Dataset Information

A 5-Gene Stemness Score for Rapid Determination of Risk in Multiple Myeloma.

ABSTRACT: Purpose:Risk stratification in patients with ?multiple ?myeloma (MM) remains a challenge. As clinicopathological characteristics have been demonstrated insufficient for exactly defining MM risk, and molecular biomarkers have become the focuses of interests. Prognostic predictions based on ?gene ?expression ?profiles (GEPs) have been the most accurate to this day. The purpose of our study was to construct a risk score based on stemness genes to evaluate the prognosis in MM. Materials and Methods:Bioinformatics studies by ingenuity pathway analyses in ?side ?population (SP) and non-SP (MP) cells of MM patients were performed. Firstly, co-expression network was built to confirm hub genes associated with the top five Kyoto Encyclopedia of Genes and Genomes pathways. Functional analyses of hub genes were used to confirm the biologic functions. Next, these selective genes were utilized for construction of prognostic model, and this model was validated in independent testing sets. Finally, five stemness genes (ROCK1, GSK3B, BRAF, MAPK1 and MAPK14) were used to build a MM side population 5 (MMSP5) gene model, which was demonstrated to be forcefully prognostic compared to usual clinical prognostic parameters by multivariate cox analysis. MM patients in MMSP5 low-risk group were significantly related to better prognosis than those in high-risk group in independent testing sets. Conclusion:Our study provided proof-of-concept that MMSP5 model can be adopted to evaluate recurrence risk and clinical outcome for MM. The MMSP5 model evaluated in different databases clearly indicated novel risk stratification for personalized anti-MM treatments.

SUBMITTER: Bai H

PROVIDER: S-EPMC7244240 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A 5-Gene Stemness Score for Rapid Determination of Risk in Multiple Myeloma.

Bai Hua H Chen Bing B

OncoTargets and therapy 20200518

<h4>Purpose</h4>Risk stratification in patients with multiple myeloma (MM) remains a challenge. As clinicopathological characteristics have been demonstrated insufficient for exactly defining MM risk, and molecular biomarkers have become the focuses of interests. Prognostic predictions based on gene expression profiles (GEPs) have been the most accurate to this day. The purpose of our study was to construct a risk score based on stemness genes to evaluate the prognosis in MM.<h4>Materials a ...[more]

PMID: 32547066

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Project 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.

Dataset Information

A 5-Gene Stemness Score for Rapid Determination of Risk in Multiple Myeloma.

Publications

A 5-Gene Stemness Score for Rapid Determination of Risk in Multiple Myeloma.

Similar Datasets

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