Identification of a Glycolysis-Related LncRNA Signature to Predict Survival in Diffuse Glioma Patients

Glycolysis refers to one of the critical phenotypes of tumor cells, regulating tumor cell phenotypes and generating sufficient energy for glioma cells. A range of noticeable genes [such as isocitrate dehydrogenase (IDH), phosphatase, and tensin homolog (PTEN), or Ras] overall impact cell proliferation, invasion, cell cycle, and metastasis through glycolysis. Moreover, long non-coding RNAs (LncRNAs) are increasingly critical to disease progression. Accordingly, this study aimed to identify whether glycolysis-related LncRNAs have potential prognostic value for glioma patients. First, co-expression network between glycolysis-related protein-coding RNAs and LncRNAs was established according to Pearson correlation (Filter: |r| > 0.5 & P < 0.001). Furthermore, based on univariate Cox regression, the Least Absolute Shrinkage and Selection Operator (LASSO) analysis and multivariate Cox regression, a predictive model were built; vital glycolysis-related LncRNAs were identified; the risk score of every single patient was calculated. Moreover, receiver operating characteristic (ROC) curve analysis, gene set enrichment analysis (GSEA), GO and KEGG enrichment analysis were performed to assess the effect of risk score among glioma patients. 685 cases (including RNA sequences and clinical information) from two different cohorts of the Chinese Glioma Genome Atlas (CGGA) database were acquired. Based on the mentioned methods, the risk score calculation formula was yielded as follows: Risk score = (0.19 × EXPFOXD2-AS1) + (−0.27 × EXPAC062021.1) + (−0.16 × EXPAF131216.5) + (−0.05 × EXPLINC00844) + (0.11 × EXPCRNDE) + (0.35 × EXPLINC00665). The risk score was independently related to prognosis, and every single mentioned LncRNAs was significantly related to the overall survival of patients. Moreover, functional enrichment analysis indicated that the biologic process of the high-risk score was mainly involved in the cell cycle and DNA replication signaling pathway. This study confirmed that glycolysis-related LncRNAs significantly impact poor prognosis and short overall survival and may act as therapeutic targets in the future.

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Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.11273 ◽

2021 ◽

Vol 9 ◽

pp. e11273

Author(s):

Lei Yang ◽

Weilong Yin ◽

Xuechen Liu ◽

Fangcun Li ◽

Li Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Cycle ◽

Overall Survival ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Enrichment Score ◽

Hub Genes ◽

Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

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Identification and Validation of a Hypoxia-related lncRNA Signature for Prognostic Prediction in Endometrial Cancer

10.21203/rs.3.rs-633056/v1 ◽

2021 ◽

Author(s):

Hongyang Liu ◽

Junhu Wan ◽

Quanling Feng ◽

Jingyu Li ◽

Jun Liu ◽

...

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Roc Curve Analysis ◽

Gene Set Enrichment ◽

Cox Regression Analysis ◽

Selection Operator

Abstract Background: Endometrial cancer (EC) is one of the most common types of gynecological cancer. Hypoxia is an important clinical feature and regulates various tumor processes. However, the prognostic value of hypoxia-related lncRNA in EC remains to be further elucidated. Here, we aimed to characterize the molecular features of EC by the development of a classification system based on the expression profile of hypoxia-related lncRNA.Methods: Univariate Cox regression analysis was used to identify hypoxia-related lncRNAs associated with overall survival. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct gene signature. Multivariate Cox regression analysis and receiver operating characteristic (ROC) curve analysis were also performed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEEG) pathway, and Gene Set Enrichment Analysis (GESA) were used to identify hypoxia-related lncRNA pathway. Western blot and real-time PCR were used to detect target gene expression. The cell proliferation was determined by using WST-1 assay.Results: Based on univariate Cox regression analysis, we identified 17 hypoxia-related lncRNAs significantly associated with overall survival. Next, the least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct a multigene signature in the TCGA EC cohort. The risk score was confirmed as an independent predictor for overall survival in multivariate Cox regression analysis and receiver operating characteristic (ROC) curve analysis. Besides, the survival time of EC patients in different risk group was significantly correlated to clinicopathologic factors, such as age, stage and grade. Furthermore, hypoxia-related lncRNA associated with the high-risk group were involved in various aspects of the malignant progression of EC via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEEG) pathway, and Gene Set Enrichment Analysis (GESA). Besides, using CIBERSORT analysis, we found a different immune cell environment characterization of EC between different cluster and risk group. Moreover, the risk score was closely correlated to immunotherapy response, microsatellite instability and tumor mutation burden (TMB). Finally, we select one hypoxia-related lncRNA SOS1-IT1 to validate its role in hypoxia and EC progression. Interestingly, we found SOS1-IT1 was overexpressed in tumor tissues, and closely correlated with clinicopathological parameters of EC. The expression level of SOS1-IT1 was significantly increased under hypoxia condition. Additionally, the important hypoxia regulatory factor HIF-1α can directly bind SOS1-IT1 promoter region, and affect its expression level. Conclusions: In summary, this study established a new EC classification based on the hypoxia-related lncRNA signature, thereby provide a novel sight to understand the potential mechanism of human EC development.

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A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

10.21203/rs.3.rs-335140/v1 ◽

2021 ◽

Author(s):

Yanjia Hu ◽

Jing Zhang ◽

Jing Chen

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML)

Frontiers in Genetics ◽

10.3389/fgene.2021.681867 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chunxia Zhao ◽

Yulu Wang ◽

Famei Tu ◽

Shuai Zhao ◽

Xiaoying Ye ◽

...

Keyword(s):

Cox Regression ◽

Survival Curve ◽

Pearson Correlation ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Kaplan Meier ◽

Non Coding Rna ◽

Novel Biomarkers ◽

Meier Survival Curve

BackgroundSome studies have proven that autophagy and lncRNA play important roles in AML. Several autophagy related lncRNA signatures have been shown to affect the survival of patients in some other cancers. However, the role of autophagy related lncRNA in AML has not been explored yet. Hence, this study aims to find an autophagy related lncRNA signature that can affect survival for AML patients.MethodA Pearson correlation analysis, a Kaplan–Meier survival curve, a univariate cox regression, and a multivariate cox regression were performed to establish an autophagy related lncRNA signature. A univariate cox regression, a multivariate cox regression, a Kaplan–Meier survival curve, and a ROC curve were applied to confirm if the signature is an independent prognosis for AML patients. The relationship between the signature and the clinical features was explored by using a T test. Gene Set Enrichment Analysis (GSEA) was used to investigate the potential tumor related pathways.ResultsA four-autophagy related lncRNA (MIR133A1HG, AL359715.1, MIRLET7BHG, and AL356752.1) signature was established. The high risk score based on signature was related to the short survival time of AML patients. The signature was an independent factor for the prognosis for AML patients (HR = 1.684, 95% CI = 1.324–2.142, P < 0.001). The signature was correlated with age, leukocyte numbers, and FAB (M3 or non-M3). The P53, IL6/JAK/STAT3, TNF-α, INF-γ, and IL2/STAT5 pathways might contribute to the differences between the risk groups based on signature in AML.ConclusionThe four autophagy related lncRNAs and their signature might be novel biomarkers for predicting the survival of AML patients. Some biological pathways might be the potential mechanisms of the signature for the survival of AML patients.

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Comprehensive Analysis of Cell Cycle-Related Genes in Patients With Prostate Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.796795 ◽

2022 ◽

Vol 11 ◽

Author(s):

Zehua Liu ◽

Rongfang Pan ◽

Wenxian Li ◽

Yanjiang Li

Keyword(s):

Prostate Cancer ◽

Cell Cycle ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Functional Enrichment ◽

Cancer Center ◽

Clinical Prognosis ◽

Normal Tissues

This study aimed to identify critical cell cycle-related genes (CCRGs) in prostate cancer (PRAD) and to evaluate the clinical prognostic value of the gene panel selected. Gene set variation analysis (GSVA) of dysregulated genes between PRAD and normal tissues demonstrated that the cell cycle-related pathways played vital roles in PRAD. Patients were classified into four clusters, which were associated with recurrence-free survival (RFS). Moreover, 200 prognostic-related genes were selected using the Kaplan–Meier (KM) survival analysis and univariable Cox regression. The prognostic CCRG risk score was constructed using random forest survival and multivariate regression Cox methods, and their efficiency was validated in Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70770. We identified nine survival-related genes: CCNL2, CDCA5, KAT2A, CHTF18, SPC24, EME2, CDK5RAP3, CDC20, and PTTG1. Based on the median risk score, the patients were divided into two groups. Then the functional enrichment analyses, mutational profiles, immune components, estimated half-maximal inhibitory concentration (IC50), and candidate drugs were screened of these two groups. In addition, the characteristics of nine hub CCRGs were explored in Oncomine, cBioPortal, and the Human Protein Atlas (HPA) datasets. Finally, the expression profiles of these hub CCRGs were validated in RWPE-1 and three PRAD cell lines (PC-3, C4-2, and DU-145). In conclusion, our study systematically explored the role of CCRGs in PRAD and constructed a risk model that can predict the clinical prognosis and immunotherapeutic benefits.

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Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.780601 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ya Jun Liu ◽

Alphonse Houssou Hounye ◽

Zheng Wang ◽

Xiaowei Liu ◽

Jun Yi ◽

...

Keyword(s):

Risk Score ◽

Immune Cell ◽

Noncoding Rnas ◽

Enrichment Analysis ◽

Long Noncoding Rnas ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Roc Curve Analysis ◽

Good Reliability ◽

Normal Tissues

Cholangiocarcinoma (CCA) is featured by common occurrence and poor prognosis. Autophagy is a biological process that has been extensively involved in the progression of tumors. Long noncoding RNAs (lncRNAs) have been discovered to be critical in diagnosing and predicting various tumors. It may be valuable to elaborate autophagy-related lncRNAs (ARlncRNAs) in CCA, and indeed, there are still few studies concerning the role of ARlncRNAs in CCA. Here, a prognostic ARlncRNA signature was constructed to predict the survival outcome of CCA patients. Through identification, three differentially expressed ARlncRNAs (DEARlncRNAs), including CHRM3.AS2, MIR205HG, and LINC00661, were screened and were considered predictive signatures. Furthermore, the overall survival (OS) of patients with high-risk scores was significantly lower than that of patients with low scores. Interestingly, the risk score was an independent factor for the OS of patients with CCA. Moreover, receiver operating characteristic (ROC) curve analysis showed that the screened and constructed prognosis signature for 1 year (AUC = 0.884), 3 years (AUC =0.759), and 5 years (AUC = 0.788) presented a high score of accuracy in predicting OS of CCA patients. Gene set enrichment analysis (GSEA) revealed that the three DEARlncRNAs were significantly enriched in CCA-related signaling pathways, including “pathways of basal cell carcinoma”, “glycerolipid metabolism”, etc. Quantitative real-time PCR (qRT-PCR) showed that expressions of CHRM3.AS2, MIR205HG, and LINC00661 were higher in CCA tissues than those in normal tissues, similar to the trends detected in the CCA dataset. Furthermore, Pearson’s analysis reported an intimate correlation of the risk score with immune cell infiltration, indicating a predictive value of the signature for the efficacy of immunotherapy. In addition, the screened lncRNAs were found to have the ability to modulate the expression of mRNAs by interacting with miRNAs based on the established lncRNA-miRNA-mRNA network. In conclusion, our study develops a novel nomogram with good reliability and accuracy to predict the OS of CCA patients, providing a significant guiding value for developing tailored therapy for CCA patients.

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Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

2021 ◽

Vol 16 ◽

Author(s):

Dongqing Su ◽

Qianzi Lu ◽

Yi Pan ◽

Yao Yu ◽

Shiyuan Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

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Clinicopathological signature and prognostic value of RNA:m5C methyltransferases in gliomas

10.21203/rs.2.22601/v1 ◽

2020 ◽

Author(s):

Peng Wang ◽

Kai Huang ◽

Miaojing Wu ◽

Qing Hu ◽

Chuming Tao ◽

...

Keyword(s):

Overall Survival ◽

Risk Score ◽

Prognostic Value ◽

Cox Regression ◽

Roc Curves ◽

Clinicopathological Features ◽

Gene Set Enrichment Analysis ◽

Consensus Clustering ◽

Score Model ◽

Genome Atlas

Abstract Background: Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m5C) methyltransferases has recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m5C methyltransferases in glioma remains unclear. This study investigated RNA: m5C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas. Methods: We systematically studied the RNA-sequence data of RNA:m5C methyltransferases underlying gliomas in the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk score model. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors. Results: Consensus clustering of RNA:m5C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, Functional annotations demonstrated that RNA:m5C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m5C methyltransferase genes were screened to construct a risk score model which can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome. Conclusion: We demonstrated the role of RNA:m5C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy.

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Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

BioMed Research International ◽

10.1155/2020/3813546 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Yun Zhong ◽

Zhe Liu ◽

Dangchi Li ◽

Qinyuan Liao ◽

Jingao Li

Keyword(s):

Gene Expression ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Test Set ◽

Cox Regression Analysis

Background. An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods. Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results. In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion. In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

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Establishment of an Autophagy-Related Clinical Prognosis Model for Predicting the Overall Survival of Osteosarcoma

BioMed Research International ◽

10.1155/2021/5428425 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Jianyi Li ◽

Xiaojie Tang ◽

Yukun Du ◽

Jun Dong ◽

Zheng Zhao ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Molecular Mechanisms ◽

Cox Regression ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Data Set ◽

Primary Tumor Site ◽

Clinical Prognosis

Purpose. Osteosarcoma is the most common primary and highly invasive bone tumor in children and adolescents. The purpose of this study is to construct a multi-gene expression feature related to autophagy, which can be used to predict the prognosis of patients with osteosarcoma. Materials and methods. The clinical and gene expression data of patients with osteosarcoma were obtained from the target database. Enrichment analysis of autophagy-related genes related to overall survival (OS-related ARGs) screened by univariate Cox regression was used to determine OS-related ARGs function and signal pathway. In addition, the selected OS-related ARGs were incorporated into multivariate Cox regression to construct prognostic signature for the overall survival (OS) of osteosarcoma. Use the dataset obtained from the GEO database to verify the signature. Besides, gene set enrichment analysis (GSEA) were applied to further elucidate the molecular mechanisms. Finally, the nomogram is established by combining the risk signature with the clinical characteristics. Results. Our study eventually included 85 patients. Survival analysis showed that patients with low riskScore had better OS. In addition, 16 genes were included in OS-related ARGs. We also generate a prognosis signature based on two OS-related ARGs. The signature can significantly divide patients into low-risk groups and high-risk groups, and has been verified in the data set of GEO. Subsequently, the riskScore, primary tumor site and metastasis status were identified as independent prognostic factors for OS and a nomogram were generated. The C-index of nomogram is 0.789 (95% CI: 0.703~0.875), ROC curve and calibration chart shows that nomogram has a good consistency between prediction and observation of patients. Conclusions. ARGs was related to the prognosis of osteosarcoma and can be used as a biomarker of prognosis in patients with osteosarcoma. Nomogram can be used to predict OS of patients and improve treatment strategies.

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