HLA-DPA1 gene is a potential predictor with prognostic values in multiple myeloma
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RESEARCH ARTICLE
Open Access
HLA-DPA1 gene is a potential predictor with prognostic values in multiple myeloma Jie Yang, Fei Wang and Baoan Chen*
Abstract Background: Multiple myeloma (MM) is an incurable hematological tumor, which is closely related to hypoxic bone marrow microenvironment. However, the underlying mechanisms are still far from fully understood. We took integrated bioinformatics analysis with expression profile GSE110113 downloaded from National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) database, and screened out major histocompatibility complex, class II, DP alpha 1 (HLA-DPA1) as a hub gene related to hypoxia in MM. Methods: Differentially expressed genes (DEGs) were filtrated with R package “limma”. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed using “clusterProfiler” package in R. Then, protein-protein interaction (PPI) network was established. Hub genes were screened out according to Maximal Clique Centrality (MCC). PrognoScan evaluated all the significant hub genes for survival analysis. ScanGEO was used for visualization of gene expression in different clinical studies. P and Cox p value < 0.05 was considered to be statistical significance. Results: HLA-DPA1 was finally picked out as a hub gene in MM related to hypoxia. MM patients with down-regulated expression of HLA-DPA1 has statistically significantly shorter disease specific survival (DSS) (COX p = 0.005411). Based on the clinical data of GSE47552 dataset, HLA-DPA1 expression showed significantly lower in MM patients than that in healthy donors (HDs) (p = 0.017). Conclusion: We identified HLA-DPA1 as a hub gene in MM related to hypoxia. HLA-DPA1 down-regulated expression was associated with MM patients’ poor outcome. Further functional and mechanistic studies are need to investigate HLADPA1 as potential therapeutic target. Keywords: Multiple myeloma, Hypoxia, Prognosis, Bioinformatics analysis
Background Multiple myeloma (MM) is a hematological malignancy which is characterized by aberrant plasma cells infiltration in the bone marrow and complex heterogeneous cytogenetic abnormalities [1]. Accumulation of abnormal plasma cells replaces normal hematopoietic cells and * Correspondence: [email protected] Department of Hematology and Oncology, Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao, Gulou District, Nanjing 210009, Jiangsu, China
leads to “CRAB” - hypercalcemia, renal failure, anemia, and bone lesions, even fetal outcome eventually [2]. With the deepening of basic and clinicalresearches, novel drugs mainly including proteasome inhibitors and immunomodulatory drugs have improved patients’ outcome to some extend [3, 4]. Besides, high-dose chemotherapy and tandem autologous stem cell transplant (ASCT), together with supportive care have significantly prolonged patients’ progression-free survival (PFS) and overall survival (OS) [5]. However, MM remains an
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