Association of an eight-gene signature prognosis model with tumor immunity in medulloblastoma
Abstract Background The tumor microenvironment (TME) plays an important role in cancer progression. We investigated TME-specific gene signatures and established a risk score to predict the outcome of medulloblastoma (MB) patients. Methods We evaluated TME parameters of 240 MB patients at Beijing Tiantan Hospital Capital Medical University with the ESTIMATE algorithm. Co-expression network analysis of differentially expressed and weighted genes (WGCNA) was used to identify intersecting genes. Using least absolute shrinkage and selection operator regression and backward stepwise regression we obtained a TME-associated risk score (TMErisk) based on eight prognostic gene signatures (CEBPB, OLFML2B, GGTA1, GZMA, TCIM, OLFML3, NAT1, and CD1C), verified in a GEO dataset (GSE85217). Results The correlation between TMErisk and TME, immune checkpoint, mRNAsi, and tumor mutation burden (TMB) was analyzed. MB patients’ response to immunotherapy was evaluated using immune-phenoscore (IPS) and drug sensitivity. A high TMErisk score indicated a worse overall survival. TMErisk scores were negatively correlated with immune cells, immune checkpoints, and human leukocyte antigens. TMErisk scores correlated significantly negatively with TMB and IPS for specific molecular subtypes. Tumor mRNAsi was associated with TME-risk. Conclusions A prognostic model based on TME-specific gene signatures may be used as a biomarker for evaluating prognosis and predicting response to immunotherapy in MB patients..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
ResearchSquare.com - (2024) vom: 22. März Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Han, DongMing [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.21203/rs.3.rs-2723037/v2 |
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| PPN (Katalog-ID): |
XRA042247888 |
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| 520 | |a Abstract Background The tumor microenvironment (TME) plays an important role in cancer progression. We investigated TME-specific gene signatures and established a risk score to predict the outcome of medulloblastoma (MB) patients. Methods We evaluated TME parameters of 240 MB patients at Beijing Tiantan Hospital Capital Medical University with the ESTIMATE algorithm. Co-expression network analysis of differentially expressed and weighted genes (WGCNA) was used to identify intersecting genes. Using least absolute shrinkage and selection operator regression and backward stepwise regression we obtained a TME-associated risk score (TMErisk) based on eight prognostic gene signatures (CEBPB, OLFML2B, GGTA1, GZMA, TCIM, OLFML3, NAT1, and CD1C), verified in a GEO dataset (GSE85217). Results The correlation between TMErisk and TME, immune checkpoint, mRNAsi, and tumor mutation burden (TMB) was analyzed. MB patients’ response to immunotherapy was evaluated using immune-phenoscore (IPS) and drug sensitivity. A high TMErisk score indicated a worse overall survival. TMErisk scores were negatively correlated with immune cells, immune checkpoints, and human leukocyte antigens. TMErisk scores correlated significantly negatively with TMB and IPS for specific molecular subtypes. Tumor mRNAsi was associated with TME-risk. Conclusions A prognostic model based on TME-specific gene signatures may be used as a biomarker for evaluating prognosis and predicting response to immunotherapy in MB patients. | ||
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| 700 | 1 | |a Zou, Wanjing |4 aut | |
| 700 | 1 | |a Liu, Raynald |4 aut | |
| 700 | 1 | |a Hu, Yuhua |4 aut | |
| 700 | 1 | |a Qiu, Xiaoguang |4 aut | |
| 700 | 1 | |a Li, Chunde |4 aut | |
| 700 | 1 | |a Liu, Hailong |4 aut | |
| 700 | 1 | |a Li, Jiankang |4 aut | |
| 700 | 1 | |a Jiang, Tao |4 aut | |
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