Du Chenxiao, Guo Dongmei, Zhang Yuhui, Gao Chao, Bai Jie
Department of Hematology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Department of Hematology, Taian City Central Hospital, Taian, China.
Ann Transl Med. 2022 Dec;10(24):1325. doi: 10.21037/atm-22-2656.
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.
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.
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).
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.
多发性骨髓瘤（MM）对于现代临床体系而言仍是一种棘手的疾病，需要开展更多研究以制定更有效的治疗策略。本研究试图通过挖掘基因表达综合数据库（GEO）来筛选和验证MM进展过程中的生物标志物。鉴定生物标志物不仅有助于促进早期诊断和管理，还能识别MM预后不良和病情进展风险较高的个体。
利用GEO2R分析GSE87900数据集的mRNA表达谱。使用SangerBox在线程序，以P<0.05和|logFC|>1为筛选标准，筛选高危MM样本中的差异表达基因（DEG）。使用SangerBox在线分析工具分析火山图。对DEG进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析。纳入泰安市中心医院20例高危MM患者和20例标危MM患者。采用实时定量聚合酶链反应（RT-qPCR）验证MM组织中选定的关键基因。
共获得611个DEG。GO功能富集分析表明，这些DEG在生物学水平上主要富集于DNA复制过程，排名靠前的DEG有CACYBP、PCNA、MCM6、SMC1A、DTL、GINS4、MCM2、CDT1、RRM2、BRCA1、RFC5、MCM4、GINS3、GINS1、MCM10、CDC7、CDAN1、BRIP1、GINS2、CDK1、NFIB和BARD1。RT-qPCR检测显示，高危MM和标危MM中CDC7和PCNA的表达存在显著差异。受试者工作特征（ROC）分析表明，CDC7和PCNA预测的曲线下面积分别为0.900和0.8863，这表明CDC7和PCNA可能是MM的潜在生物标志物。Kaplan-Meier生存分析表明，CDC7和PCNA高表达的MM患者2年总生存期（OS）较短（P<0.05）。
CDC7和PCNA可作为高危MM预后的生物标志物，评估MM患者的预后，有助于指导MM患者的临床治疗。
