Dataset Information

Risk stratification by long non-coding RNAs profiling in COVID-19 patients.

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.

SUBMITTER: Cheng J

PROVIDER: S-EPMC8107096 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundTumor necrosis factor (TNF) is an inflammatory cytokine that can coordinate tissue homeostasis by co-regulating the production of cytokines, cell survival, or death. It widely expresses in various tumor tissues and correlates with the malignant clinical features of patients. As an important inflammatory factor, the role of TNFα is involved in all steps of tumorigenesis and development, including cell transformation, survival, proliferation, invasion and metastasis. Recent research has showed that long non-coding RNAs (lncRNAs), defined as RNA transcripts >200 nucleotides that do not encode a protein, influence numerous cellular processes. However, little is known about the genomic profile of TNF pathway related-lncRNAs in GBM. This study investigated the molecular mechanism of TNF related-lncRNAs and their immune characteristics in glioblastoma multiforme (GBM) patients.MethodsTo identify TNF associations in GBM patients, we performed bioinformatics analysis of public databases - The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). The ConsensusClusterPlus, CIBERSORT, Estimate, GSVA and TIDE and first-order bias correlation and so on approaches were conducted to comprehensively characterize and compare differences among TNF-related subtypes.ResultsBased on the comprehensive analysis of TNF-related lncRNAs expression profiles, we constructed six TNF-related lncRNAs (C1RL-AS1, LINC00968, MIR155HG, CPB2-AS1, LINC00906, and WDR11-AS1) risk signature to determine the role of TNF-related lncRNAs in GBM. This signature could divide GBM patients into subtypes with distinct clinical and immune characteristics and prognoses. We identified three molecular subtypes (C1, C2, and C3), with C2 showing the best prognosis; otherwise, C3 showing the worst prognosis. Moreover, we assessed the prognostic value, immune infiltration, immune checkpoints, chemokines cytokines and enrichment analysis of this signature in GBM. The TNF-related lncRNA signature was tightly associated with the regulation of tumor immune therapy and could serve as an independent prognostic biomarker in GBM.ConclusionThis analysis provides a comprehensive understanding of the role of TNF-related characters, which may improve the clinical outcome of GBM patients.

Dataset Information

Risk stratification by long non-coding RNAs profiling in COVID-19 patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets