Construction of mitochondrial quality regulation genes-related prognostic model based on bulk-RNA-seq analysis in multiple myeloma.

IF 5 3区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY BioFactors Pub Date : 2024-10-24 DOI:10.1002/biof.2135
Xiaohui Li, Ling Zhang, Chengcheng Liu, Yi He, Xudong Li, Yichuan Xu, Cuiyin Gu, Xiaozhen Wang, Shuoting Wang, Jingwen Zhang, Jiajun Liu
{"title":"Construction of mitochondrial quality regulation genes-related prognostic model based on bulk-RNA-seq analysis in multiple myeloma.","authors":"Xiaohui Li, Ling Zhang, Chengcheng Liu, Yi He, Xudong Li, Yichuan Xu, Cuiyin Gu, Xiaozhen Wang, Shuoting Wang, Jingwen Zhang, Jiajun Liu","doi":"10.1002/biof.2135","DOIUrl":null,"url":null,"abstract":"<p><p>Mitochondrial quality regulation plays an important role in affecting the treatment sensitivity of multiple myeloma (MM). We aimed to develop a mitochondrial quality regulation genes (MQRGs)-related prognostic model for MM patients. The Genomic Data Commons-MM of bulk RNA-seq, mutation, and single-cell RNA-seq (scRNA-seq) dataset were downloaded, and the MQRGs gene set was collected previous study. \"maftools\" and CIBERSORT were used for mutation and immune-infiltration analysis. Subsequently, the \"ConsensusClusterPlus\" was used to perform the unsupervised clustering analysis, \"survminer\" and \"ssGSEA\" R package was used for the Kaplan-Meier survival and enrichment analysis, \"limma\" R, univariate and Least Absolute Shrinkage and Selection Operator Cox were used for RiskScore model. The \"timeROC\" R package was used for Receiver Operating Characteristic Curve analysis. Finally, the \"Seurat\" R package was used for scRNA-seq analysis. These MQRGs are mainly located on chromosome-1,2,3,7, and 22 and had significant expression differences among age, gender, and stage groups, in which PPARGC1A and PPARG are the high mutation genes. Most MQRGs expression are closely associated with the plasma cells infiltration and can divide the patients into 2 different prognostic clusters (C1, C2). Then, 8 risk models were screened from 60 DEGs for RiskScore, which is an independent prognostic factor and effectively divided the patients into high and low risk groups with significant difference of immune checkpoint expression. Nomogram containing RiskScore can accurately predict patient prognosis, and a series of specific transcription factor PRDM1 and IRF1 were identified. We described the based molecular features and developed a high effective MQRGs-related prognostic model in MM.</p>","PeriodicalId":8923,"journal":{"name":"BioFactors","volume":" ","pages":""},"PeriodicalIF":5.0000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioFactors","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/biof.2135","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Mitochondrial quality regulation plays an important role in affecting the treatment sensitivity of multiple myeloma (MM). We aimed to develop a mitochondrial quality regulation genes (MQRGs)-related prognostic model for MM patients. The Genomic Data Commons-MM of bulk RNA-seq, mutation, and single-cell RNA-seq (scRNA-seq) dataset were downloaded, and the MQRGs gene set was collected previous study. "maftools" and CIBERSORT were used for mutation and immune-infiltration analysis. Subsequently, the "ConsensusClusterPlus" was used to perform the unsupervised clustering analysis, "survminer" and "ssGSEA" R package was used for the Kaplan-Meier survival and enrichment analysis, "limma" R, univariate and Least Absolute Shrinkage and Selection Operator Cox were used for RiskScore model. The "timeROC" R package was used for Receiver Operating Characteristic Curve analysis. Finally, the "Seurat" R package was used for scRNA-seq analysis. These MQRGs are mainly located on chromosome-1,2,3,7, and 22 and had significant expression differences among age, gender, and stage groups, in which PPARGC1A and PPARG are the high mutation genes. Most MQRGs expression are closely associated with the plasma cells infiltration and can divide the patients into 2 different prognostic clusters (C1, C2). Then, 8 risk models were screened from 60 DEGs for RiskScore, which is an independent prognostic factor and effectively divided the patients into high and low risk groups with significant difference of immune checkpoint expression. Nomogram containing RiskScore can accurately predict patient prognosis, and a series of specific transcription factor PRDM1 and IRF1 were identified. We described the based molecular features and developed a high effective MQRGs-related prognostic model in MM.

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
基于批量RNA-seq分析构建多发性骨髓瘤线粒体质量调控基因相关预后模型
线粒体质量调控在影响多发性骨髓瘤(MM)治疗敏感性方面发挥着重要作用。我们的目标是为多发性骨髓瘤患者建立一个与线粒体质量调控基因(MQRGs)相关的预后模型。我们从基因组数据公共共享平台(Genomic Data Commons-MM)下载了大量RNA-seq、突变和单细胞RNA-seq(scRNA-seq)数据集,并在之前的研究中收集了MQRGs基因集。使用 "maftools "和 CIBERSORT 进行突变和免疫浸润分析。随后,使用 "ConsensusClusterPlus "进行无监督聚类分析,使用 "survminer "和 "ssGSEA "R软件包进行Kaplan-Meier生存分析和富集分析,使用 "limma "R软件包、单变量和最小绝对收缩及选择操作符Cox进行RiskScore模型分析。timeROC "R软件包用于接收者工作特征曲线分析。最后,"Seurat "R软件包用于scRNA-seq分析。这些MQRGs主要位于1、2、3、7和22号染色体上,在不同年龄、性别和分期组之间存在显著的表达差异,其中PPARGC1A和PPARG是高突变基因。大多数 MQRGs 的表达与浆细胞浸润密切相关,可将患者分为两个不同的预后群(C1、C2)。然后,从 60 个 DEGs 中筛选出了 8 个风险模型 RiskScore,它是一个独立的预后因素,能有效地将免疫检查点表达差异显著的患者分为高危和低危两组。包含RiskScore的提名图能准确预测患者的预后,并确定了一系列特异性转录因子PRDM1和IRF1。我们描述了基于MQRGs的分子特征,并建立了MM的高效MQRGs相关预后模型。
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
BioFactors
BioFactors 生物-内分泌学与代谢
CiteScore
11.50
自引率
3.30%
发文量
96
审稿时长
6-12 weeks
期刊介绍: BioFactors, a journal of the International Union of Biochemistry and Molecular Biology, is devoted to the rapid publication of highly significant original research articles and reviews in experimental biology in health and disease. The word “biofactors” refers to the many compounds that regulate biological functions. Biological factors comprise many molecules produced or modified by living organisms, and present in many essential systems like the blood, the nervous or immunological systems. A non-exhaustive list of biological factors includes neurotransmitters, cytokines, chemokines, hormones, coagulation factors, transcription factors, signaling molecules, receptor ligands and many more. In the group of biofactors we can accommodate several classical molecules not synthetized in the body such as vitamins, micronutrients or essential trace elements. In keeping with this unified view of biochemistry, BioFactors publishes research dealing with the identification of new substances and the elucidation of their functions at the biophysical, biochemical, cellular and human level as well as studies revealing novel functions of already known biofactors. The journal encourages the submission of studies that use biochemistry, biophysics, cell and molecular biology and/or cell signaling approaches.
期刊最新文献
Role of miRNAs in breast cancer development and progression: Current research. Dose-dependent effects of anthocyanin-rich extracts on obesity-induced inflammation and gut microbiota modulation. Insights into an indolicidin-derived low-toxic anti-microbial peptide's efficacy against bacterial cells while preserving eukaryotic cell viability. Investigating the dysregulation of genes associated with glucose and lipid metabolism in gastric cancer and their influence on immunity and prognosis. Construction of lysosome-related prognostic signature to predict the survival outcomes and selecting suitable drugs for patients with HNSCC.
×
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