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Bioinformatics analysis of the prognostic biomarkers and predictive accuracy of differentially expressed genes in
Chenxiao Du1, Dongmei Guo2, Yuhui Zhang1
1Department of Hematology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Background:
Multiple myeloma (MM) is still an intractable disease for modern clinical system, and more researches are necessary for development of more effective therapeutic strategies. This study attempted to screen and validates the biomarkers in the progression of MM via excavating Gene Expression Omnibus (GEO) database. Identification of a biomarker may help not only facilitate early diagnosis and management but also identify individuals at risk for poor prognosis and development of MM.
Methods:
The mRNA expression profile of the GSE87900 dataset was analyzed by GEO2R. Using the SangerBox online program, differentially expressed genes (DEGs) in high-risk MM samples were screened with the filter criteria of P<0.05 and |logFC| >1. The SangerBox online analysis tool was used to analyze the volcano plot. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed for DEGs. Twenty patients with high-risk MM and 20 patients with standard-risk MM from Taian City Central Hospital were included. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the selected key genes in MM tissues.
Results:
A total of 611 DEGs were obtained. GO functional enrichment analysis showed that the DEGs were mainly enriched in the DNA replication process at the biological level, and the top DEGs were CACYBP, PCNA, MCM6, SMC1A, DTL, GINS4, MCM2, CDT1, RRM2, BRCA1, RFC5, MCM4, GINS3, GINS1, MCM10, CDC7, CDAN1, BRIP1, GINS2, CDK1, NFIB, and BARD1. The expression of CDC7 and PCNA was significantly different in high-risk MM and standard-risk MM as determined by RT-qPCR. Receiver operating characteristic (ROC) analysis showed that the areas under the curve predicted by CDC7 and PCNA were 0.900 and 0.8863, respectively, which allowed the identification of CDC7 and PCNA could be a potential biomarker of MM. Kaplan-Meier survival analysis showed that MM patients with high CDC7 and PCNA expression had shorter 2-year overall survival (OS) (P<0.05).
Conclusions:
CDC7 and PCNA can be used as biomarkers for the prognosis of high-risk MM and evaluate the prognosis of MM patients, which is helpful for guiding the clinical treatment of MM patients.
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