Identification of Two Novel Immune Subtypes Characterized by Distinct Prognosis and Tumor Microenvironment in Osteosarcoma

Yao, Shunhan; Deng, Meiling; Du, Xiaojing; Chen, Qingfeng; Huang, Rongzhi

doi:10.1155/2022/2181525

Cited by 1 publication

(1 citation statement)

References 42 publications

(45 reference statements)

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“…We found significantly higher levels of immune cells in the low-risk group than that in the high-risk group, with the exception of M0 macrophages. Previous evidence suggests that prolonged immune responses and the composition of the tumor microenvironment are significant factors that determine OS prognosis [ 41 , 42 ]. Tan et al previously reported that the tumor microenvironment was helpful in identifying the signature of immunotherapy-relevant and prognostic genes [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

A risk score model based on endoplasmic reticulum stress related genes for predicting prognostic value of osteosarcoma

Zhao

Gao

Fan

et al. 2023

BMC Musculoskelet Disord

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Background We aimed to establish an osteosarcoma prognosis prediction model based on a signature of endoplasmic reticulum stress-related genes. Methods Differentially expressed genes (DEGs) between osteosarcoma with and without metastasis from The Cancer Genome Atlas (TCGA) database were mapped to ERS genes retrieved from Gene Set Enrichment Analysis to select endoplasmic reticulum stress-related DEGs. Subsequently, we constructed a risk score model based on survival-related endoplasmic reticulum stress DEGs and a nomogram of independent survival prognostic factors. Based on the median risk score, we stratified the samples into high- and low-risk groups. The ability of the model was assessed by Kaplan–Meier, receiver operating characteristic curve, and functional analyses. Additionally, the expression of the identified prognostic endoplasmic reticulum stress-related DEGs was verified using real-time quantitative PCR (RT-qPCR). Results In total, 41 endoplasmic reticulum stress-related DEGs were identified in patients with osteosarcoma with metastasis. A risk score model consisting of six prognostic endoplasmic reticulum stress-related DEGs (ATP2A3, ERMP1, FBXO6, ITPR1, NFE2L2, and USP13) was established, and the Kaplan–Meier and receiver operating characteristic curves validated their performance in the training and validation datasets. Age, tumor metastasis, and the risk score model were demonstrated to be independent prognostic clinical factors for osteosarcoma and were used to establish a nomogram survival model. The nomogram model showed similar performance of one, three, and five year-survival rate to the actual survival rates. Nine immune cell types in the high-risk group were found to be significantly different from those in the low-risk group. These survival-related genes were significantly enriched in nine Kyoto Encyclopedia of Genes and Genomes pathways, including cell adhesion molecule cascades, and chemokine signaling pathways. Further, RT-qPCR results demonstrated that the consistency rate of bioinformatics analysis was approximately 83.33%, suggesting the relatively high reliability of the bioinformatics analysis. Conclusion We established an osteosarcoma prediction model based on six prognostic endoplasmic reticulum stress-related DEGs that could be helpful in directing personalized treatment.

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Section: Discussionmentioning

confidence: 99%