Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma
Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. Howe...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-01-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.758596/full |
| _version_ | 1819283844240506880 |
|---|---|
| author | Yudong Cao Hecheng Zhu Weidong Liu Lei Wang Wen Yin Jun Tan Quanwei Zhou Zhaoqi Xin Hailong Huang Dongcheng Xie Ming Zhao Xingjun Jiang Jiahui Peng Caiping Ren Caiping Ren |
| author_facet | Yudong Cao Hecheng Zhu Weidong Liu Lei Wang Wen Yin Jun Tan Quanwei Zhou Zhaoqi Xin Hailong Huang Dongcheng Xie Ming Zhao Xingjun Jiang Jiahui Peng Caiping Ren Caiping Ren |
| author_sort | Yudong Cao |
| collection | DOAJ |
| description | Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear.Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N6-methyladenosine (m6A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA).Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m6A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs.Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m6A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets. |
| first_indexed | 2024-12-24T01:37:56Z |
| format | Article |
| id | doaj.art-2e8bb32fa0b34b778c0d04d7e0e0b016 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-24T01:37:56Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-2e8bb32fa0b34b778c0d04d7e0e0b0162022-12-21T17:22:08ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-01-011210.3389/fgene.2021.758596758596Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade GliomaYudong Cao0Hecheng Zhu1Weidong Liu2Lei Wang3Wen Yin4Jun Tan5Quanwei Zhou6Zhaoqi Xin7Hailong Huang8Dongcheng Xie9Ming Zhao10Xingjun Jiang11Jiahui Peng12Caiping Ren13Caiping Ren14Department of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaChangsha Kexin Cancer Hospital, Changsha, ChinaKey Laboratory for Carcinogenesis of Chinese Ministry of Health, School of Basic Medical Science, Cancer Research Institute, Central South University, Changsha, ChinaKey Laboratory for Carcinogenesis of Chinese Ministry of Health, School of Basic Medical Science, Cancer Research Institute, Central South University, Changsha, ChinaDepartment of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaChangsha Kexin Cancer Hospital, Changsha, ChinaDepartment of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Medical Ultrasonics, Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, ChinaDepartment of Neurosurgery, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, ChinaKey Laboratory for Carcinogenesis of Chinese Ministry of Health, School of Basic Medical Science, Cancer Research Institute, Central South University, Changsha, ChinaBackground: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear.Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N6-methyladenosine (m6A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA).Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m6A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs.Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m6A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.https://www.frontiersin.org/articles/10.3389/fgene.2021.758596/fullgenome instabilitylower-grade gliomamulti-omics analysislong noncoding RNAsignature |
| spellingShingle | Yudong Cao Hecheng Zhu Weidong Liu Lei Wang Wen Yin Jun Tan Quanwei Zhou Zhaoqi Xin Hailong Huang Dongcheng Xie Ming Zhao Xingjun Jiang Jiahui Peng Caiping Ren Caiping Ren Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma Frontiers in Genetics genome instability lower-grade glioma multi-omics analysis long noncoding RNA signature |
| title | Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma |
| title_full | Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma |
| title_fullStr | Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma |
| title_full_unstemmed | Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma |
| title_short | Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma |
| title_sort | multi omics analysis based on genomic instability for prognostic prediction in lower grade glioma |
| topic | genome instability lower-grade glioma multi-omics analysis long noncoding RNA signature |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.758596/full |
| work_keys_str_mv | AT yudongcao multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT hechengzhu multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT weidongliu multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT leiwang multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT wenyin multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT juntan multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT quanweizhou multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT zhaoqixin multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT hailonghuang multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT dongchengxie multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT mingzhao multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT xingjunjiang multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT jiahuipeng multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT caipingren multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma AT caipingren multiomicsanalysisbasedongenomicinstabilityforprognosticpredictioninlowergradeglioma |