Ferroptosis-related gene signature as a prognostic marker for lower-grade gliomas

Ferroptosis is a newly discovered form of programmed cell death, which has unique biological effects on metabolism and redox biology. In this study, the prognostic value of ferroptosis-related genes was investigated in lower-grade gliomas (LGG). We downloaded the ferroptosis-related genes from the FerrDb dataset. Univariate Cox and LASSO regression analyses were applied to identify genes correlated with overall survival (OS). Subsequently, 12 ferroptosis-related genes were screened to establish the prognostic signature using stepwise multivariate Cox regression. According to the median value of risk scores, patients were divided into low- and high-risk subgroups. The Kaplan-Meier curves showed the high-risk group had a lower OS. The predictive power of the risk model was validated using the CGGA. Functional analysis revealed that the terms associated with plasma membrane receptor complex, immune response and glutamate metabolic process were primarily related to the risk model. Moreover, we established a nomogram that had a strong forecasting ability for the 1-, 3- and 5-year OS. In addition, we compared the risk scores between different clinical features. We also detected infiltration of macrophages and monocytes in different subgroups. Overall, our study identified the prognostic signature of 12 ferroptosis-related genes, which has the potential to predict the prognosis of LGG.     

Profiles of immune-related genes and immune cell infiltration in the tumor microenvironment of diffuse lower-grade gliomas

The tumor microenvironment is highly correlated with tumor occurrence, progress, and prognosis. We aimed to investigate the immune-related gene (IRG) expression and immune infiltration pattern in the tumor microenvironment of lower-grade glioma (LGG). We employed the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores and identify prognostic IRG based on The Cancer Genome Atlas data set. The potential molecular functions of these genes were explored with the help of functional enrichment analysis and the protein–protein interaction network. Remarkably, three cohorts that were downloaded from the Chinese Glioma Genome Atlas database were analyzed to further verify the prognostic values of these genes. Moreover, the Tumor IMmune Estimation Resource (TIMER) algorithm was used to estimate the abundance of infiltrating immune cells and explore the immune infiltration pattern in LGG. And unsupervised cluster analysis determined three clusters of the immune infiltration pattern and indicated that CD8⁺ T cells and macrophages were significantly associated with LGG outcomes. Altogether, our study identified a list of prognostic IRGs and provided a perspective to explore the immune infiltration pattern in LGG.     

Identification of immune-related hub genes and construction of an immune-related gene prognostic index for low-grade glioma

Low-grade glioma (LGG) poses significant management challenges and has a dismal prognosis. While immunotherapy has shown significant promise in cancer treatment, its progress in glioma has confronted with challenges. In our study, we aimed to develop an immune-related gene prognostic index (IRGPI) which could be used to evaluate the response and efficacy of LGG patients with immunotherapy. We included a total of 529 LGG samples from TCGA database and 1152 normal brain tissue samples from the GTEx database. Immune-related differentially expressed genes (DEGs) were screened. Then, we used weighted gene co-expression network analysis (WGCNA) to identify immune-related hub genes in LGG patients and performed Cox regression analysis to construct an IRGPI. The median IRGPI was used as the cut-off value to categorize LGG patients into IRGPI-high and low subgroups, and the molecular and immune mechanism in IRGPI-defined subgroups were analysed. Finally, we explored the relationship between IRGPI-defined subgroups and immunotherapy related indicators in patients after immunotherapy. Three genes (RHOA, NFKBIA and CCL3) were selected to construct the IRGPI. In a survival analysis using TCGA cohort as a training set, patients in the IRGPI-low subgroup had a better OS than those in IRGPI-high subgroup, consistent with the results in CGGA cohort. The comprehensive results showed that IRGPI-low subgroup had a more abundant activated immune cell population and lower TIDE score, higher MSI, higher TMB score, lower T cell dysfunction score, more likely benefit from ICIs therapy. IRGPI is a promising biomarker in the field of LGG ICIs therapy to distinguish the prognosis, the molecular and immunological characteristics of patients.     

Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer

Ovarian cancer (OC) is associated with high mortality rate. However, the correlation between immune microenvironment and prognosis of OC remains unclear. This study aimed to explore prognostic significance of OC tumour microenvironment. The OC data set was selected from the cancer genome atlas (TCGA), and 307 samples were collected. Hierarchical clustering was performed according to the expression of 756 genes. The immune and matrix scores of all immune subtypes were determined, and Kruskal-Wallis test was used to analyse the differences in the immune and matrix scores between OC samples with different immune subtypes. The model for predicting prognosis was constructed based on the expression of immune-related genes. TIDE platform was applied to predict the effect of immunotherapy on patients with OC of different immune subtypes. The 307 OC samples were classified into three immune subtypes A-C. Patients in subtype B had poorer prognosis and lower survival rate. The infiltration of helper T cells and macrophages in microenvironment indicated significant differences between immune subtypes. Enrichment analyses of immune cell molecular pathways showed that JAK–STAT3 pathway changed significantly in subtype B. Furthermore, predictive response to immunotherapy in subtype B was significantly higher than that in subtype A and C. Immune subtyping can be used as an independent predictor of the prognosis of OC patients, which may be related to the infiltration patterns of immune cells in tumour microenvironment. In addition, patients in immune subtype B have superior response to immunotherapy, suggesting that patients in subtype B are suitable for immunotherapy.     

Prognostic significance of ARL9 and its methylation in low-grade glioma

This study aimed to determine the value of ARL9 expression or methylation as a biomarker for LGG survival. We investigated the expression, methylation, prognosis and immune significance of ARL9 through bioinformatics analysis. ARL9 is negatively regulated by ARL9 methylation, leading to its low expression in LGG tissues. Both low ARL9 expression and hypermethylation predicted favorable OS and PFS in LGG patients, according to the TCGA database. Cox regression demonstrated that low ARL9 expression and ARL9 hypermethylation were independent biomarkers for OS. Moreover, three other glioma databases were utilized to verify the prognostic role of ARL9 in LGG, and the similar results were reached. A meta-analysis revealed that low ARL9 expression was closely relevant to better OS. Finally, ARL9 expression exhibited a close correlation with some immune cells, especially CD8+ T cells. ARL9 could constitute a promising prognostic biomarker, and probably plays an important role in immune cell infiltration in LGG.     

Genome‐wide methylomic analyses identify prognostic epigenetic signature in lower grade glioma

Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan‐Meier survival curves and time‐dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two‐CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune‐checkpoint blockade, immunotherapy‐related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.     

Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients

BackgroundThis study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints.MethodLGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature.ResultsImmunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes.ConclusionsThe IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.     

A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas

Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.     

Construction and validation of a TP53-associated immune prognostic model for gastric cancer

Increasing evidence indicates that TP53 mutation impacts the patients' prognosis by regulating the gastric cancer (GC) immunophenotype. An immune prognostic signature (IPS) was constructed based on TP53 status. The effects of the IPS on the immune microenvironment of GC were analyzed. We also constructed a nomogram integrating the IPS and other clinical factors. An IPS was constructed in the TCGA cohort and validated in the meta-GEO cohort. TP53 mutation resulted in the downregulation of the immune response in GC. Concretely, high-risk patients were characterized by increased monocyte, macrophage M0 and T cell follicular helper infiltration; increased stromal score, ESTIMATE score and immune score; higher TIM3 and BTLA expression; and decreased dendritic cell and T cell CD4 memory-activated infiltration and tumor purity. The nomogram also showed good predictive performance. These results suggest that the IPS is an effective prognostic indicator for GC patients, which might provide a theoretical foundation for immunotherapy.     

Prognostic and immunological role of Fam20C in pan-cancer

Background: The family with sequence similarity 20-member C (Fam20C) kinase plays important roles in physiopathological process and is responsible for majority of the secreted phosphoproteome, including substrates associated with tumor cell migration. However, it remains unclear whether Fam20C plays a role in cancers. Here, we aimed to analyze the expression and prognostic value of Fam20C in pan-cancer and to gain insights into the association between Fam20C and immune infiltration.Methods: We analyzed Fam20C expression patterns and the associations between Fam20C expression levels and prognosis in pan-cancer via the ONCOMINE, TIMER (Tumor Immune Estimation Resource), PrognoScan, GEPIA (Gene Expression Profiling Interactive Analysis), and Kaplan–Meier Plotter databases. After that, GEPIA and TIMER databases were applied to investigate the relations between Fam20C expression and immune infiltration across different cancer types, especially BLCA (bladder urothelial carcinoma), LGG (brain lower grade glioma), and STAD (stomach adenocarcinoma).Results: Compared with adjacent normal tissues, Fam20C was widely expressed across many cancers. In general, Fam20C showed a detrimental role in pan-cancer, it was positively associated with poor survival of BLCA, LGG, and STAD patients. Specifically, based on TCGA (The Cancer Genome Atlas) database, a high expression level of Fam20C was associated with worse prognostic value in stages T2–T4 and stages N0–N2 in the cohort of STAD patients. Moreover, Fam20C expression had positive associations with immune infiltration, including CD4⁺ T cells, macrophages, neutrophils, and dendritic cells, and other diverse immune cells in BLCA, LGG, and STAD.Conclusion: Fam20C may serve as a promising prognostic biomarker in pan-cancer and has positive associations with immune infiltrates.     

Immune-related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) show multiple functions, including immune response. Recently, the immune-related lncRNAs have been reported in some cancers. We first investigated the immune-related lncRNA signature as a potential target in hepatocellular carcinoma (HCC) survival. The training set (n = 368) and the independent external validation cohort (n = 115) were used. Immune genes and lncRNAs coexpression were constructed to identify immune-related lncRNAs. Cox regression analyses were perfumed to establish the immune-related lncRNA signature. Regulatory roles of this signature on cancer pathways and the immunologic features were investigated. The correlation between immune checkpoint inhibitors and this signature was examined. In this study, the immune-related lncRNA signature was identified in HCC, which could stratify patients into high- and low-risk groups. This immune-related lncRNA signature was correlated with disease progression and worse survival and was an independent prognostic biomarker. Our immune-related lncRNA signature was still a powerful tool in predicting survival in each stratum of age, gender, and tumor stage. This signature mediated cell cycle, glycolysis, DNA repair, mammalian target of rapamycin signaling, and immunologic characteristics (i.e., natural killer cells vs. Th1 cells down, etc). This signature was associated with immune cell infiltration (i.e., macrophages M0, Tregs, CD4 memory T cells, and macrophages M1, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., PD-L1, PD-L2, and IDO1). Our findings suggested that the immune-related lncRNA signature had an important value for survival prediction and may have the potential to measure the response to ICB immunotherapy. This signature may guide the selection of the immunotherapy for HCC.     

Clinical and prognostic implications of an immune‐related risk model based on TP53 status in lung adenocarcinoma

TP53 mutation is the most widespread mutation in lung adenocarcinoma (LUAD). Meanwhile, p53 (encoded by TP53) has recently been implicated in immune responses. However, it is still unknown whether TP53 mutation remodels the tumour microenvironment to influence tumour progression and prognosis in LUAD. In this study, we developed a 6‐gene immune‐related risk model (IRM) to predict the survival of patients with LUAD in The Cancer Genome Atlas (TCGA) cohort based on TP53 status, and the predictive ability was confirmed in 2 independent cohorts. TP53 mutation led to a decreased immune response in LUAD. Further analysis revealed that patients in the high‐index group had observably lower relative infiltration of memory B cells and regulatory T cells and significantly higher relative infiltration of neutrophils and resting memory CD4⁺ T cells. Additionally, the IRM index positively correlated with the expression of critical immune checkpoint genes, including PDCD1 (encoding PD‐1) and CD274 (encoding PD‐L1), which was validated in the Nanjing cohort. Furthermore, as an independent prognostic factor, the IRM index was used to establish a nomogram for clinical application. In conclusion, this IRM may serve as a powerful prognostic tool to further optimize LUAD immunotherapy.     

Pyroptosis‐related molecular classification and immune microenvironment infiltration in breast cancer: A novel therapeutic target

The underlying role of pyroptosis in breast cancer (BC) remains unknown. Herein, we investigated the correlations of 33 pyroptosis‐related genes (PRGs) with immune checkpoints and immune cell infiltrations in BC patients based on The Cancer Genome Atlas cohort (n = 996) and Gene Expression Omnibus cohort (n = 3,262). Enrichment analysis revealed that these PRGs mainly functioned in pyroptosis, inflammasomes and regulation of autophagy pathway. Four prognostic independent PRGs (CASP9, TIRAP, GSDMC and IL18) were identified. Then, cluster 1/2 was recognized using consensus clustering for these four PRGs. Patients from cluster 1 had a favourable prognosis and diverse immune cell infiltrations. A nomogram was developed based on age, TNM stage, tumour subtype and pyroptosis score. Patients with the high‐risk group exhibited worse 5‐year OS, and the result was consistent in the external cohort. Additionally, high‐risk group patients were associated with downregulated immune checkpoint expression. Further analysis suggested that the high‐risk group patients were associated with a higher IC50 of paclitaxel, doxorubicin, cisplatin, methotrexate and vinorelbine. In summarizing, the pyroptosis score‐based nomogram might serve as an independent prognostic predictor and could guide medication for chemotherapy. Additionally, it may bring novel insight into the regulation of tumour immune microenvironment in BC and help to achieve precision immunotherapy.     

Establishment and Validation of an Interferon-Stimulated Genes (ISGs) Prognostic Signature in Pan-cancer Patients: A Multicenter,Real-world Study

Our study aims at developing an interferon-stimulated genes (ISGs) signature that could predict overall survival (OS) in cancer patients, which enrolled a total of 5643 pan-cancer patients. Linear models for microarray data method analysis were conducted to identify the differentially expressed prognostic genes in the global ISGs family. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival analysis were used to test the efficiency of a multi-gene signature in predicting the prognosis of pan-cancer patients. The prognostic performance and potential biological function of gene signature were verified by quantitative real-time PCR in a pan-cancer independent cohort. Three ISGs genes were finally identified to build a classifier, a specific risk score formula, with which patients were classified into the low- or high-risk groups. Time-dependent ROC analyses proved prognostic accuracy. Then, its prognostic value was validated in seven external validation series. A nomogram was constructed to guide the individualized treatment of patients with lung adenocarcinoma. Biological pathway and tumor immune infiltration analysis showed that the signature might cause poor prognosis by blocking NK cell activation. Finally, the signature in our centers was confirmed by real-time quantitative PCR. A robust ISGs-related feature was discovered to effectively classify pan-cancer patients into subgroups with different OS.     

Identification of m6A methyltransferase-related lncRNA signature for predicting immunotherapy and prognosis in patients with hepatocellular carcinoma

N6-methyladenosine (m6A) methyltransferase has been shown to be an oncogene in a variety of cancers. Nevertheless, the relationship between the long non-coding RNAs (lncRNAs) and hepatocellular carcinoma (HCC) remains elusive. We integrated the gene expression data of 371 HCC and 50 normal tissues from The Cancer Genome Atlas (TCGA) database. Differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRs) analysis and univariate regression and Kaplan–Meier (K–M) analysis were performed to identify m6A methyltransferase-related lncRNAs. Three prognostic lncRNAs were selected by univariate and LASSO Cox regression analyses to construct the m6A methyltransferase-related lncRNA signature. Multivariate Cox regression analyses illustrated that this signature was an independent prognostic factor for overall survival (OS) prediction. The Gene Set Enrichment Analysis (GSEA) suggested that the m6A methyltransferase-related lncRNAs were involved in the immune-related biological processes (BPs) and pathways. Besides, we discovered that the lncRNAs signature was correlated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. Tumor Immune Dysfunction and Exclusion (TIDE) analysis revealed that the lncRNAs could predict the clinical response to immunotherapy. Our study had originated a prognostic signature for HCC based on the potential prognostic m6A methyltransferase-related lncRNAs. The present study had deepened the understanding of the TME status of HCC patients and laid a theoretical foundation for the choice of immunotherapy.     

The role of YTH domain containing 2 in epigenetic modification and immune infiltration of pan-cancer

YTH domain containing 2 (YTHDC2) is the largest N6-Methyladenosine (m⁶A) binding protein of the YTH protein family and the only member containing ATP-dependent RNA helicase activity. For further analysing its biological role in epigenetic modification, we comprehensively explored YTHDC2 from gene expression, genetic alteration, protein-protein interaction (PPI) network, immune infiltration, diagnostic value and prognostic value in pan-cancer, using a series of databases and bioinformatic tools. We found that YTHDC2 with Missense mutation could cause a different prognosis in uterine corpus endometrial carcinoma (UCEC), and its different methylation level could lead to a totally various prognosis in adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), lung squamous cell carcinoma (LUSC) and UCEC. The main molecular mechanisms of YTHDC2 focused on catalytic activity, helicase activity, snRNA binding, spliceosome and mRNA surveillance. Additionally, YTHDC2 was notably correlated with tumour immune infiltration. Moreover, YTHDC2 had a high diagnostic value for seven cancer types and a prognostic value for brain lower grade glioma (LGG), rectum adenocarcinoma (READ) and skin cutaneous melanoma (SKCM). Collectively, YTHDC2 plays a significant role in epigenetic modification and immune infiltration and maybe a potential biomarker for diagnosis and prognosis in certain cancers.     

Identification and validation of autophagy-related prognostic signature for head and neck squamous cell carcinoma

BackgroundMany studies have demonstrated that autophagy plays a significant role in regulating tumor growth and progression. However, the effect of autophagy-related genes (ARGs) on the prognosis have rarely been analyzed in head and neck squamous cell carcinoma (HNSCC).MethodsWe obtained differentially expressed ARGs from HNSCC mRNA data in The Cancer Genome Atlas (TCGA) database. And then we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the autophagy-related biological functions. The overall survival (OS)-related and disease specific survival (DSS)-related ARGs were identified by univariate Cox regression analyses. With these genes, we established OS-related and DSS-related risk signature by LASSO regression method, respectively. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, Kaplan-Meier survival curves, clinical correlation analysis, and nomogram. Then we analyzed relationships between risk signature and immune cell infiltration.ResultsWe established the prognostic signatures based on 14 ARGs for OS and 12 ARGs for DSS. The ROC curves, survival analysis, and nomogram validated the predictive accuracy of the models. Clinic correlation analysis showed that the risk group was closely related to Stage, pathological T stage, pathological N stage and human papilloma virus (HPV) subtype. Cox regression demonstrated that the risk score was an independent predictor for the prognosis of HNSCC patients. Furthermore, patients in low-risk score group exhibited higher immunescore and distinct immune cell infiltration than high-risk score group. And we further analysis revealed that the copy number alterations (CNAs) of ARGs-based signature affected the abundance of tumor-infiltrating immune cells.ConclusionIn this study, we identified novel autophagy-related signature for the prediction of OS and DSS in patients with HNSCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and elucidate the important role of autophagy in tumor immune microenvironment (TIME) of HNSCC.     

Construction and validation of a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis and immune infiltration

BackgroundApoptosis played vital roles in the formation and progression of osteosarcoma. However, no studies elucidated the prognostic relationships between apoptosis-associated genes (AAGs) and osteosarcoma.MethodsThe differentially expressed genes associated with osteosarcoma metastasis and apoptosis were identified from GEO and MSigDB databases. The apoptosis-associated prognostic signature was established through univariate and multivariate cox regression analyses. The Kaplan–Meier (KM) survival curve, ROC curve and nomogram were constructed to investigate the predictive value of this signature. CIBERSORT algorithm and ssGSEA were used to explore the relationships between immune infiltration and AAG signature. The above results were validated in another GEO dataset and the expression of AAGs was also validated in osteosarcoma patient samples by immunohistochemistry.ResultsHSPB1 and IER3 were involved in AAG signature. In training and validation datasets, apoptosis-associated risk scores were negatively related to patient survival rates and the AAG signature was regarded as the independent prognostic factor. ROC and calibration curves demonstrated the signature and nomogram were reliable. GSEA revealed the signature related to immune-associated pathways. ssGSEA indicated that one immune cell and three immune functions were significantly dysregulated. The immunohistochemistry analyses of patients’ samples revealed that AAGs were significantly differently expressed between metastasis and non-metastasis osteosarcomas.ConclusionsThe present study identified and validated a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis. It could serve as the potential biomarker and therapeutic targets for osteosarcoma in the future.