This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
wei xiang
Neurosrugery Department, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Municipal People's Hospital, Shenzhen, Guangdong, 518020, China
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background:
Glioblastoma (GBM) is a primary malignant tumor of the central nervous system with a poor prognosis. Long non-coding RNAs (lncRNAs) play a variety of key regulatory roles in a variety of biological processes, and have an important influence on the occurrence and development of tumors by regulating the expression of target genes. However, their role in the prognosis of GBM is still lacking in accurate prognostic markers.
This study aims to establish an effective lncRNAs model to evaluate the prognosis of GBM.
Methods:
We used data of mRNA, lncRNA and clinical follow-up from The Cancer Genome Atlas (TCGA) to conduct univariate analysis, clustering analysis, coding gene difference analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis for GBM patients. lncRNAs that are closely related to the survival and prognosis of GBM were found and a multiple regression model was constructed to calculate the risk score of the samples, so as to accurately predict the clinical prognosis of GBM patients.
Results:
Through multiple systematic analysis, we found 5 lncRNAs that are closely related to the survival and prognosis of GBM, and these 5 lncRNAs can be used as independent prognostic factors. Through GO enrichment analysis and KEGG pathway analysis, we found that there is a close relationship between GBM and G protein coupled receptors. Therefore, 93 mRNAs associated with G-protein-coupled receptors and 5 lncRNAs associated with independent prognostic factors were selected to calculate the correlation, respectively. In other words, a tumor-specific lncRNAs/mRNAs co-expression network was constructed through biological prediction based on correlation analysis. The results showed that lncRNAs and mRNAs interregulated very closely in GBM patients. In addition, we also constructed a multiple regression model with these 5 lncRNAs which was able to calculate the risk score of each sample to accurately predict the clinical prognosis of GBM patients.
Conclusion:
Our study found lncRNAs independently were related to the prognosis of GBM, and successfully constructed a multiple regression model related to lncRNAs, providing a new perspective for better evaluation of the role of lncRNAs in the clinical prognosis of GBM.
Keywords
Glioblastoma, G protein coupled receptor, LncRNA, Clinical outcomes
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
wei xiang
Neurosrugery Department, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Municipal People's Hospital, Shenzhen, Guangdong, 518020, China
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 13 Jan, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background:
Glioblastoma (GBM) is a primary malignant tumor of the central nervous system with a poor prognosis. Long non-coding RNAs (lncRNAs) play a variety of key regulatory roles in a variety of biological processes, and have an important influence on the occurrence and development of tumors by regulating the expression of target genes. However, their role in the prognosis of GBM is still lacking in accurate prognostic markers.
This study aims to establish an effective lncRNAs model to evaluate the prognosis of GBM.
Methods:
We used data of mRNA, lncRNA and clinical follow-up from The Cancer Genome Atlas (TCGA) to conduct univariate analysis, clustering analysis, coding gene difference analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis for GBM patients. lncRNAs that are closely related to the survival and prognosis of GBM were found and a multiple regression model was constructed to calculate the risk score of the samples, so as to accurately predict the clinical prognosis of GBM patients.
Results:
Through multiple systematic analysis, we found 5 lncRNAs that are closely related to the survival and prognosis of GBM, and these 5 lncRNAs can be used as independent prognostic factors. Through GO enrichment analysis and KEGG pathway analysis, we found that there is a close relationship between GBM and G protein coupled receptors. Therefore, 93 mRNAs associated with G-protein-coupled receptors and 5 lncRNAs associated with independent prognostic factors were selected to calculate the correlation, respectively. In other words, a tumor-specific lncRNAs/mRNAs co-expression network was constructed through biological prediction based on correlation analysis. The results showed that lncRNAs and mRNAs interregulated very closely in GBM patients. In addition, we also constructed a multiple regression model with these 5 lncRNAs which was able to calculate the risk score of each sample to accurately predict the clinical prognosis of GBM patients.
Conclusion:
Our study found lncRNAs independently were related to the prognosis of GBM, and successfully constructed a multiple regression model related to lncRNAs, providing a new perspective for better evaluation of the role of lncRNAs in the clinical prognosis of GBM.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
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