SMG9 is a novel prognostic-related biomarker in glioma correlating with ferroptosis and immune infiltrates
© 2024 Published by Elsevier Ltd..
Background: Glioma is the most frequent type of malignancy that may damage the brain with high morbidity and mortality rates and patients' prognoses are still dismal. Ferroptosis, a newly uncovered mode of programmed cell death, may be triggered to destroy glioma cells. Nevertheless, the significance of ferroptosis-related genes (FRGs) in predicting prognosis in glioma individuals is still a mystery.
Methods: The CGGA (The Chinese Glioma Atlas), GEO (Gene Expression Omnibus), and TCGA (The Cancer Genome Atlas) databases were all searched to obtain the glioma expression dataset. First, TCGA was searched to identify differentially expressed genes (DEGs). This was followed by a machine learning algorithm-based screening of the glioma's most relevant genes. Additionally, these genes were subjected to Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional enrichment analyses. The chosen biological markers were then submitted to single-cell, immune function, and gene set enrichment analysis (GSEA). In addition, we performed functional enrichment and Mfuzz expression profile clustering on the most promising biological markers to delve deeper into their regulatory mechanisms and assess their clinical diagnostic capacities.
Results: We identified 4444 DEGs via differential analysis and 564 FRGs from the FerrDb database. The two were subjected to intersection analysis, which led to the discovery of 143 overlapping genes. After that, glioma biological markers were identified in fourteen genes by the use of machine learning methods. In terms of its use for clinical diagnosis, SMG9 stands out as the most significant among these biomarkers.
Conclusion: In light of these findings, the identification of SMG9 as a new biological marker has the potential to provide information on the mechanism of action and the effect of the immune milieu in glioma. The promise of SMG9 in glioma prognosis prediction warrants more study.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Heliyon - 10(2024), 4 vom: 29. Feb., Seite e25716 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dai, Yong [VerfasserIn] |
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Date Revised 24.02.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.heliyon.2024.e25716 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368745821 |
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| 520 | |a © 2024 Published by Elsevier Ltd. | ||
| 520 | |a Background: Glioma is the most frequent type of malignancy that may damage the brain with high morbidity and mortality rates and patients' prognoses are still dismal. Ferroptosis, a newly uncovered mode of programmed cell death, may be triggered to destroy glioma cells. Nevertheless, the significance of ferroptosis-related genes (FRGs) in predicting prognosis in glioma individuals is still a mystery | ||
| 520 | |a Methods: The CGGA (The Chinese Glioma Atlas), GEO (Gene Expression Omnibus), and TCGA (The Cancer Genome Atlas) databases were all searched to obtain the glioma expression dataset. First, TCGA was searched to identify differentially expressed genes (DEGs). This was followed by a machine learning algorithm-based screening of the glioma's most relevant genes. Additionally, these genes were subjected to Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional enrichment analyses. The chosen biological markers were then submitted to single-cell, immune function, and gene set enrichment analysis (GSEA). In addition, we performed functional enrichment and Mfuzz expression profile clustering on the most promising biological markers to delve deeper into their regulatory mechanisms and assess their clinical diagnostic capacities | ||
| 520 | |a Results: We identified 4444 DEGs via differential analysis and 564 FRGs from the FerrDb database. The two were subjected to intersection analysis, which led to the discovery of 143 overlapping genes. After that, glioma biological markers were identified in fourteen genes by the use of machine learning methods. In terms of its use for clinical diagnosis, SMG9 stands out as the most significant among these biomarkers | ||
| 520 | |a Conclusion: In light of these findings, the identification of SMG9 as a new biological marker has the potential to provide information on the mechanism of action and the effect of the immune milieu in glioma. The promise of SMG9 in glioma prognosis prediction warrants more study | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a CGGA | |
| 650 | 4 | |a Ferroptosis | |
| 650 | 4 | |a Glioma | |
| 650 | 4 | |a SMG9 | |
| 650 | 4 | |a TCGA | |
| 700 | 1 | |a Zhang, Huan |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Sujuan |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Chao |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Wenjie |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Yimei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yi |e verfasserin |4 aut | |
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