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Development of an immune-related prognostic index associated with osteosarcoma.

ABSTRACT: Tumor immunity is closely associated with the prognosis of tumors, including osteosarcoma (OS). The aim of the present study was to construct an immune-related prognostic index (PI) to predict the prognosis of OS. Herein, OS expression data were sourced from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. We divided the OS patients into nonmetastatic and metastatic groups, allowing differentially immune-related genes (DIRGs) to be selected. After DIRGs were further investigated by enrichment analysis, four keys prognostic IRGs (CD79A, CSF3R, MTNR1B and NPPC) were identified using a Cox proportional hazards model. Then, an immune-related prognostic index was constructed. Finally, gene set enrichment analysis (GSEA) was employed to further explore the underlying mechanisms. The difference in tumor-infiltrating immune cell (TIIC) abundance was also discussed. In our study, eight upregulated genes and 30 downregulated genes were identified. Several Gene Ontology (GO) terms and the most significantly enriched KEGG pathways were immune-associated functions and pathways. Four genes, including CD79A, CSF3R, MTNR1B and NPPC, were used to establish a risk assessment model for evaluating OS prognosis. GSEA revealed that the risk score was related to cytokine receptor interaction and to the chemokine and B cell receptor signaling pathways. Furthermore, high risk markedly related to the infiltration of several immune cell types, including M2 macrophages, naïve CD4 T cells, and CD8 T cells. In sum, we developed a survival model for OS. The underlying molecular mechanisms of the high-risk group may affect immune-related biological processes and TIICs.Abbreviations TARGET: Therapeutically Applicable Research To Generate Effective Treatments; PI: Prognostic index; OS: Osteosarcoma; DIRGs: Differentially immune-related genes; GSEA: Gene set enrichment analysis; TIIC: Tumor-infiltrating immune cell.

SUBMITTER: Yin CD

PROVIDER: S-EPMC8806312 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIncreasing evidence has shown that hypoxia microenvironment relates to tumor initiation and progression. However, no studies focus on the application of hypoxia-associated genes in predicting osteosarcoma patients' prognosis. This research aims to identify the hypoxia-associated genes related to osteosarcoma metastasis and construct a gene signature to predict osteosarcoma prognosis.MethodsThe differentially expressed messenger RNAs (DEmRNAs) related to osteosarcoma metastasis were identified from Therapeutically Applicable Research to Generate Effective Treatments (Target) database. Univariate and multivariate cox regression analyses were performed to develop the hypoxia-associated prognostic signature. The Kaplan-Meier (KM) survival analyses of patients with high and low hypoxia risk scores were conducted. The nomogram was constructed and the gene signature was validated in the external Gene Expression Omnibus (GEO) cohort. Single-sample gene set enrichment analysis (ssGSEA) was conducted to investigate the relationships between immune infiltration and gene signature.ResultsTwo genes, including decorin (DCN) and prolyl 4-hydroxylase subunit alpha 1 (P4HA1), were involved in the hypoxia-associated gene signature. In training and testing datasets, patients with high-risk scores showed lower survival rates and the gene signature was identified as the independent prognostic factor. Receiver operating characteristic (ROC) curves demonstrated the robustness of signature. Functional analyses of DEmRNAs among high- and low-risk groups revealed that immune-associated functions and pathways were significantly enriched. Furthermore, ssGSEA showed that five immune cells (DCs, macrophages, neutrophils, pDCs, and TIL) and three immune features (CCR, APC co inhibition, and Check-point) were down-regulated in the high-risk group.ConclusionThe current study established and validated a novel hypoxia-associated gene signature in osteosarcoma. It could act as a prognostic biomarker and serve as therapeutic guidance in clinical applications.

Project description:BackgroundLung cancer is the most common malignant tumor in the world, and its prognosis is still not optimistic. The aim of this study was to establish an immune-related gene (IRG) prognostic index (IRGPI) for lung adenocarcinoma (LUAD) based on IRGs, and to explore the prognosis, molecular and immune features, and response to immune checkpoint inhibitor (ICI) therapy in IRGPI-classified different subgroups of LUAD.MethodsBased on the LUAD transcriptome RNA-sequencing data in TCGA database, the differentially expressed genes (DEGs) were selected. Subsequently, DEGs were intersected with IRGs to obtain differentially expressed immune-related genes (DEIRGs). Weighted gene co-expression network analysis (WGCNA) identified hub genes in DEIRGs. Finally, univariate and multivariate Cox regression analyses were used to build an IRGPI model. Subsequently, TCGA patients were divided into high- and low-risk groups, and the survival of patients in different groups was further analyzed. Besides, we validated the molecular and immune characteristics, relationship with immune checkpoints, angiogenesis-related genes, and immune subtypes distribution in different subgroups. Meanwhile, we further validated the response to ICI therapy in different subgroups.ResultsThe IRGPI was constructed based on 13 DEIRGs. Compared with the low-risk group, overall survival (OS) was lower in the high-risk group, and the high-risk score was independently associated with poorer OS. Besides, the high-risk score was associated with cell cycle pathway, high mutation rate of TP53 and KRAS, high infiltration of M0 macrophages, and immunosuppressive state, and these patients had poorer prognosis but the TIDE score of the high-risk group was lower than that of the other group, which means that the high-risk group could benefit more from ICI treatment. In contrast, the low-risk score was related to low mutation rate of TP53 and KRAS, high infiltration of plasma cells, and immunoactive state, and these patients had better prognosis but the low-risk group less benefit from ICI treatment based on the results of TIDE score.ConclusionsIRGPI is a prospective biomarker based on IRGs that can distinguish high- and low-risk groups to predict patient prognosis, help characterize the tumor immune microenvironment, and evaluate the benefit of ICI therapy in LUAD.

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Development of an immune-related prognostic index associated with osteosarcoma.

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