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Risk stratification by long non-coding RNAs profiling in COVID-19 patients |
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| Authors: | Jie Cheng Xiang Zhou Weijun Feng Min Jia Xinlu Zhang Taixue An Minyuan Luan Yi Pan Shu Zhang Zhaoming Zhou Lei Wen Yun Sun Cheng Zhou |
| Affiliation: | 1. Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China Contribution: Conceptualization (equal), Data curation (equal), Formal analysis (equal), Funding acquisition (equal), Investigation (equal), Methodology (equal), Software (equal), Supervision (equal), Validation (equal), Writing - original draft (equal), Writing - review & editing (equal);2. Department of Anesthesiology, General Hospital of Central Theater Command of PLA, Wuhan, China Contribution: Conceptualization (equal), Investigation (equal), Validation (equal), Writing - original draft (equal);3. Institute of Pediatrics, Children’s Hospital of Fudan University, the Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai, China Contribution: Formal analysis (equal), Validation (equal), Writing - original draft (equal), Writing - review & editing (equal);4. Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China Contribution: Investigation (supporting), Methodology (supporting), Writing - original draft (supporting);5. Department of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, China Contribution: Data curation (supporting), Formal analysis (supporting), Writing - original draft (supporting);6. Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China Contribution: Formal analysis (supporting), Software (supporting), Writing - original draft (supporting);7. Organ Transplantation Center, the Affiliated Hospital of Qingdao University, Qingdao, China Contribution: Software (equal), Writing - review & editing (equal);8. Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany Faculty of Medicine Heidelberg, Heidelberg University, Heidelberg, Germany Contribution: Software (equal), Writing - original draft (supporting);9. Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, China Contribution: Methodology (supporting), Validation (supporting);10. Department of Radiation Medicine, School of Public Health, Southern Medical University, Guangzhou, China Contribution: Conceptualization (supporting), Data curation (supporting), Software (supporting);11. Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, China Contribution: Writing - original draft (supporting), Writing - review & editing (supporting);12. Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China;13. Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China |
| Abstract: | Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic worldwide. Long non-coding RNAs (lncRNAs) are a subclass of endogenous, non-protein-coding RNA, which lacks an open reading frame and is more than 200 nucleotides in length. However, the functions for lncRNAs in COVID-19 have not been unravelled. The present study aimed at identifying the related lncRNAs based on RNA sequencing of peripheral blood mononuclear cells from patients with SARS-CoV-2 infection as well as health individuals. Overall, 17 severe, 12 non-severe patients and 10 healthy controls were enrolled in this study. Firstly, we reported some altered lncRNAs between severe, non-severe COVID-19 patients and healthy controls. Next, we developed a 7-lncRNA panel with a good differential ability between severe and non-severe COVID-19 patients using least absolute shrinkage and selection operator regression. Finally, we observed that COVID-19 is a heterogeneous disease among which severe COVID-19 patients have two subtypes with similar risk score and immune score based on lncRNA panel using iCluster algorithm. As the roles of lncRNAs in COVID-19 have not yet been fully identified and understood, our analysis should provide valuable resource and information for the future studies. |
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