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Qi P, Qi B, Gu C, Huo S, Dang X, Liu Y, Zhao B. Construction of an immune-related prognostic model and potential drugs screening for esophageal cancer based on bioinformatics analyses and network pharmacology. Immun Inflamm Dis 2024; 12:e1266. [PMID: 38804848 PMCID: PMC11131936 DOI: 10.1002/iid3.1266] [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: 04/04/2023] [Revised: 02/29/2024] [Accepted: 04/18/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Esophageal cancer (ESCA) is a highly invasive malignant tumor with poor prognosis. This study aimed to discover a generalized and high-sensitivity immune prognostic signature that could stratify ESCA patients and predict their overall survival, and to discover potential therapeutic drugs by the connectivity map. METHODS The key gene modules significantly related to clinical traits (survival time and state) of ESCA patients were selected by weighted gene coexpression network analysis (WCGNA), then the univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to construct a 15-immune-related gene prognostic signature. RESULTS The immune-related risk model was related to clinical and pathologic factors and remained an effective independent prognostic factor. Enrichment analyses revealed that the differentially expressed genes (DEGs) of the high- and low-risk groups were associated with tumor cell proliferation and immune mechanisms. Based on the gathered data, a small molecule drug named perphenazine (PPZ) was elected. The pharmacological analysis indicates that PPZ could help in adjuvant therapy of ESCA through regulation of metabolic process and cellular proliferation, enhancement of immunologic functions, and inhibition of inflammatory reactions. Furthermore, molecular docking was performed to explore and verify the PPZ-core target interactions. CONCLUSION We succeed in structuring the immune-related prognostic model, which could be used to distinguish and predict patients' survival outcome, and screening a small molecule drug named PPZ. Prospective studies also are needed to further validate its analytical accuracy for estimating prognoses and confirm the potential use of PPZ for treating ESCA.
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Affiliation(s)
- Pengju Qi
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiHenanChina
- Esophageal Cancer Institute of Xinxiang Medical UniversityWeihuiHenanChina
- Life Science Research CenterThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiHenanChina
| | - Bo Qi
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiHenanChina
- Esophageal Cancer Institute of Xinxiang Medical UniversityWeihuiHenanChina
| | - Chengwei Gu
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiHenanChina
- Esophageal Cancer Institute of Xinxiang Medical UniversityWeihuiHenanChina
| | - Shuhua Huo
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiHenanChina
- Esophageal Cancer Institute of Xinxiang Medical UniversityWeihuiHenanChina
| | - Xinchen Dang
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiHenanChina
- Esophageal Cancer Institute of Xinxiang Medical UniversityWeihuiHenanChina
| | - Yuzhen Liu
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiHenanChina
- Esophageal Cancer Institute of Xinxiang Medical UniversityWeihuiHenanChina
- Life Science Research CenterThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiHenanChina
| | - Baosheng Zhao
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiHenanChina
- Esophageal Cancer Institute of Xinxiang Medical UniversityWeihuiHenanChina
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Beltrán-Navarro YM, Reyes-Cruz G, Vázquez-Prado J. P-Rex1 Signaling Hub in Lower Grade Glioma Patients, Found by In Silico Data Mining, Correlates With Reduced Survival and Augmented Immune Tumor Microenvironment. Front Oncol 2022; 12:922025. [PMID: 35875157 PMCID: PMC9300953 DOI: 10.3389/fonc.2022.922025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 06/02/2022] [Indexed: 11/21/2022] Open
Abstract
Systematic analysis of tumor transcriptomes, combined with deep genome sequencing and detailed clinical assessment of hundreds of patients, constitutes a powerful strategy aimed to identify potential biomarkers and therapeutic targets to guide personalized treatments. Oncogenic signaling cascades are integrated by multidomain effector proteins such as P-Rex1, a guanine nucleotide exchange factor for the Rac GTPase (RacGEF), known to promote metastatic dissemination of cancer cells. We hypothesized that patients with high P-Rex1 expression and reduced survival might be characterized by a particular set of signaling proteins co-expressed with this effector of cell migration as a central component of a putative signaling hub indicative of poor prognosis. High P-Rex1 expression correlated with reduced survival of TCGA Lower Grade Glioma (LGG) patients. Thus, guided by PREX1 expression, we searched for signaling partners of this RacGEF by applying a systematic unbiased in silico data mining strategy. We identified 30 putative signaling partners that also correlated with reduced patient survival. These included GPCRs such as CXCR3, GPR82, FZD6, as well as MAP3K1, MAP2K3, NEK8, DYRK3 and RPS6KA3 kinases, and PTPN2 and PTPN22 phosphatases, among other transcripts of signaling proteins and phospho-substrates. This PREX1 signaling hub signature correlated with increased risk of shorter survival of LGG patients from independent datasets and coincided with immune and endothelial transcriptomic signatures, indicating that myeloid infiltration and tumor angiogenesis might contribute to worsen brain tumor pathology. In conclusion, P-Rex1 and its putative signaling partners in LGG are indicative of a signaling landscape of the tumor microenvironment that correlates with poor prognosis and might guide the characterization of signaling targets leading the eventual development of immunotherapeutic strategies.
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Affiliation(s)
| | | | - José Vázquez-Prado
- Department of Pharmacology, Cinvestav-IPN, Mexico City, Mexico
- *Correspondence: José Vázquez-Prado,
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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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Chen J, Shen S, Li Y, Fan J, Xiong S, Xu J, Zhu C, Lin L, Dong X, Duan W, Zhao Y, Qian X, Liu Z, Wei Y, Christiani DC, Zhang R, Chen F. APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance. EBioMedicine 2022; 79:104007. [PMID: 35436725 PMCID: PMC9035655 DOI: 10.1016/j.ebiom.2022.104007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions. METHODS Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review. FINDINGS APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10-69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness. INTERPRETATION APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO). FUNDING National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).
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Affiliation(s)
- Jiajin Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
| | - Sipeng Shen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China, 211166
| | - Yi Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA, 48109
| | - Juanjuan Fan
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
| | - Shiyu Xiong
- Department of Clinical Medicine, The First Clinical Medical College, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
| | - Jingtong Xu
- Department of Clinical Medicine, The First Clinical Medical College, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
| | - Chenxu Zhu
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
| | - Lijuan Lin
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
| | - Xuesi Dong
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 100021
| | - Weiwei Duan
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China 211166
| | - Yang Zhao
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
| | - Xu Qian
- Department of Nutrition and Food Hygiene, Institute for Brain Tumors, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China, 211166
| | - Zhonghua Liu
- Department of Statistics and Actuarial Science, the University of Hong Kong, Hong Kong, China, 999077
| | - Yongyue Wei
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China, 211166
| | - David C Christiani
- Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, 02114.
| | - Ruyang Zhang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China, 211166.
| | - Feng Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China, 211166; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China, 211166.
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Identification and Validation of a Gene Signature for Lower-Grade Gliomas Based on Pyroptosis-Related Genes to Predict Survival and Response to Immune Checkpoint Inhibitors. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8704127. [PMID: 35535221 PMCID: PMC9078805 DOI: 10.1155/2022/8704127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/28/2022] [Accepted: 04/18/2022] [Indexed: 02/08/2023]
Abstract
Pyroptosis plays a critical role in the immune response to immune checkpoint inhibitors (ICIs) by mediating the tumor immune microenvironment. However, the impact of pyroptosis-related biomarkers on the prognosis and efficacy of ICIs in patients with lower-grade gliomas (LGGs) is unclear. An unsupervised clustering analysis identified pyroptosis-related subtypes (PRSs) based on the expression profile of 47 pyroptosis-related genes in The Cancer Genome Atlas-LGG cohort. A PRS gene signature was established using univariate Cox regression, random survival forest, least absolute shrinkage and selection operator, and stepwise multivariable Cox regression analyses. The predictive power of this signature was validated in the Chinese Glioma Genome Atlas database. We also investigated the differences between high- and low-risk groups in terms of the tumor immune microenvironment, tumor mutation, and response to target therapy and ICIs. The PRS gene signature comprised eight PRS genes, which independently predicted the prognosis of LGG patients. High-risk patients had a worse overall survival than did the low-risk patients. The high-risk group also displayed a higher proportion of M1 macrophages and CD8+ T cells and higher immune scores, tumor mutational burden, immunophenoscore, IMmuno-PREdictive Score, MHC I association immune score, and T cell-inflamed gene expression profile scores, but lower suppressor cells scores, and were more suitable candidates for ICI treatment. Higher risk scores were more frequent in patients who responded to ICIs using data from the ImmuCellAI website. The presently established PRS gene signature can be validated in melanoma patients treated with real ICI treatment. This signature is valuable in predicting prognosis and ICI treatment of LGG patients, pending further prospective verification.
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Liu W, Liu C, Chen C, Huang X, Yi Q, Tian Y, Peng B, Yuan Y. Construction and Verification of a Glycolysis-Associated Gene Signature for the Prediction of Overall Survival in Low Grade Glioma. Front Genet 2022; 13:843711. [PMID: 35401698 PMCID: PMC8983898 DOI: 10.3389/fgene.2022.843711] [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: 03/07/2022] [Indexed: 11/24/2022] Open
Abstract
The overall survival of patients with lower grade glioma (LGG) that might develop into high-grade malignant glioma shows marked heterogeneity. The currently used clinical evaluation index is not sufficient to predict precise prognostic outcomes accurately. To optimize survival risk stratification and the personalized management of patients with LGG, there is an urgent need to develop an accurate risk prediction model. The TCGA-LGG dataset, downloaded from The Cancer Genome Atlas (TCGA) portal, was used as a training cohort, and the Chinese Glioma Genome Atlas (CGGA) dataset and Rembrandt dataset were used as validation cohorts. The levels of various cancer hallmarks were quantified, which identified glycolysis as the primary overall survival-related risk factor in LGGs. Furthermore, using various bioinformatic and statistical methods, we developed a strong glycolysis-related gene signature to predict prognosis. Gene set enrichment analysis showed that in our model, high-risk glioma correlated with the chemoradiotherapy resistance and poor survival. Moreover, based on established risk model and other clinical features, a decision tree and a nomogram were built, which could serve as useful tools in the diagnosis and treatment of LGGs. This study indicates that the glycolysis-related gene signature could distinguish high-risk and low‐risk patients precisely, and thus can be used as an independent clinical feature.
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Affiliation(s)
- Wei Liu
- Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Chunshan Liu
- Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Chengcong Chen
- Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Xiaoting Huang
- Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Qi Yi
- Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Yunhong Tian
- Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Biao Peng
- Department of Neurosurgery, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Yawei Yuan
- Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
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Chen S. Glioma Subtypes Based on the Activity Changes of Immunologic and Hallmark Gene Sets in Cancer. Front Endocrinol (Lausanne) 2022; 13:879233. [PMID: 35774141 PMCID: PMC9236851 DOI: 10.3389/fendo.2022.879233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 04/25/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Glioma is the most common primary cranial brain tumor that arises from the cancelation of glial cells (which can be in the brain or spinal cord). It is due to innate genetic risk factors or induced by a carcinogenic environment. If left untreated, the disease has a poor prognosis. METHODS In this study, we downloaded glioma data from TCGA database and GEO (GSE4412). The GSEA database was used to screen tumor microenvironment-related gene sets. Cancer subtypes were classified by GSVA enrichment method. RESULTS By GSVA enrichment analysis, we obtain three Gliomas cancer subtypes. After further survival prognosis analysis and biological function analysis, we obtained 13 tumor microenvironment gene sets and 14 core genes that affect patients' survival prognosis, and these genes have the potential to become targets for targeted therapies and disease detection. CONCLUSION We screened a total of 13 gene sets through a series of enrichment analyses, statistical and prognostic analyses, etc. Among them, 14 core genes were identified, namely: TOP2A, TPX2, BUB1, AURKB, AURKA, CDK1, BUB1B, CCNA2, CCNB2, CDCA8, CDC20, KIF11, KIF20A and KIF2C.
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Affiliation(s)
- Sihan Chen
- Taikang (Ningbo) Hospital Co., Ltd. Yinzhou, Ningbo, China
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Du Z, Wang Y, Liang J, Gao S, Cai X, Yu Y, Qi Z, Li J, Xie Y, Wang Z. Association of glioma CD44 expression with glial dynamics in the tumour microenvironment and patient prognosis. Comput Struct Biotechnol J 2022; 20:5203-5217. [PMID: 36187921 PMCID: PMC9508470 DOI: 10.1016/j.csbj.2022.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/02/2022] [Accepted: 09/03/2022] [Indexed: 11/18/2022] Open
Abstract
Because of the heterogeneity of lower-grade gliomas (LGGs), patients show various survival outcomes that are not reliably predicted by histological classification. The tumour microenvironment (TME) contributes to the initiation and progression of brain LGGs. Identifying potential prognostic markers based on the immune and stromal components in the TME will provide new insights into the dynamic modulation of these two components of the TME in LGGs. We applied ESTIMATE to calculate the ratio of immune and stromal components from The Cancer Genome Atlas database. After combined differential gene expression analysis, protein–protein interaction network construction and survival analysis, CD44 was screened as an independent prognostic factor and subsequently validated utilizing data from the Chinese Glioma Genome Atlas database. To decipher the association of glioma cell CD44 expression with stromal cells in the TME and tumour progression, RT–qPCR, cell viability and wound healing assays were employed to determine whether astrocytes enhance glioma cell viability and migration by upregulating CD44 expression. Surprisingly, M1 macrophages were identified as positively correlated with CD44 expression by CIBERSORT analysis. CD44+ glioma cells were further suggested to interact with microglia-derived macrophages (M1 phenotype) via osteopontin signalling on the basis of single-cell sequencing data. Overall, we found that astrocytes could elevate the CD44 expression level of glioma cells, enhancing the recruitment of M1 macrophages that may promote glioma stemness via osteopontin-CD44 signalling. Thus, glioma CD44 expression might coordinate with glial activities in the TME and serve as a potential therapeutic target and prognostic marker for LGGs.
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Han T, Zuo Z, Qu M, Zhou Y, Li Q, Wang H. Comprehensive Analysis of Inflammatory Response-Related Genes, and Prognosis and Immune Infiltration in Patients With Low-Grade Glioma. Front Pharmacol 2021; 12:748993. [PMID: 34712139 PMCID: PMC8545815 DOI: 10.3389/fphar.2021.748993] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/03/2021] [Indexed: 12/11/2022] Open
Abstract
Background: Although low-grade glioma (LGG) has a good prognosis, it is prone to malignant transformation into high-grade glioma. It has been confirmed that the characteristics of inflammatory factors and immune microenvironment are closely related to the occurrence and development of tumors. It is necessary to clarify the role of inflammatory genes and immune infiltration in LGG. Methods: We downloaded the transcriptome gene expression data and corresponding clinical data of LGG patients from the TCGA and GTEX databases to screen prognosis-related differentially expressed inflammatory genes with the difference analysis and single-factor Cox regression analysis. The prognostic risk model was constructed by LASSO Cox regression analysis, which enables us to compare the overall survival rate of high- and low-risk groups in the model by Kaplan–Meier analysis and subsequently draw the risk curve and survival status diagram. We analyzed the accuracy of the prediction model via ROC curves and performed GSEA enrichment analysis. The ssGSEA algorithm was used to calculate the score of immune cell infiltration and the activity of immune-related pathways. The CellMiner database was used to study drug sensitivity. Results: In this study, 3 genes (CALCRL, MMP14, and SELL) were selected from 9 prognosis-related differential inflammation genes through LASSO Cox regression analysis to construct a prognostic risk model. Further analysis showed that the risk score was negatively correlated with the prognosis, and the ROC curve showed that the accuracy of the model was better. The age, grade, and risk score can be used as independent prognostic factors (p < 0.001). GSEA analysis confirmed that 6 immune-related pathways were enriched in the high-risk group. We found that the degree of infiltration of 12 immune cell subpopulations and the scores of 13 immune functions and pathways in the high-risk group were significantly increased by applying the ssGSEA method (p < 0.05). Finally, we explored the relationship between the genes in the model and the susceptibility of drugs. Conclusion: This study analyzed the correlation between the inflammation-related risk model and the immune microenvironment. It is expected to provide a reference for the screening of LGG prognostic markers and the evaluation of immune response.
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Affiliation(s)
- Tao Han
- Department of Oncology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Zhifan Zuo
- The General Hospital of Northern Theater Command Training Base for Graduate, China Medical University, Shenyang, China
| | - Meilin Qu
- School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, Shenyang, China
| | - Yinghui Zhou
- The General Hospital of Northern Theater Command Training Base for Graduate, Jinzhou Medical University, Jinzhou, China
| | - Qing Li
- School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, China
| | - Hongjin Wang
- Department of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
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