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Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma

BACKGROUND: Osteosarcoma (OS) is a disease with high mortality in children and adolescents, and metastasis is one of its important clinical features. However, the molecular mechanism of OS occurrence is not completely clear. Thus, we screened potential biomarkers of OS and analyze their prognostic v...

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Autores principales: Liu, Hua, Zong, Chenyu, Sun, Jiacheng, Li, Haiyang, Qin, Guangzhen, Wang, Xiaojian, Zhu, Jianwei, Yang, Yang, Xue, Qiang, Liu, Xianchen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9360822/
https://www.ncbi.nlm.nih.gov/pubmed/35958002
http://dx.doi.org/10.21037/tp-22-253
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author Liu, Hua
Zong, Chenyu
Sun, Jiacheng
Li, Haiyang
Qin, Guangzhen
Wang, Xiaojian
Zhu, Jianwei
Yang, Yang
Xue, Qiang
Liu, Xianchen
author_facet Liu, Hua
Zong, Chenyu
Sun, Jiacheng
Li, Haiyang
Qin, Guangzhen
Wang, Xiaojian
Zhu, Jianwei
Yang, Yang
Xue, Qiang
Liu, Xianchen
author_sort Liu, Hua
collection PubMed
description BACKGROUND: Osteosarcoma (OS) is a disease with high mortality in children and adolescents, and metastasis is one of its important clinical features. However, the molecular mechanism of OS occurrence is not completely clear. Thus, we screened potential biomarkers of OS and analyze their prognostic value. METHODS: The Cancer Genome Atlas (TCGA) datasets were used to analyze the differential lncRNAs in patients with OS of different immune score and the lncRNAs expressed by immune cells. Cox regression was used to develop the prognosis prediction model and specify the prognosis outcomes. Risk-proportional regression model was constructed, and the samples were divided into high and low groups based on the risk scores for the survival analysis. The areas under the receiver operating characteristic (ROC) curve were calculated and the risk-score model was verified. Finally, using 4 gene sets (comprising chemokines, immune checkpoint blockades, immune activity-related genes, and immune cells), and 4 analysis tools (CIBERSORT, TIMER, XCELL and MCP) to evaluated tumor immune infiltration. RESULTS: Twenty-nine long non-coding ribonucleic acids (lncRNAs) were obtained from the intersection of the screened lncRNAs. Caspase recruitment domain-containing protein 8-antisense RNA 1 (CARD8-AS1), lncRNA five prime to Xist (FTX), KAT8 regulatory NSL complex unit 1-antisense RNA 1 (KANSL1-AS1), Neuroplastin Intronic Transcript 1 (NPTN-IT1), oligodendrocyte maturation-associated long intervening non-coding RNA (OLMALINC) and RPARP Antisense RNA 1 (RPARP-AS1) were found to be correlated with survival. Univariate and multivariate regression analysis showed risk score [HR (hazard ratio) 3.5, P value 0.0043; HR 3.7, P value 0.0033] and metastasis (HR 4.7, P value 6.60E-05; HR 4.8, P value 8.36E-05) were the key factors of patients with OS. The areas under curves (AUCs) of the 1-, 3-, and 5-year ROC curves of the prognostic model were 0.715, 0.729, and 0.771. The low-risk patients tended to have a high abundance of immune cells. CONCLUSIONS: This study showed that a risk score based on 6 lncRNAs has potential value in the prognosis of OS, and patients with low-risk scores have high immune cell infiltration and good prognosis. This study may enrich understandings of underlying mechanisms related to the occurrence and development of OS.
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spelling pubmed-93608222022-08-10 Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma Liu, Hua Zong, Chenyu Sun, Jiacheng Li, Haiyang Qin, Guangzhen Wang, Xiaojian Zhu, Jianwei Yang, Yang Xue, Qiang Liu, Xianchen Transl Pediatr Original Article BACKGROUND: Osteosarcoma (OS) is a disease with high mortality in children and adolescents, and metastasis is one of its important clinical features. However, the molecular mechanism of OS occurrence is not completely clear. Thus, we screened potential biomarkers of OS and analyze their prognostic value. METHODS: The Cancer Genome Atlas (TCGA) datasets were used to analyze the differential lncRNAs in patients with OS of different immune score and the lncRNAs expressed by immune cells. Cox regression was used to develop the prognosis prediction model and specify the prognosis outcomes. Risk-proportional regression model was constructed, and the samples were divided into high and low groups based on the risk scores for the survival analysis. The areas under the receiver operating characteristic (ROC) curve were calculated and the risk-score model was verified. Finally, using 4 gene sets (comprising chemokines, immune checkpoint blockades, immune activity-related genes, and immune cells), and 4 analysis tools (CIBERSORT, TIMER, XCELL and MCP) to evaluated tumor immune infiltration. RESULTS: Twenty-nine long non-coding ribonucleic acids (lncRNAs) were obtained from the intersection of the screened lncRNAs. Caspase recruitment domain-containing protein 8-antisense RNA 1 (CARD8-AS1), lncRNA five prime to Xist (FTX), KAT8 regulatory NSL complex unit 1-antisense RNA 1 (KANSL1-AS1), Neuroplastin Intronic Transcript 1 (NPTN-IT1), oligodendrocyte maturation-associated long intervening non-coding RNA (OLMALINC) and RPARP Antisense RNA 1 (RPARP-AS1) were found to be correlated with survival. Univariate and multivariate regression analysis showed risk score [HR (hazard ratio) 3.5, P value 0.0043; HR 3.7, P value 0.0033] and metastasis (HR 4.7, P value 6.60E-05; HR 4.8, P value 8.36E-05) were the key factors of patients with OS. The areas under curves (AUCs) of the 1-, 3-, and 5-year ROC curves of the prognostic model were 0.715, 0.729, and 0.771. The low-risk patients tended to have a high abundance of immune cells. CONCLUSIONS: This study showed that a risk score based on 6 lncRNAs has potential value in the prognosis of OS, and patients with low-risk scores have high immune cell infiltration and good prognosis. This study may enrich understandings of underlying mechanisms related to the occurrence and development of OS. AME Publishing Company 2022-07 /pmc/articles/PMC9360822/ /pubmed/35958002 http://dx.doi.org/10.21037/tp-22-253 Text en 2022 Translational Pediatrics. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Liu, Hua
Zong, Chenyu
Sun, Jiacheng
Li, Haiyang
Qin, Guangzhen
Wang, Xiaojian
Zhu, Jianwei
Yang, Yang
Xue, Qiang
Liu, Xianchen
Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma
title Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma
title_full Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma
title_fullStr Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma
title_full_unstemmed Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma
title_short Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma
title_sort bioinformatics analysis of lncrnas in the occurrence and development of osteosarcoma
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9360822/
https://www.ncbi.nlm.nih.gov/pubmed/35958002
http://dx.doi.org/10.21037/tp-22-253
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