Characterization of a lactate metabolism-related signature for evaluation of immune features and prediction prognosis in glioma
BackgroundGlioma is one of the most typical tumors in the central nervous system with a poor prognosis, and the optimal management strategy remains controversial. Lactate in the tumor microenvironment is known to promote cancer progression, but its impact on clinical outcomes of glioma is largely un...
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| Format: | Article |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2022.1064349/full |
| _version_ | 1797957416531263488 |
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| author | Zhiqiang Wu Zhiqiang Wu Jing Wang Yanan Li Jianmin Liu Zijian Kang Zijian Kang Zijian Kang Wangjun Yan Wangjun Yan |
| author_facet | Zhiqiang Wu Zhiqiang Wu Jing Wang Yanan Li Jianmin Liu Zijian Kang Zijian Kang Zijian Kang Wangjun Yan Wangjun Yan |
| author_sort | Zhiqiang Wu |
| collection | DOAJ |
| description | BackgroundGlioma is one of the most typical tumors in the central nervous system with a poor prognosis, and the optimal management strategy remains controversial. Lactate in the tumor microenvironment is known to promote cancer progression, but its impact on clinical outcomes of glioma is largely unknown.MethodsGlioma RNA-seq data were obtained from TCGA and GCGA databases. Lactate metabolism genes (LMGs) were then evaluated to construct an LMG model in glioma using Cox and LASSO regression. Immune cell infiltration, immune checkpoint gene expression, enriched pathways, genetic alteration, and drug sensitivity were compared within the risk subgroups. Based on the risk score and clinicopathological features, a nomogram was developed to predict prognosis in patients with glioma.ResultsFive genes (LDHA, LDHB, MRS2, SL16A1, and SL25A12) showed a good prognostic value and were used to construct an LMG-based risk score. This risk score was shown as an independent prognostic factor with good predictive power in both training and validation cohorts (p < 0.001). The LMG signature was found to be correlated with the expression of immune checkpoint genes and immune infiltration and could shape the tumor microenvironment. Genetic alteration, dysregulated metabolism, and tumorigenesis pathways could be the underlying contributing factors that affect LMG risk stratification. The patients with glioma in the LMG high-risk group showed high sensitivity to EGFR inhibitors. In addition, our nomogram model could effectively predict overall survival with an area under the curve value of 0.894.ConclusionWe explored the characteristics of LMGs in glioma and proposed an LMG-based signature. This prognostic model could predict the survival of patients with glioma and help clinical oncologists plan more individualized and effective therapeutic regimens. |
| first_indexed | 2024-04-11T00:04:52Z |
| format | Article |
| id | doaj.art-da936ebb46e5426eaedc56722c9d0a6f |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-11T00:04:52Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-da936ebb46e5426eaedc56722c9d0a6f2023-01-09T14:00:49ZengFrontiers Media S.A.Frontiers in Neurology1664-22952023-01-011310.3389/fneur.2022.10643491064349Characterization of a lactate metabolism-related signature for evaluation of immune features and prediction prognosis in gliomaZhiqiang Wu0Zhiqiang Wu1Jing Wang2Yanan Li3Jianmin Liu4Zijian Kang5Zijian Kang6Zijian Kang7Wangjun Yan8Wangjun Yan9Department of Musculoskeletal Surgery, Shanghai Cancer Center, Fudan University, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaNeurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, ChinaNeurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, ChinaNeurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, ChinaDepartment of Musculoskeletal Surgery, Shanghai Cancer Center, Fudan University, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaDepartment of Rheumatology and Immunology, Second Affiliated Hospital of Naval Medical University, Shanghai, ChinaDepartment of Musculoskeletal Surgery, Shanghai Cancer Center, Fudan University, Shanghai, ChinaDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, ChinaBackgroundGlioma is one of the most typical tumors in the central nervous system with a poor prognosis, and the optimal management strategy remains controversial. Lactate in the tumor microenvironment is known to promote cancer progression, but its impact on clinical outcomes of glioma is largely unknown.MethodsGlioma RNA-seq data were obtained from TCGA and GCGA databases. Lactate metabolism genes (LMGs) were then evaluated to construct an LMG model in glioma using Cox and LASSO regression. Immune cell infiltration, immune checkpoint gene expression, enriched pathways, genetic alteration, and drug sensitivity were compared within the risk subgroups. Based on the risk score and clinicopathological features, a nomogram was developed to predict prognosis in patients with glioma.ResultsFive genes (LDHA, LDHB, MRS2, SL16A1, and SL25A12) showed a good prognostic value and were used to construct an LMG-based risk score. This risk score was shown as an independent prognostic factor with good predictive power in both training and validation cohorts (p < 0.001). The LMG signature was found to be correlated with the expression of immune checkpoint genes and immune infiltration and could shape the tumor microenvironment. Genetic alteration, dysregulated metabolism, and tumorigenesis pathways could be the underlying contributing factors that affect LMG risk stratification. The patients with glioma in the LMG high-risk group showed high sensitivity to EGFR inhibitors. In addition, our nomogram model could effectively predict overall survival with an area under the curve value of 0.894.ConclusionWe explored the characteristics of LMGs in glioma and proposed an LMG-based signature. This prognostic model could predict the survival of patients with glioma and help clinical oncologists plan more individualized and effective therapeutic regimens.https://www.frontiersin.org/articles/10.3389/fneur.2022.1064349/fullgliomalactate metabolismimmune infiltrationprognostic modelCGGA |
| spellingShingle | Zhiqiang Wu Zhiqiang Wu Jing Wang Yanan Li Jianmin Liu Zijian Kang Zijian Kang Zijian Kang Wangjun Yan Wangjun Yan Characterization of a lactate metabolism-related signature for evaluation of immune features and prediction prognosis in glioma Frontiers in Neurology glioma lactate metabolism immune infiltration prognostic model CGGA |
| title | Characterization of a lactate metabolism-related signature for evaluation of immune features and prediction prognosis in glioma |
| title_full | Characterization of a lactate metabolism-related signature for evaluation of immune features and prediction prognosis in glioma |
| title_fullStr | Characterization of a lactate metabolism-related signature for evaluation of immune features and prediction prognosis in glioma |
| title_full_unstemmed | Characterization of a lactate metabolism-related signature for evaluation of immune features and prediction prognosis in glioma |
| title_short | Characterization of a lactate metabolism-related signature for evaluation of immune features and prediction prognosis in glioma |
| title_sort | characterization of a lactate metabolism related signature for evaluation of immune features and prediction prognosis in glioma |
| topic | glioma lactate metabolism immune infiltration prognostic model CGGA |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2022.1064349/full |
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