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...

Descripción completa

Detalles Bibliográficos
Autores principales: Ban, Chunmei, Yang, Feiyan, Wei, Min, Liu, Qin, Wang, Jiankun, Chen, Lei, Lu, Liuting, Xie, Dongmei, Liu, Lie, Huang, Jinxiong
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