RYR2 mutation in non-small cell lung cancer prolongs survival via down-regulation of DKK1 and up-regulation of GS1-115G20.1: A weighted gene Co-expression network analysis and risk prognostic models

IF 1.9 4区 生物学 Q4 CELL BIOLOGY IET Systems Biology Pub Date : 2021-12-07 DOI:10.1049/syb2.12038
Wenjun Ren, Yongwu Li, Xi Chen, Sheng Hu, Wanli Cheng, Yu Cao, Jingcheng Gao, Xia Chen, Da Xiong, Hongrong Li, Ping Wang
{"title":"RYR2 mutation in non-small cell lung cancer prolongs survival via down-regulation of DKK1 and up-regulation of GS1-115G20.1: A weighted gene Co-expression network analysis and risk prognostic models","authors":"Wenjun Ren,&nbsp;Yongwu Li,&nbsp;Xi Chen,&nbsp;Sheng Hu,&nbsp;Wanli Cheng,&nbsp;Yu Cao,&nbsp;Jingcheng Gao,&nbsp;Xia Chen,&nbsp;Da Xiong,&nbsp;Hongrong Li,&nbsp;Ping Wang","doi":"10.1049/syb2.12038","DOIUrl":null,"url":null,"abstract":"<p><i>RYR2</i> mutation is clinically frequent in non-small cell lung cancer (NSCLC) with its function being elusive. We downloaded lung squamous cell carcinoma and lung adenocarcinoma samples from the TCGA database, split the samples into <i>RYR2</i> mutant group (<i>n</i> = 337) and <i>RYR2</i> wild group (<i>n</i> = 634), and established Kaplan-Meier curves. The results showed that <i>RYR2</i> mutant group lived longer than the wild group (<i>p</i> = 0.027). Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs) yielded prognosis-related genes. Five mRNAs and 10 lncRNAs were selected to build survival prognostic models with other clinical features. The AUCs of 2 models are 0.622 and 0.565 for predicting survival at 3 years. Among these genes, the AUCs of <i>DKK1</i> and <i>GS1-115G20.1</i> expression levels were 0.607 and 0.560, respectively, which predicted the 3-year survival rate of NSCLC sufferers. GSEA identified an association of high <i>DKK1</i> expression with <i>TP53</i>, <i>MTOR</i>, and <i>VEGF</i> expression. Several target miRNAs interacting with <i>GS1-115G20.1</i> were observed to show the relationship with the phenotype, treatment, and survival of NSCLC. NSCLC patients with <i>RYR2</i> mutation may obtain better prognosis by down-regulating <i>DKK1</i> and up-regulating <i>GS1-115G20.1</i>.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 2","pages":"43-58"},"PeriodicalIF":1.9000,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2f/bc/SYB2-16-43.PMC8965387.pdf","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Systems Biology","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/syb2.12038","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 5

Abstract

RYR2 mutation is clinically frequent in non-small cell lung cancer (NSCLC) with its function being elusive. We downloaded lung squamous cell carcinoma and lung adenocarcinoma samples from the TCGA database, split the samples into RYR2 mutant group (n = 337) and RYR2 wild group (n = 634), and established Kaplan-Meier curves. The results showed that RYR2 mutant group lived longer than the wild group (p = 0.027). Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs) yielded prognosis-related genes. Five mRNAs and 10 lncRNAs were selected to build survival prognostic models with other clinical features. The AUCs of 2 models are 0.622 and 0.565 for predicting survival at 3 years. Among these genes, the AUCs of DKK1 and GS1-115G20.1 expression levels were 0.607 and 0.560, respectively, which predicted the 3-year survival rate of NSCLC sufferers. GSEA identified an association of high DKK1 expression with TP53, MTOR, and VEGF expression. Several target miRNAs interacting with GS1-115G20.1 were observed to show the relationship with the phenotype, treatment, and survival of NSCLC. NSCLC patients with RYR2 mutation may obtain better prognosis by down-regulating DKK1 and up-regulating GS1-115G20.1.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
非小细胞肺癌中RYR2突变通过下调DKK1和上调GS1-115G20.1延长生存期:加权基因共表达网络分析和风险预后模型
RYR2突变在非小细胞肺癌(NSCLC)中较为常见,但其功能尚不明确。我们从TCGA数据库中下载肺鳞癌和肺腺癌样本,将样本分为RYR2突变组(n = 337)和RYR2野生组(n = 634),建立Kaplan-Meier曲线。结果显示,RYR2突变组比野生组寿命更长(p = 0.027)。差异表达基因(DEGs)加权基因共表达网络分析(WGCNA)得出预后相关基因。选择5种mrna和10种lncrna构建具有其他临床特征的生存预后模型。2个模型预测3年生存率的auc分别为0.622和0.565。其中,DKK1和GS1-115G20.1表达水平的auc分别为0.607和0.560,预测NSCLC患者3年生存率。GSEA发现DKK1高表达与TP53、MTOR和VEGF表达相关。观察到与GS1-115G20.1相互作用的几个靶标mirna与NSCLC的表型、治疗和生存有关。RYR2突变的NSCLC患者下调DKK1,上调GS1-115G20.1,可获得较好的预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
期刊最新文献
DDANet: A deep dilated attention network for intracerebral haemorrhage segmentation. Human essential gene identification based on feature fusion and feature screening. Inference and analysis of cell-cell communication of non-myeloid circulating cells in late sepsis based on single-cell RNA-seq. siRNAEfficacyDB: An experimentally supported small interfering RNA efficacy database. Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1