<p>A 5-Gene Stemness Score for Rapid Determination of Risk in Multiple Myeloma</p>

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.

Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data

“…The DEGs included in GEP signatures are diverse but closely connected to similar pathways. These genes may relate to kinome 15 , autophagy 16 , cell cycle 10 , 17 , stemness 18 , cytogenetic abnormalities 9 , 19 , chromosome 1 20 , homozygous deletions, cell death 21 and immune 7 subnetworks. At least 8 to 10 molecular subgroups of MM based on the genomic and transcriptomic patterns have been reported that tend to correlate with different clinical outcomes 8 .…”

“…At least 8 to 10 molecular subgroups of MM based on the genomic and transcriptomic patterns have been reported that tend to correlate with different clinical outcomes 8 . Computational and functional analysis of hub genes, nodes, networks and pathways in MM have led to the development of risk scoring systems, relating to the seven genetic subgroups 22 , 70 genes UAMS70 risk signatures 20 , IFM15 risk stratification 17 , 5 gene stemness score 18 , UAMS 17 23 , CINGLEC 214 14 , HOVON-65/GMMG-HD4 EMC 92 9 , HZD 97 21 , M3CN 10 and others.…”

Over-Expression of Centromere Protein U Participates in the Malignant Neoplastic Progression of Breast Cancer

“…Centromere protein A (CENP-A) is over-expressed in colorectal cancer cell lines and suggested to be an independent prognostic marker for patients with estrogen receptor (ER)-positive breast cancer who received no systemic therapy (8,9). Many previous studies have reported a high expression level of centromere protein E (CENP-E) and centromere protein W (CENP-W) in several solid tumors such as uterine cervical cancer, gastric cancer, and hypopharyngeal cancer (10)(11)(12)(13)(14)(15)(16). Furthermore, centromere protein H (CENP-H) has been demonstrated to be a novel biomarker related to the survival in patients with hepatocellular carcinoma (17).…”

Genome-Wide Identification of Potential Biomarkers in Multiple Myeloma Using Meta-Analysis of mRNA and miRNA Expression Data

“…Expression pro les of differentially expressed genes (DEGs) are of paramount importance and have provided critical prognostic insights in MM. Computational and functional analysis of hub genes, nodes, networks and pathways in MM have led to the development of risk scoring systems, relating to the 8 genetic subgroups 2 , 70 genes UAMS70 risk signatures 3 , IFM15 risk strati cation 4 , 5 gene stemness score 5 , UAMS 17 3 , CINGLEC 214 6 , EMC 92 7 , HZD 97 8 , Millenium 100 9 , M3CN 10 and others.…”