Dataset Information

Artificial intelligence reveals dysregulation of osteosarcoma and cuproptosis-related biomarkers, PDHA1, CDKN2A and neutrophils.

ABSTRACT: At present, the impact of cuproptosis-related genes in the study of osteosarcoma is largely unknown. Genome-wide data of osteosarcoma and controls were downloaded from 3 different databases, and specific diagnostic models associated with cuproptosis in osteosarcoma were constructed by support vector machines with artificial intelligence, random forest trees and LASSO regression. Differential analysis of immune cell infiltration was examined using routine blood data from 25,665 cases. Differential expression was examined using immunohistochemistry and PCR. PDHA1 and CDKN2A were obtained as specific cuproptosis-related biomarkers for osteosarcoma after artificial intelligence analysis. PDHA1, CDKN2A and neutrophils were differentially expressed in OS and control groups. PDHA1 and CDKN2A are significantly dysregulated in OS and are able to serve as biomarkers of OS.

SUBMITTER: Jiang J

PROVIDER: S-EPMC10040405 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Artificial intelligence reveals dysregulation of osteosarcoma and cuproptosis-related biomarkers, PDHA1, CDKN2A and neutrophils.

Jiang Jie J Zhan Xinli X Wei Jianxun J Fan Qie Q Li Haowen H Li Hao H Li Shuzhen S Zhao Yong Y Yin Guodong G Tang Lin L Wu Yongxiang Y Lan Mindong M Qin Yijue Y Guo Quan Q Xu Weicheng W Lu Ling L Yang Yanwei Y Zhang Yitian Y Qu Haishun H

Scientific reports 20230326 1

At present, the impact of cuproptosis-related genes in the study of osteosarcoma is largely unknown. Genome-wide data of osteosarcoma and controls were downloaded from 3 different databases, and specific diagnostic models associated with cuproptosis in osteosarcoma were constructed by support vector machines with artificial intelligence, random forest trees and LASSO regression. Differential analysis of immune cell infiltration was examined using routine blood data from 25,665 cases. Differentia ...[more]

PMID: 36967449

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Project description:Osteosarcoma has the worst prognosis among malignant bone tumors, and effective biomarkers are lacking. Our study aims to explore m6A-related and immune-related biomarkers. Gene expression profiles of osteosarcoma and healthy controls were downloaded from multiple public databases, and their m6A-based gene expression was utilized for tumor typing using bioinformatics. Subsequently, a prognostic model for osteosarcoma was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression analysis, and its immune cell composition was calculated using the CIBERSORTx algorithm. We also performed drug sensitivity analysis for these two genes. Finally, analysis was validated using immunohistochemistry. We also examined the RBM15 gene by qRT-PCR in an in vitro experiment. We collected routine blood data from 1738 patients diagnosed with osteosarcoma and 24,344 non-osteosarcoma patients and used two independent sample t tests to verify the accuracy of the CIBERSORTx analysis for immune cell differences. The analysis based on m6A gene expression tumor typing was most reliable using the two typing methods. The prognostic model based on the two genes constituting RNA-binding motif protein 15 (RBM15) and YTDC1 had a much lower survival rate for patients in the high-risk group than those in the low-risk group (P < 0.05). CIBERSORTx immune cell component analysis demonstrated that RBM15 showed a negative and positive correlation with T cells gamma delta and activated natural killer cells, respectively. Drug sensitivity analysis showed that these two genes showed varying degrees of correlation with multiple drugs. The results of immunohistochemistry revealed that the expression of these two genes was significantly higher in osteosarcoma than in paraneoplastic tissues. The results of qRT-PCR experiments showed that the expression of RBM15 was significantly higher in both osteosarcomas than in the control cell lines. Absolute lymphocyte value, lymphocyte percentage, hematocrit and erythrocyte count were lower in osteosarcoma than in the control group (P < 0.001). RBM15 and YTHDC1 can serve as potential prognostic biomarkers associated with m6A in osteosarcoma.

Project description:Acute myeloblastic leukemia (AML) is the most prevalent form of AML in adults. Despite the availability of various treatment options, including radiotherapy and chemotherapy, many patients fail to respond to treatment or relapse. Copper is a necessary cofactor for all organisms; however, it turns toxic when concentrations reach a certain threshold maintained by homeostatic systems that have been conserved through evolution. However, the mechanism through which excess copper triggers cell death remains unknown. In this study, data on long non-coding RNAs (lncRNAs) related to cuproptosis were retrieved from publicly available databases. LASSO and univariate and multivariate Cox regression analyses were performed to establish an lncRNA model associated with cuproptosis specific to AML. To investigate the risk model, the Kaplan-Meier curve, principal component analysis, functional enrichment analysis, and nomographs were employed. The underlying clinicopathological characteristics were determined, and drug sensitivity predictions against the model were identified. Six cuproptosis-related lncRNA-based risk models were identified as the independent prognostic factors. By regrouping patients using a model-based method, we were able to more accurately differentiate patients according to their responses to immunotherapy. In addition, prospective compounds targeting AML subtypes have been identified. Using qRT-PCR, we examined the expression levels of six cuproptosis-associated lncRNAs in 30 clinical specimens. The cuproptosis-associated lncRNA risk-scoring model developed herein has implications in monitoring AML prognosis and in the clinical prediction of the response to immunotherapy. Furthermore, we identified and verified the ceRNA of the cuproptosis-related lncRNA HAGLR/miR-326/CDKN2A regulatory axis using bioinformatic tools. HAGLR is highly expressed in AML and AML cell lines. HAGLR inhibition significantly reduced the proliferation of AML cells and promoted apoptosis. Elesclomol promotes the degradation of CDKN2A and inhibits the proliferation of AML cells. Elesclomol combined with si-HAGLR inhibited the AML progression of AML both in vitro and in vivo.

Dataset Information

Artificial intelligence reveals dysregulation of osteosarcoma and cuproptosis-related biomarkers, PDHA1, CDKN2A and neutrophils.

Publications

Artificial intelligence reveals dysregulation of osteosarcoma and cuproptosis-related biomarkers, PDHA1, CDKN2A and neutrophils.

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