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Niu W, Yu H, Fan X, Li S, Sun S, Gong M, Zhang S, Bi W, Chen X, Fang Z. Development of stemness-related signature to optimize prognosis prediction and identify XMD8-85 as a novel therapeutic compound for glioma. Cell Signal 2024; 120:111231. [PMID: 38768760 DOI: 10.1016/j.cellsig.2024.111231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/28/2024] [Accepted: 05/15/2024] [Indexed: 05/22/2024]
Abstract
Glioma is a highly invasive and aggressive type of brain cancer with poor treatment response. Stemness-related transcription factors form a regulatory network that sustains the malignant phenotype of gliomas. We conducted an integrated analysis of stemness-related transcription factors using The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, established the characteristics of stemness-related transcription factors, including Octamer-Binding Protein 4 (OCT4), Meis Homeobox 1 (MEIS1), E2F Transcription Factor 1 (E2F1), Transcription Factor CP2 Like 1 (TFCP2L1), and RUNX Family Transcription Factor 1 (RUNX1). The characteristic of stemness-related transcription factors was identified as an independent prognostic factor for glioma patients. Patients in the high-risk group have a worse prognosis than those in the low-risk group. The glioma microenvironment in the high-risk group exhibited a more active immune status. Single-cell level analysis revealed that stem cell-like cells exhibited stronger intercellular communication than glioma cells. Meanwhile, patients in different risk stratification exhibited varying sensitivities to immunotherapy and small molecule drug therapy. XMD8-85 was more effective in the high-risk group, and its antitumor effects were validated both in vivo and in vitro. Our results indicate that this prognostic feature will assist clinicians in predicting the prognosis of glioma patients, guiding immunotherapy and personalized treatment, as well as the potential clinical application of XMD8-85 in glioma treatment, and helping to develop effective treatment strategies.
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Affiliation(s)
- Wanxiang Niu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, 230031 Hefei, Anhui, China; Science Island Branch, Graduate School of University of Science and Technology of China, No. 96, Jin Zhai Road, 230026 Hefei, Anhui, China
| | - Huihan Yu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, 230031 Hefei, Anhui, China; School of Basic Medical Sciences, Anhui Medical University, No. 81, Meishan Road, Hefei 230032, Anhui, China
| | - Xiaoqing Fan
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, 230031 Hefei, Anhui, China; Science Island Branch, Graduate School of University of Science and Technology of China, No. 96, Jin Zhai Road, 230026 Hefei, Anhui, China
| | - Shuyang Li
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, 230031 Hefei, Anhui, China; School of Basic Medical Sciences, Anhui Medical University, No. 81, Meishan Road, Hefei 230032, Anhui, China
| | - Suling Sun
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, 230031 Hefei, Anhui, China; Science Island Branch, Graduate School of University of Science and Technology of China, No. 96, Jin Zhai Road, 230026 Hefei, Anhui, China
| | - Meiting Gong
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, 230031 Hefei, Anhui, China; School of Basic Medical Sciences, Anhui Medical University, No. 81, Meishan Road, Hefei 230032, Anhui, China
| | - Siyu Zhang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, 230031 Hefei, Anhui, China; Science Island Branch, Graduate School of University of Science and Technology of China, No. 96, Jin Zhai Road, 230026 Hefei, Anhui, China
| | - Wenxu Bi
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, 230031 Hefei, Anhui, China; Science Island Branch, Graduate School of University of Science and Technology of China, No. 96, Jin Zhai Road, 230026 Hefei, Anhui, China
| | - Xueran Chen
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, 230031 Hefei, Anhui, China; Science Island Branch, Graduate School of University of Science and Technology of China, No. 96, Jin Zhai Road, 230026 Hefei, Anhui, China.
| | - Zhiyou Fang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushan Hu Road, 230031 Hefei, Anhui, China; Science Island Branch, Graduate School of University of Science and Technology of China, No. 96, Jin Zhai Road, 230026 Hefei, Anhui, China.
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Sharma A, Wang Y, Ge F, Chen P, Dakal TC, Carro MS, Schmidt-Wolf IGH, Maciaczyk J. Systematic integration of m6A regulators and autophagy-related genes in combination with long non-coding RNAs predicts survival in glioblastoma multiforme. Sci Rep 2023; 13:17232. [PMID: 37821547 PMCID: PMC10567764 DOI: 10.1038/s41598-023-44087-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 10/03/2023] [Indexed: 10/13/2023] Open
Abstract
Glioblastoma multiforme (GBM) is probably the only tumor in which a unique epigenetic alteration, namely methylation of the MGMT gene, possesses direct clinical relevance. Now with the emergence of aberrant N6 methyladenosine (m6A) modifications (the most common epigenetic modification of mRNA, closely linked to the autophagy process) in cancer, the epi-transcriptomic landscape of GBM pathobiology has been expanded. Considering this, herein, we systematically analyzed m6A regulators, assessed their correlation with autophagy-related genes (ATG), and established a long non-coding RNAs (lncRNA)-dependent prognostic signature (m6A-autophagy-lncRNAs) for GBM. Our analysis identified a novel signature of five long non-coding RNAs (lncRNAs: ITGA6-AS1, AC124248.1, NFYC-AS1, AC025171.1, and AC005229.3) associated with survival of GBM patients, and four among them clearly showed cancer-associated potential. We further validated and confirmed the altered expression of two lncRNAs (AC124248.1, AC005229.3) in GBM associated clinical samples using RT-PCR. Concerning the prognostic ability, the obtained signature determined high-/low-risk groups in GBM patients and showed sensitivity to anticancer drugs. Collectively, the m6A-autophagy-lncRNAs signature presented in the study is clinically relevant and is the first attempt to systematically predict the potential interaction between the three key determinants (m6A, autophagy, lncRNA) in cancer, particularly in GBM.
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Affiliation(s)
- Amit Sharma
- Department of Stereotacitc and Functional Neurosurgery, University Hospital of Bonn, 53127, Bonn, Germany
- Department of Integrated Oncology, Center for Integrated Oncology (CIO), University Hospital of Bonn, 53127, Bonn, Germany
| | - Yulu Wang
- Department of Integrated Oncology, Center for Integrated Oncology (CIO), University Hospital of Bonn, 53127, Bonn, Germany
| | - Fangfang Ge
- Department of Integrated Oncology, Center for Integrated Oncology (CIO), University Hospital of Bonn, 53127, Bonn, Germany
| | - Peng Chen
- Department of Integrated Oncology, Center for Integrated Oncology (CIO), University Hospital of Bonn, 53127, Bonn, Germany
| | - Tikam Chand Dakal
- Genome and Computational Biology Lab, Department of Biotechnology, Mohanlal Sukhadia University, Udaipur, India
| | - Maria Stella Carro
- Department of Neurosurgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Breisgau, Germany
| | - Ingo G H Schmidt-Wolf
- Department of Integrated Oncology, Center for Integrated Oncology (CIO), University Hospital of Bonn, 53127, Bonn, Germany
| | - Jarek Maciaczyk
- Department of Stereotacitc and Functional Neurosurgery, University Hospital of Bonn, 53127, Bonn, Germany.
- Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, 9054, New Zealand.
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Tűzesi Á, Hallal S, Satgunaseelan L, Buckland ME, Alexander KL. Understanding the Epitranscriptome for Avant-Garde Brain Tumour Diagnostics. Cancers (Basel) 2023; 15:cancers15041232. [PMID: 36831575 PMCID: PMC9954771 DOI: 10.3390/cancers15041232] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/13/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
RNA modifications are diverse, dynamic, and reversible transcript alterations rapidly gaining attention due to their newly defined RNA regulatory roles in cellular pathways and pathogenic mechanisms. The exciting emerging field of 'epitranscriptomics' is predominantly centred on studying the most abundant mRNA modification, N6-methyladenine (m6A). The m6A mark, similar to many other RNA modifications, is strictly regulated by so-called 'writer', 'reader', and 'eraser' protein species. The abundance of genes coding for the expression of these regulator proteins and m6A levels shows great potential as diagnostic and predictive tools across several cancer fields. This review explores our current understanding of RNA modifications in glioma biology and the potential of epitranscriptomics to develop new diagnostic and predictive classification tools that can stratify these highly complex and heterogeneous brain tumours.
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Affiliation(s)
- Ágota Tűzesi
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Susannah Hallal
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2050, Australia
- Department of Neurosurgery, Chris O’Brien Lifehouse, Camperdown, NSW 2050, Australia
| | - Laveniya Satgunaseelan
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
- Sydney Medical School, Faculty of Medicine and Health Sciences, The University of Sydney, Sydney, NSW 2050, Australia
| | - Michael E. Buckland
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Kimberley L. Alexander
- Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2050, Australia
- Department of Neurosurgery, Chris O’Brien Lifehouse, Camperdown, NSW 2050, Australia
- Correspondence:
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Chen J, Wu L, Yang H, Zhang X, Xv S, Qian Q. Establishment of three heterogeneous subtypes and a risk model of low-grade gliomas based on cell senescence-related genes. Front Immunol 2022; 13:982033. [PMID: 36052073 PMCID: PMC9424930 DOI: 10.3389/fimmu.2022.982033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 07/27/2022] [Indexed: 12/12/2022] Open
Abstract
Background Cellular senescence is a key element in the occurrence and progression of a variety of tumors. As a result, cellular senescence-related markers can be categorized based on the prognosis status of patients. Due to the heterogeneity and the complexity of the tumor microenvironment (TME), the long-term effectiveness of low-grade glioma (LGG) treatment remains a clinical challenge. Consequently, developing and refining effective treatment approaches to aid with LGG management is critical. Methods Based on the expressions of cell senescence-related genes (CSRGs) acquired from the cellAge database, consensus clustering was utilized to identify stable molecular subtypes. Clinical features, immune infiltration, route modifications, and genetic changes of various subtypes were also assessed. Following that, the least absolute shrinkage and selection operator (LASSO) regression and univariate Cox regression analysis were used for developing the cell senescence-related risk score (CSRS) model. Finally, a correlation study of the CSRS model with molecular, immunological, and immunotherapy parameters was performed. Results C1, C2, and C3, are the three senescence-related subtypes that were identified. Patients belonging to the C1 subtype had poor prognoses and a substantial proportion of them was in the grade G3. The differentially expressed genes (DEGs) among the three subtypes were used to develop the CSRS model. In both the training and independent validation cohort, the model had a high area under the receiver operating characteristic (ROC) curve in predicting the overall survival (OS) of patients. As a result, this model can predict clinical features and responses to immunotherapy in a variety of patients and it is a potential independent prognostic factor for LGG. Conclusion This research discovered three LGG subtypes related to cell senescence and created a CSRS model for six genes. Cell senescence was highly associated with unfavorable prognosis in LGG. The CSRS model can be used to predict the prognosis of patients and identify patients who would benefit from immunotherapy.
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Affiliation(s)
- Jing Chen
- Department of Medical Oncology, The First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China
- *Correspondence: Jing Chen,
| | - Lingjiao Wu
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Hanjin Yang
- Department of Pathology, The First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China
| | - XiaoChen Zhang
- Department of Medical Oncology, The First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China
| | - SuZhen Xv
- Department of Medical Oncology, The First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China
| | - Qiong Qian
- Department of Medical Oncology, The First Affiliated Hospital of College of Medicine, Zhejiang University, Hangzhou, China
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Bao JH, Lu WC, Duan H, Ye YQ, Li JB, Liao WT, Li YC, Sun YP. Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas. Front Immunol 2022; 13:933973. [PMID: 36045691 PMCID: PMC9420977 DOI: 10.3389/fimmu.2022.933973] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/22/2022] [Indexed: 12/20/2022] Open
Abstract
Background Cuproptosis is a newly discovered unique non-apoptotic programmed cell death distinguished from known death mechanisms like ferroptosis, pyroptosis, and necroptosis. However, the prognostic value of cuproptosis and the correlation between cuproptosis and the tumor microenvironment (TME) in lower-grade gliomas (LGGs) remain unknown. Methods In this study, we systematically investigated the genetic and transcriptional variation, prognostic value, and expression patterns of cuproptosis-related genes (CRGs). The CRG score was applied to quantify the cuproptosis subtypes. We then evaluated their values in the TME, prognostic prediction, and therapeutic responses in LGG. Lastly, we collected five paired LGG and matched normal adjacent tissue samples from Sun Yat-sen University Cancer Center (SYSUCC) to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR) and Western blotting (WB). Results Two distinct cuproptosis-related clusters were identified using consensus unsupervised clustering analysis. The correlation between multilayer CRG alterations with clinical characteristics, prognosis, and TME cell infiltration were observed. Then, a well-performed cuproptosis-related risk model (CRG score) was developed to predict LGG patients' prognosis, which was evaluated and validated in two external cohorts. We classified patients into high- and low-risk groups according to the CRG score and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P<0.001). A high CRG score implies higher TME scores, more significant TME cell infiltration, and increased mutation burden. Meanwhile, the CRG score was significantly correlated with the cancer stem cell index, chemoradiotherapy sensitivity-related genes and immune checkpoint genes, and chemotherapeutic sensitivity, indicating the association with CRGs and treatment responses. Univariate and multivariate Cox regression analyses revealed that the CRG score was an independent prognostic predictor for LGG patients. Subsequently, a highly accurate predictive model was established for facilitating the clinical application of the CRG score, showing good predictive ability and calibration. Additionally, crucial CRGs were further validated by qRT-PCR and WB. Conclusion Collectively, we demonstrated a comprehensive overview of CRG profiles in LGG and established a novel risk model for LGG patients' therapy status and prognosis. Our findings highlight the potential clinical implications of CRGs, suggesting that cuproptosis may be the potential therapeutic target for patients with LGG.
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Affiliation(s)
- Jia-hao Bao
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Wei-cheng Lu
- State Key Laboratory of Oncology in Southern China, Department of Anesthesiology, Sun Yat-sen University Cancer Center, Collaborative Innovation for Cancer Medicine, Guangzhou, China
| | - Hao Duan
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Ya-qi Ye
- State Key Laboratory of Oncology in Southern China, Department of Anesthesiology, Sun Yat-sen University Cancer Center, Collaborative Innovation for Cancer Medicine, Guangzhou, China
| | - Jiang-bo Li
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Wen-ting Liao
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China,*Correspondence: Yang-peng Sun, ; Yong-chun Li, ; Wen-ting Liao,
| | - Yong-chun Li
- State Key Laboratory of Oncology in Southern China, Department of Anesthesiology, Sun Yat-sen University Cancer Center, Collaborative Innovation for Cancer Medicine, Guangzhou, China,*Correspondence: Yang-peng Sun, ; Yong-chun Li, ; Wen-ting Liao,
| | - Yang-peng Sun
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China,*Correspondence: Yang-peng Sun, ; Yong-chun Li, ; Wen-ting Liao,
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Teng C, Zhu Y, Li Y, Dai L, Pan Z, Wanggou S, Li X. Recurrence- and Malignant Progression-Associated Biomarkers in Low-Grade Gliomas and Their Roles in Immunotherapy. Front Immunol 2022; 13:899710. [PMID: 35677036 PMCID: PMC9168984 DOI: 10.3389/fimmu.2022.899710] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 04/12/2022] [Indexed: 12/15/2022] Open
Abstract
Despite a generally better prognosis than high-grade glioma (HGG), recurrence and malignant progression are the main causes for the poor prognosis and difficulties in the treatment of low-grade glioma (LGG). It is of great importance to learn about the risk factors and underlying mechanisms of LGG recurrence and progression. In this study, the transcriptome characteristics of four groups, namely, normal brain tissue and recurrent LGG (rLGG), normal brain tissue and secondary glioblastoma (sGBM), primary LGG (pLGG) and rLGG, and pLGG and sGBM, were compared using Chinese Glioma Genome Atlas (CGGA) and Genotype-Tissue Expression Project (GTEx) databases. In this study, 296 downregulated and 396 upregulated differentially expressed genes (DEGs) with high consensus were screened out. Univariate Cox regression analysis of data from The Cancer Genome Atlas (TCGA) yielded 86 prognostically relevant DEGs; a prognostic prediction model based on five key genes (HOXA1, KIF18A, FAM133A, HGF, and MN1) was established using the least absolute shrinkage and selection operator (LASSO) regression dimensionality reduction and multivariate Cox regression analysis. LGG was divided into high- and low-risk groups using this prediction model. Gene Set Enrichment Analysis (GSEA) revealed that signaling pathway differences in the high- and low-risk groups were mainly seen in tumor immune regulation and DNA damage-related cell cycle checkpoints. Furthermore, the infiltration of immune cells in the high- and low-risk groups was analyzed, which indicated a stronger infiltration of immune cells in the high-risk group than that in the low-risk group, suggesting that an immune microenvironment more conducive to tumor growth emerged due to the interaction between tumor and immune cells. The tumor mutational burden and tumor methylation burden in the high- and low-risk groups were also analyzed, which indicated higher gene mutation burden and lower DNA methylation level in the high-risk group, suggesting that with the accumulation of genomic mutations and epigenetic changes, tumor cells continued to evolve and led to the progression of LGG to HGG. Finally, the value of potential therapeutic targets for the five key genes was analyzed, and findings demonstrated that KIF18A was the gene most likely to be a potential therapeutic target. In conclusion, the prediction model based on these five key genes can better identify the high- and low-risk groups of LGG and lay a solid foundation for evaluating the risk of LGG recurrence and malignant progression.
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Affiliation(s)
- Chubei Teng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China.,Department of Neurosurgery, The First Affiliated Hospital, University of South China, Hengyang, China
| | - Yongwei Zhu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Yueshuo Li
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Luohuan Dai
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Zhouyang Pan
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Siyi Wanggou
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
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Li X, Xiong K, Bi D, Zhao C. A Novel CRISPR/Cas9 Screening Potential Index for Prognostic and Immunological Prediction in Low-Grade Glioma. Front Genet 2022; 13:839884. [PMID: 35586564 PMCID: PMC9109250 DOI: 10.3389/fgene.2022.839884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 03/18/2022] [Indexed: 12/05/2022] Open
Abstract
Glioma is a malignancy with the highest mortality in central nervous system disorders. Here, we implemented the computational tools based on CRISPR/Cas9 to predict the clinical outcomes and biological characteristics of low-grade glioma (LGG). The transcriptional expression profiles and clinical phenotypes of LGG patients were retrieved from The Cancer Genome Atlas and Chinese Glioma Genome Atlas. The CERES algorithm was used to screen for LGG-lethal genes. Cox regression and random survival forest were adopted for survival-related gene selection. Nonnegative matrix factorization distinguished patients into different clusters. Single-sample gene set enrichment analysis was employed to create a novel CRISPR/Cas9 screening potential index (CCSPI), and patients were stratified into low- and high-CCSPI groups. Survival analysis, area under the curve values (AUCs), nomogram, and tumor microenvironment exploration were included for the model validation. A total of 20 essential genes in LGG were used to classify patients into two clusters and construct the CCSPI system. High-CCSPI patients were associated with a worse prognosis of both training and validation set (p < 0.0001) and higher immune fractions than low-CCSPI individuals. The CCSPI system had a promising performance with 1-, 3-, and 5-year AUCs of 0.816, 0.779, 0.724, respectively, and the C-index of the nomogram model reached 0.743 (95% CI = 0.725–0.760). Immune-infiltrating cells and immune checkpoints such as PD-1/PD-L1 and POLD3 were positively associated with CCSPI. In conclusion, the CCSPI had prognostic value in LGG, and the model will deepen our cognition of the interaction between the CNS and immune system in different LGG subtypes.
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Affiliation(s)
- Xiangpan Li
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Kewei Xiong
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China.,School of Mathematics and Statistics, Central China Normal University, Wuhan, China
| | - Dong Bi
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chen Zhao
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
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