A 5-Gene Stemness Score for Rapid Determination of Risk in Multiple Myeloma
Authors Bai H, Chen B
Received 15 February 2020
Accepted for publication 23 April 2020
Published 18 May 2020 Volume 2020:13 Pages 4339—4348
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Dr Federico Perche
Hua Bai, Bing Chen
Department of Hematology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, People’s Republic of China
Correspondence: Hua Bai; Bing Chen Email email@example.com; firstname.lastname@example.org
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.
Keywords: multiple myeloma, gene expression profiling, side population, risk score, prognosis
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