Related Experiment Video
Updated: Jun 2, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Identifying potential prognosis markers in relapsed multiple myeloma via integrated bioinformatics analysis and
Yong Xu1, Xinya Cao1, He Zhou2
1Department of Hematology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
Background:
Almost all multiple myeloma (MM) patients will eventually develop disease that has relapsed with or become refractory to current therapeutic regimes. However, the pervious clinical parameters have been proved inaccurate for defining MM relapse, and molecular targets have become the focuses of interests. Prognostic predictions based on molecular targets have been more effective to this day. Our research was performed to demonstrate hub genes involving relapsed MM by bioinformatics and biological experiments.
Methods And Results:
The integrated bioinformatics analysis in baseline and relapsed MM patients were executed. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were utilized to analyze biologic functions of up-regulated differentially expressed genes (DEGs). Four hub genes (CENPE, ASPM, TOP2A and FANCI) were adopted for construction of relapsed gene score model (RGS), and RGS model was evaluated in two testing sets. The CENPE inhibitor GSK923295 had anti-myeloma effect, including promoting cell death, cell cycle arrest and DNA damage of MM cell lines.
Conclusion:
Through bioinformatics analysis, we found that the four hub genes (CENPE, ASPM, TOP2A and FANCI) were associated to cell cycle, nuclear division, mitosis and spindle. Our research provided proof-of-concept that RGS model could be utilized to estimate recurrence risk and prognosis for patients, and targeting CENPE contributed to developing novel therapeutic pattern for MM.
