. However, this efficacy diminishes over time, while radiotherapy notably increases the risk of mortality. Genomic analysis using WES identified 3,215 genes with copy number alterations, including 826 amplifications and 2,389 deletions. Functional enrichment revealed key pathways like immune response and cancer metabolism, with the Hippo pathway showing significant alterations, particularly in YAP1, a core regulatory gene. YAP1 exhibited recurrent copy number gains in osteosarcoma, and 35 Hippo-related genes showed distinct CNA patterns. Pharmacogenomic analysis identified 1,299 drug-gene interactions involving 73 Hippo pathway genes, suggesting potential therapeutic targets. These findings highlight the importance of the Hippo pathway, especially YAP1, in osteosarcoma and its potential as a therapeutic target. The positive expression rate of YAP was 78.84% (41/52), while the expression rate in osteochondroma was 30% (6/20). The expression rate in osteosarcoma was significantly higher than that in osteochondroma (P = 0.000). The positive expression rate of YAP was not significantly associated with gender or age, but showed a statistically significant correlation with tumor size, staging, and metastasis status. Notably, analysis revealed a positive correlation between YAP positive expression and both Enneking staging and distant metastasis in osteosarcoma patients. Conclusion Our study demonstrated that chemotherapy efficacy in osteosarcoma patients diminishes over time, identifies significant copy number variations in osteosarcoma tissues, and highlights the elevated expression of YAP1, particularly in osteosarcoma compared to osteochondroma, suggesting its potential role in tumorigenesis and therapeutic targeting. osteosarcoma Machine learning YAP1 Bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Osteosarcoma is a prevalent and aggressive primary malignant bone tumor, It predominantly affects adolescents between the ages of 10 and 25, with a higher prevalence in males[ 1 ].Osteosarcoma is characterized by its high invasiveness, poor prognosis, and low 5-year survival rate. In cases where metastasis has not occurred, approximately 70% of patients may achieve long-term survival. However, once recurrence or metastasis develops, the 5-year survival rate decreases to approximately 20%. Genomic structural variations, particularly CNV in large segments (> 1 kb), are closely linked to disease clinical phenotypes. At the genetic level, tumor drug resistance is often driven by gene amplifications and deletions, reflecting the rate and efficiency of CNV formation. The Hippo pathway plays a significant role in tumor drug resistance[ 2 – 4 ].The Hippo signaling pathway is an important growth-regulating system made up of protein kinases, transcription factors, co-activators, and other regulatory molecules. It works by controlling cellular processes through a series of protein phosphorylation events, influencing tumor cell growth, movement, invasion, and differentiation. This pathway is essential in understanding how tumors develop[ 5 ].YAP and TAZ are phosphorylated at specific amino acid residues by the LATS1/2-Mob1 complex. When Hippo signaling is attenuated, the phosphorylation levels of YAP and TAZ are reduced, leading to their translocation into the nucleus. Once inside the nucleus, they bind to one of the TEA domain (TEAD) family transcription factors, activating target genes involved in cell proliferation, survival, and tissue growth[ 6 – 8 ]. Bioinformatics, as an interdisciplinary field, integrates information technology, computer science, mathematical modeling innovations, and statistical theories. This field analyzes biological experimental data to deeply explore the underlying biological mechanisms. Compared to traditional statistical methods, bioinformatics demonstrates more systematic and efficient analytical capabilities[ 9 ].This study aims to explore the molecular mechanisms of osteosarcoma through the integration of multi-omics data. We obtained WES data from 106 cancer patients from the National Genomics Data Center (NGDC) and collected extensive clinical data from osteosarcoma patients from the Surveillance, Epidemiology, and End Results (SEER) database. Based on these datasets, a systematic analysis was conducted. Various bioinformatics methods were used to analyze CNV and gene expression profiles, with the aim of identifying key genes and pathways associated with osteosarcoma. The biological processes and signaling pathways involved in the relevant genes were explored through KEGG and Gene Ontology (GO) enrichment analysis. In addition, experimental validation showed that YAP expression is higher in malignant bone tumors than in benign bone tumors, and is associated with tumor malignancy progression. Materials and methods Data Download We obtained dataset HRA003260 from the NGDC (National Genomics Data Center, https://ngdc.cncb.ac.cn/ ), which contains WES data from 106 cancer patients including both osteosarcoma tissues and adjacent normal tissues. Additionally, clinical data encompassing 3,115 osteosarcoma patients were acquired from the SEER (The Surveillance, Epidemiology, and End Results) database. Clinical Analysis The univariate Cox regression analysis of clinical data from osteosarcoma patients was performed using the survival package (v3.6.4) in R version 4.4.0. Time-dependent covariates for chemotherapy efficacy were modeled through linear regression analysis using the coxph function. Identification of CNVs in Genes and Genomic Regions WES data were aligned to the hg19 reference genome using HISAT2 (v2.2.1), followed by SAMtools (v1.9) for BAM file processing. Transcript-level expression quantification was performed with StringTie (v2.1.4) to generate transcripts per million (TPM) values. CNVs were statistically analyzed using edgeR (v3.32.1) with the following criteria: genes demonstrating Benjamini-Hochberg adjusted p 0 were identified as statistically significant CNV events. CNV detection in genomic regions was conducted using 100 kb sliding windows, where median-normalized sequencing depth was calculated per window via bedtools (v2.30.0) genomecov. Differential depth analysis between paired cancer-normal tissue samples was performed using the Wilcoxon signed-rank test. Read alignment visualization were conducted using the Integrative Genomics Viewer (IGV v2.12.3). KEGG and GO Enrichment Analysis Concurrent KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO enrichment analyses were systematically conducted through the KOBAS-i (v3.0) and Metascape platforms, employing a significance threshold of Benjamini-Hochberg adjusted p < 0.05 gene set. Interaction of the Hippo Pathway Genes with Drugs All gene-drug interaction data were retrieved from the DGI. Network visualization was implemented using igraph (v1.5.1) in R. Expression Profiling Analysis RNA-seq data from 101 osteosarcoma samples were obtained from the CNCB HRA003260 dataset, with additional expression profiles encompassing 20 osteoblast samples, 30 skeletal muscle tissue specimens, and 30 cultured myocyte samples retrieved from NCBI GEO datasets GSE227994 and GSE287342. RNA-seq reads were aligned to the hg19 reference genome using HISAT2 (v2.2.1) with default parameters, followed by transcript quantification of YAP1 expression levels via StringTie (v2.2.0) in transcripts per million (TPM) units. The expression of YAP in human osteosarcoma tissues. Specimens were decalcified, fixed in 4% paraformaldehyde for 24 hours, and embedded in paraffin. Sections (4µm) were mounted on 1% poly-L-lysine–treated slides and incubated at 65°C for 1.5 hours. Detection was performed using the SP method with the primary antibody diluted at YAP1:500. Staining was done with DAB, followed by counterstaining, dehydration, clearing, and mounting. PBS served as the negative control, and a known positive marker was used as the positive control. The clinical data, including patient gender, age, tumor size, staging, and metastasis status, were collected. Subsequently, statistical methods were applied to assess the correlation between YAP positive expression and these factors. Result Chemotherapeutic Efficacy in Osteosarcoma Patients Exhibits Time-dependent Attenuation. We analyzed clinical data from 3,115 osteosarcoma cases retrieved from SEERstat. Kaplan-Meier survival curves demonstrated that surgery and chemotherapy significantly reduced mortality, whereas radiotherapy markedly increased mortality risk, with surgical intervention showing the most pronounced therapeutic advantage (Fig. 1 A, Table 1 ). Subsequent evaluation of chemotherapy's time-varying hazard ratio (HR) revealed superior early-stage efficacy that progressively diminished over time (Fig. 1 B). Stratified survival analyses at 1-year, 3-year, and 5-year intervals showed narrowing survival rate disparities between chemotherapy and non-chemotherapy cohorts, with no significant difference at 1 year and nearly complete loss of efficacy by 5 years (Fig. 1 C). Collectively, these findings indicate substantial temporal decay in chemotherapy's therapeutic benefits for osteosarcoma patients. Table 1 Cox regression analysis of osteosarcoma patients Characteristic HR 95% CI P value Significance Race Black Reference - - - White 1.09 0.93–1.27 0.311 nc Other 1.07 0.87–1.33 0.514 nc Sex Female Reference - - - Male 1.11 1.00-1.22 4.60E-02 * Surgery No Reference - - - Yes 0.38 0.34–0.43 5.85E-65 *** Radiotherapy No Reference - - - Yes 2.59 2.27–2.95 1.53E-46 *** Chemotherapy No Reference - - - Yes 0.85 0.76–0.95 3.01E-03 ** Days From Diagnosis To Treatment 1.00 1.00–1.00 0.429 nc Identification of Copy Number Variations in Osteosarcoma Tissues To investigate pathway- or gene-specific amplifications in osteosarcoma, we integrated WES data from four independent cohorts: 80 cases from TCGA TARGET-OS, 212 cases (tumor/adjacent tissues) from CNCB HRA003260, 28 cases from NCBI PRJNA400619, and 24 cases from PRJNA774097. Clustering analysis based on gene-level copy number profiles revealed distinct segregation between malignant and normal tissues (Fig. 2 A), suggesting systematic genomic divergence. A total of 3,215 genes exhibited copy number alterations, including 826 amplified and 2,389 deleted loci in tumor tissues (Fig. 2 B). Genome-wide analysis identified 61 significant 100-kb genomic windows with recurrent CNAs (25 gains and 36 losses), highlighting chromosomal hotspots of instability (Fig. 2 C). Functional Enrichment Analysis All copy number-altered genes underwent functional annotation via Metascape and KEGG pathway enrichment. Metascape analysis revealed significant enrichment of pathways including adaptive immune response, response to bacterium, tube morphogenesis, and regulation of epithelial cell proliferation (Fig. 3 A-B, Table S 1). KEGG enrichment highlighted overrepresented pathways such as Metabolic pathways, Pathways in cancer, and Proteoglycans in cancer (Fig. 3 C, Table S 2). Among KEGG pathways, the PI3K − Akt, Hippo, Thyroid hormone, Rap1, and Ras signaling pathways demonstrated the highest significance, with core regulatory genes ( YAP1 in Hippo, Rap1 , and Ras ) exhibiting copy number gains. Given that the Hippo pathway exhibited greater statistical prominence than Rap1 and Ras pathways, it was prioritized for subsequent mechanistic investigation. Mechanistic Profiling of the Hippo Pathway and Its Core Gene YAP The Hippo signaling pathway serves as a master regulator of organ size determination, tissue regeneration, and cellular proliferation through its canonical kinase cascade. Core kinases LATS1/2 phosphorylate effector proteins YAP/TAZ, triggering their cytoplasmic sequestration and subsequent suppression of pro-growth transcriptional programs. Pathway dysregulation is mechanistically linked to oncogenesis and developmental anomalies (Fig. 4 A). WES analysis revealed recurrent YAP1 copy number gains across osteosarcoma specimens, with a striking 36.4-fold amplification observed in sample HRR786903 (Fig. 4 B). Furthermore, systematic characterization identified distinct copy number alteration patterns in 35 Hippo pathway-associated genes within osteosarcoma cohorts (Fig. 4 C). Pharmacogenomic Interactions of Hippo Pathway Genes Interrogation of the DGI identified 1,299 gene-drug interaction pairs involving 73 Hippo pathway components (Fig. 5 B, Table S 3). These interactions predominantly clustered within clinically actionable targets, kinase, and druggable genome categories (Fig. 5 A). Subsequent prioritization revealed 17 potent interactions, with the top-ranked pairs being DVL1 -Illudalic acid and AXIN1 -(±)-hydnocarpin (Fig. 5 C). Secondary hits included BMP2 , FZD1 , FZD4 , and FZD8 interacting with PI3K/mTOR inhibitors and Wnt pathway modulators, with particular potency observed in FZD8 -irinotecan binding. YAP1 Expression Profiling To validate whether YAP1 copy number gains correlate with transcriptional upregulation, we performed comparative analysis of YAP1 expression across osteoblasts, skeletal muscle tissues,and stage-stratified osteosarcoma specimens. YAP1 demonstrated significantly elevated expression levels in malignant tissues compared to normal counterparts, with cultured muscle cells exhibiting higher expression than primary muscle tissues (Figs. 6 A and 6 C). Notably, no significant differential expression was observed between stage IIB and III osteosarcoma cohorts, potentially attributable to the limited stage III sample size. Difference of YAP expression in osteosarcoma and chondrosarcoma In osteosarcoma tissue, the positive expression rate of YAP was 78.84% (41/52), while the expression rate in osteochondroma was 30% (6/20). The expression rate in osteosarcoma was significantly higher than that in osteochondroma (P = 0.000) (Fig. 7 and Table 2 ). The positive expression rate of YAP was not significantly associated with gender or age, but showed a statistically significant correlation with tumor size, staging, and metastasis status. Notably, analysis revealed a positive correlation between YAP positive expression and both Enneking staging and distant metastasis in osteosarcoma patients. (Table 3 ) Table 2 The expression levels of YAP in osteosarcoma and osteochondroma Groups n + - P Osteosarcoma 52 41 9 0.000 osteochondroma 20 6 14 Table 3 YAP expression and its correlation with clinical pathological features Variable YAP P - + Total 9 41 Gender Male 5 28 0.465 Female 4 13 Age(year) <20 6 22 0.477 ≥ 20 3 19 Diameter(cm 3 ) <21 7 16 0.035 ≥ 21 2 25 Ennecking stages I、II 8 13 0.002 III 1 28 Distant metastasis Yes 0 27 0.000 No 9 15 Discussion Osteosarcoma is one of the most challenging malignant tumors. It is the most common primary bone cancer, primarily affecting adolescents and elderly individuals, with a male-to-female ratio of 1.2:1[ 10 – 12 ]. Tumor cells directly produce immature osteoid tissue, which ultimately leads to the formation of osteosarcoma. Its development is primarily driven by genetic, hormonal, and environmental factors. OS is primarily treated with surgery and adjuvant chemotherapy. Under this combined therapeutic approach, the 5-year survival rate for patients with localized disease is approximately 70%[ 13 ]. However, for patients with recurrent or metastatic disease, the 5-year survival rate drops to around 20%, indicating a poor prognosis. For OS patients who are inoperable or exhibit resistance or intolerance to chemotherapy, the available treatment options remain limited. The role of chemotherapy (CT) and radiotherapy (RT) in the treatment of osteosarcoma (ESOS) remains controversial[ 14 ]. Huang et al. [ 15 ]conducted a retrospective analysis of 29 pediatric cases of knee osteosarcoma. All patients received neoadjuvant chemotherapy followed by amputation surgery, and the study found that the combined treatment achieved a 5-year survival rate of 89.1%. Michael et al[ 16 ]. reviewed the medical records of 82 dogs that underwent radiotherapy for diagnosed or suspected OS. The study found that both pain levels and radiation dosage were associated with the survival rates of dogs receiving radiotherapy alone, without chemotherapy.This study analyzed the clinical data of 3,115 osteosarcoma cases retrieved from SEERstat. The Kaplan-Meier survival curves indicated that surgery and chemotherapy significantly reduced mortality, while radiotherapy was associated with an increased risk of mortality. Surgical intervention demonstrated the most notable therapeutic advantage. The evaluation of the time-dependent hazard ratio (HR) for chemotherapy revealed that early intervention is effective, but its efficacy gradually diminishes over time. CNV in genomic structural variations (SV) are caused by genomic rearrangements. CNV typically refer to the deletion or duplication of gene copies longer than 1 kb, and are highly correlated with the clinical manifestations of diseases. Research has confirmed that both primary and acquired resistance in tumors at the genetic level originate from genomic structural variations[ 17 ]. CNVs are commonly present in both primary and metastatic tumors, with many mutations occurring in important signaling pathways and therapeutic target genes related to tumor initiation and progression, leading to changes in tumor drug sensitivity. We integrated whole exome sequencing (WES) data from four independent cohorts: 80 cases from TCGA TARGETOS, 212 cases from CNCB HRA003260, 28 cases from NCBI PRJNA400619, and 24 cases from PRJNA774097. Cluster analysis based on gene-level copy number profiles revealed significant genomic differences between malignant and normal tissues. A total of 3,215 genes exhibited copy number alterations, including 826 amplification loci and 2,389 deletion loci in tumor tissues. In addition to primary resistance, acquired resistance also poses a challenge for clinicians. During treatment, tumor cells gradually develop mutations that confer resistance. The occurrence of resistance mechanisms is often due to the formation of genomic structural variations, which block the initial action of drugs on target molecules, or through the activation of alternative signaling pathways that sustain tumor growth, ultimately leading to the development of drug resistance. Copy number-altered genes were functionally annotated using Metascape and KEGG pathway enrichment. Metascape analysis highlighted significant enrichment in pathways such as adaptive immune response, bacterial response, tube morphogenesis, and epithelial cell proliferation regulation. Among the KEGG pathways, the PI3K − Akt, Hippo, Thyroid hormone, Rap1, and Ras signaling pathways showed the highest significance. Notably, core regulatory genes, such as YAP1 in the Hippo, Rap1, and Ras pathways, exhibited copy number gains. YAP and TAZ are core effector proteins of the Hippo signaling pathway, widely expressed in various tissues and frequently upregulated in many tumor cells[ 18 ]. A literature review indicates that YAP is expressed in most solid tumors. Furthermore, recent studies have demonstrated that YAP functions as an oncogenic factor in gastrointestinal tumors, breast cancer, and lung cancer[ 19 – 21 ]. When the regulation of the Hippo pathway is impaired, YAP/TAZ lose phosphorylation by the upstream LATS1/2, leading to increased expression of YAP/TAZ. This, in turn, inhibits apoptosis and promotes epithelial-mesenchymal transition (EMT)[ 22 ]. At the genetic level, different individuals exhibit distinct resistance mechanisms, and the genetic mutations associated with resistance vary at different time points. However, most studies are based on single samples and/or assessments at a single time point, which may easily overlook important mutations. Therefore, it is crucial to predict the drug sensitivity of tumors before chemotherapy and establish a method for continuous monitoring of drug efficacy during treatment. This approach is essential for studying the mechanisms of drug resistance in osteosarcoma. The ability of tumor cells to acquire metastatic potential has long been a topic of intense debate among scientists. While a large number of tumor cells are capable of detaching from the primary tumor and entering the bloodstream, only a small fraction of these cells ultimately survive, invade distant organs, and form metastatic foci. In this study, there was a significantly higher positive expression rate of YAP in osteosarcoma tissues compared to osteochondroma. Clinical data collection revealed a positive correlation between YAP positive expression and the Enneking staging as well as distant metastasis in osteosarcoma patients. Conclusion Our study demonstrated that chemotherapy efficacy in osteosarcoma patients diminishes over time, identifies significant copy number variations in osteosarcoma tissues, and highlights the elevated expression of YAP1, particularly in osteosarcoma compared to osteochondroma, suggesting its potential role in tumorigenesis and therapeutic targeting. Abbreviations CNV copy number variations CT chemotherapy DGI Drug Gene Interaction RT radiotherapy SEER Surveillance, Epidemiology, and End Results TPM transcripts per million KEGG Kyoto Encyclopedia of Genes and Genomes NGDC National Genomics Data Center WES whole-exome sequencing GO Gene Ontology Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no conflicts of interest. Funding The research was funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2021D01C472). Authors’ contributions HXX: Conducted the study. The data were collected, analyzed, and interpreted. Wrote the manuscript. HCY: Edited the manuscript and planned the project. GLL: Designed the study, interpreted the data, and edited the manuscript. RM: Interpreted the data. TL: Edited the manuscript and reviewed the manuscript. YT: Edited the manuscript. All the authors have read and approved the final manuscript. Acknowledgments Not applicable. The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article. References Cole S, Gianferante DM, Zhu B, Mirabello L. Osteosarcoma: A Surveillance, Epidemiology, and End Results program-based analysis from 1975 to 2017. Cancer. 2022;128(11):2107–18. Yang M, Zhang H, Gao S, Huang W. DEPDC1 and KIF4A synergistically inhibit the malignant biological behavior of osteosarcoma cells through Hippo signaling pathway. J Orthop Surg Res. 2023;18(1):145. Zhang H, Zhou L, Hu S, Gu W, Li Z, Sun J, Wei X, Wang Y. The crosstalk between LINC01089 and hippo pathway inhibits osteosarcoma progression. J Bone Min Metab. 2022;40(6):890–9. Ji Z, Shen J, Lan Y, Yi Q, Liu H. 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YAP and TAZ in Lung Cancer: Oncogenic Role and Clinical Targeting. Cancers (Basel) 2018, 10(5). Pan D. The hippo signaling pathway in development and cancer. Dev Cell. 2010;19(4):491–505. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx 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-6614090","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480060888,"identity":"9fb05c0d-c7a0-4d06-aa7b-9e23c46abd57","order_by":0,"name":"Xiaoxia Huang","email":"","orcid":"","institution":"General Hospital of Xinjiang Military Region","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxia","middleName":"","lastName":"Huang","suffix":""},{"id":480060889,"identity":"9df77d78-b743-4765-93f5-f28a13695be9","order_by":1,"name":"Caiyun Huang","email":"","orcid":"","institution":"Graduate School of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Caiyun","middleName":"","lastName":"Huang","suffix":""},{"id":480060890,"identity":"40a52861-21e8-4da9-bc5f-2ad8abcaf9c2","order_by":2,"name":"Sun Xiangning","email":"","orcid":"","institution":"Graduate School of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Xiangning","suffix":""},{"id":480060891,"identity":"1777e7ee-0143-43b1-bbf2-b8c2b2e1943c","order_by":3,"name":"LeiLei Gao","email":"","orcid":"","institution":"Graduate School of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"LeiLei","middleName":"","lastName":"Gao","suffix":""},{"id":480060892,"identity":"ede7a75a-3903-49e3-bc62-c2aadca53f94","order_by":4,"name":"Rui Ma","email":"","orcid":"","institution":"Graduate School of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Ma","suffix":""},{"id":480060896,"identity":"e91a9b1f-3e00-47be-8952-8d2153647ba5","order_by":5,"name":"Yong Teng","email":"","orcid":"","institution":"General Hospital of Xinjiang Military Region","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Teng","suffix":""},{"id":480060901,"identity":"8b2859e2-f9db-44e7-9639-5c36270fee94","order_by":6,"name":"Tao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYHACNgTzg4GNHRE6mNkYDkCZjDMK0pJJ08LM8+EQYwMhDfzS5489/lBxx27D8d7Dr20MDjAzsB8+ugGfFsm+ZHaDA2eeJW84cy7NOsfgDh8DT1raDXxaDM4ws0kcbDucbHAjx8w4x+AZM4MEjxkRWv5BtVgYHGZsIE5Lw2E7oBbjxwzEaJHsYTaTOHPscILkmTNmjD0GaclshPzCz8P4TKKi5rA93/Ee4w8//tjY8bMfPoZXCwwkLjjAwCYBYrERUgoD9vINDMwfiFU9CkbBKBgFIwsAAPvKTil5A/k0AAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Air Force Medical University","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-05-07 17:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6614090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6614090/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86215466,"identity":"827403bb-c6e6-4c28-a7a9-1fdb9ec53b53","added_by":"auto","created_at":"2025-07-08 05:55:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":174729,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of clinical data of patients with osteosarcoma\u003c/p\u003e\n\u003cp\u003e(A) Overall survival rate curves for patients undergoing surgery, chemotherapy and radiotherapy. The P-values were calculated using the univariable Cox regression analysis method.\u003c/p\u003e\n\u003cp\u003e(B) Relationship of hazard ratio (HR) over time under chemotherapy. The temporal association with HR was modeled using linear regression.\u003c/p\u003e\n\u003cp\u003e(C) Overall survival rate curves for patients undergoing chemotherapy calculated from 1, 3 and 5 years, respectively. The P-values were calculated using the univariable Cox regression analysis method.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/10e80d477aaea69256997e7e.png"},{"id":86215465,"identity":"e3203bbb-8e23-4a8d-a5f8-8f6a537add2f","added_by":"auto","created_at":"2025-07-08 05:55:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":249106,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of copy number variants (CNVs) in osteosarcoma tissue.\u003c/p\u003e\n\u003cp\u003e(A) Clustering tree of normal and tumor samples constructed using relative genomic copy numbers of all genes.\u003c/p\u003e\n\u003cp\u003e(B) Volcano plot delineating CNV genes between tumor and normal tissues.\u003c/p\u003e\n\u003cp\u003e(C) Genome-wide landscape of CNVs analyzed with statistical calculations performed using 100-kilobase (kb) genomic windows.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/48ca9c3efdff93157117326c.png"},{"id":86215469,"identity":"df2e7484-7801-4e64-ba75-5f74586667db","added_by":"auto","created_at":"2025-07-08 05:55:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247234,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment of CNV genes.\u003c/p\u003e\n\u003cp\u003e(A) Network of enriched GO terms colored by cluster ID, where nodes that share the same cluster ID are typically close to each other.\u003c/p\u003e\n\u003cp\u003e(B) Bar graph of enriched GO terms colored by p-values.\u003c/p\u003e\n\u003cp\u003e(C) Dot graph of enriched KEGG terms colored by p-values.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/a4f62b537cbeb44048a5a5ca.png"},{"id":86216113,"identity":"ab40d174-7060-452b-8aa0-913972a3589b","added_by":"auto","created_at":"2025-07-08 06:03:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3641960,"visible":true,"origin":"","legend":"\u003cp\u003eSystematic profiling CNVs associated with key components of the Hippo pathway.\u003c/p\u003e\n\u003cp\u003e(A) Schematic overview of the Hippo signaling pathway's core regulatory architecture. Genes with CN gains in tumor samples are annotated in red, whereas those exhibiting CN losses are demarcated in blue.\u003c/p\u003e\n\u003cp\u003e(B) Comparative visualization of sequencing reads depth at the \u003cem\u003eYAP1\u003c/em\u003e locus performed between normal and tumor samples.\u003c/p\u003e\n\u003cp\u003e(C) Row-wise normalized heatmap of relative copy numbers across CNV genes in Hippo signaling pathway.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/6fe4c91df7c0812f37630687.png"},{"id":86215477,"identity":"f3b617e4-c930-40ef-b118-20daa93485c0","added_by":"auto","created_at":"2025-07-08 05:55:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3685899,"visible":true,"origin":"","legend":"\u003cp\u003eInteractions between Hippo pathway genes and drugs.\u003c/p\u003e\n\u003cp\u003e(A) Druggable catagories involving Hippo pathway genes.\u003c/p\u003e\n\u003cp\u003e(B) Number of drug-related genes and interactions in enriched KEGG pathways.\u003c/p\u003e\n\u003cp\u003e(C) Interaction network between Hippo pathway genes and drugs. Blue dots represent drugs. Thicker and redder lines represent stronger interactions.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/c4ba39ad65ed1fc2e946f162.png"},{"id":86215473,"identity":"8988426f-f961-4397-a368-19bcd109920a","added_by":"auto","created_at":"2025-07-08 05:55:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":183027,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptional profiling of \u003cem\u003eYAP1 \u003c/em\u003emRNA expression.\u003c/p\u003e\n\u003cp\u003e(A) Cross-tissue comparison of \u003cem\u003eYAP1\u003c/em\u003e expression levels in osteoblasts, skeletal muscle, and osteosarcoma specimens. Statistical comparisons were performed using the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003e(B) Stage-dependent differential expression of \u003cem\u003eYAP1\u003c/em\u003e between stage IIB and III osteosarcoma cohorts. Statistical comparisons were performed using the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003e(C) Comparative analysis of\u003cem\u003e YAP1\u003c/em\u003e expression in normal muscle tissues versus in vitro cultured normal muscle cell lines. Statistical comparisons were performed using the Wilcoxon signed-rank test.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/21eccee811a5704a5406d3fc.png"},{"id":86216111,"identity":"1c415f02-206c-4f19-bfc5-d8b291bdc9dd","added_by":"auto","created_at":"2025-07-08 06:03:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1963682,"visible":true,"origin":"","legend":"\u003cp\u003e(A) YAP expression in osteosarcoma tissue;(B) YAP expression in osteochondroma tissue\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/e808951b470b4898ef60222a.png"},{"id":87892026,"identity":"23e24fd8-451e-4d66-8f12-02c4c5f4ac3a","added_by":"auto","created_at":"2025-07-30 06:53:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4008382,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/c8f7bc41-18ae-4357-8a44-faf568b306ac.pdf"},{"id":86215476,"identity":"4ba7a351-f559-4269-b45f-e5430552eb8f","added_by":"auto","created_at":"2025-07-08 05:55:07","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":95083,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/72803bfdfe7acb93e5cb9b8a.xlsx"},{"id":86215501,"identity":"ab33d05f-9cbc-4c8b-82bd-b29887b3d0a9","added_by":"auto","created_at":"2025-07-08 05:55:08","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":29165,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/01e5df63254df7e7067202ac.xlsx"},{"id":86215485,"identity":"758f2e17-72d7-42c2-b80a-3053a4cb6541","added_by":"auto","created_at":"2025-07-08 05:55:07","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":46255,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6614090/v1/84aff2a0b7fcd9e06d9ecf30.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and validation of the important role of YAP in the development and progression of Osteosarcoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteosarcoma is a prevalent and aggressive primary malignant bone tumor, It predominantly affects adolescents between the ages of 10 and 25, with a higher prevalence in males[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].Osteosarcoma is characterized by its high invasiveness, poor prognosis, and low 5-year survival rate. In cases where metastasis has not occurred, approximately 70% of patients may achieve long-term survival. However, once recurrence or metastasis develops, the 5-year survival rate decreases to approximately 20%. Genomic structural variations, particularly CNV in large segments (\u0026gt;\u0026thinsp;1 kb), are closely linked to disease clinical phenotypes. At the genetic level, tumor drug resistance is often driven by gene amplifications and deletions, reflecting the rate and efficiency of CNV formation.\u003c/p\u003e \u003cp\u003eThe Hippo pathway plays a significant role in tumor drug resistance[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].The Hippo signaling pathway is an important growth-regulating system made up of protein kinases, transcription factors, co-activators, and other regulatory molecules. It works by controlling cellular processes through a series of protein phosphorylation events, influencing tumor cell growth, movement, invasion, and differentiation. This pathway is essential in understanding how tumors develop[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].YAP and TAZ are phosphorylated at specific amino acid residues by the LATS1/2-Mob1 complex. When Hippo signaling is attenuated, the phosphorylation levels of YAP and TAZ are reduced, leading to their translocation into the nucleus. Once inside the nucleus, they bind to one of the TEA domain (TEAD) family transcription factors, activating target genes involved in cell proliferation, survival, and tissue growth[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBioinformatics, as an interdisciplinary field, integrates information technology, computer science, mathematical modeling innovations, and statistical theories. This field analyzes biological experimental data to deeply explore the underlying biological mechanisms. Compared to traditional statistical methods, bioinformatics demonstrates more systematic and efficient analytical capabilities[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].This study aims to explore the molecular mechanisms of osteosarcoma through the integration of multi-omics data. We obtained WES data from 106 cancer patients from the National Genomics Data Center (NGDC) and collected extensive clinical data from osteosarcoma patients from the Surveillance, Epidemiology, and End Results (SEER) database. Based on these datasets, a systematic analysis was conducted. Various bioinformatics methods were used to analyze CNV and gene expression profiles, with the aim of identifying key genes and pathways associated with osteosarcoma. The biological processes and signaling pathways involved in the relevant genes were explored through KEGG and Gene Ontology (GO) enrichment analysis. In addition, experimental validation showed that YAP expression is higher in malignant bone tumors than in benign bone tumors, and is associated with tumor malignancy progression.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Download\u003c/h2\u003e \u003cp\u003eWe obtained dataset HRA003260 from the NGDC (National Genomics Data Center, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which contains WES data from 106 cancer patients including both osteosarcoma tissues and adjacent normal tissues. Additionally, clinical data encompassing 3,115 osteosarcoma patients were acquired from the SEER (The Surveillance, Epidemiology, and End Results) database.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical Analysis\u003c/h3\u003e\n\u003cp\u003eThe univariate Cox regression analysis of clinical data from osteosarcoma patients was performed using the survival package (v3.6.4) in R version 4.4.0. Time-dependent covariates for chemotherapy efficacy were modeled through linear regression analysis using the coxph function.\u003c/p\u003e\n\u003ch3\u003eIdentification of CNVs in Genes and Genomic Regions\u003c/h3\u003e\n\u003cp\u003eWES data were aligned to the hg19 reference genome using HISAT2 (v2.2.1), followed by SAMtools (v1.9) for BAM file processing. Transcript-level expression quantification was performed with StringTie (v2.1.4) to generate transcripts per million (TPM) values. CNVs were statistically analyzed using edgeR (v3.32.1) with the following criteria: genes demonstrating Benjamini-Hochberg adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and mean log\u003csub\u003e2\u003c/sub\u003e(CPM)\u0026thinsp;\u0026gt;\u0026thinsp;0 were identified as statistically significant CNV events. CNV detection in genomic regions was conducted using 100 kb sliding windows, where median-normalized sequencing depth was calculated per window via bedtools (v2.30.0) genomecov. Differential depth analysis between paired cancer-normal tissue samples was performed using the Wilcoxon signed-rank test. Read alignment visualization were conducted using the Integrative Genomics Viewer (IGV v2.12.3).\u003c/p\u003e\n\u003ch3\u003eKEGG and GO Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eConcurrent KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO enrichment analyses were systematically conducted through the KOBAS-i (v3.0) and Metascape platforms, employing a significance threshold of Benjamini-Hochberg adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 gene set.\u003c/p\u003e\n\u003ch3\u003eInteraction of the Hippo Pathway Genes with Drugs\u003c/h3\u003e\n\u003cp\u003eAll gene-drug interaction data were retrieved from the DGI. Network visualization was implemented using igraph (v1.5.1) in R.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExpression Profiling Analysis\u003c/h2\u003e \u003cp\u003eRNA-seq data from 101 osteosarcoma samples were obtained from the CNCB HRA003260 dataset, with additional expression profiles encompassing 20 osteoblast samples, 30 skeletal muscle tissue specimens, and 30 cultured myocyte samples retrieved from NCBI GEO datasets GSE227994 and GSE287342. RNA-seq reads were aligned to the hg19 reference genome using HISAT2 (v2.2.1) with default parameters, followed by transcript quantification of \u003cem\u003eYAP1\u003c/em\u003e expression levels via StringTie (v2.2.0) in transcripts per million (TPM) units.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe expression of YAP in human osteosarcoma tissues.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSpecimens were decalcified, fixed in 4% paraformaldehyde for 24 hours, and embedded in paraffin. Sections (4\u0026micro;m) were mounted on 1% poly-L-lysine\u0026ndash;treated slides and incubated at 65\u0026deg;C for 1.5 hours. Detection was performed using the SP method with the primary antibody diluted at YAP1:500. Staining was done with DAB, followed by counterstaining, dehydration, clearing, and mounting. PBS served as the negative control, and a known positive marker was used as the positive control. The clinical data, including patient gender, age, tumor size, staging, and metastasis status, were collected. Subsequently, statistical methods were applied to assess the correlation between YAP positive expression and these factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003e \u003cb\u003eChemotherapeutic Efficacy in Osteosarcoma Patients Exhibits Time-dependent Attenuation.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe analyzed clinical data from 3,115 osteosarcoma cases retrieved from SEERstat. Kaplan-Meier survival curves demonstrated that surgery and chemotherapy significantly reduced mortality, whereas radiotherapy markedly increased mortality risk, with surgical intervention showing the most pronounced therapeutic advantage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Subsequent evaluation of chemotherapy's time-varying hazard ratio (HR) revealed superior early-stage efficacy that progressively diminished over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Stratified survival analyses at 1-year, 3-year, and 5-year intervals showed narrowing survival rate disparities between chemotherapy and non-chemotherapy cohorts, with no significant difference at 1 year and nearly complete loss of efficacy by 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Collectively, these findings indicate substantial temporal decay in chemotherapy's therapeutic benefits for osteosarcoma patients.\u003c/p\u003e \u003cp\u003e \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\u003eCox regression analysis of osteosarcoma patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026ndash;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u0026ndash;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00-1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.60E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u0026ndash;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.85E-65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.27\u0026ndash;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.53E-46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.01E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDays From Diagnosis To Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eIdentification of Copy Number Variations in Osteosarcoma Tissues\u003c/h3\u003e\n\u003cp\u003eTo investigate pathway- or gene-specific amplifications in osteosarcoma, we integrated WES data from four independent cohorts: 80 cases from TCGA TARGET-OS, 212 cases (tumor/adjacent tissues) from CNCB HRA003260, 28 cases from NCBI PRJNA400619, and 24 cases from PRJNA774097. Clustering analysis based on gene-level copy number profiles revealed distinct segregation between malignant and normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), suggesting systematic genomic divergence. A total of 3,215 genes exhibited copy number alterations, including 826 amplified and 2,389 deleted loci in tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Genome-wide analysis identified 61 significant 100-kb genomic windows with recurrent CNAs (25 gains and 36 losses), highlighting chromosomal hotspots of instability (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eAll copy number-altered genes underwent functional annotation via Metascape and KEGG pathway enrichment. Metascape analysis revealed significant enrichment of pathways including adaptive immune response, response to bacterium, tube morphogenesis, and regulation of epithelial cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B, Table S 1). KEGG enrichment highlighted overrepresented pathways such as Metabolic pathways, Pathways in cancer, and Proteoglycans in cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Table S 2). Among KEGG pathways, the PI3K\u0026thinsp;\u0026minus;\u0026thinsp;Akt, Hippo, Thyroid hormone, Rap1, and Ras signaling pathways demonstrated the highest significance, with core regulatory genes (\u003cem\u003eYAP1\u003c/em\u003e in Hippo, \u003cem\u003eRap1\u003c/em\u003e, and \u003cem\u003eRas\u003c/em\u003e) exhibiting copy number gains. Given that the Hippo pathway exhibited greater statistical prominence than Rap1 and Ras pathways, it was prioritized for subsequent mechanistic investigation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMechanistic Profiling of the Hippo Pathway and Its Core Gene YAP\u003c/h2\u003e \u003cp\u003eThe Hippo signaling pathway serves as a master regulator of organ size determination, tissue regeneration, and cellular proliferation through its canonical kinase cascade. Core kinases LATS1/2 phosphorylate effector proteins YAP/TAZ, triggering their cytoplasmic sequestration and subsequent suppression of pro-growth transcriptional programs. Pathway dysregulation is mechanistically linked to oncogenesis and developmental anomalies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). WES analysis revealed recurrent \u003cem\u003eYAP1\u003c/em\u003e copy number gains across osteosarcoma specimens, with a striking 36.4-fold amplification observed in sample HRR786903 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, systematic characterization identified distinct copy number alteration patterns in 35 Hippo pathway-associated genes within osteosarcoma cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePharmacogenomic Interactions of Hippo Pathway Genes\u003c/h2\u003e \u003cp\u003eInterrogation of the DGI identified 1,299 gene-drug interaction pairs involving 73 Hippo pathway components (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Table S 3). These interactions predominantly clustered within clinically actionable targets, kinase, and druggable genome categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Subsequent prioritization revealed 17 potent interactions, with the top-ranked pairs being \u003cem\u003eDVL1\u003c/em\u003e-Illudalic acid and \u003cem\u003eAXIN1\u003c/em\u003e-(\u0026plusmn;)-hydnocarpin (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Secondary hits included \u003cem\u003eBMP2\u003c/em\u003e, \u003cem\u003eFZD1\u003c/em\u003e, \u003cem\u003eFZD4\u003c/em\u003e, and \u003cem\u003eFZD8\u003c/em\u003e interacting with PI3K/mTOR inhibitors and Wnt pathway modulators, with particular potency observed in \u003cem\u003eFZD8\u003c/em\u003e-irinotecan binding.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eYAP1 Expression Profiling\u003c/h2\u003e \u003cp\u003eTo validate whether YAP1 copy number gains correlate with transcriptional upregulation, we performed comparative analysis of YAP1 expression across osteoblasts, skeletal muscle tissues,and stage-stratified osteosarcoma specimens. YAP1 demonstrated significantly elevated expression levels in malignant tissues compared to normal counterparts, with cultured muscle cells exhibiting higher expression than primary muscle tissues (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Notably, no significant differential expression was observed between stage IIB and III osteosarcoma cohorts, potentially attributable to the limited stage III sample size.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDifference of YAP expression in osteosarcoma and chondrosarcoma\u003c/h2\u003e \u003cp\u003eIn osteosarcoma tissue, the positive expression rate of YAP was 78.84% (41/52), while the expression rate in osteochondroma was 30% (6/20). The expression rate in osteosarcoma was significantly higher than that in osteochondroma (P\u0026thinsp;=\u0026thinsp;0.000) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The positive expression rate of YAP was not significantly associated with gender or age, but showed a statistically significant correlation with tumor size, staging, and metastasis status. Notably, analysis revealed a positive correlation between YAP positive expression and both Enneking staging and distant metastasis in osteosarcoma patients. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \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\u003eThe expression levels of YAP in osteosarcoma and osteochondroma\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\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteosarcoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eosteochondroma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\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\u003eYAP expression and its correlation with clinical pathological features\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiameter(cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnnecking stages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI、II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOsteosarcoma is one of the most challenging malignant tumors. It is the most common primary bone cancer, primarily affecting adolescents and elderly individuals, with a male-to-female ratio of 1.2:1[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Tumor cells directly produce immature osteoid tissue, which ultimately leads to the formation of osteosarcoma. Its development is primarily driven by genetic, hormonal, and environmental factors. OS is primarily treated with surgery and adjuvant chemotherapy. Under this combined therapeutic approach, the 5-year survival rate for patients with localized disease is approximately 70%[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, for patients with recurrent or metastatic disease, the 5-year survival rate drops to around 20%, indicating a poor prognosis. For OS patients who are inoperable or exhibit resistance or intolerance to chemotherapy, the available treatment options remain limited.\u003c/p\u003e \u003cp\u003eThe role of chemotherapy (CT) and radiotherapy (RT) in the treatment of osteosarcoma (ESOS) remains controversial[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Huang et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]conducted a retrospective analysis of 29 pediatric cases of knee osteosarcoma. All patients received neoadjuvant chemotherapy followed by amputation surgery, and the study found that the combined treatment achieved a 5-year survival rate of 89.1%. Michael et al[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. reviewed the medical records of 82 dogs that underwent radiotherapy for diagnosed or suspected OS. The study found that both pain levels and radiation dosage were associated with the survival rates of dogs receiving radiotherapy alone, without chemotherapy.This study analyzed the clinical data of 3,115 osteosarcoma cases retrieved from SEERstat. The Kaplan-Meier survival curves indicated that surgery and chemotherapy significantly reduced mortality, while radiotherapy was associated with an increased risk of mortality. Surgical intervention demonstrated the most notable therapeutic advantage. The evaluation of the time-dependent hazard ratio (HR) for chemotherapy revealed that early intervention is effective, but its efficacy gradually diminishes over time.\u003c/p\u003e \u003cp\u003eCNV in genomic structural variations (SV) are caused by genomic rearrangements. CNV typically refer to the deletion or duplication of gene copies longer than 1 kb, and are highly correlated with the clinical manifestations of diseases. Research has confirmed that both primary and acquired resistance in tumors at the genetic level originate from genomic structural variations[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. CNVs are commonly present in both primary and metastatic tumors, with many mutations occurring in important signaling pathways and therapeutic target genes related to tumor initiation and progression, leading to changes in tumor drug sensitivity. We integrated whole exome sequencing (WES) data from four independent cohorts: 80 cases from TCGA TARGETOS, 212 cases from CNCB HRA003260, 28 cases from NCBI PRJNA400619, and 24 cases from PRJNA774097. Cluster analysis based on gene-level copy number profiles revealed significant genomic differences between malignant and normal tissues. A total of 3,215 genes exhibited copy number alterations, including 826 amplification loci and 2,389 deletion loci in tumor tissues. In addition to primary resistance, acquired resistance also poses a challenge for clinicians. During treatment, tumor cells gradually develop mutations that confer resistance. The occurrence of resistance mechanisms is often due to the formation of genomic structural variations, which block the initial action of drugs on target molecules, or through the activation of alternative signaling pathways that sustain tumor growth, ultimately leading to the development of drug resistance.\u003c/p\u003e \u003cp\u003eCopy number-altered genes were functionally annotated using Metascape and KEGG pathway enrichment. Metascape analysis highlighted significant enrichment in pathways such as adaptive immune response, bacterial response, tube morphogenesis, and epithelial cell proliferation regulation. Among the KEGG pathways, the PI3K\u0026thinsp;\u0026minus;\u0026thinsp;Akt, Hippo, Thyroid hormone, Rap1, and Ras signaling pathways showed the highest significance. Notably, core regulatory genes, such as YAP1 in the Hippo, Rap1, and Ras pathways, exhibited copy number gains. YAP and TAZ are core effector proteins of the Hippo signaling pathway, widely expressed in various tissues and frequently upregulated in many tumor cells[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A literature review indicates that YAP is expressed in most solid tumors. Furthermore, recent studies have demonstrated that YAP functions as an oncogenic factor in gastrointestinal tumors, breast cancer, and lung cancer[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen the regulation of the Hippo pathway is impaired, YAP/TAZ lose phosphorylation by the upstream LATS1/2, leading to increased expression of YAP/TAZ. This, in turn, inhibits apoptosis and promotes epithelial-mesenchymal transition (EMT)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. At the genetic level, different individuals exhibit distinct resistance mechanisms, and the genetic mutations associated with resistance vary at different time points. However, most studies are based on single samples and/or assessments at a single time point, which may easily overlook important mutations. Therefore, it is crucial to predict the drug sensitivity of tumors before chemotherapy and establish a method for continuous monitoring of drug efficacy during treatment. This approach is essential for studying the mechanisms of drug resistance in osteosarcoma.\u003c/p\u003e \u003cp\u003eThe ability of tumor cells to acquire metastatic potential has long been a topic of intense debate among scientists. While a large number of tumor cells are capable of detaching from the primary tumor and entering the bloodstream, only a small fraction of these cells ultimately survive, invade distant organs, and form metastatic foci. In this study, there was a significantly higher positive expression rate of YAP in osteosarcoma tissues compared to osteochondroma. Clinical data collection revealed a positive correlation between YAP positive expression and the Enneking staging as well as distant metastasis in osteosarcoma patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study demonstrated that chemotherapy efficacy in osteosarcoma patients diminishes over time, identifies significant copy number variations in osteosarcoma tissues, and highlights the elevated expression of YAP1, particularly in osteosarcoma compared to osteochondroma, suggesting its potential role in tumorigenesis and therapeutic targeting.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecopy number variations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDGI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDrug Gene Interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eradiotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurveillance, Epidemiology, and End Results\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etranscripts per million\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Genomics Data Center\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewhole-exome sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2021D01C472).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHXX: Conducted the study. The data were collected, analyzed, and interpreted. Wrote the manuscript.\u003c/p\u003e\n\u003cp\u003eHCY: Edited the manuscript and planned the project.\u003c/p\u003e\n\u003cp\u003eGLL: Designed the study, interpreted the data, and edited the manuscript.\u003c/p\u003e\n\u003cp\u003eRM:\u0026nbsp;Interpreted the data.\u003c/p\u003e\n\u003cp\u003eTL:\u0026nbsp;Edited the manuscript and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eYT: Edited the manuscript.\u003c/p\u003e\n\u003cp\u003eAll the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCole S, Gianferante DM, Zhu B, Mirabello L. Osteosarcoma: A Surveillance, Epidemiology, and End Results program-based analysis from 1975 to 2017. Cancer. 2022;128(11):2107\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang M, Zhang H, Gao S, Huang W. DEPDC1 and KIF4A synergistically inhibit the malignant biological behavior of osteosarcoma cells through Hippo signaling pathway. J Orthop Surg Res. 2023;18(1):145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Zhou L, Hu S, Gu W, Li Z, Sun J, Wei X, Wang Y. The crosstalk between LINC01089 and hippo pathway inhibits osteosarcoma progression. J Bone Min Metab. 2022;40(6):890\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi Z, Shen J, Lan Y, Yi Q, Liu H. Targeting signaling pathways in osteosarcoma: Mechanisms and clinical studies. MedComm (2020). 2023;4(4):e308.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa X, Chang J, Sun X, Zhou C, Zhao P, Yang Y. 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Bioanalysis and the oncology revolution. Bioanalysis. 2021;13(5):291\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdewuyi E, Chorya H, Muili A, Moradeyo A, Kayode A, Naik A, Odedele T, Opabode M. Chemotherapy, immunotherapy, and targeted therapy for osteosarcoma: Recent advancements. Crit Rev Oncol Hematol. 2025;206:104575.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorre I, Verrecchia F, Crenn V, Redini F, Trichet V. The Osteosarcoma Microenvironment: A Complex But Targetable Ecosystem. Cells 2020, 9(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimpson E, Brown HL. Understanding osteosarcomas. Jaapa. 2018;31(8):15\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiraga H, Ozaki T. Adjuvant and neoadjuvant chemotherapy for osteosarcoma: JCOG Bone and Soft Tissue Tumor Study Group. 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Br J Cancer. 2023;128(9):1611\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLo Sardo F, Strano S, Blandino G. YAP and TAZ in Lung Cancer: Oncogenic Role and Clinical Targeting. Cancers (Basel) 2018, 10(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan D. The hippo signaling pathway in development and cancer. Dev Cell. 2010;19(4):491\u0026ndash;505.\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, Machine learning, YAP1, Bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-6614090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6614090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThis study aims to explore the molecular mechanisms of osteosarcoma by integrating multi-omics data to identify key genes and pathways, with a focus on the Hippo pathway, and to validate the association of YAP expression with tumor malignancy progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study integrates multi-omics data to conduct a systematic analysis of osteosarcoma. We obtained whole-exome sequencing (WES) data from 106 patients from the National Genomics Data Center (NGDC) and collected clinical data from 3,115 osteosarcoma patients from the SEER database. Univariate Cox regression analysis was performed using the survival analysis package in R to construct a time-dependent covariate model for chemotherapy efficacy. WES data were analyzed using tools such as HISAT2 and SAMtools to identify copy number variations (CNVs) in genes and genomic regions. KEGG and GO enrichment analyses were conducted using the KOBAS-i and Metascape platforms. Gene-drug interaction data were retrieved from the Drug Gene Interaction (DGI) database and visualized using the igraph package in R. Additionally, RNA sequencing data were obtained to analyze the expression levels of the YAP1 gene, and its expression was further validated by immunohistochemical staining, with phosphate-buffered saline as the negative control and a known positive marker as the positive control.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eClinical data analysis indicates that both surgery and chemotherapy significantly reduce mortality rates, with chemotherapy demonstrating significant early efficacy. However, this efficacy diminishes over time, while radiotherapy notably increases the risk of mortality. Genomic analysis using WES identified 3,215 genes with copy number alterations, including 826 amplifications and 2,389 deletions. Functional enrichment revealed key pathways like immune response and cancer metabolism, with the Hippo pathway showing significant alterations, particularly in YAP1, a core regulatory gene. YAP1 exhibited recurrent copy number gains in osteosarcoma, and 35 Hippo-related genes showed distinct CNA patterns. Pharmacogenomic analysis identified 1,299 drug-gene interactions involving 73 Hippo pathway genes, suggesting potential therapeutic targets. These findings highlight the importance of the Hippo pathway, especially YAP1, in osteosarcoma and its potential as a therapeutic target. The positive expression rate of YAP was 78.84% (41/52), while the expression rate in osteochondroma was 30% (6/20). The expression rate in osteosarcoma was significantly higher than that in osteochondroma (P\u0026thinsp;=\u0026thinsp;0.000). The positive expression rate of YAP was not significantly associated with gender or age, but showed a statistically significant correlation with tumor size, staging, and metastasis status. Notably, analysis revealed a positive correlation between YAP positive expression and both Enneking staging and distant metastasis in osteosarcoma patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study demonstrated that chemotherapy efficacy in osteosarcoma patients diminishes over time, identifies significant copy number variations in osteosarcoma tissues, and highlights the elevated expression of YAP1, particularly in osteosarcoma compared to osteochondroma, suggesting its potential role in tumorigenesis and therapeutic targeting.\u003c/p\u003e","manuscriptTitle":"Identification and validation of the important role of YAP in the development and progression of Osteosarcoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 05:55:01","doi":"10.21203/rs.3.rs-6614090/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":"9b202532-1660-4341-8c02-3b19f5bcdb06","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-30T06:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 05:55:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6614090","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6614090","identity":"rs-6614090","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}