| Autor: |
Haishun Qu, Jie Jiang, Xinli Zhan, Yunxiao Liang, Quan Guo, Peifeng Liu, Ling Lu, Yanwei Yang, Weicheng Xu, Yitian Zhang, Shaohang Lan, Zeshan Chen, Yuanhong Lu, Yufu Ou, Yijue Qin |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-024-54222-6 |
| Popis: |
Abstract The principal aim of this investigation is to identify pivotal biomarkers linked to the prognosis of osteosarcoma (OS) through the application of artificial intelligence (AI), with an ultimate goal to enhance prognostic prediction. Expression profiles from 88 OS cases and 396 normal samples were procured from accessible public databases. Prognostic models were established using univariate COX regression analysis and an array of AI methodologies including the XGB method, RF method, GLM method, SVM method, and LASSO regression analysis. Multivariate COX regression analysis was also employed. Immune cell variations in OS were examined using the CIBERSORT software, and a differential analysis was conducted. Routine blood data from 20,679 normal samples and 437 OS cases were analyzed to validate lymphocyte disparity. Histological assessments of the study's postulates were performed through immunohistochemistry and hematoxylin and eosin (HE) staining. AI facilitated the identification of differentially expressed genes, which were utilized to construct a prognostic model. This model discerned that the survival rate in the high-risk category was significantly inferior compared to the low-risk cohort (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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