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Srisathaporn S, Pientong C, Heawchaiyaphum C, Nukpook T, Aromseree S, Ekalaksananan T. The Oncogenic Role of VWA8-AS1, a Long Non-Coding RNA, in Epstein-Barr Virus-Associated Oral Squamous Cell Carcinoma: An Integrative Transcriptome and Functional Analysis. Int J Mol Sci 2024; 25:12565. [PMID: 39684278 DOI: 10.3390/ijms252312565] [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: 10/24/2024] [Revised: 11/16/2024] [Accepted: 11/21/2024] [Indexed: 12/18/2024] Open
Abstract
Dysregulated long non-coding RNA (lncRNA) expression is linked to various cancers and may be influenced by oncogenic Epstein-Barr virus (EBV) infection, a known and detectable risk factor in oral squamous cell carcinoma (OSCC) patients. However, research on the oncogenic role of EBV-induced lncRNAs in OSCC is limited. To identify lncRNA-associated EBV infection and OSCC carcinogenesis, the differential expression of RNA-seq datasets from paired normal adjacent and OSCC tissues, and microarray data from EBV-negative and EBV-positive SCC25 cells, were identified and selected, respectively, for interaction, functional analysis, and CCK-8 cell proliferation, wound healing, and invasion Transwell assays. In OSCC tissues, 6731 differentially expressed lncRNAs were identified when compared to normal tissues from RNA-seq datasets, with 295 linked to EBV-induced OSCC carcinogenesis from microarray datasets. The EBV-induced lncRNA VWA8-AS1 showed significant upregulation in EBV-positive SCC25 cells and EBV-infected adjacent and OSCC tissue samples. VWA8-AS1 potentially promotes OSCC via the lncRNA-miRNA-mRNA axis or direct protein interactions, affecting various cellular processes. Studies in OSCC cell lines revealed that elevated VWA8-AS1 levels enhanced cell migration and invasion. This study demonstrates VWA8-AS1's contribution to tumor progression and possible interactions with its targets in OSCC, offering insights for future research on functional mechanisms and therapeutic targets in EBV-associated OSCC.
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Affiliation(s)
- Sawarot Srisathaporn
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
- HPV & EBV and Carcinogenesis Research Group, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Chamsai Pientong
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
- HPV & EBV and Carcinogenesis Research Group, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Chukkris Heawchaiyaphum
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
- HPV & EBV and Carcinogenesis Research Group, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Thawaree Nukpook
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
- HPV & EBV and Carcinogenesis Research Group, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Sirinart Aromseree
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
- HPV & EBV and Carcinogenesis Research Group, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Tipaya Ekalaksananan
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
- HPV & EBV and Carcinogenesis Research Group, Khon Kaen University, Khon Kaen 40002, Thailand
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Yadav G, Kulshreshtha R. Pan-cancer analyses identify MIR210HG overexpression, epigenetic regulation and oncogenic role in human tumors and its interaction with the tumor microenvironment. Life Sci 2024; 339:122438. [PMID: 38242493 DOI: 10.1016/j.lfs.2024.122438] [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: 09/30/2023] [Revised: 01/09/2024] [Accepted: 01/13/2024] [Indexed: 01/21/2024]
Abstract
BACKGROUND Molecular entities showing dysregulation in multiple cancers may hold great biomarker or therapeutic potential. There is accumulating evidence that highlights the dysregulation of a long non-coding RNA, MIR210HG, in various cancers and its oncogenic role. However, a comprehensive analysis of MIR210HG expression pattern, molecular mechanisms, diagnostic or prognostic significance or evaluation of its interaction with tumor microenvironment across various cancers remains unstudied. METHODS A systematic pan-cancer analysis was done using multiple public databases and bioinformatic tools to study the molecular role and clinical significance of MIR210HG. We have analyzed expression patterns, genome alteration, transcriptional and epigenetic regulation, correlation with patient survival, immune infiltrates, co-expressed genes, interacting proteins, and pathways associated with MIR210HG. RESULTS The Pan cancer expression analysis of MIR210HG through various tumor datasets demonstrated that MIR210HG is significantly upregulated in most cancers and increased with the tumor stage in a subset of them. Furthermore, prognostic analysis revealed high MIR210HG expression is associated with poor overall and disease-free survival in specific cancer types. Genetic alteration analysis showed minimal alterations in the MIR210HG locus, indicating that overexpression in cancers is not due to gene amplification. The exploration of SNPs on MIR210HG suggested possible structural changes that may affect its interactions with the miRNAs. The correlation of MIR210HG with promoter methylation was found to be significantly negative in nature in majority of cancers depicting the possible epigenetic regulation of expression of MIR210HG. Additionally, MIR210HG showed negative correlations with immune cells and thus may have strong impact on the tumor microenvironment. Functional analysis indicates its association with hypoxia, angiogenesis, metastasis, and DNA damage repair processes. MIR210HG was found to interact with several proteins and potentially regulate chromatin modifications and transcriptional regulation. CONCLUSIONS A first pan-can cancer analysis of MIR210HG highlights its transcriptional and epigenetic deregulation and oncogenic role in the majority of cancers, its correlation with tumor microenvironment factors such as hypoxia and immune infiltration, and its potential as a prognostic biomarker and therapeutic target in several cancers.
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Affiliation(s)
- Garima Yadav
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Ritu Kulshreshtha
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India.
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Zhou W, Su M, Jiang T, Yang Q, Sun Q, Xu K, Shi J, Yang C, Ding N, Li Y, Xu J. SORC: an integrated spatial omics resource in cancer. Nucleic Acids Res 2024; 52:D1429-D1437. [PMID: 37811897 PMCID: PMC10768140 DOI: 10.1093/nar/gkad820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/31/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023] Open
Abstract
The interactions between tumor cells and the microenvironment play pivotal roles in the initiation, progression and metastasis of cancer. The advent of spatial transcriptomics data offers an opportunity to unravel the intricate dynamics of cellular states and cell-cell interactions in cancer. Herein, we have developed an integrated spatial omics resource in cancer (SORC, http://bio-bigdata.hrbmu.edu.cn/SORC), which interactively visualizes and analyzes the spatial transcriptomics data in cancer. We manually curated currently available spatial transcriptomics datasets for 17 types of cancer, comprising 722 899 spots across 269 slices. Furthermore, we matched reference single-cell RNA sequencing data in the majority of spatial transcriptomics datasets, involving 334 379 cells and 46 distinct cell types. SORC offers five major analytical modules that address the primary requirements of spatial transcriptomics analysis, including slice annotation, identification of spatially variable genes, co-occurrence of immune cells and tumor cells, functional analysis and cell-cell communications. All these spatial transcriptomics data and in-depth analyses have been integrated into easy-to-browse and explore pages, visualized through intuitive tables and various image formats. In summary, SORC serves as a valuable resource for providing an unprecedented spatially resolved cellular map of cancer and identifying specific genes and functional pathways to enhance our understanding of the tumor microenvironment.
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Affiliation(s)
- Weiwei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Minghai Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Tiantongfei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Qingyi Yang
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Qisen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Kang Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Jingyi Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Changbo Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Yongsheng Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
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Zhang G, Song C, Fan S, Yin M, Wang X, Zhang Y, Huang X, Li Y, Shang D, Li C, Wang Q. LncSEA 2.0: an updated platform for long non-coding RNA related sets and enrichment analysis. Nucleic Acids Res 2024; 52:D919-D928. [PMID: 37986229 PMCID: PMC10767924 DOI: 10.1093/nar/gkad1008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/12/2023] [Accepted: 10/19/2023] [Indexed: 11/22/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) possess a wide range of biological functions, and research has demonstrated their significance in regulating major biological processes such as development, differentiation, and immune response. The accelerating accumulation of lncRNA research has greatly expanded our understanding of lncRNA functions. Here, we introduce LncSEA 2.0 (http://bio.liclab.net/LncSEA/index.php), aiming to provide a more comprehensive set of functional lncRNAs and enhanced enrichment analysis capabilities. Compared with LncSEA 1.0, we have made the following improvements: (i) We updated the lncRNA sets for 11 categories and extremely expanded the lncRNA scopes for each set. (ii) We newly introduced 15 functional lncRNA categories from multiple resources. This update not only included a significant amount of downstream regulatory data for lncRNAs, but also covered numerous epigenetic regulatory data sets, including lncRNA-related transcription co-factor binding, chromatin regulator binding, and chromatin interaction data. (iii) We incorporated two new lncRNA set enrichment analysis functions based on GSEA and GSVA. (iv) We adopted the snakemake analysis pipeline to track data processing and analysis. In summary, LncSEA 2.0 offers a more comprehensive collection of lncRNA sets and a greater variety of enrichment analysis modules, assisting researchers in a more comprehensive study of the functional mechanisms of lncRNAs.
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Affiliation(s)
- Guorui Zhang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Chao Song
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Shifan Fan
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Xinyue Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing, 163319, China
| | - Yuexin Zhang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Xuemei Huang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Ye Li
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Desi Shang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Chunquan Li
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- MOE Key Lab of Rare Pediatric Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Qiuyu Wang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
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Cheng S, Li S, Yang P, Wang R, Zhou P, Li J. Dissecting the tumour immune microenvironment in merkel cell carcinoma based on a machine learning framework. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2023; 51:397-407. [PMID: 37676035 DOI: 10.1080/21691401.2023.2244998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/13/2023] [Accepted: 08/02/2023] [Indexed: 09/08/2023]
Abstract
Merkel cell carcinoma (MCC) is a primary cutaneous neoplasm of neuroendocrine carcinoma of the skin, which is characterized by molecular heterogeneity with diverse tumour microenvironment (TME). However, we are still lack knowledge of the cellular states and ecosystems in MCC. Here, we systematically identified and characterized the landscape of cellular states and ecotypes in MCC based on a machine learning framework. We obtained 30 distinct cellular states from 9 immune cell types and investigated the B cell, CD8 T cell, fibroblast, and monocytes/macrophage cellular states in detail. The functional profiling of cellular states were investigated and found the genes highly expressed in cellular states were significantly enriched in immune- and cancer hallmark-related pathways. In addition, four ecotypes were further identified which were with different patient compositions. Transcriptional regulation analysis revealed the critical transcription factors (i.e. E2F1, E2F3 and E2F7), which play important roles in regulating the TME of MCC. In summary, the findings of this study may provide rich knowledge to understand the intrinsic subtypes of MCCs and the pathways involved in distinct subtype oncogenesis, and will further advance the knowledge in developing a specific therapeutic strategy for these MCC subtypes.
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Affiliation(s)
- Shaowen Cheng
- Department of Wound Repair, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Si Li
- College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China
| | - Ping Yang
- Department of Radiotherapy, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Rong Wang
- Department of Wound Repair, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Ping Zhou
- Department of Radiotherapy, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Jingquan Li
- The First Affiliated Hospital of Hainan Medical University, Haikou, China
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6
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Wan D, Li R, Huang H, Zhu X, Li G. Pan-cancer landscape of immunology PIWI-interacting RNAs. Comput Struct Biotechnol J 2023; 21:5309-5325. [PMID: 37941657 PMCID: PMC10628341 DOI: 10.1016/j.csbj.2023.10.042] [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: 06/07/2023] [Revised: 10/19/2023] [Accepted: 10/19/2023] [Indexed: 11/10/2023] Open
Abstract
PIWI-interacting RNAs (piRNAs), an emergent type of non-coding RNAs during oncogenesis, play critical roles in regulating tumor microenvironment. Systematic analysis of piRNAs' roles in modulating immune pathways is important for tumor immunotherapy. In this study, in-depth analysis of piRNAs was performed to develop an integrated computational algorithm, the immunology piRNA (ImmPI) pipeline, for uncovering the global expression landscape of piRNAs and identifying their regulatory roles in immune pathways. The immunology piRNAs show a tendency towards overexpression patterns in immune cells, causing perturbations in tumors, being significantly associated with infiltration of immune cells, and having prognostic value. The ImmPI score can contribute to prioritizing tumor-related piRNAs and distinguish two subtypes of SKCM (immune-cold and hot phenotypes), as characterized by different prognoses, immunogenicity and antitumor immunity. Finally, we developed an interactive web resource (ImmPI portal: http://www.hbpding.com/ImmPi) for the biomedical research community, with several useful modules to browse, visualize, and download the results of immunology piRNAs analysis. Overall, our work provides a comprehensive landscape of piRNAs across multiple cancer types and sheds light on their regulatory and functional roles in tumor immunity. These findings pave the way for future research and development of piRNA-based immunotherapies for cancer treatment.
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Affiliation(s)
- Dongyi Wan
- Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ran Li
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Haohao Huang
- Department of Neurosurgery, General Hospital of Central Theater Command of Chinese People’s Liberation Army, Wuhan 430070, China
| | - Xiaohua Zhu
- Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ganxun Li
- Hepatic Surgery Center and Hubei Key Laboratory of Hepato-Biliary-Pancreatic Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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7
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Zhang J, Liu L, Wei X, Zhao C, Li S, Li J, Le TD. Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers. PLoS Comput Biol 2023; 19:e1011308. [PMID: 37812646 PMCID: PMC10586676 DOI: 10.1371/journal.pcbi.1011308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/19/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023] Open
Abstract
Non-coding RNAs (ncRNAs) act as important modulators of gene expression and they have been confirmed to play critical roles in the physiology and development of malignant tumors. Understanding the synergism of multiple ncRNAs in competing endogenous RNA (ceRNA) regulation can provide important insights into the mechanisms of malignant tumors caused by ncRNA regulation. In this work, we present a framework, SCOM, for identifying ncRNA synergistic competition. We systematically construct the landscape of ncRNA synergistic competition across 31 malignant tumors, and reveal that malignant tumors tend to share hub ncRNAs rather than the ncRNA interactions involved in the synergistic competition. In addition, the synergistic competition ncRNAs (i.e. ncRNAs involved in the synergistic competition) are likely to be involved in drug resistance, contribute to distinguishing molecular subtypes of malignant tumors, and participate in immune regulation. Furthermore, SCOM can help to infer ncRNA synergistic competition across malignant tumors and uncover potential diagnostic and prognostic biomarkers of malignant tumors. Altogether, the SCOM framework (https://github.com/zhangjunpeng411/SCOM/) and the resulting web-based database SCOMdb (https://comblab.cn/SCOMdb/) serve as a useful resource for exploring ncRNA regulation and to accelerate the identification of carcinogenic biomarkers.
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Affiliation(s)
- Junpeng Zhang
- School of Engineering, Dali University, Dali, Yunnan, People’s Republic of China
| | - Lin Liu
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Xuemei Wei
- School of Engineering, Dali University, Dali, Yunnan, People’s Republic of China
| | - Chunwen Zhao
- School of Engineering, Dali University, Dali, Yunnan, People’s Republic of China
| | - Sijing Li
- School of Engineering, Dali University, Dali, Yunnan, People’s Republic of China
| | - Jiuyong Li
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Thuc Duy Le
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
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Shi X, Xue Z, Ye K, Yuan J, Zhang Y, Qu J, Su J. Roles of non-coding RNAs in eye development and diseases. WILEY INTERDISCIPLINARY REVIEWS. RNA 2023; 14:e1785. [PMID: 36849659 DOI: 10.1002/wrna.1785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/17/2022] [Accepted: 02/06/2023] [Indexed: 03/01/2023]
Abstract
The prevalence of ocular disorders is dramatically increasing worldwide, especially those that cause visual impairment and permanent loss of vision, including cataract, glaucoma, age-related macular degeneration, and diabetic retinopathy. Extensive evidence has shown that ncRNAs are key regulators in various biogenesis and biological functions, controlling gene expression related to histogenesis and cell differentiation in ocular tissues. Aberrant expression and function of ncRNA can lead to dysfunction of visual system and mediate progression of eye disorders. Here, we mainly offer an overview of the role of precise modulation of ncRNAs in eye development and function in patients with eye diseases. We also highlight the challenges and future perspectives in conducting ncRNA studies, focusing specifically on the role of ncRNAs that may hold expanded promise for their diagnostic and therapeutic applications in various eye diseases. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.
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Affiliation(s)
- Xinrui Shi
- School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zhengbo Xue
- School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Kaicheng Ye
- School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jian Yuan
- School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Yan Zhang
- School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jia Qu
- School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Jianzhong Su
- School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
- Institute of PSI Genomics, Zhejiang, China
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9
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Kan L, Huang Y, Liu Z. WITHDRAWN: JUN and ATF3 are deficient in prostate cancer patients and their delivery in vivo via lipid nanoparticles has therapeutic efficacy by enhancing immune surveillance. Pharmacol Res 2023; 194:106753. [PMID: 37011775 DOI: 10.1016/j.phrs.2023.106753] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 03/03/2023] [Accepted: 03/31/2023] [Indexed: 04/03/2023]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.
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Affiliation(s)
- Liang Kan
- Department of Geriatrics, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, PR China
| | - Yongye Huang
- College of Life and Health Sciences, Northeastern University, Shenyang, 110169, Liaoning, PR China
| | - Zhongyuan Liu
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, PR China.
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10
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Li S, Pan T, Xu G, Gao Y, Zhang Y, Xu Q, Pan J, Zhou W, Xu J, Li Q, Li Y. Deep immunophenotyping reveals clinically distinct cellular states and ecosystems in large-scale colorectal cancer. Commun Biol 2023; 6:785. [PMID: 37500893 PMCID: PMC10374645 DOI: 10.1038/s42003-023-05117-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 07/07/2023] [Indexed: 07/29/2023] Open
Abstract
Determining the diverse cell types in the tumor microenvironment (TME) and their organization into cellular communities, is critical for understanding the biological heterogeneity and therapy of cancer. Here, we deeply immunophenotype the colorectal cancer (CRC) by integrative analysis of large-scale bulk and single cell transcriptome of 2350 patients and 53,137 cells. A rich landscape of 42 cellular states and 7 ecosystems in TMEs is uncovered and extend the previous immune classifications of CRC. Functional pathways and potential transcriptional regulators analysis of cellular states and ecosystems reveal cancer hallmark-related pathways and several critical transcription factors in CRC. High-resolution characterization of the TMEs, we discover the potential utility of cellular states (i.e., Monocytes/Macrophages and CD8 T cell) and ecosystems for prognosis and clinical therapy selection of CRC. Together, our results expand our understanding of cellular organization in TMEs of CRC, with potential implications for the development of biomarkers and precision therapies.
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Affiliation(s)
- Si Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China
| | - Tao Pan
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
| | - Gang Xu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yueying Gao
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China
| | - Ya Zhang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
| | - Qi Xu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
| | - Jiwei Pan
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
| | - Weiwei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Qifu Li
- The First Affiliated Hospital, Hainan Medical University, Haikou, 571199, China.
| | - Yongsheng Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China.
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China.
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11
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Xue J, Chen H, Lu J, Zhang H, Geng J, He P, Lu X. Identification of immunity-related lncRNAs and construction of a ceRNA network of potential prognostic biomarkers in acute myeloid leukemia. Front Genet 2023; 14:1203345. [PMID: 37388937 PMCID: PMC10301753 DOI: 10.3389/fgene.2023.1203345] [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: 04/10/2023] [Accepted: 05/30/2023] [Indexed: 07/01/2023] Open
Abstract
Objective: Using bioinformatics analyses, this study aimed to identify lncRNAs related to the immune status of acute myeloid leukemia (AML) patients and ascertain the potential impact in immunity-related competing endogenous RNA (ceRNA) networks on AML prognosis. Methods: AML-related RNA-seq FPKM data, AML-related miRNA expression microarray data, and gene sets associated with immunity-related pathways were, respectively, obtained from the TCGA, GEO, and ImmReg databases. An immunity-related ceRNA network was then constructed according to the predicted interactions between AML-related mRNAs, lncRNAs, and miRNAs. After performing LASSO and multivariate Cox regression analyses, lncRNAs in the ceRNA network were used to establish an AML prognostic model. According to mutual regulatory relationships and consistent trends of expression among candidate ceRNAs, two ceRNA subnetworks related to the AML prognostic model were determined. Finally, the correlation between the expression levels of mRNAs, lncRNAs, and miRNAs in each ceRNA subnetwork and immune cell infiltration (assessed by combining the ESTIMATE and CIBERSORT methods and ssGSEA) was analyzed. Results: A total of 424 immunity-related differentially expressed (IR-DE) mRNAs (IR-DEmRNAs), 191 IR-DElncRNAs, and 69 IR-DEmiRNAs were obtained, and a ceRNA network of 20 IR-DElncRNAs, 6 IR-DEmRNAs, and 3 IR-DEmiRNAs was established. Univariate Cox regression analysis was conducted on 20 IR-DElncRNAs, and 7 of these were identified to be significantly correlated with the overall survival (OS) time in AML patients. Then, two IR-DElncRNAs (MEG3 and HCP5) were screened as independent OS-related factors by LASSO and multivariable Cox regression analyses, and a prognostic model was constructed to evaluate the survival risk in AML patients. Survival analyses indicated that the OS of patients was often poor in the high-risk group. Additionally, from this model, two ceRNA regulatory pathways, namely, MEG3/miR-125a-5p/SEMA4C and HCP5/miR-125b-5p/IL6R, which were potentially involved in the immune regulation of AML prognosis were identified. Conclusion: lncRNAs HCP5 and MEG3 may act as key ceRNAs in the pathogenesis in AML by regulating immune cell representation as part of the regulatory lncRNA-miRNA-mRNA axes. The candidate mRNAs, lncRNAs, and miRNAs included in the ceRNA network identified here may serve as useful prognostic biomarkers and immunotherapeutic targets for AML.
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Affiliation(s)
- Jia Xue
- School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Haoran Chen
- School of Management, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jinqi Lu
- Department of Computer Science, Boston University, Boston, MA, United States
| | - Haojun Zhang
- School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jie Geng
- School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Peifeng He
- School of Management, Shanxi Medical University, Taiyuan, Shanxi, China
- Shanxi Key Laboratory of Big Data for Clinical Decision Research, Taiyuan, Shanxi, China
| | - Xuechun Lu
- School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, Shanxi, China
- School of Management, Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Hematology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
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12
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McCaffrey TA, Toma I, Yang Z, Katz R, Reiner J, Mazhari R, Shah P, Falk Z, Wargowsky R, Goldman J, Jones D, Shtokalo D, Antonets D, Jepson T, Fetisova A, Jaatinen K, Ree N, Ri M. RNAseq profiling of blood from patients with coronary artery disease: Signature of a T cell imbalance. JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY PLUS 2023; 4:100033. [PMID: 37303712 PMCID: PMC10256136 DOI: 10.1016/j.jmccpl.2023.100033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Background Cardiovascular disease had a global prevalence of 523 million cases and 18.6 million deaths in 2019. The current standard for diagnosing coronary artery disease (CAD) is coronary angiography either by invasive catheterization (ICA) or computed tomography (CTA). Prior studies employed single-molecule, amplification-independent RNA sequencing of whole blood to identify an RNA signature in patients with angiographically confirmed CAD. The present studies employed Illumina RNAseq and network co-expression analysis to identify systematic changes underlying CAD. Methods Whole blood RNA was depleted of ribosomal RNA (rRNA) and analyzed by Illumina total RNA sequencing (RNAseq) to identify transcripts associated with CAD in 177 patients presenting for elective invasive coronary catheterization. The resulting transcript counts were compared between groups to identify differentially expressed genes (DEGs) and to identify patterns of changes through whole genome co-expression network analysis (WGCNA). Results The correlation between Illumina amplified RNAseq and the prior SeqLL unamplified RNAseq was quite strong (r = 0.87), but there was only 9 % overlap in the DEGs identified. Consistent with the prior RNAseq, the majority (93 %) of DEGs were down-regulated ~1.7-fold in patients with moderate to severe CAD (>20 % stenosis). DEGs were predominantly related to T cells, consistent with known reductions in Tregs in CAD. Network analysis did not identify pre-existing modules with a strong association with CAD, but patterns of T cell dysregulation were evident. DEGs were enriched for transcripts associated with ciliary and synaptic transcripts, consistent with changes in the immune synapse of developing T cells. Conclusions These studies confirm and extend a novel mRNA signature of a Treg-like defect in CAD. The pattern of changes is consistent with stress-related changes in the maturation of T and Treg cells, possibly due to changes in the immune synapse.
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Affiliation(s)
- Timothy A. McCaffrey
- Department of Medicine, Division of Genomic Medicine, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
- The St. Laurent Institute, 317 New Boston Street, Woburn, MA 01801, United States of America
- Department of Microbiology, Immunology, and Tropical Medicine, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
- True Bearing Diagnostics, 2450 Virginia Avenue, Washington, DC 20037, United States of America
| | - Ian Toma
- Department of Medicine, Division of Genomic Medicine, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
- Department of Clinical Research and Leadership, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
- True Bearing Diagnostics, 2450 Virginia Avenue, Washington, DC 20037, United States of America
| | - Zhaoqing Yang
- Department of Medicine, Division of Genomic Medicine, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
| | - Richard Katz
- Department of Medicine, Division of Cardiology, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
| | - Jonathan Reiner
- Department of Medicine, Division of Cardiology, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
| | - Ramesh Mazhari
- Department of Medicine, Division of Cardiology, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
| | - Palak Shah
- INOVA Heart and Vascular Institute, 3300 Gallows Road, Fairfax, VA 22042, United States of America
| | - Zachary Falk
- Department of Medicine, Division of Genomic Medicine, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
| | - Richard Wargowsky
- Department of Medicine, Division of Genomic Medicine, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
| | - Jennifer Goldman
- Department of Medicine, Division of Genomic Medicine, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
| | - Dan Jones
- SeqLL, Inc., 3 Federal Street, Billerica, MA 01821, United States of America
| | - Dmitry Shtokalo
- The St. Laurent Institute, 317 New Boston Street, Woburn, MA 01801, United States of America
- A.P. Ershov Institute of Informatics Systems SB RAS, 6, Acad. Lavrentyeva Ave, Novosibirsk 630090, Russia
| | - Denis Antonets
- The St. Laurent Institute, 317 New Boston Street, Woburn, MA 01801, United States of America
| | - Tisha Jepson
- Department of Medicine, Division of Genomic Medicine, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
- The St. Laurent Institute, 317 New Boston Street, Woburn, MA 01801, United States of America
- True Bearing Diagnostics, 2450 Virginia Avenue, Washington, DC 20037, United States of America
| | - Anastasia Fetisova
- Department of Medicine, Division of Genomic Medicine, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
| | - Kevin Jaatinen
- Department of Medicine, Division of Genomic Medicine, The George Washington University, 2300 I Street NW, Washington, DC 20037, United States of America
| | - Natalia Ree
- Center for Mitochondrial Functional Genomics, Institute of Living Systems, Immanuel Kant Baltic Federal University, Kalingrad 236040, Russia
| | - Maxim Ri
- The St. Laurent Institute, 317 New Boston Street, Woburn, MA 01801, United States of America
- A.P. Ershov Institute of Informatics Systems SB RAS, 6, Acad. Lavrentyeva Ave, Novosibirsk 630090, Russia
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13
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Su M, Pan T, Chen QZ, Zhou WW, Gong Y, Xu G, Yan HY, Li S, Shi QZ, Zhang Y, He X, Jiang CJ, Fan SC, Li X, Cairns MJ, Wang X, Li YS. Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications. Mil Med Res 2022; 9:68. [PMID: 36461064 PMCID: PMC9716519 DOI: 10.1186/s40779-022-00434-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.
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Affiliation(s)
- Min Su
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Tao Pan
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Qiu-Zhen Chen
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Wei-Wei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 Heilongjiang China
| | - Yi Gong
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
- Department of Immunology, Nanjing Medical University, Nanjing, 211166 China
| | - Gang Xu
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Huan-Yu Yan
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Si Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Qiao-Zhen Shi
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Ya Zhang
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Xiao He
- Department of Laboratory Medicine, Women and Children’s Hospital of Chongqing Medical University, Chongqing, 401174 China
| | | | - Shi-Cai Fan
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518110 Guangdong China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 Heilongjiang China
| | - Murray J. Cairns
- School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, the University of Newcastle, University Drive, Callaghan, NSW 2308 Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305 Australia
| | - Xi Wang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Yong-Sheng Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
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14
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Mai S, Liang L, Mai G, Liu X, Diao D, Cai R, Liu L. Development and Validation of Lactate Metabolism-Related lncRNA Signature as a Prognostic Model for Lung Adenocarcinoma. Front Endocrinol (Lausanne) 2022; 13:829175. [PMID: 35422758 PMCID: PMC9004472 DOI: 10.3389/fendo.2022.829175] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 02/21/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Lung cancer has been a prominent research focus in recent years due to its role in cancer-related fatalities globally, with lung adenocarcinoma (LUAD) being the most prevalent histological form. Nonetheless, no signature of lactate metabolism-related long non-coding RNAs (LMR-lncRNAs) has been developed for patients with LUAD. Accordingly, we aimed to develop a unique LMR-lncRNA signature to determine the prognosis of patients with LUAD. METHOD The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to derive the lncRNA expression patterns. Identification of LMR-lncRNAs was accomplished by analyzing the co-expression patterns between lncRNAs and LMR genes. Subsequently, the association between lncRNA levels and survival outcomes was determined to develop an effective signature. In the TCGA cohort, Cox regression was enlisted to build an innovative signature consisting of three LMR-lncRNAs, which was validated in the GEO validation cohort. GSEA and immune infiltration analysis were conducted to investigate the functional annotation of the signature and the function of each type of immune cell. RESULTS Fourteen differentially expressed LMR-lncRNAs were strongly correlated with the prognosis of patients with LUAD and collectively formed a new LMR-lncRNA signature. The patients could be categorized into two cohorts based on their LMR-lncRNA signatures: a low-risk and high-risk group. The overall survival of patients with LUAD in the high-risk group was considerably lower than those in the low-risk group. Using Cox regression, this signature was shown to have substantial potential as an independent prognostic factor, which was further confirmed in the GEO cohort. Moreover, the signature could anticipate survival across different groups based on stage, age, and gender, among other variables. This signature also correlated with immune cell infiltration (including B cells, neutrophils, CD4+ T cells, CD8+ T cells, etc.) as well as the immune checkpoint blockade target CTLA-4. CONCLUSION We developed and verified a new LMR-lncRNA signature useful for anticipating the survival of patients with LUAD. This signature could give potentially critical insight for immunotherapy interventions in patients with LUAD.
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Affiliation(s)
- Shijie Mai
- Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Liping Liang
- Department of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Genghui Mai
- Department of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiguang Liu
- Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Dingwei Diao
- Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ruijun Cai
- Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
- *Correspondence: Le Liu, ; Ruijun Cai,
| | - Le Liu
- Department of Gastroenterology, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- *Correspondence: Le Liu, ; Ruijun Cai,
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