Comprehensive analysis of prognostic tumor microenvironment-related genes in osteosarcoma patients

Abstract

Background Tumor microenvironment (TME) plays an important role in malignant tumors. Our study aimed to investigate the effect of the TME and related genes in osteosarcoma patients. Methods Gene expression profiles and clinical data of osteosarcoma patients were downloaded from the TARGET dataset. ESTIMATE algorithm was used to quantify the immune score. Then, the association between immune score and prognosis was studied. Afterward, a differential analysis was performed based on the high- and low-immune scores to determine TME-related genes. Additionally, Cox analyses were performed to construct two prognostic signatures for overall survival (OS) and disease-free survival (DFS), respectively. Two datasets obtained from the GEO database were used to validate signatures. Results Eighty-five patients were included in our research. The survival analysis indicated that patients with higher immune score have a favorable OS and DFS. Moreover, 769 genes were determined as TME-related genes. The unsupervised clustering analysis revealed two clusters were significantly related to immune score and T cells CD4 memory fraction. In addition, two signatures were generated based on three and two TME-related genes, respectively. Both two signatures can significantly divide patients into low- and high-risk groups and were validated in two GEO datasets. Afterward, the risk score and metastatic status were identified as independent prognostic factors for both OS and DFS and two nomograms were generated. The C-indexes of OS nomogram and DFS nomogram were 0.791 and 0.711, respectively. Conclusion TME was associated with the prognosis of osteosarcoma patients. Prognostic models based on TME-related genes can effectively predict OS and DFS of osteosarcoma patients.

Development and Validation of a Hypoxia-Associated Prognostic Signature Related to Osteosarcoma Metastasis and Immune Infiltration

“…The majority of these genes relate to occurrence or development of cancers and some could serve as the prognostic signature ( Liu et al, 2020 ; Wu et al, 2020 ). In osteosarcoma, several gene signatures related to energy metabolism, tumor microenvironment and immune system have been investigated ( Hong et al, 2020 ; Hu et al, 2020 ; Zhu et al, 2020 ). However, no hypoxia-associated prognostic signature has been established.…”

Analysis of Immune Gene Expression Subtypes Reveals Osteosarcoma Immune Heterogeneity

“…As not all OS patients have greater benefit from immunotherapy, more immune microenvironment characteristics should be incorporated to instruct clinical treatment. Researchers have made great effort to reveal the role of tumor microenvironment and tumor microenvironment-related genes in OS by a series of bioinformatics methods [ 12 – 14 , 23 , 24 ]. In the present study, the ConsensusClusterPlus algorithm was utilized to identify the five reproducible immune subtypes in 87 OS patients from the TARGET database.…”

“…OS is no exception, and the tumor immune microenvironment is closely related to the prognosis of OS patients. Zhang et al first established the prognostic signature based on immune microenvironment-related genes for OS [ 14 ], and Hu et al performed comprehensive analysis of prognostic tumor microenvironment-related genes based on several validated genes [ 23 ]. Song et al identified a set of immune gene signature related to clinical response and was verified based on 45 OSA primary tumors [ 12 ].…”

Immune classification of osteosarcoma

“…There have been many other studies that utilize a deconvolution method to study the tumor microenvironment of osteosarcoma. A number of them use the estimated immune infiltrations calculated using CIBERSORT and/or immune scores calculated using ESTIMATE to find immune-related genes that can predict the prognosis of osteosarcoma [ 22 – 25 ]. Another set of studies find genes with prognostic values by applying Cox model on survival data or performing differentially expressed genes analysis between two groups of interest, and then investigate the relationship between these genes and estimated immune infiltrates [ 26 – 30 ].…”