IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures
Recent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to explore robust signatures for clinical translation is increasingly emphasized, and this is attributed to the clini...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-07-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2021.687975/full |
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| author | Dongqiang Zeng Zilan Ye Rongfang Shen Guangchuang Yu Jiani Wu Yi Xiong Yi Xiong Yi Xiong Rui Zhou Wenjun Qiu Na Huang Li Sun Xuejun Li Xuejun Li Jianping Bin Yulin Liao Min Shi Wangjun Liao |
| author_facet | Dongqiang Zeng Zilan Ye Rongfang Shen Guangchuang Yu Jiani Wu Yi Xiong Yi Xiong Yi Xiong Rui Zhou Wenjun Qiu Na Huang Li Sun Xuejun Li Xuejun Li Jianping Bin Yulin Liao Min Shi Wangjun Liao |
| author_sort | Dongqiang Zeng |
| collection | DOAJ |
| description | Recent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to explore robust signatures for clinical translation is increasingly emphasized, and this is attributed to the clinical success of immune checkpoint blockades in diverse malignancies. However, effective tools for comprehensively interpreting multi-omics data are still warranted to provide increased granularity into the intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Therefore, we developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing a comprehensive investigation of the estimation of reported or user-built signatures, TME deconvolution, and signature construction based on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, long non-coding RNA (lncRNA) profiling, genomic characteristics, and signatures generated from single-cell RNA sequencing (scRNA-seq) data in different cancer settings. Additionally, IOBR integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool for leveraging multi-omics data to facilitate immuno-oncology exploration and to unveil tumor-immune interactions and accelerating precision immunotherapy. |
| first_indexed | 2024-12-16T18:47:04Z |
| format | Article |
| id | doaj.art-45f8899678544d519732ed31ae7caf37 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-12-16T18:47:04Z |
| publishDate | 2021-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-45f8899678544d519732ed31ae7caf372022-12-21T22:20:48ZengFrontiers Media S.A.Frontiers in Immunology1664-32242021-07-011210.3389/fimmu.2021.687975687975IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and SignaturesDongqiang Zeng0Zilan Ye1Rongfang Shen2Guangchuang Yu3Jiani Wu4Yi Xiong5Yi Xiong6Yi Xiong7Rui Zhou8Wenjun Qiu9Na Huang10Li Sun11Xuejun Li12Xuejun Li13Jianping Bin14Yulin Liao15Min Shi16Wangjun Liao17Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaState Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, ChinaDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, ChinaHunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, ChinaXiangya School of Medicine, Central South University, Changsha, ChinaDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, ChinaHunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaRecent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to explore robust signatures for clinical translation is increasingly emphasized, and this is attributed to the clinical success of immune checkpoint blockades in diverse malignancies. However, effective tools for comprehensively interpreting multi-omics data are still warranted to provide increased granularity into the intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Therefore, we developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing a comprehensive investigation of the estimation of reported or user-built signatures, TME deconvolution, and signature construction based on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, long non-coding RNA (lncRNA) profiling, genomic characteristics, and signatures generated from single-cell RNA sequencing (scRNA-seq) data in different cancer settings. Additionally, IOBR integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool for leveraging multi-omics data to facilitate immuno-oncology exploration and to unveil tumor-immune interactions and accelerating precision immunotherapy.https://www.frontiersin.org/articles/10.3389/fimmu.2021.687975/fulltumor microenvironmentmulti-omicsgene signaturesimmune-tumor interactionmetabolism |
| spellingShingle | Dongqiang Zeng Zilan Ye Rongfang Shen Guangchuang Yu Jiani Wu Yi Xiong Yi Xiong Yi Xiong Rui Zhou Wenjun Qiu Na Huang Li Sun Xuejun Li Xuejun Li Jianping Bin Yulin Liao Min Shi Wangjun Liao IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures Frontiers in Immunology tumor microenvironment multi-omics gene signatures immune-tumor interaction metabolism |
| title | IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures |
| title_full | IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures |
| title_fullStr | IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures |
| title_full_unstemmed | IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures |
| title_short | IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures |
| title_sort | iobr multi omics immuno oncology biological research to decode tumor microenvironment and signatures |
| topic | tumor microenvironment multi-omics gene signatures immune-tumor interaction metabolism |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2021.687975/full |
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