{"title":"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","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":"{\"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}","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}
Construction of mitochondrial quality regulation genes-related prognostic model based on bulk-RNA-seq analysis in multiple myeloma.
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.
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.