Cargando…
Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma
Tumor progression includes the obtainment of progenitor and stem cell-like features and the gradual loss of a differentiated phenotype. Stemness was defined as the potential for differentiation and self-renewal from the cell of origin. Previous studies have confirmed the effective application of ste...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415636/ https://www.ncbi.nlm.nih.gov/pubmed/34484287 http://dx.doi.org/10.3389/fgene.2021.666561 |
_version_ | 1783748007282343936 |
---|---|
author | Ban, Chunmei Yang, Feiyan Wei, Min Liu, Qin Wang, Jiankun Chen, Lei Lu, Liuting Xie, Dongmei Liu, Lie Huang, Jinxiong |
author_facet | Ban, Chunmei Yang, Feiyan Wei, Min Liu, Qin Wang, Jiankun Chen, Lei Lu, Liuting Xie, Dongmei Liu, Lie Huang, Jinxiong |
author_sort | Ban, Chunmei |
collection | PubMed |
description | Tumor progression includes the obtainment of progenitor and stem cell-like features and the gradual loss of a differentiated phenotype. Stemness was defined as the potential for differentiation and self-renewal from the cell of origin. Previous studies have confirmed the effective application of stemness in a number of malignancies. However, the mechanisms underlying the growth and maintenance of multiple myeloma (MM) stem cells remain unclear. We calculated the stemness index for samples of MM by utilizing a novel one-class logistic regression (OCLR) machine learning algorithm and found that mRNA expression-based stemness index (mRNAsi) was an independent prognostic factor of MM. Based on the same cutoff value, mRNAsi could stratify MM patients into low and high groups with different outcomes. We identified 127 stemness-related signatures using weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Functional annotation and pathway enrichment analysis indicated that these genes were mainly involved in the cell cycle, cell differentiation, and DNA replication and repair. Using the molecular complex detection (MCODE) algorithm, we identified 34 pivotal signatures. Meanwhile, we conducted unsupervised clustering and classified the MM cohorts into three MM stemness (MMS) clusters with distinct prognoses. Samples in MMS-cluster3 possessed the highest stemness fractions and the worst prognosis. Additionally, we applied the ESTIMATE algorithm to infer differential immune infiltration among the three MMS clusters. The immune core and stromal score were significantly lower in MMS-cluster3 than in the other clusters, supporting the negative relation between stemness and anticancer immunity. Finally, we proposed a prognostic nomogram that allows for individualized assessment of the 3- and 5-year overall survival (OS) probabilities among patients with MM. Our study comprehensively assessed the MM stemness index based on large cohorts and built a 34-gene based classifier for predicting prognosis and potential strategies for stemness treatment. |
format | Online Article Text |
id | pubmed-8415636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84156362021-09-04 Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma Ban, Chunmei Yang, Feiyan Wei, Min Liu, Qin Wang, Jiankun Chen, Lei Lu, Liuting Xie, Dongmei Liu, Lie Huang, Jinxiong Front Genet Genetics Tumor progression includes the obtainment of progenitor and stem cell-like features and the gradual loss of a differentiated phenotype. Stemness was defined as the potential for differentiation and self-renewal from the cell of origin. Previous studies have confirmed the effective application of stemness in a number of malignancies. However, the mechanisms underlying the growth and maintenance of multiple myeloma (MM) stem cells remain unclear. We calculated the stemness index for samples of MM by utilizing a novel one-class logistic regression (OCLR) machine learning algorithm and found that mRNA expression-based stemness index (mRNAsi) was an independent prognostic factor of MM. Based on the same cutoff value, mRNAsi could stratify MM patients into low and high groups with different outcomes. We identified 127 stemness-related signatures using weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Functional annotation and pathway enrichment analysis indicated that these genes were mainly involved in the cell cycle, cell differentiation, and DNA replication and repair. Using the molecular complex detection (MCODE) algorithm, we identified 34 pivotal signatures. Meanwhile, we conducted unsupervised clustering and classified the MM cohorts into three MM stemness (MMS) clusters with distinct prognoses. Samples in MMS-cluster3 possessed the highest stemness fractions and the worst prognosis. Additionally, we applied the ESTIMATE algorithm to infer differential immune infiltration among the three MMS clusters. The immune core and stromal score were significantly lower in MMS-cluster3 than in the other clusters, supporting the negative relation between stemness and anticancer immunity. Finally, we proposed a prognostic nomogram that allows for individualized assessment of the 3- and 5-year overall survival (OS) probabilities among patients with MM. Our study comprehensively assessed the MM stemness index based on large cohorts and built a 34-gene based classifier for predicting prognosis and potential strategies for stemness treatment. Frontiers Media S.A. 2021-08-16 /pmc/articles/PMC8415636/ /pubmed/34484287 http://dx.doi.org/10.3389/fgene.2021.666561 Text en Copyright © 2021 Ban, Yang, Wei, Liu, Wang, Chen, Lu, Xie, Liu and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Ban, Chunmei Yang, Feiyan Wei, Min Liu, Qin Wang, Jiankun Chen, Lei Lu, Liuting Xie, Dongmei Liu, Lie Huang, Jinxiong Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma |
title | Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma |
title_full | Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma |
title_fullStr | Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma |
title_full_unstemmed | Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma |
title_short | Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma |
title_sort | integrative analysis of gene expression through one-class logistic regression machine learning identifies stemness features in multiple myeloma |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415636/ https://www.ncbi.nlm.nih.gov/pubmed/34484287 http://dx.doi.org/10.3389/fgene.2021.666561 |
work_keys_str_mv | AT banchunmei integrativeanalysisofgeneexpressionthroughoneclasslogisticregressionmachinelearningidentifiesstemnessfeaturesinmultiplemyeloma AT yangfeiyan integrativeanalysisofgeneexpressionthroughoneclasslogisticregressionmachinelearningidentifiesstemnessfeaturesinmultiplemyeloma AT weimin integrativeanalysisofgeneexpressionthroughoneclasslogisticregressionmachinelearningidentifiesstemnessfeaturesinmultiplemyeloma AT liuqin integrativeanalysisofgeneexpressionthroughoneclasslogisticregressionmachinelearningidentifiesstemnessfeaturesinmultiplemyeloma AT wangjiankun integrativeanalysisofgeneexpressionthroughoneclasslogisticregressionmachinelearningidentifiesstemnessfeaturesinmultiplemyeloma AT chenlei integrativeanalysisofgeneexpressionthroughoneclasslogisticregressionmachinelearningidentifiesstemnessfeaturesinmultiplemyeloma AT luliuting integrativeanalysisofgeneexpressionthroughoneclasslogisticregressionmachinelearningidentifiesstemnessfeaturesinmultiplemyeloma AT xiedongmei integrativeanalysisofgeneexpressionthroughoneclasslogisticregressionmachinelearningidentifiesstemnessfeaturesinmultiplemyeloma AT liulie integrativeanalysisofgeneexpressionthroughoneclasslogisticregressionmachinelearningidentifiesstemnessfeaturesinmultiplemyeloma AT huangjinxiong integrativeanalysisofgeneexpressionthroughoneclasslogisticregressionmachinelearningidentifiesstemnessfeaturesinmultiplemyeloma |