A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer

Long non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on N7-methylguanosine (m7G)-related lncRNA in breast cancer is lacking. Therefore, the present study explored the prognostic value, gene expression characteristics, and effects of m7G...

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Main Authors: Zhidong Huang, Kaixin Lou, Hong Liu
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-10-01
Series:Frontiers in Genetics
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fgene.2022.1030275/full
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author Zhidong Huang
Kaixin Lou
Hong Liu
author_facet Zhidong Huang
Kaixin Lou
Hong Liu
author_sort Zhidong Huang
collection DOAJ
description Long non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on N7-methylguanosine (m7G)-related lncRNA in breast cancer is lacking. Therefore, the present study explored the prognostic value, gene expression characteristics, and effects of m7G-related lncRNA on tumor immune cell infiltration and tumor mutational burden (TMB) in breast cancer. lncRNA expression matrices and clinical follow-up data of patients with breast cancer were obtained from The Cancer Genome Atlas, revealing eight significantly differentially expressed and prognostically relevant m7G-related lncRNAs in breast cancer tissues: BAIAP2-DT, COL4A2-AS1, FARP1-AS1, RERE-AS1, NDUFA6-DT, TFAP2A-AS1, LINC00115, and MIR302CHG. A breast cancer prognostic signature was created based on these m7G-related lncRNAs according to least absolute shrinkage and selection operator Cox regression. The prognostic signature combined with potential prognostic factors showed independent prognostic value, reliability, and specificity. Meanwhile, we constructed a risk score-based nomogram to assist clinical decision-making. Gene set enrichment analysis revealed that low- and high-risk group were associated with metabolism-related pathways. Our study demonstrated the association between tumor immune cell infiltration based on analyses with the CIBERSORT algorithm and prognostic signature. We also assessed the correlation between prognostic signature and TMB. Lastly, quantitative real-time polymerase chain reaction analysis was performed to validate differentially expressed lncRNAs. The effective prognostic signature based on m7G-related lncRNAs has the potential to predict the survival prognosis of patients with breast cancer. The eight m7G-related lncRNAs identified in this study might represent potential biomarkers and therapeutic targets of breast cancer.
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spelling doaj.art-58ebffc427a34d72b943548442b50f6f2022-12-22T04:30:19ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-10-011310.3389/fgene.2022.10302751030275A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancerZhidong HuangKaixin LouHong LiuLong non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on N7-methylguanosine (m7G)-related lncRNA in breast cancer is lacking. Therefore, the present study explored the prognostic value, gene expression characteristics, and effects of m7G-related lncRNA on tumor immune cell infiltration and tumor mutational burden (TMB) in breast cancer. lncRNA expression matrices and clinical follow-up data of patients with breast cancer were obtained from The Cancer Genome Atlas, revealing eight significantly differentially expressed and prognostically relevant m7G-related lncRNAs in breast cancer tissues: BAIAP2-DT, COL4A2-AS1, FARP1-AS1, RERE-AS1, NDUFA6-DT, TFAP2A-AS1, LINC00115, and MIR302CHG. A breast cancer prognostic signature was created based on these m7G-related lncRNAs according to least absolute shrinkage and selection operator Cox regression. The prognostic signature combined with potential prognostic factors showed independent prognostic value, reliability, and specificity. Meanwhile, we constructed a risk score-based nomogram to assist clinical decision-making. Gene set enrichment analysis revealed that low- and high-risk group were associated with metabolism-related pathways. Our study demonstrated the association between tumor immune cell infiltration based on analyses with the CIBERSORT algorithm and prognostic signature. We also assessed the correlation between prognostic signature and TMB. Lastly, quantitative real-time polymerase chain reaction analysis was performed to validate differentially expressed lncRNAs. The effective prognostic signature based on m7G-related lncRNAs has the potential to predict the survival prognosis of patients with breast cancer. The eight m7G-related lncRNAs identified in this study might represent potential biomarkers and therapeutic targets of breast cancer.https://www.frontiersin.org/articles/10.3389/fgene.2022.1030275/fullbreast cancerm7G-related lncRNAprognostic signaturetumor immune cell infiltrationtumor mutational burden
spellingShingle Zhidong Huang
Kaixin Lou
Hong Liu
A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer
Frontiers in Genetics
breast cancer
m7G-related lncRNA
prognostic signature
tumor immune cell infiltration
tumor mutational burden
title A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer
title_full A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer
title_fullStr A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer
title_full_unstemmed A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer
title_short A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer
title_sort novel prognostic signature based on n7 methylguanosine related long non coding rnas in breast cancer
topic breast cancer
m7G-related lncRNA
prognostic signature
tumor immune cell infiltration
tumor mutational burden
url https://www.frontiersin.org/articles/10.3389/fgene.2022.1030275/full
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