Identification of a novel mitophagy-related signature for predicting clinical prognosis and immunotherapy of osteosarcoma

Identification of a novel mitophagy-related signature for predicting clinical prognosis and immunotherapy of osteosarcoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of a novel mitophagy-related signature for predicting clinical prognosis and immunotherapy of osteosarcoma Peichuan Xu, Jiangminghao Zhao, Wenrui Zhao, Jinghong Yuan, Kaihui Li, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4271624/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Osteosarcoma (OS) is a highly aggressive malignancy characterized by a poor prognosis. Mitochondrial autophagy (mitophagy) has been implicated in tumor initiation, progression, and response to therapy, highlighting it a potential prognostic indicator and therapeutic target in cancers. Despite this, the precise mechanisms underlying mitophagy in osteosarcoma remain enigmatic. This research aims to develop a mitophagy-associated signature to guide therapeutic strategies and prognosis estimations. Methods Clinical and transcriptome data for patients with osteosarcoma and skeletal muscle tissue were retrieved from UCSC Xena and GTEx. Mitophagy-related genes (MRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) website. A predictive risk model was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Cox regression analysis. To delve into the fundamental gene expression mechanisms, we employed Gene Ontology (GO), KEGG, and Gene Set Enrichment Analysis (GSEA). Moreover, the different immune-related activities between the two groups were investigated to ascertain the efficacy of immunotherapy. Lastly, the functional analysis of the key risk gene MRAS was carried out via in vitro experiments and a pan-cancer analysis and potential small molecule drugs that may target MRAS were screened through molecular docking. Results Based on seven mitophagy-related prognostic gene signatures, osteosarcoma patients were stratified into high- and low-risk categories. The predictive model exhibited strong prognostic capability, as evidenced by Kaplan-Meier analysis, time-dependent AUC, and Nomogram. Notably, compared to the low-risk group, individuals in the high-risk group exhibited lower stromal, immune, and estimate scores.The infiltration of immune cells in high-risk group decreased. Further evidence supporting MRAS's protective role against osteosarcoma was shown in vitro , where upregulating its expression could suppress the proliferation, migration, and invasion of osteosarcoma cells while stimulating their apoptosis. Pan-cancer analysis further demonstrated its role in a variety of tumors. Conclusion This study identified a mitophagy-related prognostic signature and elucidated the impact of MRAS on osteosarcoma cells. Consequently, it opened up fresh avenues for clinical prognosis prediction and established a basis for precision therapy in osteosarcoma. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Osteosarcoma (OS) predominantly affects adolescents, with an incidence rate of 3 cases per million [ 1 – 3 ] . Despite extensive research efforts and advancements in therapeutic techniques, the survival rate for osteosarcoma patients remains below 30% [ 4 , 5 ] . The disease's complex molecular mechanisms and genomic instability significantly contribute to its poor prognosis [ 6 , 7 ] . Therefore, it is imperative to detect novel molecules linked to targeted treatment and prognosis in osteosarcoma. Mitochondria, often referred to as the "energy factories" of cells, play a vital role in maintaining cellular activities and metabolism [ 8 , 9 ] . Mitophagy, a process that selectively degrades damaged mitochondria, occurs primarily in response to external stimuli such as oxidative stress, nutrient deprivation, or cellular senescence. These stimuli can cause damage to mitochondrial depolarization and result in the loss of membrane potential [ 10 , 11 ] . Mitophagy is a process that selectively breaks down damaged mitochondria to maintain intracellular homeostasis and prevent the development of tumors [ 12 , 13 ] . More and more evidence recently confirms that mitophagy is a double-edged sword in cancer development. On the one side, the reduction of mitophagy promotes cancer progression [ 14 ] . Conversely, increased mitophagy promotes cancer cell proliferation and progression by protecting cancer cells from apoptosis [ 15 ] . Nevertheless, little is known about mitophagy in OS, and more study is required to comprehend the underlying mechanisms. This study developed a novel predictive model of MRGs to improve the prognosis of OS and explore changes in the tumor microenvironment (TME), immunotherapy. The impact of MRAS on OS was elucidated via in vitro experiments and pan-cancer analysis. Potential small molecule drugs that may target MRAS were screened through molecular docking. The findings provided insights into understanding OS treatment and prognosis and paved the way for innovative interventions targeting immune evasion mechanisms. 2. Materials and Methods 2.1. Data Sources and Collection The transcriptomic and clinical data for OS were obtained from UCSC Xena ( https://xenabrowser.net/ ). The transcriptomic and clinical data for skeletal muscle were extracted from GTEx ( https://www.gtexportal.org/ ). Additionally, the transcriptome and clinical data for model validation, specifically GSE21257, were retrieved from the Gene Expression Omnibus (GEO) database, which can be found at https://www.ncbi.nlm.nih.gov/geo/ . Furthermore, 103 mitophagy-related genes (MRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) website ( https://www.kegg.jp/pathway/hsa04137 ). Figure 1 provides a comprehensive overview of the entire workflow of this study. 2.2 Analysis of differentially expressed genes and their functional implications The "limma" R package was used to identify differentially expressed genes (DEGs) in OS. The DEGs linked with mitophagy in OS were determined by intersecting DEGs with MRGs. The intersection was visualized using a Venn diagram. Moreover, enrichment analyses utilizing the "ClusterProfiler" R package were performed for both Gene Ontology (GO) and the KEGG. The visualization of the results was achieved using the "ggplot2" package. Gene-set enrichment analysis (GSEA) was also executed on the datasets c5.go.all.v2022.1.Hs.symbols.gmt and c2.kegg.all.v2022.1.Hs.symbols.gmt, employing the "clusterProfiler" program and presenting the data through the "ggplot2" tool. 2.3. Construction of the risk model Univariate Cox regression analysis identified prognosis-linked genes. Moreover, the LASSO regression analysis of the training cohort was performed to reduce the number of variables and included a new lambda value of 7 genes. The risk score for each individual was computed as follows: $$\text{R}\text{i}\text{s}\text{k}\hspace{0.17em}\text{S}\text{c}\text{o}\text{r}\text{e}={\sum }_{i=1}^{\text{n}}\left(\text{R}\text{N}\text{A} \text{e}\text{x}\text{p}\text{i}\times \text{c}\text{o}\text{e}\text{f}\text{i}\right)$$ 2.4. Evaluation of the model and construction of a predictive nomogram Using the "caret" package, 85 patients were randomly divided into training and test groups. Patients' risk scores were computed using the given formula, and based on the median risk score, high-risk and low-risk groups were categorized. Survival analysis was conducted using the "survival" package, while data visualization was achieved with the "survminer" package. The "timeROC" package was utilized to verify the model's effectiveness. Furthermore, calibration and nomogram curves were generated using the "survival" and "rms" packages. Finally, the "pec" package was used to access C-Index curves. 2.5. Immunogenomic Analysis Interactions between immune cells and genes were explored using "Cibersort" and "Xcell" algorithms. The "ESTIMATE" algorithm was also applied to assess the proportion of present immune cells. Furthermore, an immunological correlation was conducted to investigate the relationship between immune cells and risk scores. 2.6. Molecular docking The target protein MRAS (PDB: 7SD0) was sourced from the PDB database, while 2614 anticancer compounds (L2120) were provided by TOPSCIENCE ( www.tsbiochem.com ). The protein was processed, the ligand pocket was identified, and the resulting protein was docked with the small molecule database using the Molecular Operating Environment (MOE) software. Compounds with the highest scores were selected for evaluation and analysis. 2.7. Pan-Cancer Analysis The "limma" package was used to ascertain the differential expression of MRAS across various cancer types. Box plots were created using the "ggpubr" package Kaplan-Meier (K-M) survival analysis was performed for overall survival (OS), disease-free interval (DFI), and disease-specific survival (DSS) with the help of the "survival" and "survminer" packages. Data pertaining to tumor mutation burden (TMB) and microsatellite instability (MSI) were sourced from previous studies. A Spearman correlation analysis was conducted to explore the association between MRAS expression and pan-cancer TMB and MSI levels. 2.8. Verification of Hub Genes in Clinical Samples After ethical approval, 25 matched pairs of OS and adjacent normal tissues were collected from the Second Affiliated Hospital of Nanchang University between January 2021 and December 2023. The samples were preserved in liquid nitrogen at -80°C. The extraction of total RNA was performed using TRIzol and Chloroform (Thermofisher, USA). The quality and concentration of the isolated RNA were assessed using a Nanodrop One spectrophotometer (Thermofisher, USA). Afterwards, complementary DNA (cDNA) was produced using the Prime Script RT kit (TaKaRa, Japan). RT-qPCR was performed on an ABI 7500 Real-Time PCR system (Thermofisher, USA) using a reverse transcription kit (TaKaRa, Japan). Supplementary Table S1 contains primer details used for validation. 2.9. Cell Cultivation, Transfection, and Lentivirus Infection Human osteoblasts (hFOB 1.19) and osteosarcoma cell lines (HOS, MG63, and U2OS) were sourced from the American Type Culture Collection (ATCC). All cells were cultivated in the Dulbecco's Modified Eagle Medium (DMEM; Gibco, USA).All cells were maintained in a complete medium enriched with 10% fetal bovine serum (FBS; Gibco, USA) and 1% penicillin-streptomycin (NCM Biotech, China) at 37°C with 5% CO2. For MRAS overexpression, Lentivirus particles generated using Lenti-Easy Packaging (Genechem, China) were utilized. 2.10. Assessment of Cell Proliferation Cell proliferation was assessed using the CCK-8 and Edu assays. Osteosarcoma cells were seeded in a 96-well plate at a density of 4000 cells per well. CCK-8 solution (10 µL) was added to each well at 0, 24, 48, and 72 hours, followed by a 2-hour incubation. Absorbance was measured at 450 nm. Cells were also grown in 6-well plates to appropriate density, incubated with Edu for 12 hours, fixed with 4% paraformaldehyde, and stained using the EdU imaging kit (Uelandy, China). 2.11. Scratch Assay for Cell Migration Cells were grown to 90% confluence in a 6-well plate. A scratch was made using a 10 µL pipette tip to simulate a wound. After three PBS washes, the cells were cultured in serum-free media, and cell migration was tracked using a microscope. 2.12. Assays for Cell Migration and Invasion Cells were diluted to a density of 4 x 10 5 cells/mL. In a 24-well plate, 100 µL of serum-free cell suspension was added to the upper chamber, while 600 µL of growth media containing 10% FBS was added to the lower chamber. After a 24-hour incubation, cells were fixed with paraformaldehyde for 15 minutes, stained with 0.1% crystal violet for 20 minutes, and counted under a microscope. 2.13. Flow cytometry assay Cells were washed twice with PBS, centrifuged at 1600 rpm for 5 minutes, and resuspended in 200 µL of 1X buffer. The cells were then incubated in the dark for 15 minutes with propidium iodide (PI) and Annexin V-FITC. The reaction was stopped by adding 300 µL of 1X buffer. 2.14. Statistical Analysis Statistical analyses were performed using R software (version 4.2.3). Statistical significance was set at p-values less than 0.05, denoted as * p < 0.05, ** p < 0.01 and *** p < 0.001. 3. Results 3.1. Explore differentially expressed MRGs in OS The "limma" R package was utilized to pinpoint DEGs between osteosarcoma (OS) and normal skeletal muscle tissue.The selection criteria were p 1.5 (|log2 FoldChange| > 0.585). The analysis revealed 12,578 key genes exhibited differential expression (Fig. 2 A,B). Next, these genes were allowed to intersect with 103 mitophagy-related genes obtained from the KEGG website to get 95 overlapping genes (Fig. 2 C). GO enrichment analyses emphasized the involvement of these genes in processes like mitochondrion disassembly, autophagy of mitochondrion, organelle disassembly, mitochondrial outer membrane, organelle outer membrane, ubiquitin protein ligase binding, ubiquitin-like protein ligase binding, and GDP binding. Additionally, the KEGG enrichment analysis demonstrated significant enrichment of these genes in processes related to mitophagy, autophagy, and neurodegeneration pathways, providing further evidence for the functional relevance of the selected genes (Fig. 2 D-H). 3.2. Construction of prognosis prediction model Clinical data on osteosarcoma patients was collected to further identify prognostic genes. Univariable Cox regression analysis was conducted, and 10 key genes were selected (Fig. 3 A). Subsequently, LASSO regression analysis was performed to further shortlist the selected genes to 7 for model construction (Fig. 3 B,C). The bar graph presented the coef values of model genes (Fig. 3 D), while the chromosomal ring map showed the location of genes on the chromosomes (Fig. 3 E). Patients were divided into high- and low-risk groups based on the median risk score. The survival status plot revealed that patients' survival time progressively decreased, and their mortality rate increased as the risk score increased (Fig. 3 F,G). A heatmap visualized gene expression levels in OS patient samples (Fig. 3 H). The KM survival curve demonstrated that patients in the high-risk group had a poor prognosis compared to those in the low-risk group (Fig. 3 I). The AUC values for the ROC curves at 1, 3, and 5 years were 0.925, 0.936, and 0.948, respectively (Fig. 3 J). 3.3. Validation of the prognostic model Validation across multiple datasets (TARGET testing, total, and GSE21257) confirmed that as risk scores increased, patient survival time decreased, and mortality rates rose (Fig. 4 A, B). Patients in the high-risk group had a worse prognosis than those in the low-risk group, as shown by the KM survival curve (Fig. 4 C). Furthermore, the ROC curve of all groups indicated that the model exhibited robust predictive performance and stability for prognosis in OS patients (Fig. 4 D). 3.4. Construction of nomogram and subgroup analysis A nomogram integrating risk scores, age, gender, and metastasis was developed to enhance survival prediction accuracy (Fig. 5 A). Furthermore, the calibration curves of the nomogram demonstrated strong predictive accuracy and performance validation, confirming the nomogram's robustness for prediction and validation (Fig. 5 B). Based on the clinicopathological characteristics, the patients were placed into six groups: patients with ≥ 12 years of age, patients with < 12 years of age, male patients, female patients, non-metastatic group, and metastatic group. Among these six subgroups, the overall survival (OS) of patients in the high-risk group was lower than that of the low-risk group ( p < 0.05; Fig. 5 C-H). This suggests that the model possesses a remarkable capacity to differentiate between various patient outcomes. 3.5. GO, KEGG, and GSEA enrichment analysis DEGs between high- and low-risk groups underwent GO, KEGG, and GSEA analyses. The GO enrichment analysis showed that the DEGs primarily regulated Ca 2+ concentration, Ca 2+ concentration homeostasis, signal transduction and ion channels (Fig. 6 A,B). The GSEA analysis revealed that genes in the high-risk group were mainly enriched in chemical carcinogenesis, citrate cycle (TCA cycle), and metabolism of xenobiotics by cytochrome P450. In contrast, genes in the low-risk group were primarily enriched in Allograft rejection, chemokine signaling pathway, and transcriptional misregulation in cancer (Fig. 6 C,D). 3.6. Exploration of immune cell infiltration in the TME TME, the environment surrounding tumor cells, encompasses various components. These included blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, signaling molecules, and the extracellular matrix surrounding tumor cells [ 16 – 18 ] . Using the Cibersort method, we found macrophages, particularly M0 and M2, to be predominant in osteosarcoma samples. Notably, the high-risk group exhibited a higher proportion of M2 macrophages, reflecting their immunosuppressive and tumor-promoting roles (Fig. 7 A). The Xcell algorithm further revealed a reduction in activated dendritic cells (aDC), CD8 + T cells, cytotoxic cells, natural killer (NK) cells, T cells, T follicular helper (Tfh), T helper (Th) 1 cells, and T regulatory (Treg) cells in the high-risk group compared to the low-risk group (Fig. 7 B). Correlation analysis among immune cells showed the association between 21 types of immune cells. Moreover, a high expression correlation was observed between T cells, aDC, and cytotoxic cells, indicating potential relevant associations among these cells (Fig. 7 C). Afterwards, the correlation between immune cells, risk score, and the expression level of model genes was explored. An increase in the MRAS expression led to a higher infiltration of multiple immune cells, confirming that MRAS acts as a protective factor in the progression of osteosarcoma. The increasing presence of immune cells that target tumors might lead to more efficient surveillance and elimination of tumor cells, thereby impeding the growth and spread of tumors (Fig. 7 D). Furthermore, in the high-risk group, it was observed that lower scores in antigen-presenting cell (APC) co-inhibition, CCR, checkpoint, inflammation-promoting, major histocompatibility complex (MHC) class I, T cell co-stimulation, T cell co-inhibition, type I interferon (IFN) response, and parainflammation pathways compared to the low-risk group. This reflects potential functional defects or suppressive states of the immune system in high-risk patients across multiple levels (Fig. 7 E). The TME score analysis also revealed decreased ESTIMATE, stromal, and immune scores, along with increased tumor purity in the high-risk group (Fig. 7 F-I). These findings suggest insufficient infiltration of stromal cells and immune cells in the TME, potentially leading to faster tumor progression and a higher risk of metastasis. 3.7. Validation of Model Genes in OS cell line and OS Clinical Samples RT-qPCR analysis further validated the expression of model genes in three osteosarcoma cell lines (HOS, MG63, and U2OS). Normal osteoblast cells (hFOB1.19)served as the control group. Significantly elevated expression was observed for SP1, E2F1, and RPS27A in OS cell lines, while RAB5C, AMBRA1, MRAS, and SMURF1 expression decreased in osteosarcoma cell lines(Fig. 8 A-G). A total of 25 osteosarcoma tumors and corresponding tissue samples were obtained from the Second Affiliated Hospital of Nanchang University between January 2021 and December 2023. The expression levels in human tissue samples were further validated, and the results were consistent with the validation of OS cell lines. This suggests that the developed risk prediction model is effective and reliable (Fig. 8 H-N). 3.8. Overexpression of MRAS inhibits proliferation, migration, and invasion and promotes apoptosis in osteosarcoma cells. The lentiviral transfection was utilized to upregulate MRAS expression (Figs. 9 A and 10 A) and assess its effects on cell proliferation, migration, invasion, and apoptosis. CCK-8 and Edu assays demonstrated that MRAS overexpression reduced HOS and MG63 cell proliferation (Figs. 9 B and 10 B). Wound healing assay also indicated that MRAS overexpression significantly suppressed the migration ability of HOS cells (Figs. 9 C and 10 C). The Transwell experiment proved that overexpressing MRAS strongly inhibited the migration and invasion of HOS cells (Figs. 9 D,E and 10 D,E). Flow cytometry analysis revealed that overexpression of MRAS inhibited apoptosis in HOS cells (Figs. 9 F and 10 F). In conclusion, overexpression of the MRAS gene can suppress cell proliferation, migration, and invasion and simultaneously promote apoptosis. 3.9. Pan-cancer analysis Through comprehensive pan-cancer analysis, it was found that MRAS expression is elevated in cholangiocarcinoma (CHOL), kidney renal papillary cell carcinoma (KIRP), and liver hepatocellular carcinoma (LIHC). Conversely, its expression is lower in 13 different tumor types, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma (COAD), glioblastoma (GBM), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), and uterine corpus endometrial carcinoma (UCEC) (Fig. 11 A). Kaplan-Meier survival analysis was subsequently employed to assess the relationship between MRAS and patient outcomes, specifically disease-specific survival (DSS), disease-free interval (DFI), and overall survival (OS). In terms of DSS, MRAS served as a prognostic protective factor in LUAD, KIRP, and skin cutaneous melanoma (SKCM), but as a prognostic risk factor in UCEC (Fig. 11 B). For DFI, MRAS was beneficial in thymoma (THYM) and SKCM, but detrimental in adrenocortical carcinoma (ACC) and UCEC (Fig. 11 C). Regarding OS, MRAS was protective in LUAD and SKCM, but a risk factor in UCEC (Fig. 11 D). Tumor mutational burden (TMB), a biomarker indicating responsiveness to immune checkpoint inhibitors, especially those targeting the PD-1/PD-L1 pathway, was also investigated in relation to MRAS expression. Notably, MRAS expression correlated with TMB in 10 tumor types. Positive correlations were observed in THYM and KIRP, while negative correlations were found in UCEC, PRAD, LIHC, stomach adenocarcinoma (STAD), pancreatic adenocarcinoma (PAAD), brain lower grade glioma (LGG), head and neck squamous cell carcinoma (HNSC), and esophageal carcinoma (ESCA) (Fig. 11 E). Additionally, the relationship between MRAS expression and microsatellite instability (MSI), a hypermutation phenotype linked to PD-1 blockade efficacy, was examined. Among 11 tumor types, only BRCA and COAD showed a positive correlation between MRAS expression and MSI. All other tumor types, including testicular germ cell tumors (TGCT), acute myeloid leukemia (LAML), UCEC, STAD, LUSC, KICH, HNSC, ESCA, and CHOL, exhibited negative correlations (Fig. 11 F). 3.10. Molecular docking analysis To identify potential small molecule compounds interacting with MRAS, molecular docking analysis was conducted. This method aids in discovering therapeutic targets and developing innovative treatments. Among 2614 anticancer small molecules screened using MOE software, the top five scoring compounds were selected for molecular docking: LMP-400 (-17.4463), RGB-286638 (-16.9881), INH 34 (-15.9679), canertinib dihydrochloride (-15.0738), and cytidine 5'-diphosphocholine (-15.0732) (Fig. 12 ). 4. Discussion Osteosarcoma, the most common primary bone malignancy, frequently metastasizes to the lungs and carries a poor prognosis [ 21 ] . Mitophagy, a vital cellular process that identifies and eliminates damaged or dysfunctional mitochondria, is essential for mitochondrial health [ 22 ] . Recent studies suggest that mitophagy plays a significant role in osteosarcoma progression and drug sensitivity. Luo et al. found that SLC35A2 regulates mitophagy via the PI3K/AKT/mTOR pathway, thus promoting osteosarcoma development [ 23 ] . Zheng et al. demonstrated that soy isoflavones can induce mitophagy by disrupting the AKT/mTOR signaling, thereby inhibiting osteosarcoma [ 24 ] . He et al. successfully constructed zinc oxide nanoparticles that suppress β-catenin through HIF-1 alpha/BNIP3/LC3B-mediated mitophagy, impeding osteosarcoma metastasis [ 25 ] . However, research examining the potential correlation between mitophagy-related genes and osteosarcoma prognosis remains scarce. Therefore, we constructed a mitophagy-related signature comprising seven genes. Immune infiltration analysis indicated reduced infiltration of various immune cells in the high-risk group. This decrease in the ability of osteosarcoma cells to be directly killed by immune cells such as macrophages, cytotoxic cells, NK cells, CD8 + T cells, and Th1 cells likely facilitates tumor evasion from immune detection. The reduction in TFH cells diminishes germinal center reactions and efficient antibody production. The decreased number of aDC in the high-risk group suggests inadequate antigen presentation, limiting T cell activation and weakening antitumor immunity. Additionally, the lower scores across multiple immune response mechanisms in the high-risk group imply decreased T cell activation, restricted antigen-presenting cell function, and initiation of antitumor immune responses. This facilitates tumor evasion from the immune system. The reduction in MHC class I molecules may hinder T cell recognition of tumor cells. Decreased inflammation-promoting and Type I IFN responses may compromise the immune system's ability to eliminate osteosarcoma cells, affecting inflammatory reactions and the efficiency of antiviral and antitumor immune responses. Finally, reduced parainflammation and T cell co-inhibition may further weaken the immune system's antitumor capabilities. The collective reduction in immune-related features in the high-risk group may lead to immune system dysfunction or suppression, increasing the risk of disease progression and recurrence. The ESTIMATE, stromal, and immune scores suggest lower numbers or activities of stromal and immune cells in tumor tissues from the high-risk group. This may compromise immune surveillance and elimination of tumor development within the TME, favoring tumor cell dissemination and metastasis. The higher tumor purity in the high-risk group indicates a higher proportion of tumor cells and a lower proportion of non-tumor components, corroborating insufficient infiltration of stromal and immune cells. Therefore, when treating osteosarcoma, it is essential to consider both direct tumor targeting and the patient's immune status. Immune modulation strategies should aim to enhance the patient's antitumor immune response and improve prognosis. SP1 is a zinc finger transcription factor involved in vital physiological processes such as cell growth, apoptosis, and differentiation [ 26 , 27 ] . Wang et al. found that KDM3A mediates SP1 activation of PFKB4 transcription, promoting aerobic glycolysis and tumor progression in osteosarcoma [ 28 ] . Chou et al. discovered that dihydromyricetin inhibits osteosarcoma cell metastasis by suppressing SP1 nuclear factor (NF)-κB activators [ 29 ] . E2F1, a member of the E2F family, is vital in regulating the cell cycle and acting as a tumor suppressor protein. It is also targeted by tiny DNA tumor virus-transforming proteins [ 30 , 31 ] . Han et al. revealed that DJ-1 promotes osteosarcoma progression by activating the CDK4/RB/E2F1 signaling pathway [ 32 ] . Liu et al. found that long noncoding RNA (lncRNA)- TMPO-AS1 promotes osteosarcoma cell apoptosis by targeting and regulating E2F1 [ 33 ] . Zhang et al. elucidated that E2F1 impairs all-trans retinoic acid-induced osteoblastic differentiation of osteosarcoma by promoting ubiquitination-mediated RARα degradation [ 34 ] . SMURF1, a ubiquitin ligase, regulates cell motility, signaling, and polarity [ 35 , 36 ] . Zhang et al. demonstrated that UEV1A promotes osteosarcoma differentiation by facilitating SMURF1-mediated ubiquitination and degradation of smad1 [ 37 ] . Although RPS27A , RAB5C , AMBRA1 , and MRAS have not been studied in osteosarcoma, there are reports on their roles in other tumors. RPS27A encodes ribosomal protein S27a, which belongs to the ribosomal protein family involved in ribosome formation and is crucial for protein synthesis. Studies have found that high expression of RPS27A is associated with poor prognosis in human papillomavirus (HPV) 16-positive cervical cancer [ 38 ] . Mu et al. found that apolipoprotein M promotes the growth and proliferation of colorectal cancer cells and inhibits apoptosis by upregulating RPS27A [ 39 ] . RAB5C is a member of the RAS oncogene family, belonging to the small GTPase family. It primarily involves vesicle docking and fusion processes, ensuring vesicles bind to their correct receptors. Wang et al. discovered that RAB5C is a new mRNA binding target of HuR, regulating breast cancer cell proliferation [ 40 ] . Zhang et al. found that RAB5C is a tumor suppressor in thyroid cancer [ 41 ] . AMBRA1 plays a key role in the initiation stage of autophagy, regulating autophagosome formation. Studies have shown that epidermal AMBRA1 has been used as a prognostic biomarker for stage I/II cutaneous melanoma [ 42 ] . Song et al. showed that MiR-3635 blocks autophagy by targeting autophagy regulatory genes ATG12 and AMBRA1 , which inhibits epithelial-mesenchymal transition (EMT) in breast cancer cells [ 43 ] . MRAS belongs to the RAS gene family, and the RAS-MAPK signal transduction pathway is crucial for cell proliferation. This pathway is disrupted in the majority of human malignancies. Zhao et al. discovered that GNG2 significantly inhibits ERK and Akt activity in an MRAS -dependent manner, thereby inhibiting breast cancer cell growth [ 44 ] . Bonsor D. A. et al. discussed potential therapeutic approaches targeting the SMP complex in RAS/RAF-driven cancers and RASopathies [ 45 ] . Through PCR validation using human tissues and osteosarcoma cell lines, MRAS was identified as the most significant DEG. The current research further investigated the role of MRAS in osteosarcoma through in vivo experiments. The CCK-8 and Edu assay revealed that MRAS 's overexpression inhibits osteosarcoma cell growth, indicating that MRAS suppresses osteosarcoma cell proliferation. Wound healing and Transwell assays showed that overexpressing MRAS reduces the migration and invasion of osteosarcoma cells, suggesting that MRAS may inhibit these cellular processes. Flow cytometry apoptosis assays demonstrated that overexpressing MRAS increases the apoptosis rate of osteosarcoma cells, indicating that MRAS promotes osteosarcoma cell apoptosis. In a pan-cancer analysis, it was found that MRAS was differentially expressed in other cancer tissues and was associated with the prognosis of various tumors. Notably, there is a correlation between MRAS , TMB, and MSI in diverse malignancies, indicating that MRAS might be a promising target for immunosuppressant therapies in several types of cancer. To summarize, MRAS has demonstrated tumor-suppressing capabilities in osteosarcoma. Further investigation is required to elucidate the molecular pathways by which it regulates cellular processes. Understanding the precise role of MRAS in osteosarcoma might provide novel strategies for targeted treatments aimed at impeding tumor proliferation and improving patients' well-being. The results obtained in this study strongly indicate the potential therapeutic benefits of small molecule compounds in the management of osteosarcoma. The efficacy of these compounds varies depending on the specific risk factors present in individual patients. However, a comprehensive exploration of the mechanisms through which these drugs operate in osteosarcoma is expected to generate valuable insights. This, in turn, could pave the way for innovative treatment strategies that effectively restrict tumor growth and improve patient outcomes. Limitations : This study acknowledges several important limitations. Firstly, despite the bioinformatics analysis revealing a correlation between the expression of seven mitophagy-related genes and osteosarcoma, the precise mechanism by which these genes contribute to mitophagy and influence osteosarcoma remains unclear. Secondly, the validation process was limited to in vitro assessment of MRAS genes, without conducting complementary in vivo studies. Lastly, while the primary focus was on elucidating the role of MRAS in osteosarcoma, the study somewhat overlooked the activities of the other genes involved. Prospective cellular or animal investigations are necessary further to investigate the functional processes of these genes with osteosarcoma. Declarations Data Availability Statement Data is provided within the manuscript or supplementary information files. Conflict of Interest All authors report no conflicts of interest in this work. Author Contributions PX, XC, and JY contributed to the study’s concept and design. JJ and KL YH participated in gathering and analyzing the data. Administrative, technical, and material support was provided by WZ, JZ, and YW. The manuscript, authored primarily by TW, TL, and PX, benefited from the valuable input of all co-authors. Every author contributed to the article’s development and unanimously approved the final draft for submission Funding This study was supported by the Key Projects of Jiangxi Provincial Department of Education (No. GJJ210105 to Xigao Cheng). Ethics approval and consent to participate The Ethics Committee of The Second Affiliated Hospital of Nanchang University granted approval for this research (Review (2020) No. (115)), ensuring that all procedures were carried out in strict adherence to established guidelines and regulations. All participants gave their informed consent to participate in the study. Acknowledgments: The authors would like to thank all the reviewers who participated in the review and MJEditor (www.mjeditor.com) for its linguistic assistance during the preparation of this manuscript. References Isakoff MS, Bielack SS, Meltzer P, et al. Osteosarcoma: current treatment and a collaborative pathway to success. 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Cell Death Dis. 2022 Mar 23;13(3):260. doi: 10.1038/s41419-022-04690-3. Bonsor DA, Alexander P, Snead K, Hartig N, Drew M, Messing S, Finci LI, Nissley DV, McCormick F, Esposito D, Rodriguez-Viciana P, Stephen AG, Simanshu DK. Structure of the SHOC2-MRAS-PP1C complex provides insights into RAF activation and Noonan syndrome. Nat Struct Mol Biol. 2022 Oct;29(10):966-977. doi: 10.1038/s41594-022-00841-4. Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4271624","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":297181988,"identity":"5b73a861-e771-4bd3-967f-50c39853e47f","order_by":0,"name":"Peichuan Xu","email":"","orcid":"","institution":"Department of Orthopaedics, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Peichuan","middleName":"","lastName":"Xu","suffix":""},{"id":297181990,"identity":"2ef00ca9-0745-4238-83f5-7e7e7a4ae12a","order_by":1,"name":"Jiangminghao 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2","display":"","copyAsset":false,"role":"figure","size":2693477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed mitophagy-related genes in osteosarcoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u003cstrong\u003e \u003c/strong\u003eHeat map of DEGs. (B) Volcano plot of DEGs. (C) Venn diagram showing the overlap between mitochondrial autophagy genes and DEGs. (D-G) Functional enrichment analysis (D), bar chart (E), scatter plot (F), chord diagram (G), circle diagram (H), EMAP diagram.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/90bc4ada85d86a71e9a04c0d.png"},{"id":55771356,"identity":"411369eb-6b93-4ecc-bfc3-c6173fa7211f","added_by":"auto","created_at":"2024-05-02 21:08:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":759376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction evaluation of the model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Uni-cox analysis. (B, C) LASSO regression analysis. (D) The Coef value of model genes. (E) The location of model genes on the chromosomes. (F, G) Risk score and survival status distribution. (H) Risk score heatmap of samples. (I) KM survival curve. (J) 1, 3, and 5-year ROC curves.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/6e06a3d69aec266f825396cd.png"},{"id":55771890,"identity":"ba5eed4d-4598-424e-83ca-0f9bc42489aa","added_by":"auto","created_at":"2024-05-02 21:24:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":506474,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of the signature model in the testing, total, and GSE21257 cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Risk score distribution in the testing, total, and GSE21257 cohorts. (B) Survival status distribution in the testing, total, and GSE21257 cohorts. (C) KM survival curves in the testing, total, and GSE21257 cohorts. (D) ROC curve analysis for model accuracy in the testing, total, and GSE21257 cohorts.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/f2b9497f1d6b07cc4a63dd3a.png"},{"id":55770792,"identity":"6f2d5d55-a9b6-4b75-87e7-55ba28f6f946","added_by":"auto","created_at":"2024-05-02 21:00:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":629029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the nomogram and subgroup survival analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Nomogram construction based on clinical indicators and risk score. (B) Calibration curves of the nomogram. Kaplan‒Meier plots in 6 subgroups of (C) patients with ≥ 12 years of age, (D) patients with＜12 years of age, (E) male patients, (F) female patients, (G) patients with metastasis, and (H) patients without metastasis.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/284a2b07fd6f0813f2d99558.png"},{"id":55770787,"identity":"52cd57ac-5fe2-49c6-b9b2-ef406050de38","added_by":"auto","created_at":"2024-05-02 21:00:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":495133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A,B) GO and KEGG functional enrichment analysis. (C,D) GSEA functional analysis between high and low groups\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/3a448f41f6fdee1bc848ca94.png"},{"id":55771660,"identity":"166285ab-33b0-443e-8254-9c8368b6afeb","added_by":"auto","created_at":"2024-05-02 21:16:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1546038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Differences in immune cell infiltration between the high- and low-risk groups based on the Cibersort algorithm. (B) Differences in immune cell infiltration between the high- and low-risk groups based on the xCell algorithm. (C) The correlation among 21 types of immune cells. (D) The correlation of the 7 model genes, risk score, and 21 immune cells. (E) The differences in 13 immune responses between the high- and low-risk groups. (F) Immune score. (G) Stromal score. (H) Estimate score. (I) Tumor purity.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/abe771df04187aa4b8da3092.png"},{"id":55771354,"identity":"5082f11f-f0ff-4611-bcd8-d7cd31e49718","added_by":"auto","created_at":"2024-05-02 21:08:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":702931,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relative expression levels of model MRGs in cell lines and human tissues.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-G) The relative expression of model MRGs in cell lines (A) \u003cem\u003eSP1\u003c/em\u003e, (B) \u003cem\u003eMRAS\u003c/em\u003e, (C) \u003cem\u003eRPS27A\u003c/em\u003e, (D) \u003cem\u003eAMBRA1\u003c/em\u003e, (E) \u003cem\u003eE2F1\u003c/em\u003e, (F) \u003cem\u003eRAB5C\u003c/em\u003e, and (G) \u003cem\u003eSMURF1\u003c/em\u003e. (H-N) The relative expression of model MRGs in human tissues (H) \u003cem\u003eSP1\u003c/em\u003e, (I) \u003cem\u003eMRAS\u003c/em\u003e, (J) \u003cem\u003eRPS27A\u003c/em\u003e, (K) \u003cem\u003eAMBRA1\u003c/em\u003e, (L) \u003cem\u003eE2F1\u003c/em\u003e, (M) \u003cem\u003eRAB5C\u003c/em\u003e, and (N) \u003cem\u003eSMURF1\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/e4d0f2511ccdffb0a2d2fa7a.png"},{"id":55771358,"identity":"dfdaf0ff-8f20-4326-8e62-c434980b8eee","added_by":"auto","created_at":"2024-05-02 21:08:43","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3169339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverexpression of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMRAS\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in HOS cell line\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The overexpression of \u003cem\u003eMRAS via\u003c/em\u003e RT-qPCR. (B) The CCK-8 assay. (C) Edu assay. (D) Flow cytometry for apoptosis detection of cells. (E) Transwell assay. (F) Wound healing.\u003c/p\u003e","description":"","filename":"Figure9HOS.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/24719fdf40a5cbdc581eb5aa.png"},{"id":55770793,"identity":"3da84908-bda2-492a-b3f9-445315d2e21b","added_by":"auto","created_at":"2024-05-02 21:00:43","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3264281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverexpression of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMRAS\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in MG63 cell line\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The overexpression of \u003cem\u003eMRAS via\u003c/em\u003e RT-qPCR. (B) The CCK-8 assay. (C) Edu assay. (D) Flow cytometry for apoptosis detection of cells. (E) Transwell assay. (F) Wound healing.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/e3badc2fa07fba30fca83123.png"},{"id":55771662,"identity":"80455d1b-3cfb-448c-b51e-24031d51aab1","added_by":"auto","created_at":"2024-05-02 21:16:43","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1406570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePan-cancer analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMRAS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A) Pan-cancer analysis of \u003cem\u003eMRAS\u003c/em\u003e expression using TCGA database (B) KM analysis of the correlation between \u003cem\u003eMRAS\u003c/em\u003eand DSS. (C) KM analysis of the correlation between \u003cem\u003eMRAS\u003c/em\u003e and DFI. (D) KM analysis of the correlation between \u003cem\u003eMRAS\u003c/em\u003e and OS. (E) The correlation between \u003cem\u003eMRAS\u003c/em\u003e and TMB. (F) The correlation between \u003cem\u003eMRAS\u003c/em\u003e and MSI.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/5f56736d0f8fea2add195cb0.png"},{"id":55770796,"identity":"876f83c4-fb1a-490f-9d67-0f8d50d75e20","added_by":"auto","created_at":"2024-05-02 21:00:43","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":7486384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) LMP-400. (B) RGB-286638. (C) INH. (D) Canertinib dihydrochloride. (E) Cytidine 5'- Diphosphocholine.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/776ba0c6ff7e7dd19b17e8c6.png"},{"id":57265001,"identity":"2927b3ba-b71f-4540-be95-4baa6bf1eee6","added_by":"auto","created_at":"2024-05-28 10:54:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27504780,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4271624/v1/435608b9-f35d-4579-823d-23b601c4015d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of a novel mitophagy-related signature for predicting clinical prognosis and immunotherapy of osteosarcoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOsteosarcoma (OS) predominantly affects adolescents, with an incidence rate of 3 cases per million \u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Despite extensive research efforts and advancements in therapeutic techniques, the survival rate for osteosarcoma patients remains below 30% \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The disease's complex molecular mechanisms and genomic instability significantly contribute to its poor prognosis \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is imperative to detect novel molecules linked to targeted treatment and prognosis in osteosarcoma.\u003c/p\u003e \u003cp\u003eMitochondria, often referred to as the \"energy factories\" of cells, play a vital role in maintaining cellular activities and metabolism \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Mitophagy, a process that selectively degrades damaged mitochondria, occurs primarily in response to external stimuli such as oxidative stress, nutrient deprivation, or cellular senescence. These stimuli can cause damage to mitochondrial depolarization and result in the loss of membrane potential\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Mitophagy is a process that selectively breaks down damaged mitochondria to maintain intracellular homeostasis and prevent the development of tumors \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. More and more evidence recently confirms that mitophagy is a double-edged sword in cancer development. On the one side, the reduction of mitophagy promotes cancer progression \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Conversely, increased mitophagy promotes cancer cell proliferation and progression by protecting cancer cells from apoptosis\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, little is known about mitophagy in OS, and more study is required to comprehend the underlying mechanisms.\u003c/p\u003e \u003cp\u003eThis study developed a novel predictive model of MRGs to improve the prognosis of OS and explore changes in the tumor microenvironment (TME), immunotherapy. The impact of \u003cem\u003eMRAS\u003c/em\u003e on OS was elucidated via \u003cem\u003ein vitro\u003c/em\u003e experiments and pan-cancer analysis. Potential small molecule drugs that may target MRAS were screened through molecular docking. The findings provided insights into understanding OS treatment and prognosis and paved the way for innovative interventions targeting immune evasion mechanisms.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Data Sources and Collection\u003c/h2\u003e\n \u003cp\u003eThe transcriptomic and clinical data for OS were obtained from UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003c/span\u003e). The transcriptomic and clinical data for skeletal muscle were extracted from GTEx (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/\u003c/span\u003e\u003c/span\u003e). Additionally, the transcriptome and clinical data for model validation, specifically GSE21257, were retrieved from the Gene Expression Omnibus (GEO) database, which can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e. Furthermore, 103 mitophagy-related genes (MRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/pathway/hsa04137\u003c/span\u003e\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provides a comprehensive overview of the entire workflow of this study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Analysis of differentially expressed genes and their functional implications\u003c/h2\u003e\n \u003cp\u003eThe \u0026quot;limma\u0026quot; R package was used to identify differentially expressed genes (DEGs) in OS. The DEGs linked with mitophagy in OS were determined by intersecting DEGs with MRGs. The intersection was visualized using a Venn diagram. Moreover, enrichment analyses utilizing the \u0026quot;ClusterProfiler\u0026quot; R package were performed for both Gene Ontology (GO) and the KEGG. The visualization of the results was achieved using the \u0026quot;ggplot2\u0026quot; package. Gene-set enrichment analysis (GSEA) was also executed on the datasets c5.go.all.v2022.1.Hs.symbols.gmt and c2.kegg.all.v2022.1.Hs.symbols.gmt, employing the \u0026quot;clusterProfiler\u0026quot; program and presenting the data through the \u0026quot;ggplot2\u0026quot; tool.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Construction of the risk model\u003c/h2\u003e\n \u003cp\u003eUnivariate Cox regression analysis identified prognosis-linked genes. Moreover, the LASSO regression analysis of the training cohort was performed to reduce the number of variables and included a new lambda value of 7 genes. The risk score for each individual was computed as follows:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\text{R}\\text{i}\\text{s}\\text{k}\\hspace{0.17em}\\text{S}\\text{c}\\text{o}\\text{r}\\text{e}={\\sum }_{i=1}^{\\text{n}}\\left(\\text{R}\\text{N}\\text{A} \\text{e}\\text{x}\\text{p}\\text{i}\\times \\text{c}\\text{o}\\text{e}\\text{f}\\text{i}\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Evaluation of the model and construction of a predictive nomogram\u003c/h2\u003e\n \u003cp\u003eUsing the \u0026quot;caret\u0026quot; package, 85 patients were randomly divided into training and test groups. Patients\u0026apos; risk scores were computed using the given formula, and based on the median risk score, high-risk and low-risk groups were categorized. Survival analysis was conducted using the \u0026quot;survival\u0026quot; package, while data visualization was achieved with the \u0026quot;survminer\u0026quot; package. The \u0026quot;timeROC\u0026quot; package was utilized to verify the model\u0026apos;s effectiveness. Furthermore, calibration and nomogram curves were generated using the \u0026quot;survival\u0026quot; and \u0026quot;rms\u0026quot; packages. Finally, the \u0026quot;pec\u0026quot; package was used to access C-Index curves.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Immunogenomic Analysis\u003c/h2\u003e\n \u003cp\u003eInteractions between immune cells and genes were explored using \u0026quot;Cibersort\u0026quot; and \u0026quot;Xcell\u0026quot; algorithms. The \u0026quot;ESTIMATE\u0026quot; algorithm was also applied to assess the proportion of present immune cells. Furthermore, an immunological correlation was conducted to investigate the relationship between immune cells and risk scores.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Molecular docking\u003c/h2\u003e\n \u003cp\u003eThe target protein MRAS (PDB: 7SD0) was sourced from the PDB database, while 2614 anticancer compounds (L2120) were provided by TOPSCIENCE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.tsbiochem.com\u003c/span\u003e\u003c/span\u003e). The protein was processed, the ligand pocket was identified, and the resulting protein was docked with the small molecule database using the Molecular Operating Environment (MOE) software. Compounds with the highest scores were selected for evaluation and analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7. Pan-Cancer Analysis\u003c/h2\u003e\n \u003cp\u003eThe \u0026quot;limma\u0026quot; package was used to ascertain the differential expression of \u003cem\u003eMRAS\u003c/em\u003e across various cancer types. Box plots were created using the \u0026quot;ggpubr\u0026quot; package Kaplan-Meier (K-M) survival analysis was performed for overall survival (OS), disease-free interval (DFI), and disease-specific survival (DSS) with the help of the \u0026quot;survival\u0026quot; and \u0026quot;survminer\u0026quot; packages. Data pertaining to tumor mutation burden (TMB) and microsatellite instability (MSI) were sourced from previous studies. A Spearman correlation analysis was conducted to explore the association between \u003cem\u003eMRAS\u003c/em\u003e expression and pan-cancer TMB and MSI levels.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8. Verification of Hub Genes in Clinical Samples\u003c/h2\u003e\n \u003cp\u003eAfter ethical approval, 25 matched pairs of OS and adjacent normal tissues were collected from the Second Affiliated Hospital of Nanchang University between January 2021 and December 2023. The samples were preserved in liquid nitrogen at -80\u0026deg;C. The extraction of total RNA was performed using TRIzol and Chloroform (Thermofisher, USA). The quality and concentration of the isolated RNA were assessed using a Nanodrop One spectrophotometer (Thermofisher, USA). Afterwards, complementary DNA (cDNA) was produced using the Prime Script RT kit (TaKaRa, Japan). RT-qPCR was performed on an ABI 7500 Real-Time PCR system (Thermofisher, USA) using a reverse transcription kit (TaKaRa, Japan). \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e contains primer details used for validation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9. Cell Cultivation, Transfection, and Lentivirus Infection\u003c/h2\u003e\n \u003cp\u003eHuman osteoblasts (hFOB 1.19) and osteosarcoma cell lines (HOS, MG63, and U2OS) were sourced from the American Type Culture Collection (ATCC). All cells were cultivated in the Dulbecco\u0026apos;s Modified Eagle Medium (DMEM; Gibco, USA).All cells were maintained in a complete medium enriched with 10% fetal bovine serum (FBS; Gibco, USA) and 1% penicillin-streptomycin (NCM Biotech, China) at 37\u0026deg;C with 5% CO2. For MRAS overexpression, Lentivirus particles generated using Lenti-Easy Packaging (Genechem, China) were utilized.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.10. Assessment of Cell Proliferation\u003c/h2\u003e\n \u003cp\u003eCell proliferation was assessed using the CCK-8 and Edu assays. Osteosarcoma cells were seeded in a 96-well plate at a density of 4000 cells per well. CCK-8 solution (10 \u0026micro;L) was added to each well at 0, 24, 48, and 72 hours, followed by a 2-hour incubation. Absorbance was measured at 450 nm. Cells were also grown in 6-well plates to appropriate density, incubated with Edu for 12 hours, fixed with 4% paraformaldehyde, and stained using the EdU imaging kit (Uelandy, China).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.11. Scratch Assay for Cell Migration\u003c/h2\u003e\n \u003cp\u003eCells were grown to 90% confluence in a 6-well plate. A scratch was made using a 10 \u0026micro;L pipette tip to simulate a wound. After three PBS washes, the cells were cultured in serum-free media, and cell migration was tracked using a microscope.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.12. Assays for Cell Migration and Invasion\u003c/h2\u003e\n \u003cp\u003eCells were diluted to a density of 4 x 10\u003csup\u003e5\u003c/sup\u003e cells/mL. In a 24-well plate, 100 \u0026micro;L of serum-free cell suspension was added to the upper chamber, while 600 \u0026micro;L of growth media containing 10% FBS was added to the lower chamber. After a 24-hour incubation, cells were fixed with paraformaldehyde for 15 minutes, stained with 0.1% crystal violet for 20 minutes, and counted under a microscope.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e2.13. Flow cytometry assay\u003c/h2\u003e\n \u003cp\u003eCells were washed twice with PBS, centrifuged at 1600 rpm for 5 minutes, and resuspended in 200 \u0026micro;L of 1X buffer. The cells were then incubated in the dark for 15 minutes with propidium iodide (PI) and Annexin V-FITC. The reaction was stopped by adding 300 \u0026micro;L of 1X buffer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e2.14. Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analyses were performed using R software (version 4.2.3). Statistical significance was set at p-values less than 0.05, denoted as * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Explore differentially expressed MRGs in OS\u003c/h2\u003e \u003cp\u003eThe \"limma\" R package was utilized to pinpoint DEGs between osteosarcoma (OS) and normal skeletal muscle tissue.The selection criteria were \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |FoldChange| \u0026gt; 1.5 (|log2 FoldChange| \u0026gt; 0.585). The analysis revealed 12,578 key genes exhibited differential expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA,B). Next, these genes were allowed to intersect with 103 mitophagy-related genes obtained from the KEGG website to get 95 overlapping genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). GO enrichment analyses emphasized the involvement of these genes in processes like mitochondrion disassembly, autophagy of mitochondrion, organelle disassembly, mitochondrial outer membrane, organelle outer membrane, ubiquitin protein ligase binding, ubiquitin-like protein ligase binding, and GDP binding. Additionally, the KEGG enrichment analysis demonstrated significant enrichment of these genes in processes related to mitophagy, autophagy, and neurodegeneration pathways, providing further evidence for the functional relevance of the selected genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Construction of prognosis prediction model\u003c/h2\u003e \u003cp\u003eClinical data on osteosarcoma patients was collected to further identify prognostic genes. Univariable Cox regression analysis was conducted, and 10 key genes were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Subsequently, LASSO regression analysis was performed to further shortlist the selected genes to 7 for model construction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB,C). The bar graph presented the coef values of model genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), while the chromosomal ring map showed the location of genes on the chromosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Patients were divided into high- and low-risk groups based on the median risk score. The survival status plot revealed that patients' survival time progressively decreased, and their mortality rate increased as the risk score increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF,G). A heatmap visualized gene expression levels in OS patient samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). The KM survival curve demonstrated that patients in the high-risk group had a poor prognosis compared to those in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). The AUC values for the ROC curves at 1, 3, and 5 years were 0.925, 0.936, and 0.948, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Validation of the prognostic model\u003c/h2\u003e \u003cp\u003eValidation across multiple datasets (TARGET testing, total, and GSE21257) confirmed that as risk scores increased, patient survival time decreased, and mortality rates rose (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). Patients in the high-risk group had a worse prognosis than those in the low-risk group, as shown by the KM survival curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Furthermore, the ROC curve of all groups indicated that the model exhibited robust predictive performance and stability for prognosis in OS patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Construction of nomogram and subgroup analysis\u003c/h2\u003e \u003cp\u003eA nomogram integrating risk scores, age, gender, and metastasis was developed to enhance survival prediction accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Furthermore, the calibration curves of the nomogram demonstrated strong predictive accuracy and performance validation, confirming the nomogram's robustness for prediction and validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Based on the clinicopathological characteristics, the patients were placed into six groups: patients with \u0026ge;\u0026thinsp;12 years of age, patients with \u0026lt;\u0026thinsp;12 years of age, male patients, female patients, non-metastatic group, and metastatic group. Among these six subgroups, the overall survival (OS) of patients in the high-risk group was lower than that of the low-risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-H). This suggests that the model possesses a remarkable capacity to differentiate between various patient outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5. GO, KEGG, and GSEA enrichment analysis\u003c/h2\u003e \u003cp\u003eDEGs between high- and low-risk groups underwent GO, KEGG, and GSEA analyses. The GO enrichment analysis showed that the DEGs primarily regulated Ca\u003csup\u003e2+\u003c/sup\u003e concentration, Ca\u003csup\u003e2+\u003c/sup\u003e concentration homeostasis, signal transduction and ion channels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA,B). The GSEA analysis revealed that genes in the high-risk group were mainly enriched in chemical carcinogenesis, citrate cycle (TCA cycle), and metabolism of xenobiotics by cytochrome P450. In contrast, genes in the low-risk group were primarily enriched in Allograft rejection, chemokine signaling pathway, and transcriptional misregulation in cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC,D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Exploration of immune cell infiltration in the TME\u003c/h2\u003e \u003cp\u003eTME, the environment surrounding tumor cells, encompasses various components. These included blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, signaling molecules, and the extracellular matrix surrounding tumor cells\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Using the Cibersort method, we found macrophages, particularly M0 and M2, to be predominant in osteosarcoma samples. Notably, the high-risk group exhibited a higher proportion of M2 macrophages, reflecting their immunosuppressive and tumor-promoting roles (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The Xcell algorithm further revealed a reduction in activated dendritic cells (aDC), CD8\u003csup\u003e+\u003c/sup\u003e T cells, cytotoxic cells, natural killer (NK) cells, T cells, T follicular helper (Tfh), T helper (Th) 1 cells, and T regulatory (Treg) cells in the high-risk group compared to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Correlation analysis among immune cells showed the association between 21 types of immune cells. Moreover, a high expression correlation was observed between T cells, aDC, and cytotoxic cells, indicating potential relevant associations among these cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Afterwards, the correlation between immune cells, risk score, and the expression level of model genes was explored. An increase in the \u003cem\u003eMRAS\u003c/em\u003e expression led to a higher infiltration of multiple immune cells, confirming that \u003cem\u003eMRAS\u003c/em\u003e acts as a protective factor in the progression of osteosarcoma. The increasing presence of immune cells that target tumors might lead to more efficient surveillance and elimination of tumor cells, thereby impeding the growth and spread of tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Furthermore, in the high-risk group, it was observed that lower scores in antigen-presenting cell (APC) co-inhibition, CCR, checkpoint, inflammation-promoting, major histocompatibility complex (MHC) class I, T cell co-stimulation, T cell co-inhibition, type I interferon (IFN) response, and parainflammation pathways compared to the low-risk group. This reflects potential functional defects or suppressive states of the immune system in high-risk patients across multiple levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). The TME score analysis also revealed decreased ESTIMATE, stromal, and immune scores, along with increased tumor purity in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF-I). These findings suggest insufficient infiltration of stromal cells and immune cells in the TME, potentially leading to faster tumor progression and a higher risk of metastasis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Validation of Model Genes in OS cell line and OS Clinical Samples\u003c/h2\u003e \u003cp\u003eRT-qPCR analysis further validated the expression of model genes in three osteosarcoma cell lines (HOS, MG63, and U2OS). Normal osteoblast cells (hFOB1.19)served as the control group. Significantly elevated expression was observed for SP1, E2F1, and RPS27A in OS cell lines, while RAB5C, AMBRA1, MRAS, and SMURF1 expression decreased in osteosarcoma cell lines(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-G). A total of 25 osteosarcoma tumors and corresponding tissue samples were obtained from the Second Affiliated Hospital of Nanchang University between January 2021 and December 2023. The expression levels in human tissue samples were further validated, and the results were consistent with the validation of OS cell lines. This suggests that the developed risk prediction model is effective and reliable (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH-N).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Overexpression of \u003cem\u003eMRAS\u003c/em\u003e inhibits proliferation, migration, and invasion and promotes apoptosis in osteosarcoma cells.\u003c/h2\u003e \u003cp\u003eThe lentiviral transfection was utilized to upregulate MRAS expression (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA) and assess its effects on cell proliferation, migration, invasion, and apoptosis. CCK-8 and Edu assays demonstrated that MRAS overexpression reduced HOS and MG63 cell proliferation (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). Wound healing assay also indicated that \u003cem\u003eMRAS\u003c/em\u003e overexpression significantly suppressed the migration ability of HOS cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). The Transwell experiment proved that overexpressing \u003cem\u003eMRAS\u003c/em\u003e strongly inhibited the migration and invasion of HOS cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD,E and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD,E). Flow cytometry analysis revealed that overexpression of \u003cem\u003eMRAS\u003c/em\u003e inhibited apoptosis in HOS cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eF). In conclusion, overexpression of the \u003cem\u003eMRAS\u003c/em\u003e gene can suppress cell proliferation, migration, and invasion and simultaneously promote apoptosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Pan-cancer analysis\u003c/h2\u003e \u003cp\u003eThrough comprehensive pan-cancer analysis, it was found that MRAS expression is elevated in cholangiocarcinoma (CHOL), kidney renal papillary cell carcinoma (KIRP), and liver hepatocellular carcinoma (LIHC). Conversely, its expression is lower in 13 different tumor types, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma (COAD), glioblastoma (GBM), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), and uterine corpus endometrial carcinoma (UCEC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). Kaplan-Meier survival analysis was subsequently employed to assess the relationship between MRAS and patient outcomes, specifically disease-specific survival (DSS), disease-free interval (DFI), and overall survival (OS). In terms of DSS, MRAS served as a prognostic protective factor in LUAD, KIRP, and skin cutaneous melanoma (SKCM), but as a prognostic risk factor in UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB). For DFI, MRAS was beneficial in thymoma (THYM) and SKCM, but detrimental in adrenocortical carcinoma (ACC) and UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC). Regarding OS, MRAS was protective in LUAD and SKCM, but a risk factor in UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD). Tumor mutational burden (TMB), a biomarker indicating responsiveness to immune checkpoint inhibitors, especially those targeting the PD-1/PD-L1 pathway, was also investigated in relation to MRAS expression. Notably, MRAS expression correlated with TMB in 10 tumor types. Positive correlations were observed in THYM and KIRP, while negative correlations were found in UCEC, PRAD, LIHC, stomach adenocarcinoma (STAD), pancreatic adenocarcinoma (PAAD), brain lower grade glioma (LGG), head and neck squamous cell carcinoma (HNSC), and esophageal carcinoma (ESCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eE). Additionally, the relationship between MRAS expression and microsatellite instability (MSI), a hypermutation phenotype linked to PD-1 blockade efficacy, was examined. Among 11 tumor types, only BRCA and COAD showed a positive correlation between MRAS expression and MSI. All other tumor types, including testicular germ cell tumors (TGCT), acute myeloid leukemia (LAML), UCEC, STAD, LUSC, KICH, HNSC, ESCA, and CHOL, exhibited negative correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.10. Molecular docking analysis\u003c/h2\u003e \u003cp\u003eTo identify potential small molecule compounds interacting with MRAS, molecular docking analysis was conducted. This method aids in discovering therapeutic targets and developing innovative treatments. Among 2614 anticancer small molecules screened using MOE software, the top five scoring compounds were selected for molecular docking: LMP-400 (-17.4463), RGB-286638 (-16.9881), INH 34 (-15.9679), canertinib dihydrochloride (-15.0738), and cytidine 5'-diphosphocholine (-15.0732) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOsteosarcoma, the most common primary bone malignancy, frequently metastasizes to the lungs and carries a poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Mitophagy, a vital cellular process that identifies and eliminates damaged or dysfunctional mitochondria, is essential for mitochondrial health \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Recent studies suggest that mitophagy plays a significant role in osteosarcoma progression and drug sensitivity. Luo et al. found that SLC35A2 regulates mitophagy via the PI3K/AKT/mTOR pathway, thus promoting osteosarcoma development \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Zheng et al. demonstrated that soy isoflavones can induce mitophagy by disrupting the AKT/mTOR signaling, thereby inhibiting osteosarcoma \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. He et al. successfully constructed zinc oxide nanoparticles that suppress β-catenin through HIF-1 alpha/BNIP3/LC3B-mediated mitophagy, impeding osteosarcoma metastasis \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. However, research examining the potential correlation between mitophagy-related genes and osteosarcoma prognosis remains scarce. Therefore, we constructed a mitophagy-related signature comprising seven genes.\u003c/p\u003e \u003cp\u003eImmune infiltration analysis indicated reduced infiltration of various immune cells in the high-risk group. This decrease in the ability of osteosarcoma cells to be directly killed by immune cells such as macrophages, cytotoxic cells, NK cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and Th1 cells likely facilitates tumor evasion from immune detection. The reduction in TFH cells diminishes germinal center reactions and efficient antibody production. The decreased number of aDC in the high-risk group suggests inadequate antigen presentation, limiting T cell activation and weakening antitumor immunity. Additionally, the lower scores across multiple immune response mechanisms in the high-risk group imply decreased T cell activation, restricted antigen-presenting cell function, and initiation of antitumor immune responses. This facilitates tumor evasion from the immune system. The reduction in MHC class I molecules may hinder T cell recognition of tumor cells. Decreased inflammation-promoting and Type I IFN responses may compromise the immune system's ability to eliminate osteosarcoma cells, affecting inflammatory reactions and the efficiency of antiviral and antitumor immune responses. Finally, reduced parainflammation and T cell co-inhibition may further weaken the immune system's antitumor capabilities. The collective reduction in immune-related features in the high-risk group may lead to immune system dysfunction or suppression, increasing the risk of disease progression and recurrence. The ESTIMATE, stromal, and immune scores suggest lower numbers or activities of stromal and immune cells in tumor tissues from the high-risk group. This may compromise immune surveillance and elimination of tumor development within the TME, favoring tumor cell dissemination and metastasis. The higher tumor purity in the high-risk group indicates a higher proportion of tumor cells and a lower proportion of non-tumor components, corroborating insufficient infiltration of stromal and immune cells. Therefore, when treating osteosarcoma, it is essential to consider both direct tumor targeting and the patient's immune status. Immune modulation strategies should aim to enhance the patient's antitumor immune response and improve prognosis.\u003c/p\u003e \u003cp\u003eSP1 is a zinc finger transcription factor involved in vital physiological processes such as cell growth, apoptosis, and differentiation \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Wang et al. found that \u003cem\u003eKDM3A\u003c/em\u003e mediates SP1 activation of \u003cem\u003ePFKB4\u003c/em\u003e transcription, promoting aerobic glycolysis and tumor progression in osteosarcoma \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Chou et al. discovered that dihydromyricetin inhibits osteosarcoma cell metastasis by suppressing SP1 nuclear factor (NF)-κB activators \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. E2F1, a member of the E2F family, is vital in regulating the cell cycle and acting as a tumor suppressor protein. It is also targeted by tiny DNA tumor virus-transforming proteins \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Han et al. revealed that DJ-1 promotes osteosarcoma progression by activating the CDK4/RB/E2F1 signaling pathway \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Liu et al. found that long noncoding RNA (lncRNA)-\u003cem\u003eTMPO-AS1\u003c/em\u003e promotes osteosarcoma cell apoptosis by targeting and regulating E2F1 \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Zhang et al. elucidated that E2F1 impairs all-trans retinoic acid-induced osteoblastic differentiation of osteosarcoma by promoting ubiquitination-mediated RARα degradation \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. SMURF1, a ubiquitin ligase, regulates cell motility, signaling, and polarity \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Zhang et al. demonstrated that UEV1A promotes osteosarcoma differentiation by facilitating SMURF1-mediated ubiquitination and degradation of smad1 \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough \u003cem\u003eRPS27A\u003c/em\u003e, \u003cem\u003eRAB5C\u003c/em\u003e, \u003cem\u003eAMBRA1\u003c/em\u003e, and \u003cem\u003eMRAS\u003c/em\u003e have not been studied in osteosarcoma, there are reports on their roles in other tumors. \u003cem\u003eRPS27A\u003c/em\u003e encodes ribosomal protein S27a, which belongs to the ribosomal protein family involved in ribosome formation and is crucial for protein synthesis. Studies have found that high expression of \u003cem\u003eRPS27A\u003c/em\u003e is associated with poor prognosis in human papillomavirus (HPV) 16-positive cervical cancer \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Mu et al. found that apolipoprotein M promotes the growth and proliferation of colorectal cancer cells and inhibits apoptosis by upregulating \u003cem\u003eRPS27A\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eRAB5C\u003c/em\u003e is a member of the RAS oncogene family, belonging to the small GTPase family. It primarily involves vesicle docking and fusion processes, ensuring vesicles bind to their correct receptors. Wang et al. discovered that \u003cem\u003eRAB5C\u003c/em\u003e is a new mRNA binding target of HuR, regulating breast cancer cell proliferation \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Zhang et al. found that \u003cem\u003eRAB5C\u003c/em\u003e is a tumor suppressor in thyroid cancer \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eAMBRA1\u003c/em\u003e plays a key role in the initiation stage of autophagy, regulating autophagosome formation. Studies have shown that epidermal \u003cem\u003eAMBRA1\u003c/em\u003e has been used as a prognostic biomarker for stage I/II cutaneous melanoma \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Song et al. showed that MiR-3635 blocks autophagy by targeting autophagy regulatory genes \u003cem\u003eATG12\u003c/em\u003e and \u003cem\u003eAMBRA1\u003c/em\u003e, which inhibits epithelial-mesenchymal transition (EMT) in breast cancer cells \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMRAS\u003c/em\u003e belongs to the RAS gene family, and the RAS-MAPK signal transduction pathway is crucial for cell proliferation. This pathway is disrupted in the majority of human malignancies. Zhao et al. discovered that \u003cem\u003eGNG2\u003c/em\u003e significantly inhibits ERK and Akt activity in an \u003cem\u003eMRAS\u003c/em\u003e-dependent manner, thereby inhibiting breast cancer cell growth \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Bonsor D. A. et al. discussed potential therapeutic approaches targeting the SMP complex in RAS/RAF-driven cancers and RASopathies \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Through PCR validation using human tissues and osteosarcoma cell lines, \u003cem\u003eMRAS\u003c/em\u003e was identified as the most significant DEG. The current research further investigated the role of \u003cem\u003eMRAS\u003c/em\u003e in osteosarcoma through \u003cem\u003ein vivo\u003c/em\u003e experiments. The CCK-8 and Edu assay revealed that \u003cem\u003eMRAS\u003c/em\u003e's overexpression inhibits osteosarcoma cell growth, indicating that \u003cem\u003eMRAS\u003c/em\u003e suppresses osteosarcoma cell proliferation. Wound healing and Transwell assays showed that overexpressing \u003cem\u003eMRAS\u003c/em\u003e reduces the migration and invasion of osteosarcoma cells, suggesting that \u003cem\u003eMRAS\u003c/em\u003e may inhibit these cellular processes. Flow cytometry apoptosis assays demonstrated that overexpressing \u003cem\u003eMRAS\u003c/em\u003e increases the apoptosis rate of osteosarcoma cells, indicating that \u003cem\u003eMRAS\u003c/em\u003e promotes osteosarcoma cell apoptosis. In a pan-cancer analysis, it was found that \u003cem\u003eMRAS\u003c/em\u003e was differentially expressed in other cancer tissues and was associated with the prognosis of various tumors. Notably, there is a correlation between \u003cem\u003eMRAS\u003c/em\u003e, TMB, and MSI in diverse malignancies, indicating that \u003cem\u003eMRAS\u003c/em\u003e might be a promising target for immunosuppressant therapies in several types of cancer. To summarize, \u003cem\u003eMRAS\u003c/em\u003e has demonstrated tumor-suppressing capabilities in osteosarcoma. Further investigation is required to elucidate the molecular pathways by which it regulates cellular processes. Understanding the precise role of \u003cem\u003eMRAS\u003c/em\u003e in osteosarcoma might provide novel strategies for targeted treatments aimed at impeding tumor proliferation and improving patients' well-being.\u003c/p\u003e \u003cp\u003eThe results obtained in this study strongly indicate the potential therapeutic benefits of small molecule compounds in the management of osteosarcoma. The efficacy of these compounds varies depending on the specific risk factors present in individual patients. However, a comprehensive exploration of the mechanisms through which these drugs operate in osteosarcoma is expected to generate valuable insights. This, in turn, could pave the way for innovative treatment strategies that effectively restrict tumor growth and improve patient outcomes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThis study acknowledges several important limitations. Firstly, despite the bioinformatics analysis revealing a correlation between the expression of seven mitophagy-related genes and osteosarcoma, the precise mechanism by which these genes contribute to mitophagy and influence osteosarcoma remains unclear. Secondly, the validation process was limited to in vitro assessment of MRAS genes, without conducting complementary in vivo studies. Lastly, while the primary focus was on elucidating the role of MRAS in osteosarcoma, the study somewhat overlooked the activities of the other genes involved. Prospective cellular or animal investigations are necessary further to investigate the functional processes of these genes with osteosarcoma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eAll authors report no conflicts of interest in this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003ePX, XC, and JY contributed to the study\u0026rsquo;s concept and design. JJ and KL YH participated in gathering and analyzing the data. Administrative, technical, and material support was provided by WZ, JZ, and YW. The manuscript, authored primarily by TW, TL, and PX, benefited from the valuable input of all co-authors. Every author contributed to the article\u0026rsquo;s development and unanimously approved the final draft for submission\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Key Projects of Jiangxi Provincial Department of Education (No. GJJ210105 to Xigao Cheng).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of The Second Affiliated Hospital of Nanchang University granted approval for this research (Review (2020) No. (115)), ensuring that all procedures were carried out in strict adherence to established guidelines and regulations. All participants gave their informed consent to participate in the study.\u003c/p\u003e\n\u003cp\u003eAcknowledgments:\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the reviewers who participated in the review and MJEditor (www.mjeditor.com) for its linguistic assistance during the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIsakoff MS, Bielack SS, Meltzer P, et al. Osteosarcoma: current treatment and a collaborative pathway to success. J Clin Oncol. 2015;33(27):3029\u0026ndash;3035. doi:10.1200/JCO.2014.59.4895\u003c/li\u003e\n\u003cli\u003eChui MH, Kandel RA, Wong M, Griffin AM, Bell RS, Blackstein ME, Wunder JS, Dickson BC. Histopathologic Features of Prognostic Significance in High-Grade Osteosarcoma. Arch Pathol Lab Med.2016; 140:1231\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eArndt C, Rose P, Folpe A, Laack N. Common musculoskeletal tumours of childhood and adolescence. 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Nat Struct Mol Biol. 2022 Oct;29(10):966-977. doi: 10.1038/s41594-022-00841-4. \u003c/li\u003e\n\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4271624/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4271624/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOsteosarcoma (OS) is a highly aggressive malignancy characterized by a poor prognosis. Mitochondrial autophagy (mitophagy) has been implicated in tumor initiation, progression, and response to therapy, highlighting it a potential prognostic indicator and therapeutic target in cancers. Despite this, the precise mechanisms underlying mitophagy in osteosarcoma remain enigmatic. This research aims to develop a mitophagy-associated signature to guide therapeutic strategies and prognosis estimations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eClinical and transcriptome data for patients with osteosarcoma and skeletal muscle tissue were retrieved from UCSC Xena and GTEx. Mitophagy-related genes (MRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) website. A predictive risk model was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Cox regression analysis. To delve into the fundamental gene expression mechanisms, we employed Gene Ontology (GO), KEGG, and Gene Set Enrichment Analysis (GSEA). Moreover, the different immune-related activities between the two groups were investigated to ascertain the efficacy of immunotherapy. Lastly, the functional analysis of the key risk gene MRAS was carried out \u003cem\u003evia in vitro\u003c/em\u003e experiments and a pan-cancer analysis and potential small molecule drugs that may target MRAS were screened through molecular docking.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on seven mitophagy-related prognostic gene signatures, osteosarcoma patients were stratified into high- and low-risk categories. The predictive model exhibited strong prognostic capability, as evidenced by Kaplan-Meier analysis, time-dependent AUC, and Nomogram. Notably, compared to the low-risk group, individuals in the high-risk group exhibited lower stromal, immune, and estimate scores.The infiltration of immune cells in high-risk group decreased. Further evidence supporting MRAS's protective role against osteosarcoma was shown \u003cem\u003ein vitro\u003c/em\u003e, where upregulating its expression could suppress the proliferation, migration, and invasion of osteosarcoma cells while stimulating their apoptosis. Pan-cancer analysis further demonstrated its role in a variety of tumors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study identified a mitophagy-related prognostic signature and elucidated the impact of MRAS on osteosarcoma cells. Consequently, it opened up fresh avenues for clinical prognosis prediction and established a basis for precision therapy in osteosarcoma.\u003c/p\u003e","manuscriptTitle":"Identification of a novel mitophagy-related signature for predicting clinical prognosis and immunotherapy of osteosarcoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 21:00:37","doi":"10.21203/rs.3.rs-4271624/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":"b568311c-ef1e-4bc4-b198-9d527bdc3e17","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-28T10:46:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 21:00:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4271624","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4271624","identity":"rs-4271624","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}