Identification of FCER1G as a key gene in multiple myeloma based on weighted gene co-expression network analysis

Qiu, Xiao; Zhang, Jia-He; Xu, Ying; Cao, Yi-Xuan; Zhang, Rui-Ting; Hu, Li-Na1; Zhou, Ji-Hao1

2023-12-31

10.1080/16078454.2023.2210904

HEMATOLOGY   Impact Factor & Quartile

1024-5332

1607-8454

Purpose: Although the prognosis of multiple myeloma (MM) has remarkably improved with the emerge of novel agents, it remains incurable and relapses inevitably. The molecular mechanisms of MM have not been well-studied. Herein, this study aimed to identify key genes in MM. Materials and Methods: The GSE39754 dataset was used to screen differentially expressed genes (DEGs) and construct a co-expression network. Hub nodes were identified in the protein and protein interaction (PPI) network. Datasets GSE13591 and GSE2658 were used to validate hub genes. Moreover, function and gene set enrichment analyses were performed to elucidate the molecular pathogenesis of MM. Results: In this study, 11 genes were found to be hub genes in the co-expression network, among which four genes (CD68, FCER1G, PLAUR and LCP2) were also identified as hub nodes. In the test dataset GSE13591, CD68 and FCER1G were significantly downregulated in MM. Besides, the areas under the curve (AUCs) of CD68 and FCER1G were greater than 0.8 in both the training dataset and the test dataset. Our results also confirmed that FCER1G highly expressed patients had remarkably longer survival times in MM. Function and pathway enrichment analyses suggested that hub genes were associated with epithelial mesenchymal transition, TNF-alpha signaling via NF-kappa B and inflammatory response. GSEA in our study indicated that FCER1G participated in NK cell mediated cytotoxicity and the NOD-like receptor signaling pathway. Conclusion: Our study identified FCER1G as a key gene in MM, providing a novel biomarker and potential molecular mechanisms of MM for further studies.

Multiple myeloma weighted gene co-expression network analysis (WGCNA) bioinformatics hub gene biomarker survival times gene expression microarray

SCI

English

Others

Shenzhen Science and Technology Innovation Commission[RCBS20221008093314036]

Hematology

Hematology

WOS:000985670400001

TAYLOR & FRANCIS LTD

Web of Science

1.Jinan Univ, Shenzhen Peoples Hosp, Clin Med Coll 2, Dept Hematol, Shenzhen, Peoples R China
2.Southern Univ Sci & Technol, Affiliated Hosp 1, Shenzhen, Peoples R China

Qiu, Xiao,Zhang, Jia-He,Xu, Ying,et al. Identification of FCER1G as a key gene in multiple myeloma based on weighted gene co-expression network analysis[J]. HEMATOLOGY,2023,28(1).

Qiu, Xiao.,Zhang, Jia-He.,Xu, Ying.,Cao, Yi-Xuan.,Zhang, Rui-Ting.,...&Zhou, Ji-Hao.(2023).Identification of FCER1G as a key gene in multiple myeloma based on weighted gene co-expression network analysis.HEMATOLOGY,28(1).

Qiu, Xiao,et al."Identification of FCER1G as a key gene in multiple myeloma based on weighted gene co-expression network analysis".HEMATOLOGY 28.1(2023).