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Zhuang Q, Liu C, Hu Y, Liu Y, Lyu Y, Liao Y, Chen L, Yang H, Mao Y. Identification of RP11-770J1.4 as immune-related lncRNA regulating the CTXN1-cGAS-STING axis in histologically lower-grade glioma. MedComm (Beijing) 2023; 4:e458. [PMID: 38116063 PMCID: PMC10728758 DOI: 10.1002/mco2.458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 11/24/2023] [Accepted: 11/30/2023] [Indexed: 12/21/2023] Open
Abstract
Human gliomas are lethal brain cancers. Emerging evidence revealed the regulatory role of long noncoding RNAs (lncRNAs) in tumors. Here, we performed a comprehensive analysis of the expression profiles of RNAs in histologically lower-grade glioma (LGG). Enrichment analysis revealed that glioma is influenced by immune-related signatures. Survival analysis further established the close correlation between network features and glioma prognosis. Subsequent experiments showed lncRNA RP11-770J1.4 regulates CTXN1 expression through hsa-miR-124-3p. Correlation analysis identified lncRNA RP11-770J1.4 was immune related, specifically involved in the cytosolic DNA sensing pathway. Downregulated lncRNA RP11-770J1.4 resulted in increased spontaneous gene expression of the cGAS-STING pathway. Single-cell RNA sequencing analysis, along with investigations in a glioblastoma stem cell model and patient sample analysis, demonstrated the predominant localization of CTXN1 within tumor cores rather than peripheral regions. Immunohistochemistry staining established a negative correlation between CTXN1 expression and infiltration of CD8+ T cells. In vivo, Ctxn1 knockdown in GL261 cells led to decreased tumor burden and improved survival while increasing infiltration of CD8+ T cells. These findings unveil novel insights into the lncRNA RP11-770J1.4-CTXN1 as a potential immune regulatory axis, highlighting its therapeutic implications for histologically LGGs.
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Affiliation(s)
- Qiyuan Zhuang
- Department of NeurosurgeryHuashan Hospital, Fudan UniversityShanghaiChina
| | - Chaxian Liu
- Department of NeurosurgeryHuashan Hospital, Fudan UniversityShanghaiChina
| | - Yihan Hu
- School of Life Sciences, Fudan UniversityShanghaiChina
| | - Ying Liu
- Department of PathologySchool of Basic Medical Sciences, Fudan UniversityShanghaiChina
| | - Yingying Lyu
- Department of NeurosurgeryHuashan Hospital, Fudan UniversityShanghaiChina
| | - Yuheng Liao
- Key Laboratory of Medical Epigenetics and Metabolism and Molecular and Cell Biology LabInstitute of Biomedical Sciences, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Liang Chen
- Department of NeurosurgeryHuashan Hospital, Fudan UniversityShanghaiChina
- National Center for Neurological DisordersHuashan Hospital, Fudan UniversityShanghaiChina
| | - Hui Yang
- Department of NeurosurgeryHuashan Hospital, Fudan UniversityShanghaiChina
- National Center for Neurological DisordersHuashan Hospital, Fudan UniversityShanghaiChina
- Institute for Translational Brain ResearchShanghai Medical College, Fudan UniversityShanghaiChina
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan UniversityShanghaiChina
| | - Ying Mao
- Department of NeurosurgeryHuashan Hospital, Fudan UniversityShanghaiChina
- School of Life Sciences, Fudan UniversityShanghaiChina
- National Center for Neurological DisordersHuashan Hospital, Fudan UniversityShanghaiChina
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan UniversityShanghaiChina
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Gourishetti K, Balaji Easwaran V, Mostakim Y, Ranganath Pai KS, Bhere D. MicroRNA (miR)-124: A Promising Therapeutic Gateway for Oncology. BIOLOGY 2023; 12:922. [PMID: 37508353 PMCID: PMC10376116 DOI: 10.3390/biology12070922] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
MicroRNA (miR) are a class of small non-coding RNA that are involved in post-transcriptional gene regulation. Altered expression of miR has been associated with several pathological conditions. MicroRNA-124 (miR-124) is an abundantly expressed miR in the brain as well as the thymus, lymph nodes, bone marrow, and peripheral blood mono-nuclear cells. It plays a key role in the regulation of the host immune system. Emerging studies show that dysregulated expression of miR-124 is a hallmark in several cancer types and it has been attributed to the progression of these malignancies. In this review, we present a comprehensive summary of the role of miR-124 as a promising therapeutic gateway in oncology.
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Affiliation(s)
- Karthik Gourishetti
- Biotherapeutics Laboratory, School of Medicine Columbia, University of South Carolina, Columbia, SC 29209, USA
- Department of Pathology, Microbiology, and Immunology, School of Medicine Columbia, University of South Carolina, Columbia, SC 29209, USA
| | - Vignesh Balaji Easwaran
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, India
| | - Youssef Mostakim
- Biotherapeutics Laboratory, School of Medicine Columbia, University of South Carolina, Columbia, SC 29209, USA
- Department of Pathology, Microbiology, and Immunology, School of Medicine Columbia, University of South Carolina, Columbia, SC 29209, USA
- College of Arts and Sciences, University of South Carolina, Columbia, SC 29208, USA
| | - K. Sreedhara Ranganath Pai
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, India
| | - Deepak Bhere
- Biotherapeutics Laboratory, School of Medicine Columbia, University of South Carolina, Columbia, SC 29209, USA
- Department of Pathology, Microbiology, and Immunology, School of Medicine Columbia, University of South Carolina, Columbia, SC 29209, USA
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Brown JS. Comparison of Oncogenes, Tumor Suppressors, and MicroRNAs Between Schizophrenia and Glioma: The Balance of Power. Neurosci Biobehav Rev 2023; 151:105206. [PMID: 37178944 DOI: 10.1016/j.neubiorev.2023.105206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/25/2023] [Accepted: 04/30/2023] [Indexed: 05/15/2023]
Abstract
The risk of cancer in schizophrenia has been controversial. Confounders of the issue are cigarette smoking in schizophrenia, and antiproliferative effects of antipsychotic medications. The author has previously suggested comparison of a specific cancer like glioma to schizophrenia might help determine a more accurate relationship between cancer and schizophrenia. To accomplish this goal, the author performed three comparisons of data; the first a comparison of conventional tumor suppressors and oncogenes between schizophrenia and cancer including glioma. This comparison determined schizophrenia has both tumor-suppressive and tumor-promoting characteristics. A second, larger comparison between brain-expressed microRNAs in schizophrenia with their expression in glioma was then performed. This identified a core carcinogenic group of miRNAs in schizophrenia offset by a larger group of tumor-suppressive miRNAs. This proposed "balance of power" between oncogenes and tumor suppressors could cause neuroinflammation. This was assessed by a third comparison between schizophrenia, glioma and inflammation in asbestos-related lung cancer and mesothelioma (ALRCM). This revealed that schizophrenia shares more oncogenic similarity to ALRCM than glioma.
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Predictive and Prognostic Value of Non-Coding RNA in Breast Cancer. Cancers (Basel) 2022; 14:cancers14122952. [PMID: 35740618 PMCID: PMC9221286 DOI: 10.3390/cancers14122952] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/09/2022] [Accepted: 06/14/2022] [Indexed: 12/21/2022] Open
Abstract
For decades since the central dogma, cancer biology research has been focusing on the involvement of genes encoding proteins. It has been not until more recent times that a new molecular class has been discovered, named non-coding RNA (ncRNA), which has been shown to play crucial roles in shaping the activity of cells. An extraordinary number of studies has shown that ncRNAs represent an extensive and prevalent group of RNAs, including both oncogenic or tumor suppressive molecules. Henceforth, various clinical trials involving ncRNAs as extraordinary biomarkers or therapies have started to emerge. In this review, we will focus on the prognostic and diagnostic role of ncRNAs for breast cancer.
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Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041850] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool with which to find underlying patterns in data, e.g., for cancer specific biomarkers. In the past, analyses were performed on RNA-Seq data pertaining to the same cancer class as positive and negative samples, i.e., without samples of other cancer types. To perform multiple cancer type classification and to find differentially expressed genes, data for multiple cancer types need to be analyzed. Several repositories offer RNA-Seq data for various cancer types. In this paper, data from the Mendeley data repository for five cancer types are analyzed. As a first step, RNA-Seq values are converted to 2D images using normalization and zero padding. In the next step, relevant features are extracted and selected using Deep Learning (DL). In the last phase, classification is performed, and eight DL algorithms are used. Results and discussion are based on four different splitting strategies and k-fold cross validation for each DL classifier. Furthermore, a comparative analysis is performed with state of the art techniques discussed in literature. The results demonstrated that classifiers performed best at 70–30 split, and that Convolutional Neural Network (CNN) achieved the best overall results. Hence, CNN is the best DL model for classification among the eight studied DL models, and is easy to implement and simple to understand.
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