scholarly journals Identification of candidate genes and prognostic value analysis in patients with PDL1-positive and PDL1-negative lung adenocarcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9362
Author(s):  
Xiaoguang Qi ◽  
Chunyan Qi ◽  
Xindan Kang ◽  
Yi Hu ◽  
Weidong Han
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Negative Group ◽  
Hub Genes ◽  
Data Set ◽  
Value Analysis ◽  
Regulatory Pathways ◽  
Positive Group ◽  
Ppi Networks

Background Increasing bodies of evidence reveal that targeting a programmed cell death protein 1 (PD-1) monoclonal antibody is a promising immunotherapy for lung adenocarcinoma. Although PD receptor ligand 1 (PDL1) expression is widely recognized as the most powerful predictive biomarker for anti-PD-1 therapy, its regulatory mechanisms in lung adenocarcinoma remain unclear. Therefore, we conducted this study to explore differentially expressed genes (DEGs) and elucidate the regulatory mechanism of PDL1 in lung adenocarcinoma. Methods The GSE99995 data set was obtained from the Gene Expression Omnibus (GEO) database. Patients with and without PDL1 expression were divided into PDL1-positive and PDL1-negative groups, respectively. DEGs were screened using R. The Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed using the Database for Annotation, Visualization and Integrated Discovery. Protein–protein interaction (PPI) networks of DEGs was visualized using Cytoscape, and the MNC algorithm was applied to screen hub genes. A survival analysis involving Gene Expression Profiling Interactive Analysis was used to verify the GEO results. Mutation characteristics of the hub genes were further analyzed in a combined study of five datasets in The Cancer Genome Atlas (TCGA) database. Results In total, 869 DEGs were identified, 387 in the PDL1-positive group and 482 in the PDL1-negative group. GO and KEGG analysis results of the PDL1-positive group mainly exhibited enrichment of biological processes and pathways related to cell adhesion and the peroxisome proliferators-activated receptors (PPAR) signaling pathway, whereas biological process and pathways associated with cell division and repair were mainly enriched in the PDL1-negative group. The top 10 hub genes were screened during the PPI network analysis. Notably, survival analysis revealed BRCA1, mainly involved in cell cycle and DNA damage responses, to be a novel prognostic indicator in lung adenocarcinoma. Moreover, the prognosis of patients with different forms of lung adenocarcinoma was associated with differences in mutations and pathways in potential hub genes. Conclusions PDL1-positive lung adenocarcinoma and PDL1-negative lung adenocarcinoma might be different subtypes of lung adenocarcinoma. The hub genes might play an important role in PDL1 regulatory pathways. Further studies on hub genes are warranted to reveal new mechanisms underlying the regulation of PDL1 expression. These results are crucial for understanding and applying precision immunotherapy for lung adenocarcinoma.

scholarly journals Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

2021 ◽  
Author(s):  
Zimeng Wei ◽  
Min Zhao ◽  
Linnan Zang
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Analysis Tool ◽  
Hub Genes ◽  
Kegg Analysis ◽  
Ppi Networks

Abstract Background Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Methods Gene expression profiles were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, we detected the expression of hub genes in LUAD and other cancers via GEPIA, Oncomine and HPA databases. Finally, Kaplan-Meier plotter were performed to analyze the prognosis efficacy of hub genes. Results 74 up-regulated and 238 down-regulated DEGs were identified. As for the up-regulated DEGs, KEGG analysis results revealed they were mainly enrolled in protein digestion and absorption. However, the down-regulated DEGs were primarily enriched in cell adhesion molecules. Subsequently, 9 hub genes: KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2, were identified and showed higher expression in both LUAD and other cancers. Finally, all these hub genes were found significantly related to the prognosis of LUAD (p < 0.05). Conclusions Our results screened out the hub genes and pathways that were related to the development and prognosis of LUAD, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of LUAD.

scholarly journals Identification of Molecular Markers Associated with Diagnosis and Prognosis in Lung Adenocarcinoma by Bioinformatics Analysis

2020 ◽  
Author(s):  
Zimeng Wei ◽  
Linnan Zang ◽  
Min Zhao
Keyword(s):  
Gene Expression ◽  
Molecular Markers ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Diagnosis And Treatment ◽  
Analysis Tool ◽  
Hub Genes ◽  
Kegg Analysis ◽  
Diagnosis And Prognosis ◽  
Ppi Networks

Abstract Background：Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Our aim is to identify the candidate key genes and pathways associated with diagnosis and prognosis in LUAD.Methods：In this study, three gene expression profiles GSE118370, GSE32863 and GSE43458 were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, the miRNAs and the hub genes network was constructed via miRWalk. Finally, receiver operating characteristic (ROC) curve and Kaplan-Meier plotter were performed to analyze the diagnosis and prognosis efficacy of hub genes. Results: A total of 311 DEGs were identified, including 74 up-regulated and 238 down-regulated genes. GO analysis results showed that DEGs were mainly enriched in biological processes including composition of extracellular matrix, regulation of angiogenesis and so on. KEGG analysis results revealed DEGs were mainly enrolled in cell adhesion signaling pathway. Subsequently, 10 hub genes, CDC20, CENPF, TPX2, TOP2A, KIAA0101, CDCA7, ASPM, ECT2, UBE2T and COL1A1, were identified. And TOP2A, CDCA7, TPX2 and COL1A1 showed strong relationships with each other and the miRNAs nearby in miRNAs-mRNA network obtained by miRWalk website. Finally, all these 10 hub genes were found significantly related to the diagnosis and prognosis of LUAD (p<0.05). Conclusions: The identification of hub genes in this study will help us to understand the pathogenesis of LUAD, especially the molecular mechanisms of its development. Our results suggested that TOP2A, CDCA7, TPX2 and COL1A1 might present predictive value for the development and prognosis in LUAD, and might be used as potential molecular markers for the diagnosis and treatment of LUAD.

scholarly journals Identification of molecular markers associated with diagnosis and prognosis in lung adenocarcinoma by bioinformatics analysis

2021 ◽  
Author(s):  
Zimeng Wei ◽  
Linnan Zang ◽  
Min Zhao
Keyword(s):  
Gene Expression ◽  
Molecular Markers ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Diagnosis And Treatment ◽  
Analysis Tool ◽  
Hub Genes ◽  
Kegg Analysis ◽  
Diagnosis And Prognosis ◽  
Ppi Networks

Abstract Background: Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment.Methods: Gene expression profiles were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, the miRNAs and the hub genes network was constructed via miRWalk. Finally, receiver operating characteristic (ROC) curve and Kaplan-Meier plotter were performed to analyze the diagnosis and prognosis efficacy of hub genes.Results: A total of 312 DEGs were identified, including 74 up-regulated and 238 down-regulated genes. GO analysis results showed that DEGs were mainly enriched in biological processes including composition of extracellular matrix, regulation of angiogenesis and so on. KEGG analysis results revealed DEGs were mainly enrolled in cell adhesion signaling pathway. Subsequently, 10 hub genes, CDC20, CENPF, TPX2, TOP2A, KIAA0101, CDCA7, ASPM, ECT2, UBE2T and COL1A1, were identified. And TOP2A, CDCA7, TPX2 and COL1A1 showed strong relationships with each other and the miRNAs nearby in miRNAs-mRNA network obtained by miRWalk website. Finally, all these 10 hub genes were found significantly related to the diagnosis and prognosis of LUAD (p<0.05). Conclusions: Our results suggested that TOP2A, CDCA7, TPX2 and COL1A1 might present predictive value for the development and prognosis in LUAD, and might be used as potential molecular markers for the diagnosis and treatment of LUAD.

Identification of Hub Genes Associated with Development of Lung Adenocarcinoma by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Jinglei Li ◽  
Wei Hou
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Network Analysis ◽  
Lung Adenocarcinoma ◽  
Normal Control ◽  
Gene Expression Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Differential Gene Expression Analysis ◽  
Differential Gene

Abstract Purpose: Lung adenocarcinoma (LUAD) has high heterogeneity and poor prognosis, posing a major challenge to human health worldwide. Therefore, it is necessary to improve our understanding of the molecular mechanism of LUAD in order to be able to better predict its prognosis and develop new therapeutic strategies for target genes.Methods: The Cancer Genome Atlas and Gene Expression Omnibus, were selected to comprehensively analyze and explore the differences between LUAD tumors and adjacent normal tissues. Critical gene information was obtained through weighted gene co-expression network analysis (WGCNA), differential gene expression analysis, and survival analysis.Results: Using WGCNA and differential gene expression analysis, 29 differentially expressed genes were screened. The functional annotation analysis showed these genes to be mainly concentrated in heart trabecula formation, regulation of inflammatory response, collagen-containing extracellular matrix, and metalloendopeptidase inhibitor activity. Also, in the protein–protein interaction network analysis, 10 central genes were identified using Cytoscape's CytoHubba plug-in. The expression of CDH5, TEK, TIMP3, EDNRB, EPAS1, MYL9, SPARCL1, KLF4, and TGFBR3 in LUAD tissue was found to be lower than that in the normal control group, while the expression of MMP1 in LUAD tissue was higher than that in the normal control group. According to survival analysis, the low expression of MYL9 and SPARCL1 was correlated with poor overall survival in patients with LUAD. Finally, through the verification of the Oncomine database, it was found that the expression levels of MYL9 and SPARCL1 were consistent with the mRNA levels in LUAD samples, and both were downregulated.Conclusion: Two survival-related genes, MYL9 and SPARCL1, were determined to be highly correlated with the development of LUAD. Both may play an essential role in the development LUAD and may be potential biomarkers for its diagnosis and treatment in the future.

Immune-Related Hub Genes and the Competitive Endogenous RNA Network in Alzheimer’s Disease

2020 ◽  
Vol 77 (3) ◽  
pp. 1255-1265
Author(s):  
Hui Xu ◽  
Jianping Jia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Data Set ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Immune Phenomena ◽  
Competitive Endogenous Rna

Background: The pathogenesis of Alzheimer’s disease (AD) involves various immune-related phenomena; however, the mechanisms underlying these immune phenomena and the potential hub genes involved therein are unclear. An understanding of AD-related immune hub genes and regulatory mechanisms would help develop new immunotherapeutic targets. Objective: The aim of this study was to explore the hub genes and the mechanisms underlying the regulation of competitive endogenous RNA (ceRNA) in immune-related phenomena in AD pathogenesis. Methods: We used the GSE48350 data set from the Gene Expression Omnibus database and identified AD immune-related differentially expressed RNAs (DERNAs). We constructed protein–protein interaction (PPI) networks for differentially expressed mRNAs and determined the degree for screening hub genes. By determining Pearson’s correlation coefficient and using StarBase, DIANA-LncBase, and Human MicroRNA Disease Database (HMDD), the AD immune-related ceRNA network was generated. Furthermore, we assessed the upregulated and downregulated ceRNA subnetworks to identify key lncRNAs. Results: In total, 552 AD immune-related DERNAs were obtained. Twenty hub genes, including PIK3R1, B2M, HLA-DPB1, HLA-DQB1, PIK3CA, APP, CDC42, PPBP, C3AR1, HRAS, PTAFR, RAB37, FYN, PSMD1, ACTR10, HLA-E, ARRB2, GGH, ALDOA, and VAMP2 were identified on PPI network analysis. Furthermore, upon microRNAs (miRNAs) inhibition, we identified LINC00836 and DCTN1-AS1 as key lncRNAs regulating the aforementioned hub genes. Conclusion: AD-related immune hub genes include B2M, FYN, PIK3R1, and PIK3CA, and lncRNAs LINC00836 and DCTN1-AS1 potentially contribute to AD immune-related phenomena by regulating AD-related hub genes.

scholarly journals Cross-species comparison of genomewide gene expression profiles reveals induction of hypoxia-inducible factor-responsive genes in iron-deprived intestinal epithelial cells

2010 ◽  
Vol 299 (5) ◽  
pp. C930-C938 ◽  
Author(s):  
Zihua Hu ◽  
Sukru Gulec ◽  
James F. Collins
Keyword(s):  
Gene Expression ◽  
Iron Deficiency ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Chip ◽  
Intestinal Epithelial ◽  
Data Set ◽  
Regulatory Pathways ◽  
Metal Ion Homeostasis ◽  
Iron Deficient

Molecular mechanisms mediating the induction of metal ion homeostasis-related genes in the mammalian intestine during iron deficiency remain unknown. To elucidate relevant regulatory pathways, genomewide gene expression profiles were determined in fully differentiated human intestinal epithelial (Caco-2) cells. Cells were deprived of iron (or not) for 6 or 18 h, and Gene Chip analyses were subsequently performed (Affymetrix). More than 2,000 genes were differentially expressed; genes related to monosaccharide metabolism, regulation of gene expression, hypoxia, and cell death were upregulated, while those related to mitotic cell cycle were downregulated. A large proportion of induced genes are hypoxia responsive, and promoter enrichment analyses revealed a statistical overrepresentation of hypoxia response elements (HREs). Immunoblot experiments demonstrated a >60-fold increase in HIF2α protein abundance in iron-deprived cells; HIF1α levels were unchanged. Furthermore, comparison of the Caco-2 cell data set with a Gene Chip data set from iron-deficient rat intestine revealed 29 common upregulated genes; the majority are hypoxia responsive, and their promoters are enriched for HREs. We conclude that the compensatory response of the intestinal epithelium to iron deprivation relates to hypoxia and that stabilization of HIF2α may be the primary event mediating metabolic and morphological changes observed during iron deficiency.

scholarly journals Identification of Key Biomarkers and Immune Infiltration in Sciatic Nerve of Diabetic Neuropathy BKS-db/db Mice by Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Yixuan Lin ◽  
Fanjing Wang ◽  
Lianzhi Cheng ◽  
Zhaohui Fang ◽  
Guoming Shen
Keyword(s):  
Diabetic Neuropathy ◽  
Signaling Pathway ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Data Set ◽  
Immune Infiltration ◽  
P53 Signaling ◽  
Ppi Networks ◽  
Key Genes ◽  
P53 Signaling Pathway

Diabetic neuropathy (DN) is one of the chronic complications of diabetes which can cause severe harm to patients. In order to determine the key genes and pathways related to the pathogenesis of DN, we downloaded the microarray data set GSE27382 from Gene Expression Omnibus (GEO) and adopted bioinformatics methods for comprehensive analysis, including functional enrichment, construction of PPI networks, central genes screening, TFs-target interaction analysis, and evaluation of immune infiltration characteristics. Finally, we examined quantitative real- time PCR (qPCR) to validate the expression of hub genes. A total of 318 differentially expressed genes (DEGs) were identified, among which 125 upregulated DEGs were enriched in the mitotic nuclear division, extracellular region, immunoglobulin receptor binding, and p53 signaling pathway, while 193 downregulated DEGs were enriched in ion transport, membrane, synapse, sodium channel activity, and retrograde endocannabinoid signaling. GSEA plots showed that condensed nuclear chromosome kinetochore were the most significant enriched gene set positively correlated with the DN group. Importantly, we identified five central genes (Birc5, Bub1, Cdk1, Ccnb2, and Ccnb1), and KEGG pathway analysis showed that the five hub genes were focused on progesterone-mediated oocyte maturation, cell cycle, and p53 signaling pathway. The proportion of immune cells from DN tissue and normal group showed significant individual differences. In DN samples, T cells CD4 memory resting and dendritic cells resting accounted for a higher proportion, and macrophage M2 accounted for a lower proportion. In addition, all five central genes showed consistent correlation with immune cell infiltration levels. qPCR showed the same expression trend of five central genes as in our analysis. Our research identified key genes related to differential genes and immune infiltration related to the pathogenesis of DN and provided new diagnostic and potential therapeutic targets for DN.

scholarly journals Bioinformatics Analysis for Identifying Micro-RNAs, Long Noncoding RNAs, Transcription Factors, Immune Genes Regulatory Networks, and Potential Therapeutic Agents for Diabetic Cardiomyopathy Using an Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Jiefang Zhou ◽  
Xiaowei Ji ◽  
Xiuwei Shen ◽  
Kefeng Yan ◽  
Peng Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Diabetic Cardiomyopathy ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Therapeutic Agents ◽  
Hub Genes ◽  
Ppi Networks ◽  
Micro Rnas ◽  
Rna Interaction

Abstract Objectives We identified functional genes and studied the underlying molecular mechanisms of diabetic cardiomyopathy (DCM) using bioinformatics tools. Methods Original gene expression profiles were obtained from the GSE21610 and GSE112556 datasets. We used GEO2R to screen the differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on DEGs. Protein–protein interaction (PPI) networks of DEGs were constructed using STRING and hub genes of signaling pathways were identified using Cytoscape. Aberrant hub gene expression was verified using The Cancer Genome Atlas dataset. Connectivity Map was used to predict the drugs that could treat DCM. Results The DEGs in DCM were mainly enriched in the nuclei and cytoplasm and involved in DCM- and chemokine-related signaling pathways. In the PPI network, 32 nodes were chosen as hub nodes and an RNA interaction network was constructed with 517 interactions. The expression of key genes (JPIK3R1, CCR9, XIST, WDFY3.AS2, hsa-miR-144-5p, and hsa-miR-146b-5p) was significantly different between DCM and normal tissues. Danazol, ikarugamycin, and semustine were identified as therapeutic agents against DCM using CMAP. Conclusion The identified hub genes could be associated with DCM pathogenesis and the above drugs could be used for treating DCM.

scholarly journals Identification of Key Genes and miRNA-mRNA Regulatory Pathways in Bronchopulmonary Dysplasia in Preterm Infants by Bioinformatics Methods

2020 ◽  
Author(s):  
Tong Sun ◽  
Haiyang Yu ◽  
Jianhua Fu
Keyword(s):  
Gene Expression ◽  
Preterm Infants ◽  
Bronchopulmonary Dysplasia ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Regulatory Pathways ◽  
Ppi Networks ◽  
Key Genes

Abstract Background: Bronchopulmonary dysplasia (BPD) remains a severe respiratory complication of preterm infants in neonatal intensive care units (NICUs). However, its pathogenesis has been unclear. Bioinformatics analysis, which can help us explore genetic alternations and recognize latent diagnostic biomarkers, has recently promoted the comprehension of the molecular mechanisms underlying disease occurrence and development. Methods: In this study, we identified key genes and miRNA-mRNA regulatory networks in BPD in preterm infants to elucidate the pathogenesis of BPD. We downloaded and analyzed miRNA and gene expression microarray datasets from the Gene Expression Omnibus database (GEO). Differentially expressed miRNA (DEMs) and differentially expressed genes (DEGs) were obtained through NetworkAnalyst. We performed pathway enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery program (DAVID), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then we used the STRING to establish protein–protein interactions and the Cytoscape tool to establish miRNA–mRNA regulatory networks. Results: We identified 19 significant DEMs and 140 and 33 significantly upregulated and downregulated DEGs, respectively. Functional enrichment analysis indicated that significant DEGs were associated with the antigen processing and presentation, and B-cell receptor signaling pathways in BPD. Key DEGs, such as CD19, CD79B, MS4A1, and FCGR2B were selected as hub genes in PPI networks. Conclusions: In this study, we screened out 19 DEMs that might play important roles in the regulatory networks of BPD. Higher expression of miRNAs such as miR-15b-5p, hsa-miR-32-5p, miR-3613-3p, and miR-33a-5p and lower expression of miRNAs such as miR-3960, miR-425-5p, and miR-3202 might be correlated with the process of BPD.

scholarly journals Construction and Validation of a Macrophage-Associated Risk Model for Predicting the Prognosis of Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Kang-Wen Xiao ◽  
Zhi-Bo Liu ◽  
Zi-Hang Zeng ◽  
Fei-Fei Yan ◽  
Ling-Fei Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Risk Group ◽  
Curve Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Set ◽  
Cox Regression Analysis

Background. Osteosarcoma is one of the most common bone tumors among children. Tumor-associated macrophages have been found to interact with tumor cells, secreting a variety of cytokines about tumor growth, metastasis, and prognosis. This study aimed to identify macrophage-associated genes (MAGs) signatures to predict the prognosis of osteosarcoma. Methods. Totally 384 MAGs were collected from GSEA software C7: immunologic signature gene sets. Differential gene expression (DGE) analysis was performed between normal bone samples and osteosarcoma samples in GSE99671. Kaplan–Meier survival analysis was performed to identify prognostic MAGs in TARGET-OS. Decision curve analysis (DCA), nomogram, receiver operating characteristic (ROC), and survival curve analysis were further used to assess our risk model. All genes from TARGET-OS were used for gene set enrichment analysis (GSEA). Immune infiltration of osteosarcoma sample was calculated using CIBERSORT and ESTIMATE packages. The independent test data set GSE21257 from gene expression omnibus (GEO) was used to validate our risk model. Results. 5 MAGs (MAP3K5, PML, WDR1, BAMBI, and GNPDA2) were screened based on protein-protein interaction (PPI), DGE, and survival analysis. A novel macrophage-associated risk model was constructed to predict a risk score based on multivariate Cox regression analysis. The high-risk group showed a worse prognosis of osteosarcoma ( p  < 0.001) while the low-risk group had higher immune and stromal scores. The risk score was identified as an independent prognostic factor for osteosarcoma. MAGs model for diagnosis of osteosarcoma had a better net clinical benefit based on DCA. The nomogram and ROC curve also effectively predicted the prognosis of osteosarcoma. Besides, the validation result was consistent with the result of TARGET-OS. Conclusions. A novel macrophage-associated risk score to differentiate low- and high-risk groups of osteosarcoma was constructed based on integrative bioinformatics analysis. Macrophages might affect the prognosis of osteosarcoma through macrophage differentiation pathways and bring novel sights for the progression and prognosis of osteosarcoma.

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